Sample records for cancer risk prediction

  1. Breast cancer risks and risk prediction models.

    PubMed

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  2. Cancer Risk Prediction and Assessment

    Cancer.gov

    Cancer prediction models provide an important approach to assessing risk and prognosis by identifying individuals at high risk, facilitating the design and planning of clinical cancer trials, fostering the development of benefit-risk indices, and enabling estimates of the population burden and cost of cancer.

  3. Mammographic density, breast cancer risk and risk prediction

    PubMed Central

    Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

    2007-01-01

    In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

  4. Breast Cancer Risk Prediction and Mammography Biopsy Decisions

    PubMed Central

    Armstrong, Katrina; Handorf, Elizabeth A.; Chen, Jinbo; Demeter, Mirar N. Bristol

    2012-01-01

    Background Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening. Purpose To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy. Methods The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011. Results Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BIRADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold. Conclusions The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors. PMID:23253645

  5. Prediction of near-term breast cancer risk using a Bayesian belief network

    NASA Astrophysics Data System (ADS)

    Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

    2013-03-01

    Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (p<0.01), while the positive predictive value (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.

  6. Applying a new mammographic imaging marker to predict breast cancer risk

    NASA Astrophysics Data System (ADS)

    Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

    2018-02-01

    Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using "prior" negative mammograms, the area under a ROC curve is 0.70+/-0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

  7. Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

    PubMed

    Hüsing, Anika; Canzian, Federico; Beckmann, Lars; Garcia-Closas, Montserrat; Diver, W Ryan; Thun, Michael J; Berg, Christine D; Hoover, Robert N; Ziegler, Regina G; Figueroa, Jonine D; Isaacs, Claudine; Olsen, Anja; Viallon, Vivian; Boeing, Heiner; Masala, Giovanna; Trichopoulos, Dimitrios; Peeters, Petra H M; Lund, Eiliv; Ardanaz, Eva; Khaw, Kay-Tee; Lenner, Per; Kolonel, Laurence N; Stram, Daniel O; Le Marchand, Loïc; McCarty, Catherine A; Buring, Julie E; Lee, I-Min; Zhang, Shumin; Lindström, Sara; Hankinson, Susan E; Riboli, Elio; Hunter, David J; Henderson, Brian E; Chanock, Stephen J; Haiman, Christopher A; Kraft, Peter; Kaaks, Rudolf

    2012-09-01

    There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

  8. Prediction of breast cancer risk with volatile biomarkers in breath.

    PubMed

    Phillips, Michael; Cataneo, Renee N; Cruz-Ramos, Jose Alfonso; Huston, Jan; Ornelas, Omar; Pappas, Nadine; Pathak, Sonali

    2018-03-23

    Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer.

  9. Evaluation of polygenic risk scores for predicting breast and prostate cancer risk.

    PubMed

    Machiela, Mitchell J; Chen, Chia-Yen; Chen, Constance; Chanock, Stephen J; Hunter, David J; Kraft, Peter

    2011-09-01

    Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores. © 2011 Wiley-Liss, Inc.

  10. Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients

    PubMed Central

    Pfeiffer, Ruth M.; Miglioretti, Diana L.; Kerlikowske, Karla; Tice, Jeffery; Vacek, Pamela M.; Gierach, Gretchen L.

    2016-01-01

    Purpose Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown. Methods Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996–2007), we estimated 5-year breast cancer risk (<1%; 1–1.66%; ≥1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1–13 years; median: 5.4–5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35–44; 45–54; 55–69; 70–89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years. Results Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75–0.90); BCRAT: HR = 0.72 (95% CI = 0.65–0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75–0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55–69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35–44 years. Conclusions Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering

  11. A utility/cost analysis of breast cancer risk prediction algorithms

    NASA Astrophysics Data System (ADS)

    Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

    2016-03-01

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

  12. Observed and Predicted Risk of Breast Cancer Death in Randomized Trials on Breast Cancer Screening

    PubMed Central

    Autier, Philippe; Sullivan, Richard; Boyle, Peter

    2016-01-01

    Background The role of breast screening in breast cancer mortality declines is debated. Screening impacts cancer mortality through decreasing the number of advanced cancers with poor diagnosis, while cancer treatment works through decreasing the case-fatality rate. Hence, reductions in cancer death rates thanks to screening should directly reflect reductions in advanced cancer rates. We verified whether in breast screening trials, the observed reductions in the risk of breast cancer death could be predicted from reductions of advanced breast cancer rates. Patients and Methods The Greater New York Health Insurance Plan trial (HIP) is the only breast screening trial that reported stage-specific cancer fatality for the screening and for the control group separately. The Swedish Two-County trial (TCT)) reported size-specific fatalities for cancer patients in both screening and control groups. We computed predicted numbers of breast cancer deaths, from which we calculated predicted relative risks (RR) and (95% confidence intervals). The Age trial in England performed its own calculations of predicted relative risk. Results The observed and predicted RR of breast cancer death were 0.72 (0.56–0.94) and 0.98 (0.77–1.24) in the HIP trial, and 0.79 (0.78–1.01) and 0.90 (0.80–1.01) in the Age trial. In the TCT, the observed RR was 0.73 (0.62–0.87), while the predicted RR was 0.89 (0.75–1.05) if overdiagnosis was assumed to be negligible and 0.83 (0.70–0.97) if extra cancers were excluded. Conclusions In breast screening trials, factors other than screening have contributed to reductions in the risk of breast cancer death most probably by reducing the fatality of advanced cancers in screening groups. These factors were the better management of breast cancer patients and the underreporting of breast cancer as the underlying cause of death. Breast screening trials should publish stage-specific fatalities observed in each group. PMID:27100174

  13. Observed and Predicted Risk of Breast Cancer Death in Randomized Trials on Breast Cancer Screening.

    PubMed

    Autier, Philippe; Boniol, Mathieu; Smans, Michel; Sullivan, Richard; Boyle, Peter

    2016-01-01

    The role of breast screening in breast cancer mortality declines is debated. Screening impacts cancer mortality through decreasing the number of advanced cancers with poor diagnosis, while cancer treatment works through decreasing the case-fatality rate. Hence, reductions in cancer death rates thanks to screening should directly reflect reductions in advanced cancer rates. We verified whether in breast screening trials, the observed reductions in the risk of breast cancer death could be predicted from reductions of advanced breast cancer rates. The Greater New York Health Insurance Plan trial (HIP) is the only breast screening trial that reported stage-specific cancer fatality for the screening and for the control group separately. The Swedish Two-County trial (TCT)) reported size-specific fatalities for cancer patients in both screening and control groups. We computed predicted numbers of breast cancer deaths, from which we calculated predicted relative risks (RR) and (95% confidence intervals). The Age trial in England performed its own calculations of predicted relative risk. The observed and predicted RR of breast cancer death were 0.72 (0.56-0.94) and 0.98 (0.77-1.24) in the HIP trial, and 0.79 (0.78-1.01) and 0.90 (0.80-1.01) in the Age trial. In the TCT, the observed RR was 0.73 (0.62-0.87), while the predicted RR was 0.89 (0.75-1.05) if overdiagnosis was assumed to be negligible and 0.83 (0.70-0.97) if extra cancers were excluded. In breast screening trials, factors other than screening have contributed to reductions in the risk of breast cancer death most probably by reducing the fatality of advanced cancers in screening groups. These factors were the better management of breast cancer patients and the underreporting of breast cancer as the underlying cause of death. Breast screening trials should publish stage-specific fatalities observed in each group.

  14. Updating Risk Prediction Tools: A Case Study in Prostate Cancer

    PubMed Central

    Ankerst, Donna P.; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J.; Feng, Ziding; Sanda, Martin G.; Partin, Alan W.; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M.

    2013-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [−2]proPSA measured on an external case control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. PMID:22095849

  15. Updating risk prediction tools: a case study in prostate cancer.

    PubMed

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

    PubMed

    Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

    2017-07-01

    To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  17. Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach.

    PubMed

    Journy, Neige; Ancelet, Sophie; Rehel, Jean-Luc; Mezzarobba, Myriam; Aubert, Bernard; Laurier, Dominique; Bernier, Marie-Odile

    2014-03-01

    The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI <0.1-4) leukemia case (all in excess of risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols.

  18. Quantitative prediction of oral cancer risk in patients with oral leukoplakia.

    PubMed

    Liu, Yao; Li, Yicheng; Fu, Yue; Liu, Tong; Liu, Xiaoyong; Zhang, Xinyan; Fu, Jie; Guan, Xiaobing; Chen, Tong; Chen, Xiaoxin; Sun, Zheng

    2017-07-11

    Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.

  19. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk

    DTIC Science & Technology

    2008-06-01

    of benign histology in predicting risk of future breast cancer, examining in detail the role of proliferative disease, atypia , papillomas, radial...who had proliferative disease with atypia , especially those of younger age. • We identified a marked increased risk of breast cancer in women with...imparts an increased risk of developing a subsequent carcinoma similar to other forms of proliferative breast disease without atypia . Atypical

  20. Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy.

    PubMed

    Eley, John G; Friedrich, Thomas; Homann, Kenneth L; Howell, Rebecca M; Scholz, Michael; Durante, Marco; Newhauser, Wayne D

    2016-05-01

    This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breast by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.

    PubMed

    Sakoda, Lori C; Henderson, Louise M; Caverly, Tanner J; Wernli, Karen J; Katki, Hormuzd A

    2017-12-01

    Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

  2. 68Ga-PSMA-617 PET/CT: a promising new technique for predicting risk stratification and metastatic risk of prostate cancer patients.

    PubMed

    Liu, Chen; Liu, Teli; Zhang, Ning; Liu, Yiqiang; Li, Nan; Du, Peng; Yang, Yong; Liu, Ming; Gong, Kan; Yang, Xing; Zhu, Hua; Yan, Kun; Yang, Zhi

    2018-05-02

    The purpose of this study was to investigate the performance of 68 Ga-PSMA-617 PET/CT in predicting risk stratification and metastatic risk of prostate cancer. Fifty newly diagnosed patients with prostate cancer as confirmed by needle biopsy were continuously included, 40 in a train set and ten in a test set. 68 Ga-PSMA-617 PET/CT and clinical data of all patients were retrospectively analyzed. Semi-quantitative analysis of PET images provided maximum standardized uptake (SUVmax) of primary prostate cancer and volumetric parameters including intraprostatic PSMA-derived tumor volume (iPSMA-TV) and intraprostatic total lesion PSMA (iTL-PSMA). According to prostate cancer risk stratification criteria of the NCCN Guideline, all patients were simplified into a low-intermediate risk group or a high-risk group. The semi-quantitative parameters of 68 Ga-PSMA-617 PET/CT were used to establish a univariate logistic regression model for high-risk prostate cancer and its metastatic risk, and to evaluate the diagnostic efficacy of the predictive model. In the train set, 30/40 (75%) patients had high-risk prostate cancer and 10/40 (25%) patients had low-to-moderate-risk prostate cancer; in the test set, 8/10 (80%) patients had high-risk prostate cancer while 2/10 (20%) had low-intermediate risk prostate cancer. The univariate logistic regression model established with SUVmax, iPSMA-TV and iTL-PSMA could all effectively predict high-risk prostate cancer; the AUC of ROC were 0.843, 0.802 and 0.900, respectively. Based on the test set, the sensitivity and specificity of each model were 87.5% and 50% for SUVmax, 62.5% and 100% for iPSMA-TV, and 87.5% and 100% for iTL-PSMA, respectively. The iPSMA-TV and iTL-PSMA-based predictive model could predict the metastatic risk of prostate cancer, the AUC of ROC was 0.863 and 0.848, respectively, but the SUVmax-based prediction model could not predict metastatic risk. Semi-quantitative analysis indexes of 68 Ga-PSMA-617 PET/CT imaging can be

  3. Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Eley, John G., E-mail: jeley@som.umaryland.edu; University of Texas Graduate School of Biomedical Sciences, Houston, Texas; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland

    Purpose: This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. Methods and Materials: We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breastmore » by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. Results: For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Conclusions: Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy.« less

  4. Prediction of individual genetic risk to prostate cancer using a polygenic score.

    PubMed

    Szulkin, Robert; Whitington, Thomas; Eklund, Martin; Aly, Markus; Eeles, Rosalind A; Easton, Douglas; Kote-Jarai, Z Sofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Southey, Melissa C; Fitzgerald, Liesel M; Henderson, Brian E; Schumacher, Fredrick; Haiman, Christopher A; Schleutker, Johanna; Wahlfors, Tiina; Tammela, Teuvo L J; Nordestgaard, Børge G; Key, Tim J; Travis, Ruth C; Neal, David E; Donovan, Jenny L; Hamdy, Freddie C; Pharoah, Paul; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Thibodeau, Stephen N; McDonnell, Shannon K; Schaid, Daniel J; Maier, Christiane; Vogel, Walther; Luedeke, Manuel; Herkommer, Kathleen; Kibel, Adam S; Cybulski, Cezary; Lubiński, Jan; Kluźniak, Wojciech; Cannon-Albright, Lisa; Brenner, Hermann; Butterbach, Katja; Stegmaier, Christa; Park, Jong Y; Sellers, Thomas; Lin, Hui-Yi; Lim, Hui-Yi; Slavov, Chavdar; Kaneva, Radka; Mitev, Vanio; Batra, Jyotsna; Clements, Judith A; Spurdle, Amanda; Teixeira, Manuel R; Paulo, Paula; Maia, Sofia; Pandha, Hardev; Michael, Agnieszka; Kierzek, Andrzej; Gronberg, Henrik; Wiklund, Fredrik

    2015-09-01

    Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. © 2015 Wiley Periodicals, Inc.

  5. Clinical Utility of Five Genetic Variants for Predicting Prostate Cancer Risk and Mortality

    PubMed Central

    Salinas, Claudia A.; Koopmeiners, Joseph S.; Kwon, Erika M.; FitzGerald, Liesel; Lin, Daniel W.; Ostrander, Elaine A.; Feng, Ziding; Stanford, Janet L.

    2009-01-01

    Background A recent report suggests that the combination of five single-nucleotide polymorphisms (SNPs) at 8q24, 17q12, 17q24.3 and a family history of the disease may predict risk of prostate cancer. The present study tests the performance of these factors in prediction models for prostate cancer risk and prostate cancer-specific mortality. Methods SNPs were genotyped in population-based samples from Caucasians in King County, Washington. Incident cases (n=1308), aged 35–74, were compared to age-matched controls (n=1266) using logistic regression to estimate odds ratios (OR) associated with genotypes and family history. Cox proportional hazards models estimated hazard ratios for prostate cancer-specific mortality according to genotypes. Results The combination of SNP genotypes and family history was significantly associated with prostate cancer risk (ptrend=1.5 × 10−20). Men with ≥ five risk factors had an OR of 4.9 (95% CI 1.6 to 18.5) compared to men with none. However, this combination of factors did not improve the ROC curve after accounting for known risk predictors (i.e., age, serum PSA, family history). Neither the individual nor combined risk factors was associated with prostate cancer-specific mortality. Conclusion Genotypes for five SNPs plus family history are associated with a significant elevation in risk for prostate cancer and may explain up to 45% of prostate cancer in our population. However, they do not improve prediction models for assessing who is at risk of getting or dying from the disease, once known risk or prognostic factors are taken into account. Thus, this SNP panel may have limited clinical utility. PMID:19058137

  6. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface

    PubMed Central

    Lee, A J; Cunningham, A P; Kuchenbaecker, K B; Mavaddat, N; Easton, D F; Antoniou, A C

    2014-01-01

    Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions. Methods: We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting. Results: We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions. Conclusion: All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/. PMID:24346285

  7. Lung cancer in never smokers Epidemiology and risk prediction models

    PubMed Central

    McCarthy, William J.; Meza, Rafael; Jeon, Jihyoun; Moolgavkar, Suresh

    2012-01-01

    In this chapter we review the epidemiology of lung cancer incidence and mortality among never smokers/ nonsmokers and describe the never smoker lung cancer risk models used by CISNET modelers. Our review focuses on those influences likely to have measurable population impact on never smoker risk, such as secondhand smoke, even though the individual-level impact may be small. Occupational exposures may also contribute importantly to the population attributable risk of lung cancer. We examine the following risk factors in this chapter: age, environmental tobacco smoke, cooking fumes, ionizing radiation including radon gas, inherited genetic susceptibility, selected occupational exposures, preexisting lung disease, and oncogenic viruses. We also compare the prevalence of never smokers between the three CISNET smoking scenarios and present the corresponding lung cancer mortality estimates among never smokers as predicted by a typical CISNET model. PMID:22882894

  8. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

    PubMed Central

    ten Haaf, Kevin; Tammemägi, Martin C.; Han, Summer S.; Kong, Chung Yin; Plevritis, Sylvia K.; de Koning, Harry J.; Steyerberg, Ewout W.

    2017-01-01

    Background Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer. Methods and findings Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher

  9. Prostate cancer: predicting high-risk prostate cancer-a novel stratification tool.

    PubMed

    Buck, Jessica; Chughtai, Bilal

    2014-05-01

    Currently, numerous systems exist for the identification of high-risk prostate cancer, but few of these systems can guide treatment strategies. A new stratification tool that uses common diagnostic factors can help to predict outcomes after radical prostatectomy. The tool aids physicians in the identification of appropriate candidates for aggressive, local treatment.

  10. Prostate Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Bladder Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Ovarian Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Pancreatic Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Testicular Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Breast Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Esophageal Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Cervical Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Liver Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Lung Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Colorectal Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

    PubMed

    Kerlikowske, Karla; Gard, Charlotte C; Sprague, Brian L; Tice, Jeffrey A; Miglioretti, Diana L

    2015-06-01

    One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure. We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. A two-density model should be considered for women whose density decreases when calculating breast cancer risk. ©2015 American Association for Cancer Research.

  2. Cross-sectional study to assess the association of population density with predicted breast cancer risk.

    PubMed

    Lee, Jeannette Y; Klimberg, Suzanne; Bondurant, Kristina L; Phillips, Martha M; Kadlubar, Susan A

    2014-01-01

    The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85 years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p < 0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p < 0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p = 0.039) and lifetime risks were 8.94%, and 8.85% (p = 0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5 years, but not over a lifetime. Population density was not an independent risk factor for breast cancer. © 2014 Wiley Periodicals, Inc.

  3. One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk

    PubMed Central

    Kerlikowske, Karla; Gard, Charlotte C.; Sprague, Brian L.; Tice, Jeffrey A.; Miglioretti, Diana L.

    2015-01-01

    Background One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure. Methods We included 722,654 women aged 35–74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. Results The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. Conclusion The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact A two-density model should be considered for women whose density decreases when calculating breast cancer risk. PMID:25824444

  4. Assessment of uncertainties in radiation-induced cancer risk predictions at clinically relevant doses

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nguyen, J.; Moteabbed, M.; Paganetti, H., E-mail: hpaganetti@mgh.harvard.edu

    2015-01-15

    Purpose: Theoretical dose–response models offer the possibility to assess second cancer induction risks after external beam therapy. The parameters used in these models are determined with limited data from epidemiological studies. Risk estimations are thus associated with considerable uncertainties. This study aims at illustrating uncertainties when predicting the risk for organ-specific second cancers in the primary radiation field illustrated by choosing selected treatment plans for brain cancer patients. Methods: A widely used risk model was considered in this study. The uncertainties of the model parameters were estimated with reported data of second cancer incidences for various organs. Standard error propagationmore » was then subsequently applied to assess the uncertainty in the risk model. Next, second cancer risks of five pediatric patients treated for cancer in the head and neck regions were calculated. For each case, treatment plans for proton and photon therapy were designed to estimate the uncertainties (a) in the lifetime attributable risk (LAR) for a given treatment modality and (b) when comparing risks of two different treatment modalities. Results: Uncertainties in excess of 100% of the risk were found for almost all organs considered. When applied to treatment plans, the calculated LAR values have uncertainties of the same magnitude. A comparison between cancer risks of different treatment modalities, however, does allow statistically significant conclusions. In the studied cases, the patient averaged LAR ratio of proton and photon treatments was 0.35, 0.56, and 0.59 for brain carcinoma, brain sarcoma, and bone sarcoma, respectively. Their corresponding uncertainties were estimated to be potentially below 5%, depending on uncertainties in dosimetry. Conclusions: The uncertainty in the dose–response curve in cancer risk models makes it currently impractical to predict the risk for an individual external beam treatment. On the other hand, the

  5. Atypia and DNA methylation in nipple duct lavage in relation to predicted breast cancer risk.

    PubMed

    Euhus, David M; Bu, Dawei; Ashfaq, Raheela; Xie, Xian-Jin; Bian, Aihua; Leitch, A Marilyn; Lewis, Cheryl M

    2007-09-01

    Tumor suppressor gene (TSG) methylation is identified more frequently in random periareolar fine needle aspiration samples from women at high risk for breast cancer than women at lower risk. It is not known whether TSG methylation or atypia in nipple duct lavage (NDL) samples is related to predicted breast cancer risk. 514 NDL samples obtained from 150 women selected to represent a wide range of breast cancer risk were evaluated cytologically and by quantitative multiplex methylation-specific PCR for methylation of cyclin D2, APC, HIN1, RASSF1A, and RAR-beta2. Based on methylation patterns and cytology, NDL retrieved cancer cells from only 9% of breasts ipsilateral to a breast cancer. Methylation of >/=2 genes correlated with marked atypia by univariate analysis, but not multivariate analysis, that adjusted for sample cellularity and risk group classification. Both marked atypia and TSG methylation independently predicted abundant cellularity in multivariate analyses. Discrimination between Gail lower-risk ducts and Gail high-risk ducts was similar for marked atypia [odds ratio (OR), 3.48; P = 0.06] and measures of TSG methylation (OR, 3.51; P = 0.03). However, marked atypia provided better discrimination between Gail lower-risk ducts and ducts contralateral to a breast cancer (OR, 6.91; P = 0.003, compared with methylation OR, 4.21; P = 0.02). TSG methylation in NDL samples does not predict marked atypia after correcting for sample cellularity and risk group classification. Rather, both methylation and marked atypia are independently associated with highly cellular samples, Gail model risk classifications, and a personal history of breast cancer. This suggests the existence of related, but independent, pathogenic pathways in breast epithelium.

  6. Predicting neutropenia risk in patients with cancer using electronic data.

    PubMed

    Pawloski, Pamala A; Thomas, Avis J; Kane, Sheryl; Vazquez-Benitez, Gabriela; Shapiro, Gary R; Lyman, Gary H

    2017-04-01

    Clinical guidelines recommending the use of myeloid growth factors are largely based on the prescribed chemotherapy regimen. The guidelines suggest that oncologists consider patient-specific characteristics when prescribing granulocyte-colony stimulating factor (G-CSF) prophylaxis; however, a mechanism to quantify individual patient risk is lacking. Readily available electronic health record (EHR) data can provide patient-specific information needed for individualized neutropenia risk estimation. An evidence-based, individualized neutropenia risk estimation algorithm has been developed. This study evaluated the automated extraction of EHR chemotherapy treatment data and externally validated the neutropenia risk prediction model. A retrospective cohort of adult patients with newly diagnosed breast, colorectal, lung, lymphoid, or ovarian cancer who received the first cycle of a cytotoxic chemotherapy regimen from 2008 to 2013 were recruited from a single cancer clinic. Electronically extracted EHR chemotherapy treatment data were validated by chart review. Neutropenia risk stratification was conducted and risk model performance was assessed using calibration and discrimination. Chemotherapy treatment data electronically extracted from the EHR were verified by chart review. The neutropenia risk prediction tool classified 126 patients (57%) as being low risk for febrile neutropenia, 44 (20%) as intermediate risk, and 51 (23%) as high risk. The model was well calibrated (Hosmer-Lemeshow goodness-of-fit test = 0.24). Discrimination was adequate and slightly less than in the original internal validation (c-statistic 0.75 vs 0.81). Chemotherapy treatment data were electronically extracted from the EHR successfully. The individualized neutropenia risk prediction model performed well in our retrospective external cohort. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions

  7. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry.

    PubMed

    Wen, Wanqing; Shu, Xiao-Ou; Guo, Xingyi; Cai, Qiuyin; Long, Jirong; Bolla, Manjeet K; Michailidou, Kyriaki; Dennis, Joe; Wang, Qin; Gao, Yu-Tang; Zheng, Ying; Dunning, Alison M; García-Closas, Montserrat; Brennan, Paul; Chen, Shou-Tung; Choi, Ji-Yeob; Hartman, Mikael; Ito, Hidemi; Lophatananon, Artitaya; Matsuo, Keitaro; Miao, Hui; Muir, Kenneth; Sangrajrang, Suleeporn; Shen, Chen-Yang; Teo, Soo H; Tseng, Chiu-Chen; Wu, Anna H; Yip, Cheng Har; Simard, Jacques; Pharoah, Paul D P; Hall, Per; Kang, Daehee; Xiang, Yongbing; Easton, Douglas F; Zheng, Wei

    2016-12-08

    Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P < 0.05. Compared with women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.

  8. Predicted 25(OH)D score and colorectal cancer risk according to vitamin D receptor expression.

    PubMed

    Jung, Seungyoun; Qian, Zhi Rong; Yamauchi, Mai; Bertrand, Kimberly A; Fitzgerald, Kathryn C; Inamura, Kentaro; Kim, Sun A; Mima, Kosuke; Sukawa, Yasutaka; Zhang, Xuehong; Wang, Molin; Smith-Warner, Stephanie A; Wu, Kana; Fuchs, Charles S; Chan, Andrew T; Giovannucci, Edward L; Ng, Kimmie; Cho, Eunyoung; Ogino, Shuji; Nishihara, Reiko

    2014-08-01

    Despite accumulating evidence for the preventive effect of vitamin D on colorectal carcinogenesis, its precise mechanisms remain unclear. We hypothesized that vitamin D was associated with a lower risk of colorectal cancer with high-level vitamin D receptor (VDR) expression, but not with risk of tumor with low-level VDR expression. Among 140,418 participants followed from 1986 through 2008 in the Nurses' Health Study and the Health Professionals' Follow-up Study, we identified 1,059 incident colorectal cancer cases with tumor molecular data. The predicted 25-hydroxyvitamin D [25(OH)D] score was developed using the known determinants of plasma 25(OH)D. We estimated the HR for cancer subtypes using the duplication method Cox proportional hazards model. A higher predicted 25(OH)D score was associated with a lower risk of colorectal cancer irrespective of VDR expression level (P(heterogeneity) for subtypes = 0.75). Multivariate HRs (95% confidence intervals) comparing the highest with the lowest quintile of predicted 25(OH)D scores were 0.48 (0.30-0.78) for VDR-negative tumor and 0.56 (0.42-0.75) for VDR-positive tumor. Similarly, the significant inverse associations of the predicted 25(OH)D score with colorectal cancer risk did not significantly differ by KRAS, BRAF, or PIK3CA status (P(heterogeneity) for subtypes ≥ 0.22). A higher predicted vitamin D score was significantly associated with a lower colorectal cancer risk, regardless of VDR status and other molecular features examined. The preventive effect of vitamin D on colorectal carcinogenesis may not totally depend on tumor factors. Host factors (such as local and systemic immunity) may need to be considered. ©2014 American Association for Cancer Research.

  9. Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies.

    PubMed

    Van Neste, Leander; Partin, Alan W; Stewart, Grant D; Epstein, Jonathan I; Harrison, David J; Van Criekinge, Wim

    2016-09-01

    Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over-diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA-methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation-positive men, DNA-methylation intensities could help to identify those men harboring high-grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value. Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high-grade (Gleason Score ≥7) cancer. Compared to other risk factors, detection of DNA-methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, EpiScore was significantly higher for those with high-grade cancer detected upon repeat biopsy, compared to those with either no or low-grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision-making tool for repeat biopsy. Low DNA-methylation levels in PCa-negative biopsies led

  10. Longitudinal study of mammographic density measures that predict breast cancer risk

    PubMed Central

    Krishnan, Kavitha; Baglietto, Laura; Stone, Jennifer; Simpson, Julie A; Severi, Gianluca; Evans, Christopher F; MacInnis, Robert J; Giles, Graham G; Apicella, Carmel; Hopper, John L

    2016-01-01

    Background After adjusting for age and body mass index (BMI), mammographic measures - dense area (DA), percent dense area (PDA) and non-dense area (NDA) - are associated with breast cancer risk. Our aim was to use longitudinal data to estimate the extent to which these risk-predicting measures track over time. Methods We collected 4,320 mammograms (age range, 24-83 years) from 970 women in the Melbourne Collaborative Cohort Study and the Australian Breast Cancer Family Registry. Women had on average 4.5 mammograms (range, 1-14). DA, PDA and NDA were measured using the Cumulus software and normalised using the Box-Cox method. Correlations in the normalised risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modelling of Gompertz curves. Results Mean normalised DA and PDA were constant with age to the early 40s, decreased over the next two decades, and were almost constant from the mid 60s onwards. Mean normalised NDA increased non-linearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalised DA were 0.94, 0.93, 0.91, 0.91 and 0.91 for mammograms taken 2, 4, 6, 8 and 10 years apart, respectively. Similar correlations were estimated for the age and BMI adjusted normalized PDA and NDA. Conclusion The mammographic measures that predict breast cancer risk are highly correlated over time. Impact This has implications for etiologic research and clinical management whereby women at increased risk could be identified at a young age (e.g. early 40s or even younger) and recommended appropriate screening and prevention strategies. PMID:28062399

  11. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

    PubMed

    Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

    2012-04-01

    A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the

  12. An individual risk prediction model for lung cancer based on a study in a Chinese population.

    PubMed

    Wang, Xu; Ma, Kewei; Cui, Jiuwei; Chen, Xiao; Jin, Lina; Li, Wei

    2015-01-01

    Early detection and diagnosis remains an effective yet challenging approach to improve the clinical outcome of patients with cancer. Low-dose computed tomography screening has been suggested to improve the diagnosis of lung cancer in high-risk individuals. To make screening more efficient, it is necessary to identify individuals who are at high risk. We conducted a case-control study to develop a predictive model for identification of such high-risk individuals. Clinical data from 705 lung cancer patients and 988 population-based controls were used for the development and evaluation of the model. Associations between environmental variants and lung cancer risk were analyzed with a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic curve and the optimal operating point. Our results indicate that lung cancer risk factors included older age, male gender, lower education level, family history of cancer, history of chronic obstructive pulmonary disease, lower body mass index, smoking cigarettes, a diet with less seafood, vegetables, fruits, dairy products, soybean products and nuts, a diet rich in meat, and exposure to pesticides and cooking emissions. The area under the curve was 0.8851 and the optimal operating point was obtained. With a cutoff of 0.35, the false positive rate, true positive rate, and Youden index were 0.21, 0.87, and 0.66, respectively. The risk prediction model for lung cancer developed in this study could discriminate high-risk from low-risk individuals.

  13. Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction.

    PubMed

    Antoniou, Antonis C; Beesley, Jonathan; McGuffog, Lesley; Sinilnikova, Olga M; Healey, Sue; Neuhausen, Susan L; Ding, Yuan Chun; Rebbeck, Timothy R; Weitzel, Jeffrey N; Lynch, Henry T; Isaacs, Claudine; Ganz, Patricia A; Tomlinson, Gail; Olopade, Olufunmilayo I; Couch, Fergus J; Wang, Xianshu; Lindor, Noralane M; Pankratz, Vernon S; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Barile, Monica; Viel, Alessandra; Allavena, Anna; Dall'Olio, Valentina; Peterlongo, Paolo; Szabo, Csilla I; Zikan, Michal; Claes, Kathleen; Poppe, Bruce; Foretova, Lenka; Mai, Phuong L; Greene, Mark H; Rennert, Gad; Lejbkowicz, Flavio; Glendon, Gord; Ozcelik, Hilmi; Andrulis, Irene L; Thomassen, Mads; Gerdes, Anne-Marie; Sunde, Lone; Cruger, Dorthe; Birk Jensen, Uffe; Caligo, Maria; Friedman, Eitan; Kaufman, Bella; Laitman, Yael; Milgrom, Roni; Dubrovsky, Maya; Cohen, Shimrit; Borg, Ake; Jernström, Helena; Lindblom, Annika; Rantala, Johanna; Stenmark-Askmalm, Marie; Melin, Beatrice; Nathanson, Kate; Domchek, Susan; Jakubowska, Ania; Lubinski, Jan; Huzarski, Tomasz; Osorio, Ana; Lasa, Adriana; Durán, Mercedes; Tejada, Maria-Isabel; Godino, Javier; Benitez, Javier; Hamann, Ute; Kriege, Mieke; Hoogerbrugge, Nicoline; van der Luijt, Rob B; van Asperen, Christi J; Devilee, Peter; Meijers-Heijboer, E J; Blok, Marinus J; Aalfs, Cora M; Hogervorst, Frans; Rookus, Matti; Cook, Margaret; Oliver, Clare; Frost, Debra; Conroy, Don; Evans, D Gareth; Lalloo, Fiona; Pichert, Gabriella; Davidson, Rosemarie; Cole, Trevor; Cook, Jackie; Paterson, Joan; Hodgson, Shirley; Morrison, Patrick J; Porteous, Mary E; Walker, Lisa; Kennedy, M John; Dorkins, Huw; Peock, Susan; Godwin, Andrew K; Stoppa-Lyonnet, Dominique; de Pauw, Antoine; Mazoyer, Sylvie; Bonadona, Valérie; Lasset, Christine; Dreyfus, Hélène; Leroux, Dominique; Hardouin, Agnès; Berthet, Pascaline; Faivre, Laurence; Loustalot, Catherine; Noguchi, Tetsuro; Sobol, Hagay; Rouleau, Etienne; Nogues, Catherine; Frénay, Marc; Vénat-Bouvet, Laurence; Hopper, John L; Daly, Mary B; Terry, Mary B; John, Esther M; Buys, Saundra S; Yassin, Yosuf; Miron, Alexander; Goldgar, David; Singer, Christian F; Dressler, Anne Catharina; Gschwantler-Kaulich, Daphne; Pfeiler, Georg; Hansen, Thomas V O; Jønson, Lars; Agnarsson, Bjarni A; Kirchhoff, Tomas; Offit, Kenneth; Devlin, Vincent; Dutra-Clarke, Ana; Piedmonte, Marion; Rodriguez, Gustavo C; Wakeley, Katie; Boggess, John F; Basil, Jack; Schwartz, Peter E; Blank, Stephanie V; Toland, Amanda Ewart; Montagna, Marco; Casella, Cinzia; Imyanitov, Evgeny; Tihomirova, Laima; Blanco, Ignacio; Lazaro, Conxi; Ramus, Susan J; Sucheston, Lara; Karlan, Beth Y; Gross, Jenny; Schmutzler, Rita; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Lochmann, Magdalena; Arnold, Norbert; Heidemann, Simone; Varon-Mateeva, Raymonda; Niederacher, Dieter; Sutter, Christian; Deissler, Helmut; Gadzicki, Dorothea; Preisler-Adams, Sabine; Kast, Karin; Schönbuchner, Ines; Caldes, Trinidad; de la Hoya, Miguel; Aittomäki, Kristiina; Nevanlinna, Heli; Simard, Jacques; Spurdle, Amanda B; Holland, Helene; Chen, Xiaoqing; Platte, Radka; Chenevix-Trench, Georgia; Easton, Douglas F

    2010-12-01

    The known breast cancer susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1, LSP1, and 2q35 confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of 3 additional single nucleotide polymorphisms (SNPs), rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11, and rs10941679 at 5p12, and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased breast cancer risk for BRCA2 carriers (per-allele HR = 1.10, 95% CI: 1.03-1.18, P = 0.006 and HR = 1.09, 95% CI: 1.01-1.19, P = 0.03, respectively). Neither SNP was associated with breast cancer risk for BRCA1 carriers, and rs6504950 was not associated with breast cancer for either BRCA1 or BRCA2 carriers. Of the 9 polymorphisms investigated, 7 were associated with breast cancer for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, P = 7 × 10(-11) - 0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (P = 0.0049, 0.03, respectively). All risk-associated polymorphisms appear to interact multiplicatively on breast cancer risk for mutation carriers. Based on the joint genotype distribution of the 7 risk-associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e., between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing breast cancer by age 80, compared with 42% to 50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences might be sufficient to influence the clinical management of mutation carriers.

  14. Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk

    NASA Astrophysics Data System (ADS)

    Aghaei, Faranak; Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Wang, Yunzhi; Qiu, Yuchen; Liu, Hong; Zheng, Bin

    2017-03-01

    Although breast density has been widely considered an important breast cancer risk factor, it is not very effective to predict risk of developing breast cancer in a short-term or harboring cancer in mammograms. Based on our recent studies to build short-term breast cancer risk stratification models based on bilateral mammographic density asymmetry, we in this study explored a new quantitative image marker based on bilateral focal density asymmetry to predict the risk of harboring cancers in mammograms. For this purpose, we assembled a testing dataset involving 100 positive and 100 negative cases. In each of positive case, no any solid masses are visible on mammograms. We developed a computer-aided detection (CAD) scheme to automatically detect focal dense regions depicting on two bilateral mammograms of left and right breasts. CAD selects one focal dense region with the maximum size on each image and computes its asymmetrical ratio. We used this focal density asymmetry as a new imaging marker to divide testing cases into two groups of higher and lower focal density asymmetry. The first group included 70 cases in which 62.9% are positive, while the second group included 130 cases in which 43.1% are positive. The odds ratio is 2.24. As a result, this preliminary study supported the feasibility of applying a new focal density asymmetry based imaging marker to predict the risk of having mammography-occult cancers. The goal is to assist radiologists more effectively and accurately detect early subtle cancers using mammography and/or other adjunctive imaging modalities in the future.

  15. Colorectal Cancer Risk Assessment Tool

    MedlinePlus

    ... 11/12/2014 Risk Calculator About the Tool Colorectal Cancer Risk Factors Download SAS and Gauss Code Page ... Rectal Cancer: Prevention, Genetics, Causes Tests to Detect Colorectal Cancer and Polyps Cancer Risk Prediction Resources Update November ...

  16. Inclusion of Endogenous Hormone Levels in Risk Prediction Models of Postmenopausal Breast Cancer

    PubMed Central

    Tworoger, Shelley S.; Zhang, Xuehong; Eliassen, A. Heather; Qian, Jing; Colditz, Graham A.; Willett, Walter C.; Rosner, Bernard A.; Kraft, Peter; Hankinson, Susan E.

    2014-01-01

    Purpose Endogenous hormones are risk factors for postmenopausal breast cancer, and their measurement may improve our ability to identify high-risk women. Therefore, we evaluated whether inclusion of plasma estradiol, estrone, estrone sulfate, testosterone, dehydroepiandrosterone sulfate, prolactin, and sex hormone–binding globulin (SHBG) improved risk prediction for postmenopausal invasive breast cancer (n = 437 patient cases and n = 775 controls not using postmenopausal hormones) in the Nurses' Health Study. Methods We evaluated improvement in the area under the curve (AUC) for 5-year risk of invasive breast cancer by adding each hormone to the Gail and Rosner-Colditz risk scores. We used stepwise regression to identify the subset of hormones most associated with risk and assessed AUC improvement; we used 10-fold cross validation to assess model overfitting. Results Each hormone was associated with breast cancer risk (odds ratio doubling, 0.82 [SHBG] to 1.37 [estrone sulfate]). Individual hormones improved the AUC by 1.3 to 5.2 units relative to the Gail score and 0.3 to 2.9 for the Rosner-Colditz score. Estrone sulfate, testosterone, and prolactin were selected by stepwise regression and increased the AUC by 5.9 units (P = .003) for the Gail score and 3.4 (P = .04) for the Rosner-Colditz score. In cross validation, the average AUC change across the validation data sets was 6.0 (P = .002) and 3.0 units (P = .03), respectively. Similar results were observed for estrogen receptor–positive disease (selected hormones: estrone sulfate, testosterone, prolactin, and SHBG; change in AUC, 8.8 [P < .001] for Gail score and 5.8 [P = .004] for Rosner-Colditz score). Conclusion Our results support that endogenous hormones improve risk prediction for invasive breast cancer and could help identify women who may benefit from chemoprevention or more screening. PMID:25135988

  17. Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

    PubMed

    Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

    2017-03-15

    Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

  18. Gene Expression Profiling Predicts the Development of Oral Cancer

    PubMed Central

    Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

    2011-01-01

    Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

  19. Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer

    PubMed Central

    Raji, Olaide Y.; Duffy, Stephen W.; Agbaje, Olorunshola F.; Baker, Stuart G.; Christiani, David C.; Cassidy, Adrian; Field, John K.

    2013-01-01

    Background External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. Objective To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. Design Case–control and prospective cohort study. Setting Europe and North America. Patients Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study. Measurements 5-year absolute risks for lung cancer predicted by the LLP model. Results The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. Limitations The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. Conclusion Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population

  20. Prediction of prostate cancer in unscreened men: external validation of a risk calculator.

    PubMed

    van Vugt, Heidi A; Roobol, Monique J; Kranse, Ries; Määttänen, Liisa; Finne, Patrik; Hugosson, Jonas; Bangma, Chris H; Schröder, Fritz H; Steyerberg, Ewout W

    2011-04-01

    Prediction models need external validation to assess their value beyond the setting where the model was derived from. To assess the external validity of the European Randomized study of Screening for Prostate Cancer (ERSPC) risk calculator (www.prostatecancer-riskcalculator.com) for the probability of having a positive prostate biopsy (P(posb)). The ERSPC risk calculator was based on data of the initial screening round of the ERSPC section Rotterdam and validated in 1825 and 531 men biopsied at the initial screening round in the Finnish and Swedish sections of the ERSPC respectively. P(posb) was calculated using serum prostate specific antigen (PSA), outcome of digital rectal examination (DRE), transrectal ultrasound and ultrasound assessed prostate volume. The external validity was assessed for the presence of cancer at biopsy by calibration (agreement between observed and predicted outcomes), discrimination (separation of those with and without cancer), and decision curves (for clinical usefulness). Prostate cancer was detected in 469 men (26%) of the Finnish cohort and in 124 men (23%) of the Swedish cohort. Systematic miscalibration was present in both cohorts (mean predicted probability 34% versus 26% observed, and 29% versus 23% observed, both p<0.001). The areas under the curves were 0.76 and 0.78, and substantially lower for the model with PSA only (0.64 and 0.68 respectively). The model proved clinically useful for any decision threshold compared with a model with PSA only, PSA and DRE, or biopsying all men. A limitation is that the model is based on sextant biopsies results. The ERSPC risk calculator discriminated well between those with and without prostate cancer among initially screened men, but overestimated the risk of a positive biopsy. Further research is necessary to assess the performance and applicability of the ERSPC risk calculator when a clinical setting is considered rather than a screening setting. Copyright © 2010 Elsevier Ltd. All rights

  1. Update on breast cancer risk prediction and prevention.

    PubMed

    Sestak, Ivana; Cuzick, Jack

    2015-02-01

    Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk. The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings. There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

  2. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

    PubMed

    Heidari, Morteza; Khuzani, Abolfazl Zargari; Hollingsworth, Alan B; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

    2018-01-30

    In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

  3. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

    NASA Astrophysics Data System (ADS)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

    2018-02-01

    In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

  4. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    PubMed

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  5. Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study

    PubMed Central

    Hippisley-Cox, Julia; Coupland, Carol

    2015-01-01

    Objective To derive and validate a set of clinical risk prediction algorithm to estimate the 10-year risk of 11 common cancers. Design Prospective open cohort study using routinely collected data from 753 QResearch general practices in England. We used 565 practices to develop the scores and 188 for validation. Subjects 4.96 million patients aged 25–84 years in the derivation cohort; 1.64 million in the validation cohort. Patients were free of the relevant cancer at baseline. Methods Cox proportional hazards models in the derivation cohort to derive 10-year risk algorithms. Risk factors considered included age, ethnicity, deprivation, body mass index, smoking, alcohol, previous cancer diagnoses, family history of cancer, relevant comorbidities and medication. Measures of calibration and discrimination in the validation cohort. Outcomes Incident cases of blood, breast, bowel, gastro-oesophageal, lung, oral, ovarian, pancreas, prostate, renal tract and uterine cancers. Cancers were recorded on any one of four linked data sources (general practitioner (GP), mortality, hospital or cancer records). Results We identified 228 241 incident cases during follow-up of the 11 types of cancer. Of these 25 444 were blood; 41 315 breast; 32 626 bowel, 12 808 gastro-oesophageal; 32 187 lung; 4811 oral; 6635 ovarian; 7119 pancreatic; 35 256 prostate; 23 091 renal tract; 6949 uterine cancers. The lung cancer algorithm had the best performance with an R2 of 64.2%; D statistic of 2.74; receiver operating characteristic curve statistic of 0.91 in women. The sensitivity for the top 10% of women at highest risk of lung cancer was 67%. Performance of the algorithms in men was very similar to that for women. Conclusions We have developed and validated a prediction models to quantify absolute risk of 11 common cancers. They can be used to identify patients at high risk of cancers for prevention or further assessment. The algorithms could be integrated into clinical

  6. Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent.

    PubMed

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou; Michailidou, Kyriaki; Bolla, Manjeet K; Wang, Qin; Garcia-Closas, Montserrat; Milne, Roger L; Schmidt, Marjanka K; Chang-Claude, Jenny; Dunning, Allison; Bojesen, Stig E; Ahsan, Habibul; Aittomäki, Kristiina; Andrulis, Irene L; Anton-Culver, Hoda; Arndt, Volker; Beckmann, Matthias W; Beeghly-Fadiel, Alicia; Benitez, Javier; Bogdanova, Natalia V; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Casey, Graham; Chenevix-Trench, Georgia; Couch, Fergus J; Cox, Angela; Cross, Simon S; Czene, Kamila; Devilee, Peter; Dörk, Thilo; Dumont, Martine; Fasching, Peter A; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fostira, Florentia; Gammon, Marilie; Giles, Graham G; Guénel, Pascal; Haiman, Christopher A; Hamann, Ute; Hooning, Maartje J; Hopper, John L; Jakubowska, Anna; Jasmine, Farzana; Jenkins, Mark; John, Esther M; Johnson, Nichola; Jones, Michael E; Kabisch, Maria; Kibriya, Muhammad; Knight, Julia A; Koppert, Linetta B; Kosma, Veli-Matti; Kristensen, Vessela; Le Marchand, Loic; Lee, Eunjung; Li, Jingmei; Lindblom, Annika; Luben, Robert; Lubinski, Jan; Malone, Kathi E; Mannermaa, Arto; Margolin, Sara; Marme, Frederik; McLean, Catriona; Meijers-Heijboer, Hanne; Meindl, Alfons; Neuhausen, Susan L; Nevanlinna, Heli; Neven, Patrick; Olson, Janet E; Perez, Jose I A; Perkins, Barbara; Peterlongo, Paolo; Phillips, Kelly-Anne; Pylkäs, Katri; Rudolph, Anja; Santella, Regina; Sawyer, Elinor J; Schmutzler, Rita K; Seynaeve, Caroline; Shah, Mitul; Shrubsole, Martha J; Southey, Melissa C; Swerdlow, Anthony J; Toland, Amanda E; Tomlinson, Ian; Torres, Diana; Truong, Thérèse; Ursin, Giske; Van Der Luijt, Rob B; Verhoef, Senno; Whittemore, Alice S; Winqvist, Robert; Zhao, Hui; Zhao, Shilin; Hall, Per; Simard, Jacques; Kraft, Peter; Pharoah, Paul; Hunter, David; Easton, Douglas F; Zheng, Wei

    2016-08-01

    Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases  =  46,325, controls  =  42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 × 10-10). The associations were similar for both premenopausal (OR   =   0.44, 95% CI:0.31-0.62, p  =  9.91 × 10-8) and postmenopausal breast cancer (OR  =  0.57, 95% CI: 0.46-0.71, p  =  1.88 × 10-8). This association was replicated in the data from the DRIVE consortium (OR  =  0.72, 95% CI: 0.60-0.84, p   =   1.64 × 10-7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele

  7. Application of the Rosner-Colditz Risk Prediction Model to Estimate Sexual Orientation Group Disparities in Breast Cancer Risk in a U.S. Cohort of Premenopausal Women

    PubMed Central

    Austin, S. Bryn; Pazaris, Mathew J.; Rosner, Bernard; Bowen, Deborah; Rich-Edwards, Janet; Spiegelman, Donna

    2012-01-01

    Background Lesbian and bisexual women may be at greater risk of breast cancer than heterosexual women during the premenopausal period due to disparities in risk factors. Methods With 16 years of prospective data from a large cohort of U.S. women ages 25–58 years, we conducted a breast cancer risk assessment for 87,392 premenopausal women by applying the Rosner-Colditz biomathematical risk-prediction model to estimate breast cancer risk based on known risk factors. Based on each woman’s comprehensive risk factor profile, we calculated the predicted one-year incidence rate (IR) per 100,000 person-years and estimated incidence rate ratios (IRR) and 95% confidence intervals (CI) for lesbian and bisexual women compared to heterosexual women. Results 87,392 premenopausal women provided 1,091,871 person-years of data included in analyses. Mean predicted one-year breast cancer IRs per 100,000 person-years for each sexual orientation group were: heterosexual 122.55, lesbian 131.61, and bisexual 131.72. IRs were significantly elevated in both lesbian (IRR 1.06; 95 CI 1.06, 1.06) and bisexual (IRR 1.10; 95% CI 1.10, 1.10) women compared to heterosexual women. Conclusions Our findings suggest both lesbian and bisexual women have slightly elevated predicted breast cancer incidence compared to heterosexual women throughout the premenopausal period. Impact Health professionals must ensure that breast cancer prevention efforts are reaching these women. As more health systems around the country collect data on patient sexual orientation, the National Cancer Institute’s SEER cancer registry should add this information to its data system to monitor progress in reducing sexual orientation-related disparities in cancer incidence and mortality. PMID:23035180

  8. Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.

    PubMed

    Katki, Hormuzd A; Kovalchik, Stephanie A; Petito, Lucia C; Cheung, Li C; Jacobs, Eric; Jemal, Ahmedin; Berg, Christine D; Chaturvedi, Anil K

    2018-05-15

    Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown. To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts. Population-based prospective studies. United States. Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort. Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]). At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the

  9. Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

    PubMed Central

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou; Michailidou, Kyriaki; Bolla, Manjeet K.; Wang, Qin; Garcia-Closas, Montserrat; Milne, Roger L.; Schmidt, Marjanka K.; Chang-Claude, Jenny; Dunning, Allison; Bojesen, Stig E.; Ahsan, Habibul; Aittomäki, Kristiina; Andrulis, Irene L.; Anton-Culver, Hoda; Beckmann, Matthias W.; Beeghly-Fadiel, Alicia; Benitez, Javier; Bogdanova, Natalia V.; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Casey, Graham; Chenevix-Trench, Georgia; Couch, Fergus J.; Cross, Simon S.; Czene, Kamila; Dörk, Thilo; Dumont, Martine; Fasching, Peter A.; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fostira, Florentia; Gammon, Marilie; Giles, Graham G.; Guénel, Pascal; Haiman, Christopher A.; Hamann, Ute; Hooning, Maartje J.; Hopper, John L.; Jakubowska, Anna; Jasmine, Farzana; Jenkins, Mark; John, Esther M.; Johnson, Nichola; Jones, Michael E.; Kabisch, Maria; Knight, Julia A.; Koppert, Linetta B.; Kosma, Veli-Matti; Kristensen, Vessela; Le Marchand, Loic; Lee, Eunjung; Li, Jingmei; Lindblom, Annika; Lubinski, Jan; Malone, Kathi E.; Mannermaa, Arto; Margolin, Sara; McLean, Catriona; Meindl, Alfons; Neuhausen, Susan L.; Nevanlinna, Heli; Neven, Patrick; Olson, Janet E.; Perez, Jose I. A.; Perkins, Barbara; Phillips, Kelly-Anne; Pylkäs, Katri; Rudolph, Anja; Santella, Regina; Sawyer, Elinor J.; Schmutzler, Rita K.; Seynaeve, Caroline; Shah, Mitul; Shrubsole, Martha J.; Southey, Melissa C.; Swerdlow, Anthony J.; Toland, Amanda E.; Tomlinson, Ian; Torres, Diana; Truong, Thérèse; Ursin, Giske; Van Der Luijt, Rob B.; Verhoef, Senno; Whittemore, Alice S.; Winqvist, Robert; Zhao, Hui; Zhao, Shilin; Hall, Per; Simard, Jacques; Kraft, Peter; Hunter, David; Easton, Douglas F.; Zheng, Wei

    2016-01-01

    Background Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. Methods We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases  =  46,325, controls  =  42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. Results In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56–0.75, p = 3.32 × 10−10). The associations were similar for both premenopausal (OR   =   0.44, 95% CI:0.31–0.62, p  =  9.91 × 10−8) and postmenopausal breast cancer (OR  =  0.57, 95% CI: 0.46–0.71, p  =  1.88 × 10−8). This association was replicated in the data from the DRIVE consortium (OR  =  0.72, 95% CI: 0.60–0.84, p   =   1.64 × 10−7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p

  10. Prediction of Breast and Prostate Cancer Risks in Male BRCA1 and BRCA2 Mutation Carriers Using Polygenic Risk Scores

    PubMed Central

    Lecarpentier, Julie; Silvestri, Valentina; Kuchenbaecker, Karoline B.; Barrowdale, Daniel; Dennis, Joe; McGuffog, Lesley; Soucy, Penny; Leslie, Goska; Rizzolo, Piera; Navazio, Anna Sara; Valentini, Virginia; Zelli, Veronica; Lee, Andrew; Amin Al Olama, Ali; Tyrer, Jonathan P.; Southey, Melissa; John, Esther M.; Conner, Thomas A.; Goldgar, David E.; Buys, Saundra S.; Janavicius, Ramunas; Steele, Linda; Ding, Yuan Chun; Neuhausen, Susan L.; Hansen, Thomas V.O.; Osorio, Ana; Weitzel, Jeffrey N.; Toss, Angela; Medici, Veronica; Cortesi, Laura; Zanna, Ines; Palli, Domenico; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Azzollini, Jacopo; Viel, Alessandra; Cini, Giulia; Damante, Giuseppe; Tommasi, Stefania; Peterlongo, Paolo; Fostira, Florentia; Hamann, Ute; Evans, D. Gareth; Henderson, Alex; Brewer, Carole; Eccles, Diana; Cook, Jackie; Ong, Kai-ren; Walker, Lisa; Side, Lucy E.; Porteous, Mary E.; Davidson, Rosemarie; Hodgson, Shirley; Frost, Debra; Adlard, Julian; Izatt, Louise; Eeles, Ros; Ellis, Steve; Tischkowitz, Marc; Godwin, Andrew K.; Meindl, Alfons; Gehrig, Andrea; Dworniczak, Bernd; Sutter, Christian; Engel, Christoph; Niederacher, Dieter; Steinemann, Doris; Hahnen, Eric; Hauke, Jan; Rhiem, Kerstin; Kast, Karin; Arnold, Norbert; Ditsch, Nina; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Wand, Dorothea; Lasset, Christine; Stoppa-Lyonnet, Dominique; Belotti, Muriel; Damiola, Francesca; Barjhoux, Laure; Mazoyer, Sylvie; Van Heetvelde, Mattias; Poppe, Bruce; De Leeneer, Kim; Claes, Kathleen B.M.; de la Hoya, Miguel; Garcia-Barberan, Vanesa; Caldes, Trinidad; Perez Segura, Pedro; Kiiski, Johanna I.; Aittomäki, Kristiina; Khan, Sofia; Nevanlinna, Heli; van Asperen, Christi J.; Vaszko, Tibor; Kasler, Miklos; Olah, Edith; Balmaña, Judith; Gutiérrez-Enríquez, Sara; Diez, Orland; Teulé, Alex; Izquierdo, Angel; Darder, Esther; Brunet, Joan; Del Valle, Jesús; Feliubadalo, Lidia; Pujana, Miquel Angel; Lazaro, Conxi; Arason, Adalgeir; Agnarsson, Bjarni A.; Johannsson, Oskar Th.; Barkardottir, Rosa B.; Alducci, Elisa; Tognazzo, Silvia; Montagna, Marco; Teixeira, Manuel R.; Pinto, Pedro; Spurdle, Amanda B.; Holland, Helene; Lee, Jong Won; Lee, Min Hyuk; Lee, Jihyoun; Kim, Sung-Won; Kang, Eunyoung; Kim, Zisun; Sharma, Priyanka; Rebbeck, Timothy R.; Vijai, Joseph; Robson, Mark; Lincoln, Anne; Musinsky, Jacob; Gaddam, Pragna; Tan, Yen Y.; Berger, Andreas; Singer, Christian F.; Loud, Jennifer T.; Greene, Mark H.; Mulligan, Anna Marie; Glendon, Gord; Andrulis, Irene L.; Toland, Amanda Ewart; Senter, Leigha; Bojesen, Anders; Nielsen, Henriette Roed; Skytte, Anne-Bine; Sunde, Lone; Jensen, Uffe Birk; Pedersen, Inge Sokilde; Krogh, Lotte; Kruse, Torben A.; Caligo, Maria A.; Yoon, Sook-Yee; Teo, Soo-Hwang; von Wachenfeldt, Anna; Huo, Dezheng; Nielsen, Sarah M.; Olopade, Olufunmilayo I.; Nathanson, Katherine L.; Domchek, Susan M.; Lorenchick, Christa; Jankowitz, Rachel C.; Campbell, Ian; James, Paul; Mitchell, Gillian; Orr, Nick; Park, Sue Kyung; Thomassen, Mads; Offit, Kenneth; Couch, Fergus J.; Simard, Jacques; Easton, Douglas F.; Chenevix-Trench, Georgia; Schmutzler, Rita K.; Antoniou, Antonis C.; Ottini, Laura

    2017-01-01

    Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated—for the first time to our knowledge—associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10−6). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10−9). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management. PMID:28448241

  11. Prediction of Breast and Prostate Cancer Risks in Male BRCA1 and BRCA2 Mutation Carriers Using Polygenic Risk Scores.

    PubMed

    Lecarpentier, Julie; Silvestri, Valentina; Kuchenbaecker, Karoline B; Barrowdale, Daniel; Dennis, Joe; McGuffog, Lesley; Soucy, Penny; Leslie, Goska; Rizzolo, Piera; Navazio, Anna Sara; Valentini, Virginia; Zelli, Veronica; Lee, Andrew; Amin Al Olama, Ali; Tyrer, Jonathan P; Southey, Melissa; John, Esther M; Conner, Thomas A; Goldgar, David E; Buys, Saundra S; Janavicius, Ramunas; Steele, Linda; Ding, Yuan Chun; Neuhausen, Susan L; Hansen, Thomas V O; Osorio, Ana; Weitzel, Jeffrey N; Toss, Angela; Medici, Veronica; Cortesi, Laura; Zanna, Ines; Palli, Domenico; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Azzollini, Jacopo; Viel, Alessandra; Cini, Giulia; Damante, Giuseppe; Tommasi, Stefania; Peterlongo, Paolo; Fostira, Florentia; Hamann, Ute; Evans, D Gareth; Henderson, Alex; Brewer, Carole; Eccles, Diana; Cook, Jackie; Ong, Kai-Ren; Walker, Lisa; Side, Lucy E; Porteous, Mary E; Davidson, Rosemarie; Hodgson, Shirley; Frost, Debra; Adlard, Julian; Izatt, Louise; Eeles, Ros; Ellis, Steve; Tischkowitz, Marc; Godwin, Andrew K; Meindl, Alfons; Gehrig, Andrea; Dworniczak, Bernd; Sutter, Christian; Engel, Christoph; Niederacher, Dieter; Steinemann, Doris; Hahnen, Eric; Hauke, Jan; Rhiem, Kerstin; Kast, Karin; Arnold, Norbert; Ditsch, Nina; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Wand, Dorothea; Lasset, Christine; Stoppa-Lyonnet, Dominique; Belotti, Muriel; Damiola, Francesca; Barjhoux, Laure; Mazoyer, Sylvie; Van Heetvelde, Mattias; Poppe, Bruce; De Leeneer, Kim; Claes, Kathleen B M; de la Hoya, Miguel; Garcia-Barberan, Vanesa; Caldes, Trinidad; Perez Segura, Pedro; Kiiski, Johanna I; Aittomäki, Kristiina; Khan, Sofia; Nevanlinna, Heli; van Asperen, Christi J; Vaszko, Tibor; Kasler, Miklos; Olah, Edith; Balmaña, Judith; Gutiérrez-Enríquez, Sara; Diez, Orland; Teulé, Alex; Izquierdo, Angel; Darder, Esther; Brunet, Joan; Del Valle, Jesús; Feliubadalo, Lidia; Pujana, Miquel Angel; Lazaro, Conxi; Arason, Adalgeir; Agnarsson, Bjarni A; Johannsson, Oskar Th; Barkardottir, Rosa B; Alducci, Elisa; Tognazzo, Silvia; Montagna, Marco; Teixeira, Manuel R; Pinto, Pedro; Spurdle, Amanda B; Holland, Helene; Lee, Jong Won; Lee, Min Hyuk; Lee, Jihyoun; Kim, Sung-Won; Kang, Eunyoung; Kim, Zisun; Sharma, Priyanka; Rebbeck, Timothy R; Vijai, Joseph; Robson, Mark; Lincoln, Anne; Musinsky, Jacob; Gaddam, Pragna; Tan, Yen Y; Berger, Andreas; Singer, Christian F; Loud, Jennifer T; Greene, Mark H; Mulligan, Anna Marie; Glendon, Gord; Andrulis, Irene L; Toland, Amanda Ewart; Senter, Leigha; Bojesen, Anders; Nielsen, Henriette Roed; Skytte, Anne-Bine; Sunde, Lone; Jensen, Uffe Birk; Pedersen, Inge Sokilde; Krogh, Lotte; Kruse, Torben A; Caligo, Maria A; Yoon, Sook-Yee; Teo, Soo-Hwang; von Wachenfeldt, Anna; Huo, Dezheng; Nielsen, Sarah M; Olopade, Olufunmilayo I; Nathanson, Katherine L; Domchek, Susan M; Lorenchick, Christa; Jankowitz, Rachel C; Campbell, Ian; James, Paul; Mitchell, Gillian; Orr, Nick; Park, Sue Kyung; Thomassen, Mads; Offit, Kenneth; Couch, Fergus J; Simard, Jacques; Easton, Douglas F; Chenevix-Trench, Georgia; Schmutzler, Rita K; Antoniou, Antonis C; Ottini, Laura

    2017-07-10

    Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated-for the first time to our knowledge-associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/ 2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10 -6 ). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10 -9 ). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management.

  12. Breast cancer risk prediction using a clinical risk model and polygenic risk score.

    PubMed

    Shieh, Yiwey; Hu, Donglei; Ma, Lin; Huntsman, Scott; Gard, Charlotte C; Leung, Jessica W T; Tice, Jeffrey A; Vachon, Celine M; Cummings, Steven R; Kerlikowske, Karla; Ziv, Elad

    2016-10-01

    Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ≥3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.

  13. Breast Cancer Risk Prediction Using a Clinical Risk Model and Polygenic Risk Score

    PubMed Central

    Shieh, Yiwey; Hu, Donglei; Ma, Lin; Huntsman, Scott; Gard, Charlotte C.; Leung, Jessica W.T.; Tice, Jeffrey A.; Vachon, Celine M.; Cummings, Steven R.; Kerlikowske, Karla; Ziv, Elad

    2016-01-01

    Purpose Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome wide association studies. Methods We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Results Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95% CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95% CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95% CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18% of cases as high-risk (5-year risk ≥ 3%), compared with 7% using the BCSC model. Conclusion The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Impact Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited. PMID:27565998

  14. Assessment of two mammographic density related features in predicting near-term breast cancer risk

    NASA Astrophysics Data System (ADS)

    Zheng, Bin; Sumkin, Jules H.; Zuley, Margarita L.; Wang, Xingwei; Klym, Amy H.; Gur, David

    2012-02-01

    In order to establish a personalized breast cancer screening program, it is important to develop risk models that have high discriminatory power in predicting the likelihood of a woman developing an imaging detectable breast cancer in near-term (e.g., <3 years after a negative examination in question). In epidemiology-based breast cancer risk models, mammographic density is considered the second highest breast cancer risk factor (second to woman's age). In this study we explored a new feature, namely bilateral mammographic density asymmetry, and investigated the feasibility of predicting near-term screening outcome. The database consisted of 343 negative examinations, of which 187 depicted cancers that were detected during the subsequent screening examination and 155 that remained negative. We computed the average pixel value of the segmented breast areas depicted on each cranio-caudal view of the initial negative examinations. We then computed the mean and difference mammographic density for paired bilateral images. Using woman's age, subjectively rated density (BIRADS), and computed mammographic density related features we compared classification performance in estimating the likelihood of detecting cancer during the subsequent examination using areas under the ROC curves (AUC). The AUCs were 0.63+/-0.03, 0.54+/-0.04, 0.57+/-0.03, 0.68+/-0.03 when using woman's age, BIRADS rating, computed mean density and difference in computed bilateral mammographic density, respectively. Performance increased to 0.62+/-0.03 and 0.72+/-0.03 when we fused mean and difference in density with woman's age. The results suggest that, in this study, bilateral mammographic tissue density is a significantly stronger (p<0.01) risk indicator than both woman's age and mean breast density.

  15. Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study.

    PubMed

    Muller, David C; Johansson, Mattias; Brennan, Paul

    2017-03-10

    Purpose Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study. Methods This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]). Results During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; p FEV1 = 3.4 × 10 -13 ). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]). Conclusion A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.

  16. Biological and statistical approaches to predicting human lung cancer risk from silica.

    PubMed

    Kuempel, E D; Tran, C L; Bailer, A J; Porter, D W; Hubbs, A F; Castranova, V

    2001-01-01

    Chronic inflammation is a key step in the pathogenesis of particle-elicited fibrosis and lung cancer in rats, and possibly in humans. In this study, we compute the excess risk estimates for lung cancer in humans with occupational exposure to crystalline silica, using both rat and human data, and using both a threshold approach and linear models. From a toxicokinetic/dynamic model fit to lung burden and pulmonary response data from a subchronic inhalation study in rats, we estimated the minimum critical quartz lung burden (Mcrit) associated with reduced pulmonary clearance and increased neutrophilic inflammation. A chronic study in rats was also used to predict the human excess risk of lung cancer at various quartz burdens, including mean Mcrit (0.39 mg/g lung). We used a human kinetic lung model to link the equivalent lung burdens to external exposures in humans. We then computed the excess risk of lung cancer at these external exposures, using data of workers exposed to respirable crystalline silica and using Poisson regression and lifetable analyses. Finally, we compared the lung cancer excess risks estimated from male rat and human data. We found that the rat-based linear model estimates were approximately three times higher than those based on human data (e.g., 2.8% in rats vs. 0.9-1% in humans, at mean Mcrit lung burden or associated mean working lifetime exposure of 0.036 mg/m3). Accounting for variability and uncertainty resulted in 100-1000 times lower estimates of human critical lung burden and airborne exposure. This study illustrates that assumptions about the relevant biological mechanism, animal model, and statistical approach can all influence the magnitude of lung cancer risk estimates in humans exposed to crystalline silica.

  17. Risk prediction models of breast cancer: a systematic review of model performances.

    PubMed

    Anothaisintawee, Thunyarat; Teerawattananon, Yot; Wiratkapun, Chollathip; Kasamesup, Vijj; Thakkinstian, Ammarin

    2012-05-01

    The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.

  18. Clinical audit in gynecological cancer surgery: development of a risk scoring system to predict adverse events.

    PubMed

    Kondalsamy-Chennakesavan, Srinivas; Bouman, Chantal; De Jong, Suzanne; Sanday, Karen; Nicklin, Jim; Land, Russell; Obermair, Andreas

    2009-12-01

    Advanced gynecological surgery undertaken in a specialized gynecologic oncology unit may be associated with significant perioperative morbidity. Validated risk prediction models are available for general surgical specialties but currently not for gynecological cancer surgery. The objective of this study was to evaluate risk factors for adverse events (AEs) of patients treated for suspected or proven gynecological cancer and to develop a clinical risk score (RS) to predict such AEs. AEs were prospectively recorded and matched with demographical, clinical and histopathological data on 369 patients who had an abdominal or laparoscopic procedure for proven or suspected gynecological cancer at a tertiary gynecological cancer center. Stepwise multiple logistic regression was used to determine the best predictors of AEs. For the risk score (RS), the coefficients from the model were scaled using a factor of 2 and rounded to the nearest integer to derive the risk points. Sum of all the risk points form the RS. Ninety-five patients (25.8%) had at least one AE. Twenty-nine (7.9%) and 77 (20.9%) patients experienced intra- and postoperative AEs respectively with 11 patients (3.0%) experiencing both. The independent predictors for any AE were complexity of the surgical procedure, elevated SGOT (serum glutamic oxaloacetic transaminase, > or /=35 U/L), higher ASA scores and overweight. The risk score can vary from 0 to 14. The risk for developing any AE is described by the formula 100 / (1 + e((3.697 - (RS /2)))). RS allows for quantification of the risk for AEs. Risk factors are generally not modifiable with the possible exception of obesity.

  19. Thyroid Cancer Risk Assessment Tool

    Cancer.gov

    The R package thyroid implements a risk prediction model developed by NCI researchers to calculate the absolute risk of developing a second primary thyroid cancer (SPTC) in individuals who were diagnosed with a cancer during their childhood.

  20. The more from East-Asian, the better: risk prediction of colorectal cancer risk by GWAS-identified SNPs among Japanese.

    PubMed

    Abe, Makiko; Ito, Hidemi; Oze, Isao; Nomura, Masatoshi; Ogawa, Yoshihiro; Matsuo, Keitaro

    2017-12-01

    Little is known about the difference of genetic predisposition for CRC between ethnicities; however, many genetic traits common to colorectal cancer have been identified. This study investigated whether more SNPs identified in GWAS in East Asian population could improve the risk prediction of Japanese and explored possible application of genetic risk groups as an instrument of the risk communication. 558 Patients histologically verified colorectal cancer and 1116 first-visit outpatients were included for derivation study, and 547 cases and 547 controls were for replication study. Among each population, we evaluated prediction models for the risk of CRC that combined the genetic risk group based on SNPs from GWASs in European-population and a similarly developed model adding SNPs from GWASs in East Asian-population. We examined whether adding East Asian-specific SNPs would improve the discrimination. Six SNPs (rs6983267, rs4779584, rs4444235, rs9929218, rs10936599, rs16969681) from 23 SNPs by European-based GWAS and five SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279) among ten SNPs by Asian-based GWAS were selected in CRC risk prediction model. Compared with a 6-SNP-based model, an 11-SNP model including Asian GWAS-SNPs showed improved discrimination capacity in Receiver operator characteristic analysis. A model with 11 SNPs resulted in statistically significant improvement in both derivation (P = 0.0039) and replication studies (P = 0.0018) compared with six SNP model. We estimated cumulative risk of CRC by using genetic risk group based on 11 SNPs and found that the cumulative risk at age 80 is approximately 13% in the high-risk group while 6% in the low-risk group. We constructed a more efficient CRC risk prediction model with 11 SNPs including newly identified East Asian-based GWAS SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279). Risk grouping based on 11 SNPs depicted lifetime difference of CRC risk. This might be useful for

  1. Predicting reattendance at a high-risk breast cancer clinic.

    PubMed

    Ormseth, Sarah R; Wellisch, David K; Aréchiga, Adam E; Draper, Taylor L

    2015-10-01

    The research about follow-up patterns of women attending high-risk breast-cancer clinics is sparse. This study sought to profile daughters of breast-cancer patients who are likely to return versus those unlikely to return for follow-up care in a high-risk clinic. Our investigation included 131 patients attending the UCLA Revlon Breast Center High Risk Clinic. Predictor variables included age, computed breast-cancer risk, participants' perceived personal risk, clinically significant depressive symptomatology (CES-D score ≥ 16), current level of anxiety (State-Trait Anxiety Inventory), and survival status of participants' mothers (survived or passed away from breast cancer). A greater likelihood of reattendance was associated with older age (adjusted odds ratio [AOR] = 1.07, p = 0.004), computed breast-cancer risk (AOR = 1.10, p = 0.017), absence of depressive symptomatology (AOR = 0.25, p = 0.009), past psychiatric diagnosis (AOR = 3.14, p = 0.029), and maternal loss to breast cancer (AOR = 2.59, p = 0.034). Also, an interaction was found between mother's survival and perceived risk (p = 0.019), such that reattendance was associated with higher perceived risk among participants whose mothers survived (AOR = 1.04, p = 0.002), but not those whose mothers died (AOR = 0.99, p = 0.685). Furthermore, a nonlinear inverted "U" relationship was observed between state anxiety and reattendance (p = 0.037); participants with moderate anxiety were more likely to reattend than those with low or high anxiety levels. Demographic, medical, and psychosocial factors were found to be independently associated with reattendance to a high-risk breast-cancer clinic. Explication of the profiles of women who may or may not reattend may serve to inform the development and implementation of interventions to increase the likelihood of follow-up care.

  2. Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

    NASA Astrophysics Data System (ADS)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

    2018-02-01

    Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

  3. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

    NASA Astrophysics Data System (ADS)

    Qiu, Yuchen; Wang, Yunzhi; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

    2016-03-01

    In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 "prior" negative screening cases was assembled. In the next sequential ("current") screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.

  4. Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

    DOE PAGES

    Cucinotta, Francis A.; Cacao, Eliedonna

    2017-05-12

    Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

  5. Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Cucinotta, Francis A.; Cacao, Eliedonna

    Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

  6. Beyond D'Amico risk classes for predicting recurrence after external beam radiotherapy for prostate cancer: the Candiolo classifier.

    PubMed

    Gabriele, Domenico; Jereczek-Fossa, Barbara A; Krengli, Marco; Garibaldi, Elisabetta; Tessa, Maria; Moro, Gregorio; Girelli, Giuseppe; Gabriele, Pietro

    2016-02-24

    The aim of this work is to develop an algorithm to predict recurrence in prostate cancer patients treated with radical radiotherapy, getting up to a prognostic power higher than traditional D'Amico risk classification. Two thousand four hundred ninety-three men belonging to the EUREKA-2 retrospective multi-centric database on prostate cancer and treated with external-beam radiotherapy as primary treatment comprised the study population. A Cox regression time to PSA failure analysis was performed in univariate and multivariate settings, evaluating the predictive ability of age, pre-treatment PSA, clinical-radiological staging, Gleason score and percentage of positive cores at biopsy (%PC). The accuracy of this model was checked with bootstrapping statistics. Subgroups for all the variables' combinations were combined to classify patients into five different "Candiolo" risk-classes for biochemical Progression Free Survival (bPFS); thereafter, they were also applied to clinical PFS (cPFS), systemic PFS (sPFS) and Prostate Cancer Specific Survival (PCSS), and compared to D'Amico risk grouping performances. The Candiolo classifier splits patients in 5 risk-groups with the following 10-years bPFS, cPFS, sPFS and PCSS: for very-low-risk 90 %, 94 %, 100 % and 100 %; for low-risk 74 %, 88 %, 94 % and 98 %; for intermediate-risk 60 %, 82 %, 91 % and 92 %; for high-risk 43 %, 55 %, 80 % and 89 % and for very-high-risk 14 %, 38 %, 56 % and 70 %. Our classifier outperforms D'Amico risk classes for all the end-points evaluated, with concordance indexes of 71.5 %, 75.5 %, 80 % and 80.5 % versus 63 %, 65.5 %, 69.5 % and 69 %, respectively. Our classification tool, combining five clinical and easily available parameters, seems to better stratify patients in predicting prostate cancer recurrence after radiotherapy compared to the traditional D'Amico risk classes.

  7. Lung cancer risk prediction to select smokers for screening CT--a model based on the Italian COSMOS trial.

    PubMed

    Maisonneuve, Patrick; Bagnardi, Vincenzo; Bellomi, Massimo; Spaggiari, Lorenzo; Pelosi, Giuseppe; Rampinelli, Cristiano; Bertolotti, Raffaella; Rotmensz, Nicole; Field, John K; Decensi, Andrea; Veronesi, Giulia

    2011-11-01

    Screening with low-dose helical computed tomography (CT) has been shown to significantly reduce lung cancer mortality but the optimal target population and time interval to subsequent screening are yet to be defined. We developed two models to stratify individual smokers according to risk of developing lung cancer. We first used the number of lung cancers detected at baseline screening CT in the 5,203 asymptomatic participants of the COSMOS trial to recalibrate the Bach model, which we propose using to select smokers for screening. Next, we incorporated lung nodule characteristics and presence of emphysema identified at baseline CT into the Bach model and proposed the resulting multivariable model to predict lung cancer risk in screened smokers after baseline CT. Age and smoking exposure were the main determinants of lung cancer risk. The recalibrated Bach model accurately predicted lung cancers detected during the first year of screening. Presence of nonsolid nodules (RR = 10.1, 95% CI = 5.57-18.5), nodule size more than 8 mm (RR = 9.89, 95% CI = 5.84-16.8), and emphysema (RR = 2.36, 95% CI = 1.59-3.49) at baseline CT were all significant predictors of subsequent lung cancers. Incorporation of these variables into the Bach model increased the predictive value of the multivariable model (c-index = 0.759, internal validation). The recalibrated Bach model seems suitable for selecting the higher risk population for recruitment for large-scale CT screening. The Bach model incorporating CT findings at baseline screening could help defining the time interval to subsequent screening in individual participants. Further studies are necessary to validate these models.

  8. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers.

    PubMed

    Kuchenbaecker, Karoline B; McGuffog, Lesley; Barrowdale, Daniel; Lee, Andrew; Soucy, Penny; Dennis, Joe; Domchek, Susan M; Robson, Mark; Spurdle, Amanda B; Ramus, Susan J; Mavaddat, Nasim; Terry, Mary Beth; Neuhausen, Susan L; Schmutzler, Rita Katharina; Simard, Jacques; Pharoah, Paul D P; Offit, Kenneth; Couch, Fergus J; Chenevix-Trench, Georgia; Easton, Douglas F; Antoniou, Antonis C

    2017-07-01

    Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P =  8.2×10 -53 ). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P =  7.2×10 -20 ). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management. © The Author 2017. Published by Oxford

  9. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

    PubMed Central

    Kuchenbaecker, Karoline B.; McGuffog, Lesley; Barrowdale, Daniel; Lee, Andrew; Soucy, Penny; Healey, Sue; Dennis, Joe; Lush, Michael; Robson, Mark; Spurdle, Amanda B.; Ramus, Susan J.; Mavaddat, Nasim; Terry, Mary Beth; Neuhausen, Susan L.; Hamann, Ute; Southey, Melissa; John, Esther M.; Chung, Wendy K.; Daly, Mary B.; Buys, Saundra S.; Goldgar, David E.; Dorfling, Cecilia M.; van Rensburg, Elizabeth J.; Ding, Yuan Chun; Ejlertsen, Bent; Gerdes, Anne-Marie; Hansen, Thomas V. O.; Slager, Susan; Hallberg, Emily; Benitez, Javier; Osorio, Ana; Cohen, Nancy; Lawler, William; Weitzel, Jeffrey N.; Peterlongo, Paolo; Pensotti, Valeria; Dolcetti, Riccardo; Barile, Monica; Bonanni, Bernardo; Azzollini, Jacopo; Manoukian, Siranoush; Peissel, Bernard; Radice, Paolo; Savarese, Antonella; Papi, Laura; Giannini, Giuseppe; Fostira, Florentia; Konstantopoulou, Irene; Adlard, Julian; Brewer, Carole; Cook, Jackie; Davidson, Rosemarie; Eccles, Diana; Eeles, Ros; Ellis, Steve; Frost, Debra; Hodgson, Shirley; Izatt, Louise; Lalloo, Fiona; Ong, Kai-ren; Godwin, Andrew K.; Arnold, Norbert; Dworniczak, Bernd; Engel, Christoph; Gehrig, Andrea; Hahnen, Eric; Hauke, Jan; Kast, Karin; Meindl, Alfons; Niederacher, Dieter; Schmutzler, Rita Katharina; Varon-Mateeva, Raymonda; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Barjhoux, Laure; Collonge-Rame, Marie-Agnès; Elan, Camille; Golmard, Lisa; Barouk-Simonet, Emmanuelle; Lesueur, Fabienne; Mazoyer, Sylvie; Sokolowska, Joanna; Stoppa-Lyonnet, Dominique; Isaacs, Claudine; Claes, Kathleen B. M.; Poppe, Bruce; de la Hoya, Miguel; Garcia-Barberan, Vanesa; Aittomäki, Kristiina; Nevanlinna, Heli; Ausems, Margreet G. E. M.; de Lange, J. L.; Gómez Garcia, Encarna B.; Hogervorst, Frans B. L.; Kets, Carolien M.; Meijers-Heijboer, Hanne E. J.; Oosterwijk, Jan C.; Rookus, Matti A.; van Asperen, Christi J.; van den Ouweland, Ans M. W.; van Doorn, Helena C.; van Os, Theo A. M.; Kwong, Ava; Olah, Edith; Diez, Orland; Brunet, Joan; Lazaro, Conxi; Teulé, Alex; Gronwald, Jacek; Jakubowska, Anna; Kaczmarek, Katarzyna; Lubinski, Jan; Sukiennicki, Grzegorz; Barkardottir, Rosa B.; Chiquette, Jocelyne; Agata, Simona; Montagna, Marco; Teixeira, Manuel R.; Park, Sue Kyung; Olswold, Curtis; Tischkowitz, Marc; Foretova, Lenka; Gaddam, Pragna; Vijai, Joseph; Pfeiler, Georg; Rappaport-Fuerhauser, Christine; Singer, Christian F.; Tea, Muy-Kheng M.; Greene, Mark H.; Loud, Jennifer T.; Rennert, Gad; Imyanitov, Evgeny N.; Hulick, Peter J.; Hays, John L.; Piedmonte, Marion; Rodriguez, Gustavo C.; Martyn, Julie; Glendon, Gord; Mulligan, Anna Marie; Andrulis, Irene L.; Toland, Amanda Ewart; Jensen, Uffe Birk; Kruse, Torben A.; Pedersen, Inge Sokilde; Thomassen, Mads; Caligo, Maria A.; Teo, Soo-Hwang; Berger, Raanan; Friedman, Eitan; Laitman, Yael; Arver, Brita; Borg, Ake; Ehrencrona, Hans; Rantala, Johanna; Olopade, Olufunmilayo I.; Ganz, Patricia A.; Nussbaum, Robert L.; Bradbury, Angela R.; Domchek, Susan M.; Nathanson, Katherine L.; Arun, Banu K.; James, Paul; Karlan, Beth Y.; Lester, Jenny; Simard, Jacques; Pharoah, Paul D. P.; Offit, Kenneth; Couch, Fergus J.; Chenevix-Trench, Georgia; Easton, Douglas F.

    2017-01-01

    Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]–positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2×10−53). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2×10−20). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management. PMID

  10. Risk determination and prevention of breast cancer.

    PubMed

    Howell, Anthony; Anderson, Annie S; Clarke, Robert B; Duffy, Stephen W; Evans, D Gareth; Garcia-Closas, Montserat; Gescher, Andy J; Key, Timothy J; Saxton, John M; Harvie, Michelle N

    2014-09-28

    Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our

  11. Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk.

    PubMed

    Schonberg, Mara A; Li, Vicky W; Eliassen, A Heather; Davis, Roger B; LaCroix, Andrea Z; McCarthy, Ellen P; Rosner, Bernard A; Chlebowski, Rowan T; Hankinson, Susan E; Marcantonio, Edward R; Ngo, Long H

    2016-12-01

    Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). Within 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]). We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.

  12. Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk

    PubMed Central

    Schonberg, Mara A.; Li, Vicky W.; Eliassen, A. Heather; Davis, Roger B.; LaCroix, Andrea Z.; McCarthy, Ellen P.; Rosner, Bernard A.; Chlebowski, Rowan T.; Hankinson, Susan E.; Marcantonio, Edward R.; Ngo, Long H.

    2016-01-01

    Purpose Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. Methods We included 73,066 women who completed the 2004 Nurses’ Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors), 7 risk factors for non-breast cancer death (comorbidities, functional dependency), and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women’s Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). Results Within 5 years, 1.8% of NHS participants were diagnosed with breast cancer (vs. 2.0% in WHI-ES, p=0.02) and 6.6% experienced non-breast cancer death (vs. 5.2% in WHI-ES, p<0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model’s c-statistic was 0.61 (95% CI [0.60–0.63]) in NHS and 0.57 (0.55–0.58) in WHI-ES. On average our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88–0.97]). Conclusions We developed a novel prediction model that factors in postmenopausal women’s individualized competing risks of non-breast cancer death when estimating breast cancer risk. PMID:27770283

  13. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

    PubMed Central

    Stone, Jennifer; Thompson, Deborah J.; dos-Santos-Silva, Isabel; Scott, Christopher; Tamimi, Rulla M.; Lindstrom, Sara; Kraft, Peter; Hazra, Aditi; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Hall, Per; Jensen, Matt; Cunningham, Julie; Olson, Janet E.; Purrington, Kristen; Couch, Fergus J.; Brown, Judith; Leyland, Jean; Warren, Ruth M. L.; Luben, Robert N.; Khaw, Kay-Tee; Smith, Paula; Wareham, Nicholas J.; Jud, Sebastian M.; Heusinger, Katharina; Beckmann, Matthias W.; Douglas, Julie A.; Shah, Kaanan P.; Chan, Heang-Ping; Helvie, Mark A.; Le Marchand, Loic; Kolonel, Laurence N.; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher; Giles, Graham G.; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C.; Apicella, Carmel; Andrulis, Irene L.; Knight, Julia A.; Ursin, Giske; Grenaker Alnaes, Grethe I.; Kristensen, Vessela N.; Borresen-Dale, Anne-Lise; Gram, Inger Torhild; Bolla, Manjeet K.; Wang, Qin; Michailidou, Kyriaki; Dennis, Joe; Simard, Jacques; Paroah, Paul; Dunning, Alison M.; Easton, Douglas F.; Fasching, Peter A.; Pankratz, V. Shane; Hopper, John; Vachon, Celine M.

    2015-01-01

    Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute non-dense area adjusted for study, age and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1) and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all p <10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and non-dense areas, and between rs17356907 (NTN4) and adjusted absolute non-dense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiological pathways implicated in how mammographic density predicts breast cancer risk. PMID:25862352

  14. Risk Prediction Models for Other Cancers or Multiple Sites

    Cancer.gov

    Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Estrogen Metabolism and Exposure in a Genotypic-Phenotypic Model for Breast Cancer Risk Prediction

    PubMed Central

    Crooke, Philip S.; Justenhoven, Christina; Brauch, Hiltrud; Dawling, Sheila; Roodi, Nady; Higginbotham, Kathryn S. P.; Plummer, W. Dale; Schuyler, Peggy A.; Sanders, Melinda E; Page, David L.; Smith, Jeffrey R.; Dupont, William D.; Parl, Fritz F.

    2012-01-01

    Background Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. Methods We developed a model that simulates the kinetic effect of genetic variants of the enzymes CYP1A1, CYP1B1, and COMT on the production of the main carcinogenic estrogen metabolite, 4-hydroxyestradiol (4-OHE2), expressed as area under the curve metric (4-OHE2-AUC). The model also incorporates phenotypic factors (age, body mass index, hormone replacement therapy, oral contraceptives, family history), which plausibly influence estrogen metabolism and the production of 4-OHE2. We applied the model to two independent, population-based breast cancer case-control groups, the German GENICA study (967 cases, 971 controls) and the Nashville Breast Cohort (NBC; 465 cases, 885 controls). Results In the GENICA study, premenopausal women at the 90th percentile of 4-OHE2-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE2-AUC percentile (95% CI 1.7 – 3.2, P = 2.9 × 10−7). This relative risk was 1.89 (95% CI 1.5 – 2.4, P = 2.2 × 10−8) in postmenopausal women. In the NBC, this relative risk in postmenopausal women was 1.81 (95% CI 1.3 – 2.6, P = 7.6 × 10−4), which increased to 1.83 (95% CI 1.4 – 2.3, P = 9.5 × 10−7) when a history of proliferative breast disease was included in the model. Conclusions The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. Impact The estrogen carcinogenesis-based model has the potential to provide personalized risk estimates. PMID:21610218

  16. Healthy eating index and breast cancer risk among Malaysian women.

    PubMed

    Shahril, Mohd Razif; Sulaiman, Suhaina; Shaharudin, Soraya Hanie; Akmal, Sharifah Noor

    2013-07-01

    Healthy Eating Index-2005 (HEI-2005), an index-based dietary pattern, has been shown to predict the risk of chronic diseases among Americans. This study aims to examine the ability of HEI-2005 in predicting the probability for risk of premenopausal and postmenopausal breast cancer among Malaysian women. Data from a case-control nutritional epidemiology study among 764 participants including 382 breast cancer cases and 382 healthy women were extracted and scored. Multivariate odds ratios (OR) with 95% confidence intervals (CI) were used to evaluate the relationship between the risk of breast cancer and quartiles (Q) of HEI-2005 total scores and its component, whereas the risk prediction ability of HEI-2005 was investigated using diagnostics analysis. The results of this study showed that there is a significant reduction in the risk of breast cancer, with a higher HEI-2005 total score among premenopausal women (OR Q1 vs. Q4=0.34, 95% CI; 0.15-0.76) and postmenopausal women (OR Q1 vs. Q4=0.20, 95% CI; 0.06-0.63). However, HEI-2005 has a sensitivity of 56-60%, a specificity of 55-60%, and a positive predictive value and negative predictive value of 57-58%, which indicates a moderate ability to predict the risk of breast cancer according to menopausal status. The breast cancer incidence observed poorly agrees with risk outcomes from HEI-2005 as shown by low κ statistics (κ=0.15). In conclusion, although the total HEI-2005 scores were associated with a risk of breast cancer among Malaysian women, the ability of HEI-2005 to predict risk is poor as indicated by the diagnostic analysis. A local index-based dietary pattern, which is disease specific, is required to predict the risk of breast cancer among Malaysian women for early prevention.

  17. Predicting Cancer-Prevention Behavior: Disentangling the Effects of Risk Aversion and Risk Perceptions.

    PubMed

    Riddel, Mary; Hales, David

    2018-05-16

    Experimental and survey research spanning the last two decades concludes that people who are more risk tolerant are more likely to engage in risky health activities such as smoking and heavy alcohol consumption, and are more likely to be obese. Subjective perceptions of the risk associated with different activities have also been found to be associated with health behaviors. While there are numerous studies that link risk perceptions with risky behavior, it is notable that none of these controls for risk aversion. Similarly, studies that control for risk aversion fail to control for risk misperceptions. We use a survey of 474 men and women to investigate the influence of risk aversion, risk misperceptions, and cognitive ability on the choice to engage in behaviors that either increase or mitigate cancer risk. We measure optimism in two dimensions: baseline optimists are those who inaccurately believe their cancer risk to be below its expert-assessed level, while control optimists are those who believe they can reduce their risk of cancer (by changing their lifestyle choices) to a greater extent than is actually the case. Our results indicate that baseline optimism is significantly and negatively correlated with subjects' tendencies to engage in cancer-risk-reducing behaviors, and positively correlated with risky behaviors. Subjects' control misperceptions also appear to play a role in their tendency to engage in risky and prevention behaviors. When controlling for both of these types of risk misperception, risk aversion plays a much smaller role in determining health behaviors than found in past studies. © 2018 Society for Risk Analysis.

  18. Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

    PubMed Central

    O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

    2011-01-01

    Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

  19. Predicting the risk of patients with biopsy Gleason score 6 to harbor a higher grade cancer.

    PubMed

    Gofrit, Ofer N; Zorn, Kevin C; Taxy, Jerome B; Lin, Shang; Zagaja, Gregory P; Steinberg, Gary D; Shalhav, Arieh L

    2007-11-01

    Prostate cancer Gleason score 3 + 3 = 6 is currently the most common score assigned on prostatic biopsies. We analyzed the clinical variables that predict the likelihood of a patient with biopsy Gleason score 6 to harbor a higher grade tumor. The study population consisted of 448 patients with a mean age of 59.1 years who underwent radical prostatectomy between February 2003 to October 2006 for Gleason score 6 adenocarcinoma. The effect of preoperative variables on the probability of a Gleason score upgrade on final pathological evaluation was evaluated using logistic regression, and classification and regression tree analysis. Gleason score upgrade was found in 91 of 448 patients (20.3%). Logistic regression showed that only serum prostate specific antigen and the greatest percent of cancer in a core were significantly associated with a score upgrade (p = 0.0014 and 0.023, respectively). Classification and regression tree analysis showed that the risk of a Gleason score upgrade was 62% when serum prostate specific antigen was higher than 12 ng/ml and 18% when serum prostate specific antigen was 12 ng/ml or less. In patients with serum prostate specific antigen lower than 12 ng/ml the risk of a score upgrade could be dichotomized at a greatest percent of cancer in a core of 5%. The risk was 22.6% and 10.5% when the greatest percent of cancer in a core was higher than 5% and 5% or lower, respectively. The probability of patients with a prostate biopsy Gleason score of 6 to conceal a Gleason score of 7 or higher can be predicted using serum prostate specific antigen and the greatest percent of cancer in a core. With these parameters it is possible to predict upgrade rates as high as 62% and as low as 10.5%.

  20. An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study.

    PubMed

    Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

    2017-01-01

    No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; the area under the curve was used to evaluate discriminatory ability of models. Area under the curve was 0.93 for the full model (including age, smoking and coffee habits, DNA adducts, 12 genotypes) and 0.86 for the short model (including smoking, DNA adducts, 3 genotypes). Using the "best cut-off" of predicted probability of a positive outcome, percentage of cases correctly classified was 92% (full model) against 75% (short model). Cancers classified as "positive outcome" are those to be referred for evaluation by an occupational physician for etiological diagnosis; these patients were 28 (full model) or 60 (short model). Using 3 genotypes instead of 12 can double the number of patients with suspect of aromatic amine related cancer, thus increasing costs of etiologic appraisal. Integrating clinical, laboratory and genetic factors, we developed the first etiologic prediction model for aromatic amine related bladder cancer. Discriminatory ability was excellent, particularly for the full model, allowing individualized predictions. Validation of our model in external populations is essential for practical use in the clinical setting.

  1. Significant SNPs have limited prediction ability for thyroid cancer

    PubMed Central

    Guo, Shicheng; Wang, Yu-Long; Li, Yi; Jin, Li; Xiong, Momiao; Ji, Qing-Hai; Wang, Jiucun

    2014-01-01

    Recently, five thyroid cancer significantly associated genetic variants (rs965513, rs944289, rs116909374, rs966423, and rs2439302) have been discovered and validated in two independent GWAS and numerous case–control studies, which were conducted in different populations. We genotyped the above five single nucleotide polymorphisms (SNPs) in Han Chinese populations and performed thyroid cancer-risk predictions with nine machine learning methods. We found that four SNPs were significantly associated with thyroid cancer in Han Chinese population, while no polymorphism was observed for rs116909374. Small familial relative risks (1.02–1.05) and limited power to predict thyroid cancer (AUCs: 0.54–0.60) indicate limited clinical potential. Four significant SNPs have limited prediction ability for thyroid cancer. PMID:24591304

  2. Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

    PubMed Central

    Tice, Jeffrey A.; Cummings, Steven R.; Smith-Bindman, Rebecca; Ichikawa, Laura; Barlow, William E.; Kerlikowske, Karla

    2009-01-01

    Background Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography. Objective To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density. Design Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort. Setting Screening mammography sites participating in the Breast Cancer Surveillance Consortium. Patients 1 095 484 women undergoing mammography who had no previous diagnosis of breast cancer. Measurements Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories. Results During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14 766 women. The breast density model was well calibrated overall (expected–observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years. Limitation The model has only modest ability to discriminate between women who will develop breast cancer and those who will not. Conclusion A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use. PMID:18316752

  3. Do repeated assessments of performance status improve predictions for risk of death among patients with cancer? A population-based cohort study.

    PubMed

    Su, Jiandong; Barbera, Lisa; Sutradhar, Rinku

    2015-06-01

    Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancer patients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death. To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis. This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. Our study consisted of 15,487 cancer patients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis. When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status. © The Author(s) 2015.

  4. ERβ Expression and Breast Cancer Risk Prediction for Women with Atypias

    PubMed Central

    Hieken, Tina J; Carter, Jodi M; Hawse, John R; Hoskin, Tanya L; Bois, Melanie; Frost, Marlene; Hartmann, Lynn C; Radisky, Derek C; Visscher, Daniel W; Degnim, Amy C

    2015-01-01

    Estrogen receptor beta (ERβ) is highly expressed in normal breast epithelium and a putative tumor suppressor. Atypical hyperplasia substantially increases breast cancer risk, but identification of biomarkers to further improve risk stratification is needed. We evaluated ERβ expression in breast tissues from women with atypical hyperplasia and association with subsequent breast cancer risk. ERβ expression was examined by immunohistochemistry in a well-characterized 171 women cohort with atypical hyperplasia diagnosed 1967–1991. Nuclear ERβ percent and intensity was scored in the atypia and adjacent normal lobules. An ERβ sum score (percent + intensity) was calculated and grouped as low, moderate or high. Competing risks regression was used to assess associations of ERβ expression with breast cancer risk. After 15 years median follow-up, 36 women developed breast cancer. ERβ expression was lower in atypia lobules than normal lobules, by percent staining and intensity (both p<0.001). Higher ERβ expression in the atypia or normal lobules, evaluated by percent staining, intensity or sum score, decreased the risk of subsequent breast cancer by 2 (p=0.04) and 2.5-fold (p=0.006). High normal lobule ERβ expression conferred the strongest protective effect in pre-menopausal women: the 20-year cumulative incidence of breast cancer was 0% for women risk of breast cancer in women with atypical hyperplasia. These data suggest ERβ may be a useful biomarker for risk stratification and a novel therapeutic target for breast cancer risk reduction. PMID:26276747

  5. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    PubMed Central

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  6. Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test.

    PubMed

    Li, Wen; Zhao, Li-Zhong; Ma, Dong-Wang; Wang, De-Zheng; Shi, Lei; Wang, Hong-Lei; Dong, Mo; Zhang, Shu-Yi; Cao, Lei; Zhang, Wei-Hua; Zhang, Xi-Peng; Zhang, Qing-Huai; Yu, Lin; Qin, Hai; Wang, Xi-Mo; Chen, Sam Li-Sheng

    2018-05-01

    We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%-86%), followed by 76% (95% CI: 74%-79%) for a FIT alone, and 73% (95% CI: 71%-76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

  7. Prediction of breast cancer risk based on profiling with common genetic variants.

    PubMed

    Mavaddat, Nasim; Pharoah, Paul D P; Michailidou, Kyriaki; Tyrer, Jonathan; Brook, Mark N; Bolla, Manjeet K; Wang, Qin; Dennis, Joe; Dunning, Alison M; Shah, Mitul; Luben, Robert; Brown, Judith; Bojesen, Stig E; Nordestgaard, Børge G; Nielsen, Sune F; Flyger, Henrik; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Peto, Julian; Dos-Santos-Silva, Isabel; Dudbridge, Frank; Johnson, Nichola; Schmidt, Marjanka K; Broeks, Annegien; Verhoef, Senno; Rutgers, Emiel J; Swerdlow, Anthony; Ashworth, Alan; Orr, Nick; Schoemaker, Minouk J; Figueroa, Jonine; Chanock, Stephen J; Brinton, Louise; Lissowska, Jolanta; Couch, Fergus J; Olson, Janet E; Vachon, Celine; Pankratz, Vernon S; Lambrechts, Diether; Wildiers, Hans; Van Ongeval, Chantal; van Limbergen, Erik; Kristensen, Vessela; Grenaker Alnæs, Grethe; Nord, Silje; Borresen-Dale, Anne-Lise; Nevanlinna, Heli; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Fasching, Peter A; Haeberle, Lothar; Ekici, Arif B; Beckmann, Matthias W; Burwinkel, Barbara; Marme, Frederik; Schneeweiss, Andreas; Sohn, Christof; Trentham-Dietz, Amy; Newcomb, Polly; Titus, Linda; Egan, Kathleen M; Hunter, David J; Lindstrom, Sara; Tamimi, Rulla M; Kraft, Peter; Rahman, Nazneen; Turnbull, Clare; Renwick, Anthony; Seal, Sheila; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Benitez, Javier; Pilar Zamora, M; Arias Perez, Jose Ignacio; Menéndez, Primitiva; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Bogdanova, Natalia V; Antonenkova, Natalia N; Dörk, Thilo; Anton-Culver, Hoda; Neuhausen, Susan L; Ziogas, Argyrios; Bernstein, Leslie; Devilee, Peter; Tollenaar, Robert A E M; Seynaeve, Caroline; van Asperen, Christi J; Cox, Angela; Cross, Simon S; Reed, Malcolm W R; Khusnutdinova, Elza; Bermisheva, Marina; Prokofyeva, Darya; Takhirova, Zalina; Meindl, Alfons; Schmutzler, Rita K; Sutter, Christian; Yang, Rongxi; Schürmann, Peter; Bremer, Michael; Christiansen, Hans; Park-Simon, Tjoung-Won; Hillemanns, Peter; Guénel, Pascal; Truong, Thérèse; Menegaux, Florence; Sanchez, Marie; Radice, Paolo; Peterlongo, Paolo; Manoukian, Siranoush; Pensotti, Valeria; Hopper, John L; Tsimiklis, Helen; Apicella, Carmel; Southey, Melissa C; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Sigurdson, Alice J; Doody, Michele M; Hamann, Ute; Torres, Diana; Ulmer, Hans-Ulrich; Försti, Asta; Sawyer, Elinor J; Tomlinson, Ian; Kerin, Michael J; Miller, Nicola; Andrulis, Irene L; Knight, Julia A; Glendon, Gord; Marie Mulligan, Anna; Chenevix-Trench, Georgia; Balleine, Rosemary; Giles, Graham G; Milne, Roger L; McLean, Catriona; Lindblom, Annika; Margolin, Sara; Haiman, Christopher A; Henderson, Brian E; Schumacher, Fredrick; Le Marchand, Loic; Eilber, Ursula; Wang-Gohrke, Shan; Hooning, Maartje J; Hollestelle, Antoinette; van den Ouweland, Ans M W; Koppert, Linetta B; Carpenter, Jane; Clarke, Christine; Scott, Rodney; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Brenner, Hermann; Arndt, Volker; Stegmaier, Christa; Karina Dieffenbach, Aida; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Offit, Kenneth; Vijai, Joseph; Robson, Mark; Rau-Murthy, Rohini; Dwek, Miriam; Swann, Ruth; Annie Perkins, Katherine; Goldberg, Mark S; Labrèche, France; Dumont, Martine; Eccles, Diana M; Tapper, William J; Rafiq, Sajjad; John, Esther M; Whittemore, Alice S; Slager, Susan; Yannoukakos, Drakoulis; Toland, Amanda E; Yao, Song; Zheng, Wei; Halverson, Sandra L; González-Neira, Anna; Pita, Guillermo; Rosario Alonso, M; Álvarez, Nuria; Herrero, Daniel; Tessier, Daniel C; Vincent, Daniel; Bacot, Francois; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S; Simard, Jacques; Hall, Per; Easton, Douglas F; Garcia-Closas, Montserrat

    2015-05-01

    Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report. © The Author 2015. Published by Oxford University Press.

  8. Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants

    PubMed Central

    Pharoah, Paul D. P.; Michailidou, Kyriaki; Tyrer, Jonathan; Brook, Mark N.; Bolla, Manjeet K.; Wang, Qin; Dennis, Joe; Dunning, Alison M.; Shah, Mitul; Luben, Robert; Brown, Judith; Bojesen, Stig E.; Nordestgaard, Børge G.; Nielsen, Sune F.; Flyger, Henrik; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Peto, Julian; dos-Santos-Silva, Isabel; Dudbridge, Frank; Johnson, Nichola; Schmidt, Marjanka K.; Broeks, Annegien; Verhoef, Senno; Rutgers, Emiel J.; Swerdlow, Anthony; Ashworth, Alan; Orr, Nick; Schoemaker, Minouk J.; Figueroa, Jonine; Chanock, Stephen J.; Brinton, Louise; Lissowska, Jolanta; Couch, Fergus J.; Olson, Janet E.; Vachon, Celine; Pankratz, Vernon S.; Lambrechts, Diether; Wildiers, Hans; Van Ongeval, Chantal; van Limbergen, Erik; Kristensen, Vessela; Grenaker Alnæs, Grethe; Nord, Silje; Borresen-Dale, Anne-Lise; Nevanlinna, Heli; Muranen, Taru A.; Aittomäki, Kristiina; Blomqvist, Carl; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Fasching, Peter A.; Haeberle, Lothar; Ekici, Arif B.; Beckmann, Matthias W.; Burwinkel, Barbara; Marme, Frederik; Schneeweiss, Andreas; Sohn, Christof; Trentham-Dietz, Amy; Newcomb, Polly; Titus, Linda; Egan, Kathleen M.; Hunter, David J.; Lindstrom, Sara; Tamimi, Rulla M.; Kraft, Peter; Rahman, Nazneen; Turnbull, Clare; Renwick, Anthony; Seal, Sheila; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Benitez, Javier; Pilar Zamora, M.; Arias Perez, Jose Ignacio; Menéndez, Primitiva; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Bogdanova, Natalia V.; Antonenkova, Natalia N.; Dörk, Thilo; Anton-Culver, Hoda; Neuhausen, Susan L.; Ziogas, Argyrios; Bernstein, Leslie; Devilee, Peter; Tollenaar, Robert A. E. M.; Seynaeve, Caroline; van Asperen, Christi J.; Cox, Angela; Cross, Simon S.; Reed, Malcolm W. R.; Khusnutdinova, Elza; Bermisheva, Marina; Prokofyeva, Darya; Takhirova, Zalina; Meindl, Alfons; Schmutzler, Rita K.; Sutter, Christian; Yang, Rongxi; Schürmann, Peter; Bremer, Michael; Christiansen, Hans; Park-Simon, Tjoung-Won; Hillemanns, Peter; Guénel, Pascal; Truong, Thérèse; Menegaux, Florence; Sanchez, Marie; Radice, Paolo; Peterlongo, Paolo; Manoukian, Siranoush; Pensotti, Valeria; Hopper, John L.; Tsimiklis, Helen; Apicella, Carmel; Southey, Melissa C.; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Sigurdson, Alice J.; Doody, Michele M.; Hamann, Ute; Torres, Diana; Ulmer, Hans-Ulrich; Försti, Asta; Sawyer, Elinor J.; Tomlinson, Ian; Kerin, Michael J.; Miller, Nicola; Andrulis, Irene L.; Knight, Julia A.; Glendon, Gord; Marie Mulligan, Anna; Chenevix-Trench, Georgia; Balleine, Rosemary; Giles, Graham G.; Milne, Roger L.; McLean, Catriona; Lindblom, Annika; Margolin, Sara; Haiman, Christopher A.; Henderson, Brian E.; Schumacher, Fredrick; Le Marchand, Loic; Eilber, Ursula; Wang-Gohrke, Shan; Hooning, Maartje J.; Hollestelle, Antoinette; van den Ouweland, Ans M. W.; Koppert, Linetta B.; Carpenter, Jane; Clarke, Christine; Scott, Rodney; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M.; Brenner, Hermann; Arndt, Volker; Stegmaier, Christa; Karina Dieffenbach, Aida; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Offit, Kenneth; Vijai, Joseph; Robson, Mark; Rau-Murthy, Rohini; Dwek, Miriam; Swann, Ruth; Annie Perkins, Katherine; Goldberg, Mark S.; Labrèche, France; Dumont, Martine; Eccles, Diana M.; Tapper, William J.; Rafiq, Sajjad; John, Esther M.; Whittemore, Alice S.; Slager, Susan; Yannoukakos, Drakoulis; Toland, Amanda E.; Yao, Song; Zheng, Wei; Halverson, Sandra L.; González-Neira, Anna; Pita, Guillermo; Rosario Alonso, M.; Álvarez, Nuria; Herrero, Daniel; Tessier, Daniel C.; Vincent, Daniel; Bacot, Francois; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S.; Simard, Jacques; Hall, Per; Easton, Douglas F.; Garcia-Closas, Montserrat

    2015-01-01

    Background: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. Results: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. Conclusions: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report. PMID:25855707

  9. Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk

    NASA Astrophysics Data System (ADS)

    Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Patel, Bhavika; Heidari, Morteza; Liu, Hong; Zheng, Bin

    2018-05-01

    This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied ‘as is’ to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC  =  0.65  ±  0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p  <  0.01). Thus, this study demonstrated that CAD-generated false-positives might include valuable information, which needs to be further explored for identifying and/or developing more effective imaging markers for predicting short-term breast cancer risk.

  10. Risk of colon cancer in hereditary non-polyposis colorectal cancer patients as predicted by fuzzy modeling: Influence of smoking

    PubMed Central

    Brand, Rhonda M; Jones, David D; Lynch, Henry T; Brand, Randall E; Watson, Patrice; Ashwathnayaran, Ramesh; Roy, Hemant K

    2006-01-01

    AIM: To investigate whether a fuzzy logic model could predict colorectal cancer (CRC) risk engendered by smoking in hereditary non-polyposis colorectal cancer (HNPCC) patients. METHODS: Three hundred and forty HNPCC mismatch repair (MMR) mutation carriers from the Creighton University Hereditary Cancer Institute Registry were selected for modeling. Age-dependent curves were generated to elucidate the joint effects between gene mutation (hMLH1 or hMSH2), gender, and smoking status on the probability of developing CRC. RESULTS: Smoking significantly increased CRC risk in male hMSH2 mutation carriers (P < 0.05). hMLH1 mutations augmented CRC risk relative to hMSH2 mutation carriers for males (P < 0.05). Males had a significantly higher risk of CRC than females for hMLH1 non smokers (P < 0.05), hMLH1 smokers (P < 0.1) and hMSH2 smokers (P < 0.1). Smoking promoted CRC in a dose-dependent manner in hMSH2 in males (P < 0.05). Females with hMSH2 mutations and both sexes with the hMLH1 groups only demonstrated a smoking effect after an extensive smoking history (P < 0.05). CONCLUSION: CRC promotion by smoking in HNPCC patients is dependent on gene mutation, gender and age. These data demonstrate that fuzzy modeling may enable formulation of clinical risk scores, thereby allowing individualization of CRC prevention strategies. PMID:16874859

  11. Repeated assessments of symptom severity improve predictions for risk of death among patients with cancer.

    PubMed

    Sutradhar, Rinku; Atzema, Clare; Seow, Hsien; Earle, Craig; Porter, Joan; Barbera, Lisa

    2014-12-01

    Although prior studies show the importance of self-reported symptom scores as predictors of cancer survival, most are based on scores recorded at a single point in time. To show that information on repeated assessments of symptom severity improves predictions for risk of death and to use updated symptom information for determining whether worsening of symptom scores is associated with a higher hazard of death. This was a province-based longitudinal study of adult outpatients who had a cancer diagnosis and had assessments of symptom severity. We implemented a time-to-death Cox model with a time-varying covariate for each symptom to account for changing symptom scores over time. This model was compared with that using only a time-fixed (baseline) covariate for each symptom. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive performance of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. This study had 66,112 patients diagnosed with cancer and more than 310,000 assessments of symptoms. The use of repeated assessments of symptom scores improved predictions for risk of death compared with using only baseline symptom scores. Increased pain and fatigue and reduced appetite were the strongest predictors for death. If available, researchers should consider including changing information on symptom scores, as opposed to only baseline information on symptom scores, when examining hazard of death among patients with cancer. Worsening of pain, fatigue, and appetite may be a flag for impending death. Copyright © 2014 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  12. External validation of risk prediction models for incident colorectal cancer using UK Biobank

    PubMed Central

    Usher-Smith, J A; Harshfield, A; Saunders, C L; Sharp, S J; Emery, J; Walter, F M; Muir, K; Griffin, S J

    2018-01-01

    Background: This study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire. Methods: External validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. Results: There were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals. Conclusions: Several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening. PMID:29381683

  13. Development of a clinical prediction rule for risk stratification of recurrent venous thromboembolism in patients with cancer-associated venous thromboembolism.

    PubMed

    Louzada, Martha L; Carrier, Marc; Lazo-Langner, Alejandro; Dao, Vi; Kovacs, Michael J; Ramsay, Timothy O; Rodger, Marc A; Zhang, Jerry; Lee, Agnes Y Y; Meyer, Guy; Wells, Philip S

    2012-07-24

    Long-term low-molecular-weight heparin (LMWH) is the current standard for treatment of venous thromboembolism (VTE) in cancer patients. Whether treatment strategies should vary according to individual risk of VTE recurrence remains unknown. We performed a retrospective cohort study and a validation study in patients with cancer-associated VTE to derive a clinical prediction rule that stratifies VTE recurrence risk. The cohort study of 543 patients determined the model with the best classification performance included 4 independent predictors (sex, primary tumor site, stage, and prior VTE) with 100% sensitivity, a wide separation of recurrence rates, 98.1% negative predictive value, and a negative likelihood ratio of 0.16. In this model, the score sum ranged between -3 and 3 score points. Patients with a score ≤ 0 had low risk (≤ 4.5%) for recurrence and patients with a score >1 had a high risk (≥ 19%) for VTE recurrence. Subsequently, we applied and validated the rule in an independent set of 819 patients from 2 randomized, controlled trials comparing low-molecular-weight heparin to coumarin treatment in cancer patients. By identifying VTE recurrence risk in cancer patients with VTE, we may be able to tailor treatment, improving clinical outcomes while minimizing costs.

  14. Does perceived risk predict breast cancer screening use? Findings from a prospective cohort study of female relatives from the Ontario site of the breast cancer family registry.

    PubMed

    Walker, Meghan J; Mirea, Lucia; Glendon, Gord; Ritvo, Paul; Andrulis, Irene L; Knight, Julia A; Chiarelli, Anna M

    2014-08-01

    While the relationship between perceived risk and breast cancer screening use has been studied extensively, most studies are cross-sectional. We prospectively examined this relationship among 913 women, aged 25-72 with varying levels of familial breast cancer risk from the Ontario site of the Breast Cancer Family Registry. Associations between perceived lifetime breast cancer risk and subsequent use of mammography, clinical breast examination (CBE) and genetic testing were assessed using logistic regression. Overall, perceived risk did not predict subsequent use of mammography, CBE or genetic testing. Among women at moderate/high familial risk, those reporting a perceived risk greater than 50% were significantly less likely to have a CBE (odds ratio (OR) = 0.52, 95% confidence interval (CI): 0.30-0.91, p = 0.04), and non-significantly less likely to have a mammogram (OR = 0.70, 95% CI: 0.40-1.20, p = 0.70) or genetic test (OR = 0.61, 95% CI: 0.34-1.10, p = 0.09) compared to women reporting a perceived risk of 50%. In contrast, among women at low familial risk, those reporting a perceived risk greater than 50% were non-significantly more likely to have a mammogram (OR = 1.13, 95% CI: 0.59-2.16, p = 0.78), CBE (OR = 1.11, 95% CI: 0.63-1.95, p = 0.74) or genetic test (OR = 1.29, 95% CI: 0.50-3.33, p = 0.35) compared to women reporting a perceived risk of 50%. Perceived risk did not significantly predict screening use overall, however this relationship may be moderated by level of familial risk. Results may inform risk education and management strategies for women with varying levels of familial breast cancer risk. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Construction of a model predicting the risk of tube feeding intolerance after gastrectomy for gastric cancer based on 225 cases from a single Chinese center

    PubMed Central

    Xiaoyong, Wu; Xuzhao, Li; Deliang, Yu; Pengfei, Yu; Zhenning, Hang; Bin, Bai; zhengyan, Li; Fangning, Pang; Shiqi, Wang; Qingchuan, Zhao

    2017-01-01

    Identifying patients at high risk of tube feeding intolerance (TFI) after gastric cancer surgery may prevent the occurrence of TFI; however, a predictive model is lacking. We therefore analyzed the incidence of TFI and its associated risk factors after gastric cancer surgery in 225 gastric cancer patients divided into without-TFI (n = 114) and with-TFI (n = 111) groups. A total of 49.3% of patients experienced TFI after gastric cancer. Multivariate analysis identified a history of functional constipation (FC), a preoperative American Society of Anesthesiologists (ASA) score of III, a high pain score at 6-hour postoperation, and a high white blood cell (WBC) count on the first day after surgery as independent risk factors for TFI. The area under the curve (AUC) was 0.756, with an optimal cut-off value of 0.5410. In order to identify patients at high risk of TFI after gastric cancer surgery, we constructed a predictive nomogram model based on the selected independent risk factors to indicate the probability of developing TFI. Use of our predictive nomogram model in screening, if a probability > 0.5410, indicated a high-risk patients would with a 70.1% likelihood of developing TFI. These high-risk individuals should take measures to prevent TFI before feeding with enteral nutrition. PMID:29245951

  16. Post-bronchoscopy pneumonia in patients suffering from lung cancer: Development and validation of a risk prediction score.

    PubMed

    Takiguchi, Hiroto; Hayama, Naoki; Oguma, Tsuyoshi; Harada, Kazuki; Sato, Masako; Horio, Yukihiro; Tanaka, Jun; Tomomatsu, Hiromi; Tomomatsu, Katsuyoshi; Takihara, Takahisa; Niimi, Kyoko; Nakagawa, Tomoki; Masuda, Ryota; Aoki, Takuya; Urano, Tetsuya; Iwazaki, Masayuki; Asano, Koichiro

    2017-05-01

    The incidence, risk factors, and consequences of pneumonia after flexible bronchoscopy in patients with lung cancer have not been studied in detail. We retrospectively analyzed the data from 237 patients with lung cancer who underwent diagnostic bronchoscopy between April 2012 and July 2013 (derivation sample) and 241 patients diagnosed between August 2013 and July 2014 (validation sample) in a tertiary referral hospital in Japan. A score predictive of post-bronchoscopy pneumonia was developed in the derivation sample and tested in the validation sample. Pneumonia developed after bronchoscopy in 6.3% and 4.1% of patients in the derivation and validation samples, respectively. Patients who developed post-bronchoscopy pneumonia needed to change or cancel their planned cancer therapy more frequently than those without pneumonia (56% vs. 6%, p<0.001). Age ≥70 years, current smoking, and central location of the tumor were independent predictors of pneumonia, which we added to develop our predictive score. The incidence of pneumonia associated with scores=0, 1, and ≥2 was 0, 3.7, and 13.4% respectively in the derivation sample (p=0.003), and 0, 2.9, and 9.7% respectively in the validation sample (p=0.016). The incidence of post-bronchoscopy pneumonia in patients with lung cancer was not rare and associated with adverse effects on the clinical course. A simple 3-point predictive score identified patients with lung cancer at high risk of post-bronchoscopy pneumonia prior to the procedure. Copyright © 2017 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

  17. Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

    PubMed

    Whitney, Jon; Corredor, German; Janowczyk, Andrew; Ganesan, Shridar; Doyle, Scott; Tomaszewski, John; Feldman, Michael; Gilmore, Hannah; Madabhushi, Anant

    2018-05-30

    Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the

  18. Toward risk reduction: predicting the future burden of occupational cancer.

    PubMed

    Hutchings, Sally; Rushton, Lesley

    2011-05-01

    Interventions to reduce cancers related to certain occupations should be evidence-based. The authors have developed a method for forecasting the future burden of occupational cancer to inform strategies for risk reduction. They project risk exposure periods, accounting for cancer latencies of up to 50 years, forward in time to estimate attributable fractions for a series of forecast target years given past and projected exposure trends and under targeted reduction scenarios. Adjustment factors for changes in exposed numbers and levels are applied in estimation intervals within the risk-exposure periods. The authors illustrate the methods by using a range of scenarios for reducing lung cancer due to occupational exposure to respirable crystalline silica. Attributable fractions for lung cancer due to respirable crystalline silica could be potentially reduced from 2.07% in 2010 to nearly 0% by 2060, depending on the timing and success of interventions. Focusing on achieving compliance with current exposure standards in small industries can be more effective than setting standards at a lower level. The method can be used to highlight high-risk carcinogens, industries, and occupations. It is adaptable for other countries and other exposure situations in the general environment and can be extended to include socioeconomic impact assessment.

  19. Variations in pelvic dimensions do not predict the risk of circumferential resection margin (CRM) involvement in rectal cancer.

    PubMed

    Salerno, G; Daniels, I R; Brown, G; Norman, A R; Moran, B J; Heald, R J

    2007-06-01

    The objective of this study was to assess the value of preoperative pelvimetry, using magnetic resonance imaging (MRI), in predicting the risk of an involved circumferential resection margin (CRM) in a group of patients with operable rectal cancer. A cohort of 186 patients from the MERCURY study was selected. These patients' histological CRM status was compared against 14 pelvimetry parameters measured from the preoperative MRI. These measurements were taken by one of the investigators (G.S.), who was blinded to the final CRM status. There was no correlation between the pelvimetry and the CRM status. However, there was a difference in the height of the rectal cancer and the positive CRM rate (p = 0.011). Of 61 patients with low rectal cancer, 10 had positive CRM at histology (16.4% with CI 8.2%-22.1%) compared with 5 of 110 patients with mid/upper rectal cancers (4.5% with CI 0.7%-8.4%). Magnetic resonance imaging can predict clear margins in most cases of rectal cancer. Circumferential resection margin positivity cannot be predicted from pelvimetry in patients with rectal cancer selected for curative surgery. The only predictive factor for a positive CRM in the patients studied was tumor height.

  20. Family history of cancer predicts endometrial cancer risk independently of Lynch Syndrome: Implications for genetic counselling.

    PubMed

    Johnatty, Sharon E; Tan, Yen Y; Buchanan, Daniel D; Bowman, Michael; Walters, Rhiannon J; Obermair, Andreas; Quinn, Michael A; Blomfield, Penelope B; Brand, Alison; Leung, Yee; Oehler, Martin K; Kirk, Judy A; O'Mara, Tracy A; Webb, Penelope M; Spurdle, Amanda B

    2017-11-01

    To determine endometrial cancer (EC) risk according to family cancer history, including assessment by degree of relatedness, type of and age at cancer diagnosis of relatives. Self-reported family cancer history was available for 1353 EC patients and 628 controls. Logistic regression was used to quantify the association between EC and cancer diagnosis in ≥1 first or second degree relative, and to assess whether level of risk differed by degree of relationship and/or relative's age at diagnosis. Risk was also evaluated for family history of up to three cancers from known familial syndromes (Lynch, Cowden, hereditary breast and ovarian cancer) overall, by histological subtype and, for a subset of 678 patients, by EC tumor mismatch repair (MMR) gene expression. Report of EC in ≥1 first- or second-degree relative was associated with significantly increased risk of EC (P=3.8×10 -7 ), independent of lifestyle risk factors. There was a trend in increasing EC risk with closer relatedness and younger age at EC diagnosis in relatives (P Trend =4.43×10 -6 ), and with increasing numbers of Lynch cancers in relatives (P Trend ≤0.0001). EC risk associated with family history did not differ by proband tumor MMR status, or histological subtype. Reported EC in first- or second-degree relatives remained associated with EC risk after conservative correction for potential misreported family history (OR 2.0; 95% CI, 1.24-3.37, P=0.004). The strongest predictor of EC risk was closer relatedness and younger EC diagnosis age in ≥1 relative. Associations remained significant irrespective of proband MMR status, and after excluding MMR pathogenic variant carriers, indicating that Lynch syndrome genes do not fully explain familial EC risk. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

    PubMed Central

    Clyde, Merlise A.; Palmieri Weber, Rachel; Iversen, Edwin S.; Poole, Elizabeth M.; Doherty, Jennifer A.; Goodman, Marc T.; Ness, Roberta B.; Risch, Harvey A.; Rossing, Mary Anne; Terry, Kathryn L.; Wentzensen, Nicolas; Whittemore, Alice S.; Anton-Culver, Hoda; Bandera, Elisa V.; Berchuck, Andrew; Carney, Michael E.; Cramer, Daniel W.; Cunningham, Julie M.; Cushing-Haugen, Kara L.; Edwards, Robert P.; Fridley, Brooke L.; Goode, Ellen L.; Lurie, Galina; McGuire, Valerie; Modugno, Francesmary; Moysich, Kirsten B.; Olson, Sara H.; Pearce, Celeste Leigh; Pike, Malcolm C.; Rothstein, Joseph H.; Sellers, Thomas A.; Sieh, Weiva; Stram, Daniel; Thompson, Pamela J.; Vierkant, Robert A.; Wicklund, Kristine G.; Wu, Anna H.; Ziogas, Argyrios; Tworoger, Shelley S.; Schildkraut, Joellen M.

    2016-01-01

    Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted. PMID:27698005

  2. Concepts and challenges in cancer risk prediction for the space radiation environment

    NASA Astrophysics Data System (ADS)

    Barcellos-Hoff, Mary Helen; Blakely, Eleanor A.; Burma, Sandeep; Fornace, Albert J.; Gerson, Stanton; Hlatky, Lynn; Kirsch, David G.; Luderer, Ulrike; Shay, Jerry; Wang, Ya; Weil, Michael M.

    2015-07-01

    Cancer is an important long-term risk for astronauts exposed to protons and high-energy charged particles during travel and residence on asteroids, the moon, and other planets. NASA's Biomedical Critical Path Roadmap defines the carcinogenic risks of radiation exposure as one of four type I risks. A type I risk represents a demonstrated, serious problem with no countermeasure concepts, and may be a potential "show-stopper" for long duration spaceflight. Estimating the carcinogenic risks for humans who will be exposed to heavy ions during deep space exploration has very large uncertainties at present. There are no human data that address risk from extended exposure to complex radiation fields. The overarching goal in this area to improve risk modeling is to provide biological insight and mechanistic analysis of radiation quality effects on carcinogenesis. Understanding mechanisms will provide routes to modeling and predicting risk and designing countermeasures. This white paper reviews broad issues related to experimental models and concepts in space radiation carcinogenesis as well as the current state of the field to place into context recent findings and concepts derived from the NASA Space Radiation Program.

  3. Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer.

    PubMed

    Engmann, Natalie J; Golmakani, Marzieh K; Miglioretti, Diana L; Sprague, Brian L; Kerlikowske, Karla

    2017-09-01

    Many established breast cancer risk factors are used in clinical risk prediction models, although the proportion of breast cancers explained by these factors is unknown. To determine the population-attributable risk proportion (PARP) for breast cancer associated with clinical breast cancer risk factors among premenopausal and postmenopausal women. Case-control study with 1:10 matching on age, year of risk factor assessment, and Breast Cancer Surveillance Consortium (BCSC) registry. Risk factor data were collected prospectively from January 1, 1996, through October 31, 2012, from BCSC community-based breast imaging facilities. A total of 18 437 women with invasive breast cancer or ductal carcinoma in situ were enrolled as cases and matched to 184 309 women without breast cancer, with a total of 58 146 premenopausal and 144 600 postmenopausal women enrolled in the study. Breast Imaging Reporting and Data System (BI-RADS) breast density (heterogeneously or extremely dense vs scattered fibroglandular densities), first-degree family history of breast cancer, body mass index (>25 vs 18.5-25), history of benign breast biopsy, and nulliparity or age at first birth (≥30 years vs <30 years). Population-attributable risk proportion of breast cancer. Of the 18 437 women with breast cancer, the mean (SD) age was 46.3 (3.7) years among premenopausal women and 61.7 (7.2) years among the postmenopausal women. Overall, 4747 (89.8%) premenopausal and 12 502 (95.1%) postmenopausal women with breast cancer had at least 1 breast cancer risk factor. The combined PARP of all risk factors was 52.7% (95% CI, 49.1%-56.3%) among premenopausal women and 54.7% (95% CI, 46.5%-54.7%) among postmenopausal women. Breast density was the most prevalent risk factor for both premenopausal and postmenopausal women and had the largest effect on the PARP; 39.3% (95% CI, 36.6%-42.0%) of premenopausal and 26.2% (95% CI, 24.4%-28.0%) of postmenopausal breast cancers could potentially be

  4. Hypermethylation of the breast cancer-associated gene 1 promoter does not predict cytologic atypia or correlate with surrogate end points of breast cancer risk.

    PubMed

    Bean, Gregory R; Ibarra Drendall, Catherine; Goldenberg, Vanessa K; Baker, Joseph C; Troch, Michelle M; Paisie, Carolyn; Wilke, Lee G; Yee, Lisa; Marcom, Paul K; Kimler, Bruce F; Fabian, Carol J; Zalles, Carola M; Broadwater, Gloria; Scott, Victoria; Seewaldt, Victoria L

    2007-01-01

    Mutation of the breast cancer-associated gene 1 (BRCA1) plays an important role in familial breast cancer. Although hypermethylation of the BRCA1 promoter has been observed in sporadic breast cancer, its exact role in breast cancer initiation and association with breast cancer risk is unknown. The frequency of BRCA1 promoter hypermethylation was tested in (a) 14 primary breast cancer biopsies and (b) the initial random periareolar fine-needle aspiration (RPFNA) cytologic samples obtained from 61 asymptomatic women who were at increased risk for breast cancer. BRCA1 promoter hypermethylation was assessed from nucleotide -150 to nucleotide +32 relative to the transcription start site. RPFNA specimens were stratified for cytologic atypia using the Masood cytology index. BRCA1 promoter hypermethylation was observed at similar frequency in nonproliferative (normal; Masood cancer or (b) calculated Gail or BRCAPRO risk score. BRCA1 promoter hypermethylation was associated with (a) age (P = 0.028) and (b) the combined frequency of promoter hypermethylation of the retinoic acid receptor-beta2 (RARB) gene, estrogen receptor-alpha (ESR1) gene, and p16 (INK4A) gene (P = 0.003). These observations show that BRCA1 promoter hypermethylation (a) is not associated with breast cancer risk as measured by mathematical risk models and (b) does not predict mammary atypia in RPFNA cytologic samples obtained from high-risk women.

  5. Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools

    PubMed Central

    Schmidt-Hansen, Mia; Berendse, Sabine; Hamilton, Willie; Baldwin, David R

    2017-01-01

    Background Lung cancer is the leading cause of cancer deaths. Around 70% of patients first presenting to specialist care have advanced disease, at which point current treatments have little effect on survival. The issue for primary care is how to recognise patients earlier and investigate appropriately. This requires an assessment of the risk of lung cancer. Aim The aim of this study was to systematically review the existing risk prediction tools for patients presenting in primary care with symptoms that may indicate lung cancer Design and setting Systematic review of primary care data. Method Medline, PreMedline, Embase, the Cochrane Library, Web of Science, and ISI Proceedings (1980 to March 2016) were searched. The final list of included studies was agreed between two of the authors, who also appraised and summarised them. Results Seven studies with between 1482 and 2 406 127 patients were included. The tools were all based on UK primary care data, but differed in complexity of development, number/type of variables examined/included, and outcome time frame. There were four multivariable tools with internal validation area under the curves between 0.88 and 0.92. The tools all had a number of limitations, and none have been externally validated, or had their clinical and cost impact examined. Conclusion There is insufficient evidence for the recommendation of any one of the available risk prediction tools. However, some multivariable tools showed promising discrimination. What is needed to guide clinical practice is both external validation of the existing tools and a comparative study, so that the best tools can be incorporated into clinical decision tools used in primary care. PMID:28483820

  6. Screening frequency and atypical cells and the prediction of cervical cancer risk.

    PubMed

    Chen, Yun-Yuan; You, San-Lin; Koong, Shin-Lan; Liu, Jessica; Chen, Chi-An; Chen, Chien-Jen

    2014-05-01

    To evaluate the screening efficacy and importance of atypical squamous cells and atypical glandular cells in predicting subsequent cervical cancer risk. This national cohort study in Taiwan analyzed associations between Pap test screening frequency and findings in 1995-2000 and subsequent risk of squamous cell carcinoma and adenocarcinoma after 2002. Women aged 30 years or older in 1995 without a cervical cancer history were included. Multivariate-adjusted hazard ratios and their 95% confidence intervals (CIs) were assessed using Cox regression analysis. During a total follow-up of 31,693,980 person-years in 2002-2008, 9,471 squamous cell carcinoma and 1,455 adenocarcinoma cases were newly diagnosed, resulting in 2,067 deaths. The risk of developing and dying from squamous cell carcinoma decreased significantly with increasing attendance frequency between 1995 and 2000 (all P values for trend<.001). Women who attended more than three screenings in 1995-2000 had 0.69-fold and 0.35-fold decrease in incidence and mortality of adenocarcinoma, respectively, compared with women who never attended any screenings. Abnormal cytologic findings were significant predictors of the incidence and mortality of cervical cancers. The adjusted hazard ratio (95% CI) of developing squamous cell carcinoma was 29.94 (22.83-39.25) for atypical squamous cells, cannot exclude high-grade squamous intraepithelial lesions, and the adjusted hazard ratio (95% CI) of developing adenocarcinoma was 49.43 (36.49-66.97) for atypical glandular cells. Significant reductions in cervical adenocarcinoma occurred in women who attend three or more annual screenings in 6 years. High-grade atypical squamous cells and atypical glandular cells are important predictors of subsequent adenocarcinoma and squamous cell carcinoma. II.

  7. Concepts and challenges in cancer risk prediction for the space radiation environment.

    PubMed

    Barcellos-Hoff, Mary Helen; Blakely, Eleanor A; Burma, Sandeep; Fornace, Albert J; Gerson, Stanton; Hlatky, Lynn; Kirsch, David G; Luderer, Ulrike; Shay, Jerry; Wang, Ya; Weil, Michael M

    2015-07-01

    Cancer is an important long-term risk for astronauts exposed to protons and high-energy charged particles during travel and residence on asteroids, the moon, and other planets. NASA's Biomedical Critical Path Roadmap defines the carcinogenic risks of radiation exposure as one of four type I risks. A type I risk represents a demonstrated, serious problem with no countermeasure concepts, and may be a potential "show-stopper" for long duration spaceflight. Estimating the carcinogenic risks for humans who will be exposed to heavy ions during deep space exploration has very large uncertainties at present. There are no human data that address risk from extended exposure to complex radiation fields. The overarching goal in this area to improve risk modeling is to provide biological insight and mechanistic analysis of radiation quality effects on carcinogenesis. Understanding mechanisms will provide routes to modeling and predicting risk and designing countermeasures. This white paper reviews broad issues related to experimental models and concepts in space radiation carcinogenesis as well as the current state of the field to place into context recent findings and concepts derived from the NASA Space Radiation Program. Copyright © 2015 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

  8. Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.

    PubMed

    Luo, Xin; Zang, Xiao; Yang, Lin; Huang, Junzhou; Liang, Faming; Rodriguez-Canales, Jaime; Wistuba, Ignacio I; Gazdar, Adi; Xie, Yang; Xiao, Guanghua

    2017-03-01

    Pathological examination of histopathological slides is a routine clinical procedure for lung cancer diagnosis and prognosis. Although the classification of lung cancer has been updated to become more specific, only a small subset of the total morphological features are taken into consideration. The vast majority of the detailed morphological features of tumor tissues, particularly tumor cells' surrounding microenvironment, are not fully analyzed. The heterogeneity of tumor cells and close interactions between tumor cells and their microenvironments are closely related to tumor development and progression. The goal of this study is to develop morphological feature-based prediction models for the prognosis of patients with lung cancer. We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for patients with NSCLC. Tissue pathological images were analyzed for 523 patients with adenocarcinoma (ADC) and 511 patients with squamous cell carcinoma (SCC) from The Cancer Genome Atlas lung cancer cohorts. The features extracted from the pathological images were used to develop statistical models that predict patients' survival outcomes in ADC and SCC, respectively. We extracted 943 morphological features from pathological images of hematoxylin and eosin-stained tissue and identified morphological features that are significantly associated with prognosis in ADC and SCC, respectively. Statistical models based on these extracted features stratified NSCLC patients into high-risk and low-risk groups. The models were developed from training sets and validated in independent testing sets: a predicted high-risk group versus a predicted low-risk group (for patients with ADC: hazard ratio = 2.34, 95% confidence interval: 1.12-4.91, p = 0.024; for patients with SCC: hazard ratio = 2.22, 95% confidence interval: 1.15-4.27, p = 0.017) after adjustment for age, sex, smoking status, and pathologic tumor stage. The

  9. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    PubMed

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

  10. Reminders of cancer risk and pain catastrophizing: relationships with cancer worry and perceived risk in women with a first-degree relative with breast cancer.

    PubMed

    Whitney, Colette A; Dorfman, Caroline S; Shelby, Rebecca A; Keefe, Francis J; Gandhi, Vicky; Somers, Tamara J

    2018-04-20

    First-degree relatives of women with breast cancer may experience increased worry or perceived risk when faced with reminders of their own cancer risk. Worry and risk reminders may include physical symptoms (e.g., persistent breast pain) and caregiving experiences. Women who engage in pain catastrophizing may be particularly likely to experience increased distress when risk reminders are present. We examined the degree to which persistent breast pain and experience as a cancer caregiver were related to cancer worry and perceived risk in first-degree relatives of women with breast cancer (N = 85) and how catastrophic thoughts about breast pain could impact these relationships. There was a significant interaction between persistent breast pain and pain catastrophizing in predicting cancer worry (p = .03); among women who engaged in pain catastrophizing, cancer worry remained high even in the absence of breast pain. Pain catastrophizing also moderated the relationships between caregiving involvement and cancer worry (p = .003) and perceived risk (p = .03). As the degree of caregiving responsibility increased, cancer worry and perceived risk increased for women who engaged in pain catastrophizing; levels of cancer worry and perceived risk remained low and stable for women who did not engage in pain catastrophizing regardless of caregiving experience. The results suggest that first-degree relatives of breast cancer survivors who engage in pain catastrophizing may experience greater cancer worry and perceived risk and may benefit from interventions aimed at reducing catastrophic thoughts about pain.

  11. Individual prediction of heart failure among childhood cancer survivors.

    PubMed

    Chow, Eric J; Chen, Yan; Kremer, Leontien C; Breslow, Norman E; Hudson, Melissa M; Armstrong, Gregory T; Border, William L; Feijen, Elizabeth A M; Green, Daniel M; Meacham, Lillian R; Meeske, Kathleen A; Mulrooney, Daniel A; Ness, Kirsten K; Oeffinger, Kevin C; Sklar, Charles A; Stovall, Marilyn; van der Pal, Helena J; Weathers, Rita E; Robison, Leslie L; Yasui, Yutaka

    2015-02-10

    To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Survivors in the Childhood Cancer Survivor Study (CCSS) free of significant cardiovascular disease 5 years after cancer diagnosis (n = 13,060) were observed through age 40 years for the development of heart failure (ie, requiring medications or heart transplantation or leading to death). Siblings (n = 4,023) established the baseline population risk. An additional 3,421 survivors from Emma Children's Hospital (Amsterdam, the Netherlands), the National Wilms Tumor Study, and the St Jude Lifetime Cohort Study were used to validate the CCSS prediction models. Heart failure occurred in 285 CCSS participants. Risk scores based on selected exposures (sex, age at cancer diagnosis, and anthracycline and chest radiotherapy doses) achieved an area under the curve of 0.74 and concordance statistic of 0.76 at or through age 40 years. Validation cohort estimates ranged from 0.68 to 0.82. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups, corresponding to cumulative incidences of heart failure at age 40 years of 0.5% (95% CI, 0.2% to 0.8%), 2.4% (95% CI, 1.8% to 3.0%), and 11.7% (95% CI, 8.8% to 14.5%), respectively. In comparison, siblings had a cumulative incidence of 0.3% (95% CI, 0.1% to 0.5%). Using information available to clinicians soon after completion of childhood cancer therapy, individual risk for subsequent heart failure can be predicted with reasonable accuracy and discrimination. These validated models provide a framework on which to base future screening strategies and interventions. © 2014 by American Society of Clinical Oncology.

  12. Prediction of Febrile Neutropenia after Chemotherapy Based on Pretreatment Risk Factors among Cancer Patients

    PubMed Central

    Aagaard, Theis; Roen, Ashley; Daugaard, Gedske; Brown, Peter; Sengeløv, Henrik; Mocroft, Amanda; Lundgren, Jens; Helleberg, Marie

    2017-01-01

    Abstract Background Febrile neutropenia (FN) is a common complication to chemotherapy associated with a high burden of morbidity and mortality. Reliable prediction of individual risk based on pretreatment risk factors allows for stratification of preventive interventions. We aimed to develop such a risk stratification model to predict FN in the 30 days after initiation of chemotherapy. Methods We included consecutive treatment-naïve patients with solid cancers and diffuse large B-cell lymphomas at Copenhagen University Hospital, 2010–2015. Data were obtained from the PERSIMUNE repository of electronic health records. FN was defined as neutrophils ≤0.5 × 10E9/L ​at the time of either a blood culture sample or death. Time from initiation of chemotherapy to FN was analyzed using Fine-Gray models with death as a competing event. Risk factors investigated were: age, sex, body surface area, haemoglobin, albumin, neutrophil-to-lymphocyte ratio, Charlson Comorbidity Index (CCI) and chemotherapy drugs. Parameter estimates were scaled and summed to create the risk score. The scores were grouped into four: low, intermediate, high and very high risk. Results Among 8,585 patients, 467 experienced FN, incidence rate/30 person-days 0.05 (95% CI, 0.05–0.06). Age (1 point if > 65 years), albumin (1 point if < 39 g/L), CCI (1 point if > 2) and chemotherapy (range -5 to 6 points/drug) predicted FN. Median score at inclusion was 2 points (range –5 to 9). The cumulative incidence and the incidence rates and hazard ratios of FN are shown in Figure 1 and Table 1, respectively. Conclusion We developed a risk score to predict FN the first month after initiation of chemotherapy. The score is easy to use and provides good differentiation of risk groups; the score needs independent validation before routine use. Disclosures All authors: No reported disclosures.

  13. A prediction model for colon cancer surveillance data.

    PubMed

    Good, Norm M; Suresh, Krithika; Young, Graeme P; Lockett, Trevor J; Macrae, Finlay A; Taylor, Jeremy M G

    2015-08-15

    Dynamic prediction models make use of patient-specific longitudinal data to update individualized survival probability predictions based on current and past information. Colonoscopy (COL) and fecal occult blood test (FOBT) results were collected from two Australian surveillance studies on individuals characterized as high-risk based on a personal or family history of colorectal cancer. Motivated by a Poisson process, this paper proposes a generalized nonlinear model with a complementary log-log link as a dynamic prediction tool that produces individualized probabilities for the risk of developing advanced adenoma or colorectal cancer (AAC). This model allows predicted risk to depend on a patient's baseline characteristics and time-dependent covariates. Information on the dates and results of COLs and FOBTs were incorporated using time-dependent covariates that contributed to patient risk of AAC for a specified period following the test result. These covariates serve to update a person's risk as additional COL, and FOBT test information becomes available. Model selection was conducted systematically through the comparison of Akaike information criterion. Goodness-of-fit was assessed with the use of calibration plots to compare the predicted probability of event occurrence with the proportion of events observed. Abnormal COL results were found to significantly increase risk of AAC for 1 year following the test. Positive FOBTs were found to significantly increase the risk of AAC for 3 months following the result. The covariates that incorporated the updated test results were of greater significance and had a larger effect on risk than the baseline variables. Copyright © 2015 John Wiley & Sons, Ltd.

  14. The Cancer of the Prostate Risk Assessment (CAPRA) score predicts biochemical recurrence in intermediate-risk prostate cancer treated with external beam radiotherapy (EBRT) dose escalation or low-dose rate (LDR) brachytherapy.

    PubMed

    Krishnan, Vimal; Delouya, Guila; Bahary, Jean-Paul; Larrivée, Sandra; Taussky, Daniel

    2014-12-01

    To study the prognostic value of the University of California, San Francisco Cancer of the Prostate Risk Assessment (CAPRA) score to predict biochemical failure (bF) after various doses of external beam radiotherapy (EBRT) and/or permanent seed low-dose rate (LDR) prostate brachytherapy (PB). We retrospectively analysed 345 patients with intermediate-risk prostate cancer, with PSA levels of 10-20 ng/mL and/or Gleason 7 including 244 EBRT patients (70.2-79.2 Gy) and 101 patients treated with LDR PB. The minimum follow-up was 3 years. No patient received primary androgen-deprivation therapy. bF was defined according to the Phoenix definition. Cox regression analysis was used to estimate the differences between CAPRA groups. The overall bF rate was 13% (45/345). The CAPRA score, as a continuous variable, was statistically significant in multivariate analysis for predicting bF (hazard ratio [HR] 1.37, 95% confidence interval [CI] 1.10-1.72, P = 0.006). There was a trend for a lower bF rate in patients treated with LDR PB when compared with those treated by EBRT ≤ 74 Gy (HR 0.234, 95% CI 0.05-1.03, P = 0.055) in multivariate analysis. In the subgroup of patients with a CAPRA score of 3-5, CAPRA remained predictive of bF as a continuous variable (HR 1.51, 95% CI 1.01-2.27, P = 0.047) in multivariate analysis. The CAPRA score is useful for predicting biochemical recurrence in patients treated for intermediate-risk prostate cancer with EBRT or LDR PB. It could help in treatment decisions. © 2013 The Authors. BJU International © 2013 BJU International.

  15. Clinical factors predicting bacteremia in low-risk febrile neutropenia after anti-cancer chemotherapy.

    PubMed

    Ha, Young Eun; Song, Jae-Hoon; Kang, Won Ki; Peck, Kyong Ran; Chung, Doo Ryeon; Kang, Cheol-In; Joung, Mi-Kyong; Joo, Eun-Jeong; Shon, Kyung Mok

    2011-11-01

    Bacteremia is an important clinical condition in febrile neutropenia that can cause clinical failure of antimicrobial therapy. The purpose of this study was to investigate the clinical factors predictive of bacteremia in low-risk febrile neutropenia at initial patient evaluation. We performed a retrospective cohort study in a university hospital in Seoul, Korea, between May 1995 and May 2007. Patients who met the criteria of low-risk febrile neutropenia at the time of visit to emergency department after anti-cancer chemotherapy were included in the analysis. During the study period, 102 episodes of bacteremia were documented among the 993 episodes of low-risk febrile neutropenia. Single gram-negative bacteremia was most frequent. In multivariate regression analysis, initial body temperature ≥39°C, initial hypotension, presence of clinical sites of infection, presence of central venous catheter, initial absolute neutrophil count <50/mm(3), and the CRP ≥10 mg/dL were statistically significant predictors for bacteremia. A scoring system using these variables was derived and the likelihood of bacteremia was well correlated with the score points with AUC under ROC curve of 0.785. Patients with low score points had low rate of bacteremia, thus, would be candidates for outpatient-based or oral antibiotic therapy. We identified major clinical factors that can predict bacteremia in low-risk febrile neutropenia.

  16. Prostate cancer risk prediction in a urology clinic in Mexico

    PubMed Central

    Liang, Yuanyuan; Messer, Jamie C; Louden, Christopher; Jimenez-Rios, Miguel A; Thompson, Ian M; Camarena-Reynoso, Hector R

    2012-01-01

    Objectives To evaluate factors affecting the risk of prostate cancer (PCa) and high-grade disease (HGPCa, Gleason score ≥7) in a Mexican referral population, with comparison to the Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (PCPTRC). Methods and Materials From a retrospective study of 826 patients who underwent prostate biopsy between January 2005 and December 2009 at the Instituto Nacional de Cancerología, Mexico, logistic regression was used to assess the effects of age, prostate-specific antigen (PSA), digital rectal exam (DRE), first-degree family history of PCa, and history of a prior prostate biopsy on PCa and HGPCa separately. Internal discrimination, goodness-of-fit and clinical utility of the resulting models were assessed with comparison to the PCPTRC. Results Rates of both PCa (73.2%) and HGPCa (33.3%) were high among referral patients in this Mexican urology clinic. The PCPTRC generally underestimated the risk of PCa but overestimated the risk of HGPCa. Four factors influencing PCa on biopsy were logPSA, DRE, family history and a prior biopsy history (all p<0.001). The internal AUC of the logistic model was 0.823 compared to 0.785 of the PCPTRC for PCa (p<0.001). The same four factors were significantly associated with HGPCa as well and the AUC was 0.779 compared to 0.766 of the PCPTRC for HGPCa (p=0.13). Conclusions Lack of screening programs or regular urological checkups in Mexico imply that men typically first reach specialized clinics with a high cancer risk. This renders diagnostic tools developed on comparatively healthy populations, such as the PCPTRC, of lesser utility. Continued efforts are needed to develop and externally validate new clinical diagnostic tools specific to high-risk referral populations incorporating new biomarkers and more clinical characteristics. PMID:22306115

  17. Prostate cancer risk prediction in a urology clinic in Mexico.

    PubMed

    Liang, Yuanyuan; Messer, Jamie C; Louden, Christopher; Jimenez-Rios, Miguel A; Thompson, Ian M; Camarena-Reynoso, Hector R

    2013-10-01

    To evaluate factors affecting the risk of prostate cancer (CaP) and high-grade disease (HGCaP, Gleason score ≥ 7) in a Mexican referral population, with comparison to the Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (PCPTRC). From a retrospective study of 826 patients who underwent prostate biopsy between January 2005 and December 2009 at the Instituto Nacional de Cancerología, Mexico, logistic regression was used to assess the effects of age, prostate-specific antigen (PSA), digital rectal exam (DRE), first-degree family history of CaP, and history of a prior prostate biopsy on CaP and HGCaP, separately. Internal discrimination, goodness-of-fit, and clinical utility of the resulting models were assessed with comparison to the PCPTRC. Rates of both CaP (73.2%) and HGCaP (33.3%) were high among referral patients in this Mexican urology clinic. The PCPTRC generally underestimated the risk of CaP but overestimated the risk of HGCaP. Four factors influencing CaP on biopsy were logPSA, DRE, family history and a prior biopsy history (all P < 0.001). The internal AUC of the logistic model was 0.823 compared with 0.785 of the PCPTRC for CaP (P < 0.001). The same 4 factors were significantly associated with HGCaP as well and the AUC was 0.779 compared with 0.766 of the PCPTRC for HGCaP (P = 0.13). Lack of screening programs or regular urologic checkups in Mexico imply that men typically first reach specialized clinics with a high cancer risk. This renders diagnostic tools developed on comparatively healthy populations, such as the PCPTRC, of lesser utility. Continued efforts are needed to develop and externally validate new clinical diagnostic tools specific to high-risk referral populations incorporating new biomarkers and more clinical characteristics. Copyright © 2013 Elsevier Inc. All rights reserved.

  18. Meta-Prediction of MTHFR Gene Polymorphism Mutations and Associated Risk for Colorectal Cancer

    PubMed Central

    Yu, C. H.

    2016-01-01

    The methylenetetrahydrofolate reductase (MTHFR) gene is one of the most investigated of the genes associated with chronic human diseases because of its associations with hyperhomocysteinemia and toxicity. It has been proposed as a prototype gene for the prevention of colorectal cancer (CRC). The major objectives of this meta-analysis were to examine the polymorphism-mutation patterns of MTHFR and their associations with risk for CRC as well as potential contributing factors for mutations and disease risks. This analysis included 33,626 CRC cases and 48,688 controls across 92 studies for MTHFR 677 and 16,367 cases and 24,874 controls across 54 studies for MTHFR 1298, comprising data for various racial and ethnic groups, both genders, and multiple cancer sites. MTHFR 677 homozygous TT genotype was protective (p < .05) for CRC for all included populations; however, with heterogeneity across various racial–ethnic groups and opposing findings, it was a risk genotype for the subgroup of Hispanics (p < .01). Additional countries for which subgroup analyses resulted in 677 TT as a risk genotype included Turkey, Romania, Croatia, Hungary, Portugal, Mexico, Brazil, U.S. Hawai’i, Taiwan, India, and Egypt. Countries with the highest mutation rates and risks for both MTHFR 677 and 1298 genotypes are presented using global maps to visualize the grouping patterns. Meta-predictive analyses revealed that air pollution levels were associated with gene polymorphisms for both genotypes. Future nursing research should be conducted to develop proactive measures to protect populations in cities where air pollution causes more deaths. PMID:26858257

  19. Prediction of near-term breast cancer risk using local region-based bilateral asymmetry features in mammography

    NASA Astrophysics Data System (ADS)

    Li, Yane; Fan, Ming; Li, Lihua; Zheng, Bin

    2017-03-01

    This study proposed a near-term breast cancer risk assessment model based on local region bilateral asymmetry features in Mammography. The database includes 566 cases who underwent at least two sequential FFDM examinations. The `prior' examination in the two series all interpreted as negative (not recalled). In the "current" examination, 283 women were diagnosed cancers and 283 remained negative. Age of cancers and negative cases completely matched. These cases were divided into three subgroups according to age: 152 cases among the 37-49 age-bracket, 220 cases in the age-bracket 50- 60, and 194 cases with the 61-86 age-bracket. For each image, two local regions including strip-based regions and difference-of-Gaussian basic element regions were segmented. After that, structural variation features among pixel values and structural similarity features were computed for strip regions. Meanwhile, positional features were extracted for basic element regions. The absolute subtraction value was computed between each feature of the left and right local-regions. Next, a multi-layer perception classifier was implemented to assess performance of features for prediction. Features were then selected according stepwise regression analysis. The AUC achieved 0.72, 0.75 and 0.71 for these 3 age-based subgroups, respectively. The maximum adjustable odds ratios were 12.4, 20.56 and 4.91 for these three groups, respectively. This study demonstrate that the local region-based bilateral asymmetry features extracted from CC-view mammography could provide useful information to predict near-term breast cancer risk.

  20. Psychological opportunities and hazards in predictive genetic testing for cancer risk.

    PubMed

    Codori, A M

    1997-03-01

    Although the availability of genetic tests seems like an unequivocally favorable turn of events, they are, in fact, not without controversy. At the center of the controversy is a question regarding the risks and benefits of genetic testing. Many geneticists, ethicists, psychologists, and persons at risk for cancer are concerned about the potentially adverse psychological effects of genetic testing on tested persons and their families. In addition, the screening and interventions that are useful in the general population remain to be shown effective in those with high genetic cancer risk. Consequently, there have been calls for caution in moving genetic testing out of research laboratories and into commercial laboratories until their impact and the effectiveness of cancer prevention strategies can be studied. This article examines the arguments and data for and against this caution, citing examples related to hereditary nonpolyposis colon cancer and drawing upon literature on testing for other genetic diseases.

  1. Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators.

    PubMed

    Nam, Robert K; Kattan, Michael W; Chin, Joseph L; Trachtenberg, John; Singal, Rajiv; Rendon, Ricardo; Klotz, Laurence H; Sugar, Linda; Sherman, Christopher; Izawa, Jonathan; Bell, David; Stanimirovic, Aleksandra; Venkateswaran, Vasundara; Diamandis, Eleftherios P; Yu, Changhong; Loblaw, D Andrew; Narod, Steven A

    2011-08-01

    Prostate cancer risk calculators incorporate many factors to evaluate an individual's risk for prostate cancer. We validated two common North American-based, prostate cancer risk calculators. We conducted a prospective, multi-institutional study of 2,130 patients who underwent a prostate biopsy for prostate cancer detection from five centers. We evaluated the performance of the Sunnybrook nomogram-based prostate cancer risk calculator (SRC) and the Prostate Cancer Prevention Trial (PCPT) -based risk calculator (PRC) to predict the presence of any cancer and high-grade cancer. We examined discrimination, calibration, and decision curve analysis techniques to evaluate the prediction models. Of the 2,130 patients, 867 men (40.7%) were found to have cancer, and 1,263 (59.3%) did not have cancer. Of the patients with cancer, 403 (46.5%) had a Gleason score of 7 or more. The area under the [concentration-time] curve (AUC) for the SRC was 0.67 (95% CI, 0.65 to 0.69); the AUC for the PRC was 0.61 (95% CI, 0.59 to 0.64). The AUC was higher for predicting aggressive disease from the SRC (0.72; 95% CI, 0.70 to 0.75) compared with that from the PRC (0.67; 95% CI, 0.64 to 0.70). Decision curve analyses showed that the SRC performed better than the PRC for risk thresholds of more than 30% for any cancer and more than 15% for aggressive cancer. The SRC performed better than the PRC, but neither one added clinical benefit for risk thresholds of less than 30%. Further research is needed to improve the AUCs of the risk calculators, particularly for higher-grade cancer.

  2. Breast cancer and the "materiality of risk": the rise of morphological prediction.

    PubMed

    Löwy, Ilana

    2007-01-01

    This paper follows the history of "morphological risk" of breast cancer. In the early twentieth century, surgeons and pathologists arrived at the conclusion that specific anatomical and cytological changes in the breast are related to a heightened risk of developing a malignancy in the future. This conclusion was directly related to a shift from macroscopic to microscopic diagnosis of malignancies, and to the integration of the frozen section into routine surgery for breast cancer. In the interwar era, conditions such as "chronic mastitis" and "cystic disease of the breast" were defined as precancerous, and women diagnosed with these conditions were advised to undergo mastectomy. In the post-World War II era, these entities were replaced by "carcinoma in situ." The recent development of tests for hereditary predisposition to breast cancer is a continuation of attempts to detect an "embodied risk" of cancer and to eliminate this risk by cutting it out.

  3. Risk prediction and impaired tactile sensory perception among cancer patients during chemotherapy.

    PubMed

    Cardoso, Ana Carolina Lima Ramos; Araújo, Diego Dias de; Chianca, Tânia Couto Machado

    2018-01-08

    to estimate the prevalence of impaired tactile sensory perception, identify risk factors, and establish a risk prediction model among adult patients receiving antineoplastic chemotherapy. historical cohort study based on information obtained from the medical files of 127 patients cared for in the cancer unit of a private hospital in a city in Minas Gerais, Brazil. Data were analyzed using descriptive and bivariate statistics, with survival and multivariate analysis by Cox regression. 57% of the 127 patients included in the study developed impaired tactile sensory perception. The independent variables that caused significant impact, together with time elapsed from the beginning of treatment up to the onset of the condition, were: bone, hepatic and regional lymph node metastases; alcoholism; palliative chemotherapy; and discomfort in lower limbs. impaired tactile sensory perception was common among adult patients during chemotherapy, indicating the need to implement interventions designed for early identification and treatment of this condition.

  4. Assessing Breast Cancer Risk with an Artificial Neural Network

    PubMed

    Sepandi, Mojtaba; Taghdir, Maryam; Rezaianzadeh, Abbas; Rahimikazerooni, Salar

    2018-04-25

    Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer. This study aimed to establish a model to aid radiologists in breast cancer risk estimation. This incorporated imaging methods and fine needle aspiration biopsy (FNAB) for cyto-pathological diagnosis. Methods: An artificial neural network (ANN) technique was used on a retrospectively collected dataset including mammographic results, risk factors, and clinical findings to accurately predict the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: The network incorporating the selected features performed best (AUC = 0.955). Sensitivity and specificity of the ANN were respectively calculated as 0.82 and 0.90. In addition, negative and positive predictive values were respectively computed as 0.90 and 0.80. Conclusion: ANN has potential applications as a decision-support tool to help underperforming practitioners to improve the positive predictive value of biopsy recommendations. Creative Commons Attribution License

  5. Functional polymorphisms in NFκB1/IκBα predict risks of chronic obstructive pulmonary disease and lung cancer in Chinese.

    PubMed

    Huang, Dongsheng; Yang, Lei; Liu, Yehua; Zhou, Yumin; Guo, Yuan; Pan, Mingan; Wang, Yunnan; Tan, Yigang; Zhong, Haibo; Hu, Min; Lu, Wenju; Ji, Weidong; Wang, Jian; Ran, Pixin; Zhong, Nanshan; Zhou, Yifeng; Lu, Jiachun

    2013-04-01

    Lung inflammation is the major pathogenetic feature for both chronic obstructive pulmonary disease (COPD) and lung cancer. The nuclear factor-kappa B (NFκB) and its inhibitor (IκB) play crucial roles in inflammatory. Here, we tested the hypothesis that single nucleotide polymorphisms (SNPs) in NFκB/IκB confer consistent risks for COPD and lung cancer. Four putative functional SNPs (NFκB1: -94del>insATTG; NFκB2: -2966G>A; IκBα: -826C>T, 2758G>A) were analyzed in southern and validated in eastern Chineses to test their associations with COPD risk in 1,511 COPD patients and 1,677 normal lung function controls, as well as lung cancer risk in 1,559 lung cancer cases and 1,679 cancer-free controls. We found that the -94ins ATTG variants (ins/del + ins/ins) in NFκB1 conferred an increased risk of COPD (OR 1.27, 95% CI 1.06-1.52) and promoted COPD progression by accelerating annual FEV1 decline (P = 0.015). The 2758AA variant in IκBα had an increased risk of lung cancer (OR 1.53, 95% CI 1.30-1.80) by decreasing IκBα expression due to the modulation of microRNA hsa-miR-449a but not hsa-miR-34b. Furthermore, both adverse genotypes exerted effect on increasing lung cancer risk in individuals with pre-existing COPD, while the -94del>insATTG did not in those without pre-existing COPD. However, no significant association with COPD or lung cancer was observed for -2966G>A and -826C>T. Our data suggested a common susceptible mechanism of inflammation in lung induced by genetic variants in NFκB1 (-94del>ins ATTG) or IκBα (2758G>A) to predict risk of COPD or lung cancer.

  6. The potential of large studies for building genetic risk prediction models

    Cancer.gov

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  7. Prediction of Breast Cancer Risk by Aberrant Methylation in Mammary Duct Lavage

    DTIC Science & Technology

    2006-07-01

    Assessment of breast epithelial cells obtained by nipple duct lavage (NDL) may have value for breast cancer risk stratification. NDL was performed in 150...contribute to risk stratification. 15. SUBJECT TERMS breast cancer, DNA methylation, Methylation Specific PCR, Nipple Duct Lavage, Risk assessment 16...carcinogenesis. Nipple duct lavage (NDL) is a minimally invasive approach for obtaining breast epithelial cells. Cytological atypia identified in nipple

  8. Race, Genetic West African Ancestry, and Prostate Cancer Prediction by PSA in Prospectively Screened High-Risk Men

    PubMed Central

    Giri, Veda N.; Egleston, Brian; Ruth, Karen; Uzzo, Robert G.; Chen, David Y.T.; Buyyounouski, Mark; Raysor, Susan; Hooker, Stanley; Torres, Jada Benn; Ramike, Teniel; Mastalski, Kathleen; Kim, Taylor Y.; Kittles, Rick

    2008-01-01

    Introduction “Race-specific” PSA needs evaluation in men at high-risk for prostate cancer (PCA) for optimizing early detection. Baseline PSA and longitudinal prediction for PCA was examined by self-reported race and genetic West African (WA) ancestry in the Prostate Cancer Risk Assessment Program, a prospective high-risk cohort. Materials and Methods Eligibility criteria are age 35–69 years, FH of PCA, African American (AA) race, or BRCA1/2 mutations. Biopsies have been performed at low PSA values (<4.0 ng/mL). WA ancestry was discerned by genotyping 100 ancestry informative markers. Cox proportional hazards models evaluated baseline PSA, self-reported race, and genetic WA ancestry. Cox models were used for 3-year predictions for PCA. Results 646 men (63% AA) were analyzed. Individual WA ancestry estimates varied widely among self-reported AA men. “Race-specific” differences in baseline PSA were not found by self-reported race or genetic WA ancestry. Among men with ≥ 1 follow-up visit (405 total, 54% AA), three-year prediction for PCA with a PSA of 1.5–4.0 ng/mL was higher in AA men with age in the model (p=0.025) compared to EA men. Hazard ratios of PSA for PCA were also higher by self-reported race (1.59 for AA vs. 1.32 for EA, p=0.04). There was a trend for increasing prediction for PCA with increasing genetic WA ancestry. Conclusions “Race-specific” PSA may need to be redefined as higher prediction for PCA at any given PSA in AA men. Large-scale studies are needed to confirm if genetic WA ancestry explains these findings to make progress in personalizing PCA early detection. PMID:19240249

  9. Family History of Breast Cancer, Breast Density, and Breast Cancer Risk in a U.S. Breast Cancer Screening Population.

    PubMed

    Ahern, Thomas P; Sprague, Brian L; Bissell, Michael C S; Miglioretti, Diana L; Buist, Diana S M; Braithwaite, Dejana; Kerlikowske, Karla

    2017-06-01

    Background: The utility of incorporating detailed family history into breast cancer risk prediction hinges on its independent contribution to breast cancer risk. We evaluated associations between detailed family history and breast cancer risk while accounting for breast density. Methods: We followed 222,019 participants ages 35 to 74 in the Breast Cancer Surveillance Consortium, of whom 2,456 developed invasive breast cancer. We calculated standardized breast cancer risks within joint strata of breast density and simple (1 st -degree female relative) or detailed (first-degree, second-degree, or first- and second-degree female relative) breast cancer family history. We fit log-binomial models to estimate age-specific breast cancer associations for simple and detailed family history, accounting for breast density. Results: Simple first-degree family history was associated with increased breast cancer risk compared with no first-degree history [Risk ratio (RR), 1.5; 95% confidence interval (CI), 1.0-2.1 at age 40; RR, 1.5; 95% CI, 1.3-1.7 at age 50; RR, 1.4; 95% CI, 1.2-1.6 at age 60; RR, 1.3; 95% CI, 1.1-1.5 at age 70). Breast cancer associations with detailed family history were strongest for women with first- and second-degree family history compared with no history (RR, 1.9; 95% CI, 1.1-3.2 at age 40); this association weakened in higher age groups (RR, 1.2; 95% CI, 0.88-1.5 at age 70). Associations did not change substantially when adjusted for breast density. Conclusions: Even with adjustment for breast density, a history of breast cancer in both first- and second-degree relatives is more strongly associated with breast cancer than simple first-degree family history. Impact: Future efforts to improve breast cancer risk prediction models should evaluate detailed family history as a risk factor. Cancer Epidemiol Biomarkers Prev; 26(6); 938-44. ©2017 AACR . ©2017 American Association for Cancer Research.

  10. Fall-risk prediction in older adults with cancer: an unmet need.

    PubMed

    Wildes, Tanya M; Depp, Brittany; Colditz, Graham; Stark, Susan

    2016-09-01

    Falls in older adults with cancer are more common than in noncancer controls, yet no fall-risk screening tool has been validated in this population. We undertook a cross-sectional pilot study of the Falls Risk Questionnaire (FRQ) in 21 adults aged ≥65 receiving systemic cancer therapy. Participants completed the FRQ, geriatric assessment measures, and a measure of fear-of-falling. The recruitment rate was 87.5 %, with 95.2 % completion of the FRQ and additional geriatric assessment and quality of life measures. The FRQ correlated significantly with the Timed Up and Go test (Pearson r 0.479, p = 0.028). In addition, the FRQ score correlated directly with fear-of-falling and inversely with QOL, particularly physical health and neurotoxicity subscales. In conclusion, the FRQ was feasible in older adults receiving cancer therapy and correlates with measures of physical performance, functional status, and fear-of-falling. The FRQ may prove to be a valuable fall-risk screening tool to implement fall-prevention interventions in this vulnerable population of older adults with cancer.

  11. Predictions of space radiation fatality risk for exploration missions.

    PubMed

    Cucinotta, Francis A; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits. Copyright © 2017 The Committee on Space Research (COSPAR

  12. Predictions of space radiation fatality risk for exploration missions

    NASA Astrophysics Data System (ADS)

    Cucinotta, Francis A.; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. population. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits.

  13. Prediction of Prostate Cancer Recurrence Using Quantitative Phase Imaging

    NASA Astrophysics Data System (ADS)

    Sridharan, Shamira; Macias, Virgilia; Tangella, Krishnarao; Kajdacsy-Balla, André; Popescu, Gabriel

    2015-05-01

    The risk of biochemical recurrence of prostate cancer among individuals who undergo radical prostatectomy for treatment is around 25%. Current clinical methods often fail at successfully predicting recurrence among patients at intermediate risk for recurrence. We used a label-free method, spatial light interference microscopy, to perform localized measurements of light scattering in prostatectomy tissue microarrays. We show, for the first time to our knowledge, that anisotropy of light scattering in the stroma immediately adjoining cancerous glands can be used to identify patients at higher risk for recurrence. The data show that lower value of anisotropy corresponds to a higher risk for recurrence, meaning that the stroma adjoining the glands of recurrent patients is more fractionated than in non-recurrent patients. Our method outperformed the widely accepted clinical tool CAPRA-S in the cases we interrogated irrespective of Gleason grade, prostate-specific antigen (PSA) levels and pathological tumor-node-metastasis (pTNM) stage. These results suggest that QPI shows promise in assisting pathologists to improve prediction of prostate cancer recurrence.

  14. Risk prediction for early-onset gastric carcinoma: a case-control study of polygenic gastric cancer in Han Chinese with hereditary background.

    PubMed

    Yuan, Jiajia; Li, Yanyan; Tian, Tiantian; Li, Na; Zhu, Yan; Zou, Jianling; Gao, Jing; Shen, Lin

    2016-06-07

    Recent genomewide studies have identified several germline variations associated with gastric cancer. The aim of the present study was to identify, in a Chinese Han population, the individual and combined effects of those single nucleotide polymorphisms (SNPs) that increase the risk of early-onset gastric cancer. We conducted a case-control study comprising 116 patients with gastric cancer as well as 102 sex- and age-matched controls and confirmed that the SNPs MUC1 (mucin 1) rs9841504 and ZBTB20 (zinc finger and BTB domain containing 20) rs4072037 were associated with an increased gastric cancer risk. Of the 116 patients diagnosed with cancer, 65 had at least 1 direct lineal relative with carcinoma of the digestive system or breast/ovarian cancer. These 65 had another 4 SNPs associated with gastric cancer susceptibility: PSCA (prostate stem cell antigen) rs2294008, PLCE1 (phospholipase C epsilon 1) rs2274223, PTGER4/PRKAA1 (prostaglandin E receptor 4/ protein kinase AMP-activated catalytic subunit alpha 1) rs13361707, and TYMS (thymidylate synthetase) rs2790. However, each of these low-penetrance susceptibility polymorphisms alone is not considered influential enough to predict the absolute risk of early-onset gastric cancer. Thus we decided to study different combinations of polygenes as they affected for our population. Those subjects with both the risk alleles MUC1 rs9841504 and ZBTB20 rs4072037 had a greater than 3-fold increased risk of gastric cancer. Also those with a hereditary background including the risk alleles PLCE1 rs2274223 and PTGER4/PRKAA1 rs13361707 were 3 times more susceptible to cardia cancer than those without. These findings show that the study of combined polymorphisms, instead of single low-penetrance variations in susceptibility, may lead to a high-risk classification for a specific population.

  15. Risk prediction for early-onset gastric carcinoma: a case-control study of polygenic gastric cancer in Han Chinese with hereditary background

    PubMed Central

    Yuan, Jiajia; Li, Yanyan; Tian, Tiantian; Li, Na; Zhu, Yan; Zou, Jianling; Gao, Jing; Shen, Lin

    2016-01-01

    Recent genomewide studies have identified several germline variations associated with gastric cancer. The aim of the present study was to identify, in a Chinese Han population, the individual and combined effects of those single nucleotide polymorphisms (SNPs) that increase the risk of early-onset gastric cancer. We conducted a case-control study comprising 116 patients with gastric cancer as well as 102 sex- and age-matched controls and confirmed that the SNPs MUC1 (mucin 1) rs9841504 and ZBTB20 (zinc finger and BTB domain containing 20) rs4072037 were associated with an increased gastric cancer risk. Of the 116 patients diagnosed with cancer, 65 had at least 1 direct lineal relative with carcinoma of the digestive system or breast/ovarian cancer. These 65 had another 4 SNPs associated with gastric cancer susceptibility: PSCA (prostate stem cell antigen) rs2294008, PLCE1 (phospholipase C epsilon 1) rs2274223, PTGER4/PRKAA1 (prostaglandin E receptor 4/protein kinase AMP-activated catalytic subunit alpha 1) rs13361707, and TYMS (thymidylate synthetase) rs2790. However, each of these low-penetrance susceptibility polymorphisms alone is not considered influential enough to predict the absolute risk of early-onset gastric cancer. Thus we decided to study different combinations of polygenes as they affected for our population. Those subjects with both the risk alleles MUC1 rs9841504 and ZBTB20 rs4072037 had a greater than 3-fold increased risk of gastric cancer. Also those with a hereditary background including the risk alleles PLCE1 rs2274223 and PTGER4/PRKAA1 rs13361707 were 3 times more susceptible to cardia cancer than those without. These findings show that the study of combined polymorphisms, instead of single low-penetrance variations in susceptibility, may lead to a high-risk classification for a specific population. PMID:27127881

  16. Distinguishing Low-Risk Luminal A Breast Cancer Subtypes with Ki-67 and p53 Is More Predictive of Long-Term Survival

    PubMed Central

    Lee, Se Kyung; Bae, Soo Youn; Lee, Jun Ho; Lee, Hyun-Chul; Yi, Hawoo; Kil, Won Ho; Lee, Jeong Eon; Kim, Seok Won; Nam, Seok Jin

    2015-01-01

    Overexpression of p53 is the most frequent genetic alteration in breast cancer. Recently, many studies have shown that the expression of mutant p53 differs for each subtype of breast cancer and is associated with different prognoses. In this study, we aimed to determine the suitable cut-off value to predict the clinical outcome of p53 overexpression and its usefulness as a prognostic factor in each subtype of breast cancer, especially in luminal A breast cancer. Approval was granted by the Institutional Review Board of Samsung Medical Center. We analyzed a total of 7,739 patients who were surgically treated for invasive breast cancer at Samsung Medical Center between Dec 1995 and Apr 2013. Luminal A subtype was defined as ER&PR + and HER2- and was further subclassified according to Ki-67 and p53 expression as follows: luminal A (Ki-67-,p53-), luminal A (Ki-67+, p53-), luminal A (Ki-67 -, p53+) and luminal A (Ki-67+, p53+). Low-risk luminal A subtype was defined as negative for both Ki-67 and p53 (luminal A [ki-67-, p53-]), and others subtypes were considered to be high-risk luminal A breast cancer. A cut-off value of 10% for p53 was a good predictor of clinical outcome in all patients and luminal A breast cancer patients. The prognostic role of p53 overexpression for OS and DFS was only significant in luminal A subtype. The combination of p53 and Ki-67 has been shown to have the best predictive power as calculated by the area under curve (AUC), especially for long-term overall survival. In this study, we have shown that overexpression of p53 and Ki-67 could be used to discriminate low-risk luminal A subtype in breast cancer. Therefore, using the combination of p53 and Ki-67 expression in discriminating low-risk luminal A breast cancer may improve the prognostic power and provide the greatest clinical utility. PMID:26241661

  17. Distinguishing Low-Risk Luminal A Breast Cancer Subtypes with Ki-67 and p53 Is More Predictive of Long-Term Survival.

    PubMed

    Lee, Se Kyung; Bae, Soo Youn; Lee, Jun Ho; Lee, Hyun-Chul; Yi, Hawoo; Kil, Won Ho; Lee, Jeong Eon; Kim, Seok Won; Nam, Seok Jin

    2015-01-01

    Overexpression of p53 is the most frequent genetic alteration in breast cancer. Recently, many studies have shown that the expression of mutant p53 differs for each subtype of breast cancer and is associated with different prognoses. In this study, we aimed to determine the suitable cut-off value to predict the clinical outcome of p53 overexpression and its usefulness as a prognostic factor in each subtype of breast cancer, especially in luminal A breast cancer. Approval was granted by the Institutional Review Board of Samsung Medical Center. We analyzed a total of 7,739 patients who were surgically treated for invasive breast cancer at Samsung Medical Center between Dec 1995 and Apr 2013. Luminal A subtype was defined as ER&PR + and HER2- and was further subclassified according to Ki-67 and p53 expression as follows: luminal A (Ki-67-,p53-), luminal A (Ki-67+, p53-), luminal A (Ki-67 -, p53+) and luminal A (Ki-67+, p53+). Low-risk luminal A subtype was defined as negative for both Ki-67 and p53 (luminal A [ki-67-, p53-]), and others subtypes were considered to be high-risk luminal A breast cancer. A cut-off value of 10% for p53 was a good predictor of clinical outcome in all patients and luminal A breast cancer patients. The prognostic role of p53 overexpression for OS and DFS was only significant in luminal A subtype. The combination of p53 and Ki-67 has been shown to have the best predictive power as calculated by the area under curve (AUC), especially for long-term overall survival. In this study, we have shown that overexpression of p53 and Ki-67 could be used to discriminate low-risk luminal A subtype in breast cancer. Therefore, using the combination of p53 and Ki-67 expression in discriminating low-risk luminal A breast cancer may improve the prognostic power and provide the greatest clinical utility.

  18. Predicting cancer risk knowledge and information seeking: the role of social and cognitive factors.

    PubMed

    Hovick, Shelly R; Liang, Ming-Ching; Kahlor, Leeann

    2014-01-01

    This study tests an expanded Structural Influence Model (SIM) to gain a greater understanding of the social and cognitive factors that contribute to disparities in cancer risk knowledge and information seeking. At the core of this expansion is the planned risk information seeking model (PRISM). This study employed an online sample (N = 1,007) of African American, Hispanic, and non-Hispanic White adults. The addition of four cognitive predictors to the SIM substantially increased variance explained in cancer risk knowledge (R(2) = .29) and information seeking (R(2) = .56). Health literacy mediated the effects of social determinants (socioeconomic status [SES] and race/ethnicity) on cancer risk knowledge, while subjective norms mediated their effects on cancer risk information seeking. Social capital and perceived seeking control were also shown to be important mediators of the relationships between SES and cancer communication outcomes. Our results illustrate the social and cognitive mechanisms by which social determinants impact cancer communication outcomes, as well as several points of intervention to reduce communication disparities.

  19. Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction

    PubMed Central

    Antoniou, Antonis C; Beesley, Jonathan; McGuffog, Lesley; Sinilnikova, Olga M.; Healey, Sue; Neuhausen, Susan L.; Ding, Yuan Chun; Rebbeck, Timothy R.; Weitzel, Jeffrey N.; Lynch, Henry T.; Isaacs, Claudine; Ganz, Patricia A.; Tomlinson, Gail; Olopade, Olufunmilayo I.; Couch, Fergus J.; Wang, Xianshu; Lindor, Noralane M.; Pankratz, Vernon S.; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Barile, Monica; Viel, Alessandra; Allavena, Anna; Dall’Olio, Valentina; Peterlongo, Paolo; Szabo, Csilla I.; Zikan, Michal; Claes, Kathleen; Poppe, Bruce; Foretova, Lenka; Mai, Phuong L.; Greene, Mark H.; Rennert, Gad; Lejbkowicz, Flavio; Glendon, Gord; Ozcelik, Hilmi; Andrulis, Irene L.; Thomassen, Mads; Gerdes, Anne-Marie; Sunde, Lone; Cruger, Dorthe; Jensen, Uffe Birk; Caligo, Maria; Friedman, Eitan; Kaufman, Bella; Laitman, Yael; Milgrom, Roni; Dubrovsky, Maya; Cohen, Shimrit; Borg, Ake; Jernström, Helena; Lindblom, Annika; Rantala, Johanna; Stenmark-Askmalm, Marie; Melin, Beatrice; Nathanson, Kate; Domchek, Susan; Jakubowska, Ania; Lubinski, Jan; Huzarski, Tomasz; Osorio, Ana; Lasa, Adriana; Durán, Mercedes; Tejada, Maria-Isabel; Godino, Javier; Benitez, Javier; Hamann, Ute; Kriege, Mieke; Hoogerbrugge, Nicoline; van der Luijt, Rob B; van Asperen, Christi J; Devilee, Peter; Meijers-Heijboer, E.J.; Blok, Marinus J; Aalfs, Cora M.; Hogervorst, Frans; Rookus, Matti; Cook, Margaret; Oliver, Clare; Frost, Debra; Conroy, Don; Evans, D. Gareth; Lalloo, Fiona; Pichert, Gabriella; Davidson, Rosemarie; Cole, Trevor; Cook, Jackie; Paterson, Joan; Hodgson, Shirley; Morrison, Patrick J.; Porteous, Mary E.; Walker, Lisa; Kennedy, M. John; Dorkins, Huw; Peock, Susan; Godwin, Andrew K.; Stoppa-Lyonnet, Dominique; de Pauw, Antoine; Mazoyer, Sylvie; Bonadona, Valérie; Lasset, Christine; Dreyfus, Hélène; Leroux, Dominique; Hardouin, Agnès; Berthet, Pascaline; Faivre, Laurence; Loustalot, Catherine; Noguchi, Tetsuro; Sobol, Hagay; Rouleau, Etienne; Nogues, Catherine; Frénay, Marc; Vénat-Bouvet, Laurence; Hopper, John L.; Daly, Mary B.; Terry, Mary B.; John, Esther M.; Buys, Saundra S.; Yassin, Yosuf; Miron, Alex; Goldgar, David; Singer, Christian F.; Dressler, Anne Catharina; Gschwantler-Kaulich, Daphne; Pfeiler, Georg; Hansen, Thomas V. O.; Jønson, Lars; Agnarsson, Bjarni A.; Kirchhoff, Tomas; Offit, Kenneth; Devlin, Vincent; Dutra-Clarke, Ana; Piedmonte, Marion; Rodriguez, Gustavo C.; Wakeley, Katie; Boggess, John F.; Basil, Jack; Schwartz, Peter E.; Blank, Stephanie V.; Toland, Amanda Ewart; Montagna, Marco; Casella, Cinzia; Imyanitov, Evgeny; Tihomirova, Laima; Blanco, Ignacio; Lazaro, Conxi; Ramus, Susan J.; Sucheston, Lara; Karlan, Beth Y.; Gross, Jenny; Schmutzler, Rita; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Lochmann, Magdalena; Arnold, Norbert; Heidemann, Simone; Varon-Mateeva, Raymonda; Niederacher, Dieter; Sutter, Christian; Deissler, Helmut; Gadzicki, Dorothea; Preisler-Adams, Sabine; Kast, Karin; Schönbuchner, Ines; Caldes, Trinidad; de la Hoya, Miguel; Aittomäki, Kristiina; Nevanlinna, Heli; Simard, Jacques; Spurdle, Amanda B.; Holland, Helene; Chen, Xiaoqing; Platte, Radka; Chenevix-Trench, Georgia; Easton, Douglas F.

    2010-01-01

    The known breast cancer (BC) susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1,LSP1 and 2q35 confer increased risks of BC for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of three additional SNPs, rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11 and rs10941679 at 5p12 and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased BC risk for BRCA2 carriers (per-allele Hazard Ratio (HR)=1.10, 95%CI:1.03-1.18, p=0.006 and HR=1.09, 95%CI:1.01-1.19, p=0.03, respectively). Neither SNP was associated with BC risk for BRCA1 carriers and rs6504950 was not associated with BC for either BRCA1 or BRCA2 carriers. Of the nine polymorphisms investigated, seven were associated with BC for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, p-values:7×10−11-0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (p=0.0049, 0.03 respectively). All risk associated polymorphisms appear to interact multiplicatively on BC risk for mutation carriers. Based on the joint genotype distribution of the seven risk associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e. between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing BC by age 80, compared with 42-50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences may be sufficient to influence the clinical management of mutation carriers. PMID:21118973

  20. Indoor tanning and the MC1R genotype: risk prediction for basal cell carcinoma risk in young people.

    PubMed

    Molinaro, Annette M; Ferrucci, Leah M; Cartmel, Brenda; Loftfield, Erikka; Leffell, David J; Bale, Allen E; Mayne, Susan T

    2015-06-01

    Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients seen between 2006 and 2010 in New Haven, Connecticut. In our data, the adjusted area under the receiver operating characteristic curve (AUC) for a model by Han et al. (Int J Cancer. 2006;119(8):1976-1984) with 7 MC1R variants was 0.72 (95% confidence interval (CI): 0.66, 0.78), while that by Smith et al. (J Clin Oncol. 2012;30(15 suppl):8574) with MC1R and indoor tanning had an AUC of 0.69 (95% CI: 0.63, 0.75). Our base model had greater predictive ability than existing models and was significantly improved when we added ever-indoor tanning, burns from indoor tanning, and MC1R (AUC = 0.77, 95% CI: 0.74, 0.81). Our early-onset BCC risk prediction model incorporating MC1R and indoor tanning extends the work of other skin cancer risk prediction models, emphasizes the value of both genotype and indoor tanning in skin cancer risk prediction in young people, and should be validated with an independent cohort. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  1. Quantifying predictive capability of electronic health records for the most harmful breast cancer

    NASA Astrophysics Data System (ADS)

    Wu, Yirong; Fan, Jun; Peissig, Peggy; Berg, Richard; Tafti, Ahmad Pahlavan; Yin, Jie; Yuan, Ming; Page, David; Cox, Jennifer; Burnside, Elizabeth S.

    2018-03-01

    Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and LassoLR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

  2. Quantifying predictive capability of electronic health records for the most harmful breast cancer.

    PubMed

    Wu, Yirong; Fan, Jun; Peissig, Peggy; Berg, Richard; Tafti, Ahmad Pahlavan; Yin, Jie; Yuan, Ming; Page, David; Cox, Jennifer; Burnside, Elizabeth S

    2018-02-01

    Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and Lasso-LR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

  3. Life history theory and breast cancer risk: methodological and theoretical challenges: Response to "Is estrogen receptor negative breast cancer risk associated with a fast life history strategy?".

    PubMed

    Aktipis, Athena

    2016-01-01

    In a meta-analysis published by myself and co-authors, we report differences in the life history risk factors for estrogen receptor negative (ER-) and estrogen receptor positive (ER+) breast cancers. Our meta-analysis did not find the association of ER- breast cancer risk with fast life history characteristics that Hidaka and Boddy suggest in their response to our article. There are a number of possible explanations for the differences between their conclusions and the conclusions we drew from our meta-analysis, including limitations of our meta-analysis and methodological challenges in measuring and categorizing estrogen receptor status. These challenges, along with the association of ER+ breast cancer with slow life history characteristics, may make it challenging to find a clear signal of ER- breast cancer with fast life history characteristics, even if that relationship does exist. The contradictory results regarding breast cancer risk and life history characteristics illustrate a more general challenge in evolutionary medicine: often different sub-theories in evolutionary biology make contradictory predictions about disease risk. In this case, life history models predict that breast cancer risk should increase with faster life history characteristics, while the evolutionary mismatch hypothesis predicts that breast cancer risk should increase with delayed reproduction. Whether life history tradeoffs contribute to ER- breast cancer is still an open question, but current models and several lines of evidence suggest that it is a possibility. © The Author(s) 2016. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health.

  4. Australian validation of the Cancer of the Prostate Risk Assessment Post-Surgical score to predict biochemical recurrence after radical prostatectomy.

    PubMed

    Beckmann, Kerri; O'Callaghan, Michael; Vincent, Andrew; Roder, David; Millar, Jeremy; Evans, Sue; McNeil, John; Moretti, Kim

    2018-03-01

    The Cancer of the Prostate Risk Assessment Post-Surgical (CAPRA-S) score is a simple post-operative risk assessment tool predicting disease recurrence after radical prostatectomy, which is easily calculated using available clinical data. To be widely useful, risk tools require multiple external validations. We aimed to validate the CAPRA-S score in an Australian multi-institutional population, including private and public settings and reflecting community practice. The study population were all men on the South Australian Prostate Cancer Clinical Outcomes Collaborative Database with localized prostate cancer diagnosed during 1998-2013, who underwent radical prostatectomy without adjuvant therapy (n = 1664). Predictive performance was assessed via Kaplan-Meier and Cox proportional regression analyses, Harrell's Concordance index, calibration plots and decision curve analysis. Biochemical recurrence occurred in 342 (21%) cases. Five-year recurrence-free probabilities for CAPRA-S scores indicating low (0-2), intermediate (3-5) and high risk were 95, 79 and 46%, respectively. The hazard ratio for CAPRA-S score increments was 1.56 (95% confidence interval 1.49-1.64). The Concordance index for 5-year recurrence-free survival was 0.77. The calibration plot showed good correlation between predicted and observed recurrence-free survival across scores. Limitations include the retrospective nature and small numbers with higher CAPRA-S scores. The CAPRA-S score is an accurate predictor of recurrence after radical prostatectomy in our cohort, supporting its utility in the Australian setting. This simple tool can assist in post-surgical selection of patients who would benefit from adjuvant therapy while avoiding morbidity among those less likely to benefit. © 2017 Royal Australasian College of Surgeons.

  5. Cancer risk communication in mainstream and ethnic newspapers.

    PubMed

    Stryker, Jo Ellen; Fishman, Jessica; Emmons, Karen M; Viswanath, Kasisomayajula

    2009-01-01

    We wanted to understand how cancer risks are communicated in mainstream and ethnic newspapers, to determine whether the 2 kinds of newspapers differ and to examine features of news stories and sources that might predict optimal risk communication. Optimal risk communication was defined as presenting the combination of absolute risk, relative risk, and prevention response efficacy information. We collected data by conducting a content analysis of cancer news coverage from 2003 (5,327 stories in major newspapers, 565 stories in ethnic newspapers). Comparisons of mainstream and ethnic newspapers were conducted by using cross-tabulations and Pearson chi2 tests for significance. Logistic regression equations were computed to calculate odds ratios and 95% confidence intervals for optimal risk communication. In both kinds of newspapers, cancer risks were rarely communicated numerically. When numeric presentations of cancer risks were used, only 26.2% of mainstream and 29.5% of ethnic newspaper stories provided estimates of both absolute and relative risk. For both kinds of papers, only 19% of news stories presented risk communication optimally. Cancer risks were more likely to be communicated optimally if they focused on prostate cancer, were reports of new research, or discussed medical or demographic risks. Research is needed to understand how these nonnumeric and decontextualized presentations of risk might contribute to inaccurate risk perceptions among news consumers.

  6. Differentiated thyroid cancer in children: Heterogeneity of predictive risk factors.

    PubMed

    Russo, Marco; Malandrino, Pasqualino; Moleti, Mariacarla; Vermiglio, Francesco; D'Angelo, Antonio; La Rosa, Giuliana; Sapuppo, Giulia; Calaciura, Francesca; Regalbuto, Concetto; Belfiore, Antonino; Vigneri, Riccardo; Pellegriti, Gabriella

    2018-05-16

    To correlate clinical and pathological characteristics at diagnosis with patient long-term outcomes and to evaluate ongoing risk stratifications in a large series of paediatric differentiated thyroid cancers (DTC). Retrospective analysis of clinical and pathological prognostic factors of 124 paediatric patients with DTC (age at diagnosis <19 years) followed up for 10.4 ± 8.4 years. Patients with a follow-up >3 years (n = 104) were re-classified 18 months after surgery on the basis of their response to therapy (ongoing risk stratification). Most patients had a papillary histotype (96.0%), were older than 15 years (75.0%) and were diagnosed because of clinical local symptoms (63.7%). Persistent/recurrent disease was present in 31.5% of cases during follow-up, but at the last evaluation, only 12.9% had biochemical or structural disease. The presence of metastases in the lymph nodes of the lateral compartment (OR 3.2, 95% CI, 1.28-7.16, P = 0.01) was the only independent factor associated with recurrent/persistent disease during follow-up. At the last evaluation, biochemical/structural disease was associated with node metastases (N1a, N1b) by univariate but not multivariate analysis. Ongoing risk stratification compared to the initial risk classification method better identified patients with a lower probability of persistent/recurrent disease (NPV = 100%). In spite of the aggressive presentations at diagnosis, paediatric patients with DTC show an excellent response to treatment and often a favourable outcome. N1b status should be considered a strong predictor of persistent/recurrent disease which, as in adults, is better predicted by ongoing risk stratification. © 2018 Wiley Periodicals, Inc.

  7. Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics.

    PubMed

    Sammour, T; Cohen, L; Karunatillake, A I; Lewis, M; Lawrence, M J; Hunter, A; Moore, J W; Thomas, M L

    2017-11-01

    Recently published data support the use of a web-based risk calculator ( www.anastomoticleak.com ) for the prediction of anastomotic leak after colectomy. The aim of this study was to externally validate this calculator on a larger dataset. Consecutive adult patients undergoing elective or emergency colectomy for colon cancer at a single institution over a 9-year period were identified using the Binational Colorectal Cancer Audit database. Patients with a rectosigmoid cancer, an R2 resection, or a diverting ostomy were excluded. The primary outcome was anastomotic leak within 90 days as defined by previously published criteria. Area under receiver operating characteristic curve (AUROC) was derived and compared with that of the American College of Surgeons National Surgical Quality Improvement Program ® (ACS NSQIP) calculator and the colon leakage score (CLS) calculator for left colectomy. Commercially available artificial intelligence-based analytics software was used to further interrogate the prediction algorithm. A total of 626 patients were identified. Four hundred and fifty-six patients met the inclusion criteria, and 402 had complete data available for all the calculator variables (126 had a left colectomy). Laparoscopic surgery was performed in 39.6% and emergency surgery in 14.7%. The anastomotic leak rate was 7.2%, with 31.0% requiring reoperation. The anastomoticleak.com calculator was significantly predictive of leak and performed better than the ACS NSQIP calculator (AUROC 0.73 vs 0.58) and the CLS calculator (AUROC 0.96 vs 0.80) for left colectomy. Artificial intelligence-predictive analysis supported these findings and identified an improved prediction model. The anastomotic leak risk calculator is significantly predictive of anastomotic leak after colon cancer resection. Wider investigation of artificial intelligence-based analytics for risk prediction is warranted.

  8. Combining lymphovascular invasion with reactive stromal grade predicts prostate cancer mortality.

    PubMed

    Saeter, Thorstein; Vlatkovic, Ljiljana; Waaler, Gudmund; Servoll, Einar; Nesland, Jahn M; Axcrona, Karol; Axcrona, Ulrika

    2016-09-01

    Previous studies suggest that lymphovascular invasion (LVI) has a weak and variable effect on prognosis. It is uncertain whether LVI, determined by diagnostic prostate biopsy, predicts prostate cancer death. Data from experimental studies have indicated that carcinoma-associated fibroblasts in the reactive stroma could promote LVI and progression to metastasis. Thus, combining LVI with reactive stromal grade may identify prostate cancer patients at high risk of an unfavorable outcome. The purpose of the present study was to examine if LVI, determined by diagnostic biopsy, alone and in combination with reactive stromal grade could predict prostate cancer death. This population-based study included 283 patients with prostate cancer diagnosed by needle biopsy in Aust-Agder County (Norway) from 1991 to 1999. Clinical data were obtained by medical charts review. Two uropathologists evaluated LVI and reactive stromal grade. The endpoint was prostate cancer death. Patients with LVI had marginally higher risk of prostate cancer death compared to patients without LVI (hazard ratio: 1.8, P-value = 0.04). LVI had a stronger effect on prostate cancer death risk when a high reactive stromal grade was present (hazard ratio: 16.0, P-value <0.001). Therefore, patients with concomitant LVI and high reactive stromal grade were at particularly high risk for prostate cancer death. Evaluating LVI together with reactive stromal grade on diagnostic biopsies could be used to identify patients at high risk of death from prostate cancer. Prostate 76:1088-1094, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  9. Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk

    NASA Astrophysics Data System (ADS)

    Li, Yane; Fan, Ming; Cheng, Hu; Zhang, Peng; Zheng, Bin; Li, Lihua

    2018-01-01

    This study aims to develop and test a new imaging marker-based short-term breast cancer risk prediction model. An age-matched dataset of 566 screening mammography cases was used. All ‘prior’ images acquired in the two screening series were negative, while in the ‘current’ screening images, 283 cases were positive for cancer and 283 cases remained negative. For each case, two bilateral cranio-caudal view mammograms acquired from the ‘prior’ negative screenings were selected and processed by a computer-aided image processing scheme, which segmented the entire breast area into nine strip-based local regions, extracted the element regions using difference of Gaussian filters, and computed both global- and local-based bilateral asymmetrical image features. An initial feature pool included 190 features related to the spatial distribution and structural similarity of grayscale values, as well as of the magnitude and phase responses of multidirectional Gabor filters. Next, a short-term breast cancer risk prediction model based on a generalized linear model was built using an embedded stepwise regression analysis method to select features and a leave-one-case-out cross-validation method to predict the likelihood of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) values significantly increased from 0.5863  ±  0.0237 to 0.6870  ±  0.0220 when the model trained by the image features extracted from the global regions and by the features extracted from both the global and the matched local regions (p  =  0.0001). The odds ratio values monotonically increased from 1.00-8.11 with a significantly increasing trend in slope (p  =  0.0028) as the model-generated risk score increased. In addition, the AUC values were 0.6555  ±  0.0437, 0.6958  ±  0.0290, and 0.7054  ±  0.0529 for the three age groups of 37

  10. Cancer Risk Assessment for Space Radiation

    NASA Technical Reports Server (NTRS)

    Richmond, Robert C.; Curreri, Peter A. (Technical Monitor)

    2002-01-01

    Predicting the occurrence of human cancer following exposure to any agent causing genetic damage is a difficult task. This is because the uncertainty of uniform exposure to the damaging agent, and the uncertainty of uniform processing of that damage within a complex set of biological variables, degrade the confidence of predicting the delayed expression of cancer as a relatively rare event within any given clinically normal individual. The radiation health research priorities for enabling long-duration human exploration of space were established in the 1996 NRC Report entitled "Radiation Hazards to Crews of Interplanetary Missions: Biological Issues and Research Strategies". This report emphasized that a 15-fold uncertainty in predicting radiation-induced cancer incidence must be reduced before NASA can commit humans to extended interplanetary missions. That report concluded that the great majority of this uncertainty is biologically based, while a minority is physically based due to uncertainties in radiation dosimetry and radiation transport codes. Since that report, the biologically based uncertainty has remained large, and the relatively small uncertainty associated with radiation dosimetry has increased due to the considerations raised by concepts of microdosimetry. In a practical sense, however, the additional uncertainties introduced by microdosimetry are encouraging since they are in a direction of lowered effective dose absorbed through infrequent interactions of any given cell with the high energy particle component of space radiation. The biological uncertainty in predicting cancer risk for space radiation derives from two primary facts. 1) One animal tumor study has been reported that includes a relevant spectrum of particle radiation energies, and that is the Harderian gland model in mice. Fact #1: Extension of cancer risk from animal models, and especially from a single study in an animal model, to humans is inherently uncertain. 2) One human database

  11. Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

    NASA Astrophysics Data System (ADS)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

    2018-03-01

    Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p < 0.05) and odds ratio was 4.60 with a 95% confidence interval of [3.16, 6.70]. Study demonstrated that this new LPP-based feature regeneration approach enabled to produce an optimal feature vector and yield improved performance in assisting to predict risk of women having breast cancer detected in the next subsequent mammography screening.

  12. Prognostic factors, predictive markers and cancer biology: the triad for successful oral cancer chemoprevention.

    PubMed

    Monteiro de Oliveira Novaes, Jose Augusto; William, William N

    2016-10-01

    Oral squamous cell carcinomas represent a significant cancer burden worldwide. Unfortunately, chemoprevention strategies investigated to date have failed to produce an agent considered standard of care to prevent oral cancers. Nonetheless, recent advances in clinical trial design may streamline drug development in this setting. In this manuscript, we review some of these improvements, including risk prediction tools based on molecular markers that help select patients most suitable for chemoprevention. We also discuss the opportunities that novel preclinical models and modern molecular profiling techniques will bring to the prevention field in the near future, and propose a clinical trials framework that incorporates molecular prognostic factors, predictive markers and cancer biology as a roadmap to improve chemoprevention strategies for oral cancers.

  13. Predicting risk of cancer during HIV infection: the role of inflammatory and coagulation biomarkers.

    PubMed

    Borges, Álvaro H; Silverberg, Michael J; Wentworth, Deborah; Grulich, Andrew E; Fätkenheuer, Gerd; Mitsuyasu, Ronald; Tambussi, Giuseppe; Sabin, Caroline A; Neaton, James D; Lundgren, Jens D

    2013-06-01

    To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection. A prospective cohort. HIV-infected patients on continuous antiretroviral therapy (ART) in the control arms of three randomized trials (N=5023) were included in an analysis of predictors of cancer (any type, infection-related or infection-unrelated). Hazard ratios for IL-6, CRP and D-dimer levels (log2-transformed) were calculated using Cox models stratified by trial and adjusted for demographics and CD4+ cell counts and adjusted also for all biomarkers simultaneously. To assess the possibility that biomarker levels were elevated at entry due to undiagnosed cancer, analyses were repeated excluding early cancer events (i.e. diagnosed during first 2 years of follow-up). During approximately 24,000 person-years of follow-up (PYFU), 172 patients developed cancer (70 infection-related; 102 infection-unrelated). The risk of developing cancer was associated with higher levels (per doubling) of IL-6 (hazard ratio 1.38, P<0.001), CRP (hazard ratio 1.16, P=0.001) and D-dimer (hazard ratio 1.17, P=0.03). However, only IL-6 (hazard ratio 1.29, P=0.003) remained associated with cancer risk when all biomarkers were considered simultaneously. Results for infection-related and infection-unrelated cancers were similar to results for any cancer. Hazard ratios excluding 69 early cancer events were 1.31 (P=0.007), 1.14 (P=0.02) and 1.07 (P=0.49) for IL-6, CRP and D-dimer, respectively. Activated inflammation and coagulation pathways are associated with increased cancer risk during HIV infection. This association was stronger for IL-6 and persisted after excluding early cancer. Trials of interventions may be warranted to assess whether cancer risk can be reduced by lowering IL-6 levels in HIV-positive individuals.

  14. Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer.

    PubMed

    Rauh, C; Hack, C C; Häberle, L; Hein, A; Engel, A; Schrauder, M G; Fasching, P A; Jud, S M; Ekici, A B; Loehberg, C R; Meier-Meitinger, M; Ozan, S; Schulz-Wendtland, R; Uder, M; Hartmann, A; Wachter, D L; Beckmann, M W; Heusinger, K

    2012-08-01

    Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model. Results: Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (ρ = -0.56) was stronger correlated to BMI than DA (ρ = -0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95 % CI: 1.25-3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD. Conclusions: The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast

  15. Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data

    DOE PAGES

    Agrawal, Ankit; Misra, Sanchit; Narayanan, Ramanathan; ...

    2012-01-01

    We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best prediction performance in terms of accuracy and area under the ROC curve. We have developedmore » an on-line lung cancer outcome calculator for estimating the risk of mortality after 6 months, 9 months, 1 year, 2 year and 5 years of diagnosis, for which a smaller non-redundant subset of 13 attributes was carefully selected using attribute selection techniques, while trying to retain the predictive power of the original set of attributes. Further, ensemble voting models were also created for predicting conditional survival outcome for lung cancer (estimating risk of mortality after 5 years of diagnosis, given that the patient has already survived for a period of time), and included in the calculator. The on-line lung cancer outcome calculator developed as a result of this study is available at http://info.eecs.northwestern.edu:8080/LungCancerOutcomeCalculator/.« less

  16. Evaluation of the Prostate Cancer Prevention Trial Risk Calculator in a High-Risk Screening Population

    PubMed Central

    Kaplan, David J.; Boorjian, Stephen A.; Ruth, Karen; Egleston, Brian L.; Chen, David Y.T.; Viterbo, Rosalia; Uzzo, Robert G.; Buyyounouski, Mark K.; Raysor, Susan; Giri, Veda N.

    2009-01-01

    Introduction Clinical factors in addition to PSA have been evaluated to improve risk assessment for prostate cancer. The Prostate Cancer Prevention Trial (PCPT) risk calculator provides an assessment of prostate cancer risk based on age, PSA, race, prior biopsy, and family history. This study evaluated the risk calculator in a screening cohort of young, racially diverse, high-risk men with a low baseline PSA enrolled in the Prostate Cancer Risk Assessment Program. Patients and Methods Eligibility for PRAP include men ages 35-69 who are African-American, have a family history of prostate cancer, or have a known BRCA1/2 mutation. PCPT risk scores were determined for PRAP participants, and were compared to observed prostate cancer rates. Results 624 participants were evaluated, including 382 (61.2%) African-American men and 375 (60%) men with a family history of prostate cancer. Median age was 49.0 years (range 34.0-69.0), and median PSA was 0.9 (range 0.1-27.2). PCPT risk score correlated with prostate cancer diagnosis, as the median baseline risk score in patients diagnosed with prostate cancer was 31.3%, versus 14.2% in patients not diagnosed with prostate cancer (p<0.0001). The PCPT calculator similarly stratified the risk of diagnosis of Gleason score ≥7 disease, as the median risk score was 36.2% in patients diagnosed with Gleason ≥7 prostate cancer versus 15.2% in all other participants (p<0.0001). Conclusion PCPT risk calculator score was found to stratify prostate cancer risk in a cohort of young, primarily African-American men with a low baseline PSA. These results support further evaluation of this predictive tool for prostate cancer risk assessment in high-risk men. PMID:19709072

  17. Understanding PSA and its derivatives in prediction of tumor volume: addressing health disparities in prostate cancer risk stratification

    PubMed Central

    Chinea, Felix M; Lyapichev, Kirill; Epstein, Jonathan I; Kwon, Deukwoo; Smith, Paul Taylor; Pollack, Alan; Cote, Richard J; Kryvenko, Oleksandr N

    2017-01-01

    Objectives To address health disparities in risk stratification of U.S. Hispanic/Latino men by characterizing influences of prostate weight, body mass index, and race/ethnicity on the correlation of PSA derivatives with Gleason score 6 (Grade Group 1) tumor volume in a diverse cohort. Results Using published PSA density and PSA mass density cutoff values, men with higher body mass indices and prostate weights were less likely to have a tumor volume <0.5 cm3. Variability across race/ethnicity was found in the univariable analysis for all PSA derivatives when predicting for tumor volume. In receiver operator characteristic analysis, area under the curve values for all PSA derivatives varied across race/ethnicity with lower optimal cutoff values for Hispanic/Latino (PSA=2.79, PSA density=0.06, PSA mass=0.37, PSA mass density=0.011) and Non-Hispanic Black (PSA=3.75, PSA density=0.07, PSA mass=0.46, PSA mass density=0.008) compared to Non-Hispanic White men (PSA=4.20, PSA density=0.11 PSA mass=0.53, PSA mass density=0.014). Materials and Methods We retrospectively analyzed 589 patients with low-risk prostate cancer at radical prostatectomy. Pre-operative PSA, patient height, body weight, and prostate weight were used to calculate all PSA derivatives. Receiver operating characteristic curves were constructed for each PSA derivative per racial/ethnic group to establish optimal cutoff values predicting for tumor volume ≥0.5 cm3. Conclusions Increasing prostate weight and body mass index negatively influence PSA derivatives for predicting tumor volume. PSA derivatives’ ability to predict tumor volume varies significantly across race/ethnicity. Hispanic/Latino and Non-Hispanic Black men have lower optimal cutoff values for all PSA derivatives, which may impact risk assessment for prostate cancer. PMID:28160549

  18. Understanding PSA and its derivatives in prediction of tumor volume: Addressing health disparities in prostate cancer risk stratification.

    PubMed

    Chinea, Felix M; Lyapichev, Kirill; Epstein, Jonathan I; Kwon, Deukwoo; Smith, Paul Taylor; Pollack, Alan; Cote, Richard J; Kryvenko, Oleksandr N

    2017-03-28

    To address health disparities in risk stratification of U.S. Hispanic/Latino men by characterizing influences of prostate weight, body mass index, and race/ethnicity on the correlation of PSA derivatives with Gleason score 6 (Grade Group 1) tumor volume in a diverse cohort. Using published PSA density and PSA mass density cutoff values, men with higher body mass indices and prostate weights were less likely to have a tumor volume <0.5 cm3. Variability across race/ethnicity was found in the univariable analysis for all PSA derivatives when predicting for tumor volume. In receiver operator characteristic analysis, area under the curve values for all PSA derivatives varied across race/ethnicity with lower optimal cutoff values for Hispanic/Latino (PSA=2.79, PSA density=0.06, PSA mass=0.37, PSA mass density=0.011) and Non-Hispanic Black (PSA=3.75, PSA density=0.07, PSA mass=0.46, PSA mass density=0.008) compared to Non-Hispanic White men (PSA=4.20, PSA density=0.11 PSA mass=0.53, PSA mass density=0.014). We retrospectively analyzed 589 patients with low-risk prostate cancer at radical prostatectomy. Pre-operative PSA, patient height, body weight, and prostate weight were used to calculate all PSA derivatives. Receiver operating characteristic curves were constructed for each PSA derivative per racial/ethnic group to establish optimal cutoff values predicting for tumor volume ≥0.5 cm3. Increasing prostate weight and body mass index negatively influence PSA derivatives for predicting tumor volume. PSA derivatives' ability to predict tumor volume varies significantly across race/ethnicity. Hispanic/Latino and Non-Hispanic Black men have lower optimal cutoff values for all PSA derivatives, which may impact risk assessment for prostate cancer.

  19. Cancer risks after radiation exposure in middle age.

    PubMed

    Shuryak, Igor; Sachs, Rainer K; Brenner, David J

    2010-11-03

    Epidemiological data show that radiation exposure during childhood is associated with larger cancer risks compared with exposure at older ages. For exposures in adulthood, however, the relative risks of radiation-induced cancer in Japanese atomic bomb survivors generally do not decrease monotonically with increasing age of adult exposure. These observations are inconsistent with most standard models of radiation-induced cancer, which predict that relative risks decrease monotonically with increasing age at exposure, at all ages. We analyzed observed cancer risk patterns as a function of age at exposure in Japanese atomic bomb survivors by using a biologically based quantitative model of radiation carcinogenesis that incorporates both radiation induction of premalignant cells (initiation) and radiation-induced promotion of premalignant damage. This approach emphasizes the kinetics of radiation-induced initiation and promotion, and tracks the yields of premalignant cells before, during, shortly after, and long after radiation exposure. Radiation risks after exposure in younger individuals are dominated by initiation processes, whereas radiation risks after exposure at later ages are more influenced by promotion of preexisting premalignant cells. Thus, the cancer site-dependent balance between initiation and promotion determines the dependence of cancer risk on age at radiation exposure. For example, in terms of radiation induction of premalignant cells, a quantitative measure of the relative contribution of initiation vs promotion is 10-fold larger for breast cancer than for lung cancer. Reflecting this difference, radiation-induced breast cancer risks decrease with age at exposure at all ages, whereas radiation-induced lung cancer risks do not. For radiation exposure in middle age, most radiation-induced cancer risks do not, as often assumed, decrease with increasing age at exposure. This observation suggests that promotional processes in radiation carcinogenesis

  20. Evidence that breast tissue stiffness is associated with risk of breast cancer.

    PubMed

    Boyd, Norman F; Li, Qing; Melnichouk, Olga; Huszti, Ella; Martin, Lisa J; Gunasekara, Anoma; Mawdsley, Gord; Yaffe, Martin J; Minkin, Salomon

    2014-01-01

    Evidence from animal models shows that tissue stiffness increases the invasion and progression of cancers, including mammary cancer. We here use measurements of the volume and the projected area of the compressed breast during mammography to derive estimates of breast tissue stiffness and examine the relationship of stiffness to risk of breast cancer. Mammograms were used to measure the volume and projected areas of total and radiologically dense breast tissue in the unaffected breasts of 362 women with newly diagnosed breast cancer (cases) and 656 women of the same age who did not have breast cancer (controls). Measures of breast tissue volume and the projected area of the compressed breast during mammography were used to calculate the deformation of the breast during compression and, with the recorded compression force, to estimate the stiffness of breast tissue. Stiffness was compared in cases and controls, and associations with breast cancer risk examined after adjustment for other risk factors. After adjustment for percent mammographic density by area measurements, and other risk factors, our estimate of breast tissue stiffness was significantly associated with breast cancer (odds ratio = 1.21, 95% confidence interval = 1.03, 1.43, p = 0.02) and improved breast cancer risk prediction in models with percent mammographic density, by both area and volume measurements. An estimate of breast tissue stiffness was associated with breast cancer risk and improved risk prediction based on mammographic measures and other risk factors. Stiffness may provide an additional mechanism by which breast tissue composition is associated with risk of breast cancer and merits examination using more direct methods of measurement.

  1. Evidence That Breast Tissue Stiffness Is Associated with Risk of Breast Cancer

    PubMed Central

    Boyd, Norman F.; Li, Qing; Melnichouk, Olga; Huszti, Ella; Martin, Lisa J.; Gunasekara, Anoma; Mawdsley, Gord; Yaffe, Martin J.; Minkin, Salomon

    2014-01-01

    Background Evidence from animal models shows that tissue stiffness increases the invasion and progression of cancers, including mammary cancer. We here use measurements of the volume and the projected area of the compressed breast during mammography to derive estimates of breast tissue stiffness and examine the relationship of stiffness to risk of breast cancer. Methods Mammograms were used to measure the volume and projected areas of total and radiologically dense breast tissue in the unaffected breasts of 362 women with newly diagnosed breast cancer (cases) and 656 women of the same age who did not have breast cancer (controls). Measures of breast tissue volume and the projected area of the compressed breast during mammography were used to calculate the deformation of the breast during compression and, with the recorded compression force, to estimate the stiffness of breast tissue. Stiffness was compared in cases and controls, and associations with breast cancer risk examined after adjustment for other risk factors. Results After adjustment for percent mammographic density by area measurements, and other risk factors, our estimate of breast tissue stiffness was significantly associated with breast cancer (odds ratio = 1.21, 95% confidence interval = 1.03, 1.43, p = 0.02) and improved breast cancer risk prediction in models with percent mammographic density, by both area and volume measurements. Conclusion An estimate of breast tissue stiffness was associated with breast cancer risk and improved risk prediction based on mammographic measures and other risk factors. Stiffness may provide an additional mechanism by which breast tissue composition is associated with risk of breast cancer and merits examination using more direct methods of measurement. PMID:25010427

  2. Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort.

    PubMed

    Park, Jae Young; Yoon, Sungroh; Park, Man Sik; Choi, Hoon; Bae, Jae Hyun; Moon, Du Geon; Hong, Sung Kyu; Lee, Sang Eun; Park, Chanwang; Byun, Seok-Soo

    2017-01-01

    We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort. Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots. PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed. KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in

  3. Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

    PubMed Central

    Parker, Joel S.; Mullins, Michael; Cheang, Maggie C.U.; Leung, Samuel; Voduc, David; Vickery, Tammi; Davies, Sherri; Fauron, Christiane; He, Xiaping; Hu, Zhiyuan; Quackenbush, John F.; Stijleman, Inge J.; Palazzo, Juan; Marron, J.S.; Nobel, Andrew B.; Mardis, Elaine; Nielsen, Torsten O.; Ellis, Matthew J.; Perou, Charles M.; Bernard, Philip S.

    2009-01-01

    Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy. PMID:19204204

  4. Psychosocial factors and uptake of risk-reducing salpingo-oophorectomy in women at high risk for ovarian cancer.

    PubMed

    Meiser, Bettina; Price, Melanie A; Butow, Phyllis N; Karatas, Janan; Wilson, Judy; Heiniger, Louise; Baylock, Brandi; Charles, Margaret; McLachlan, Sue-Anne; Phillips, Kelly-Anne

    2013-03-01

    Bilateral risk-reducing salpingo-oophorectomy (RRSO) has been shown to significantly reduce the risk of ovarian cancer. This study assessed factors predicting uptake of RRSO. Women participating in a large multiple-case breast cancer family cohort study who were at increased risk for ovarian and fallopian tube cancer (i.e. BRCA1 or BRCA2 mutation carrier or family history including at least one first- or second-degree relative with ovarian or fallopian tube cancer), with no personal history of cancer and with at least one ovary in situ at cohort enrolment, were eligible for this study. Women who knew they did not carry the BRCA1 or BRCA2 mutation segregating in their family (true negatives) were excluded. Sociodemographic, biological and psychosocial factors, including cancer-specific anxiety, perceived ovarian cancer risk, optimism and social support, were assessed using self-administered questionnaires and interviews at cohort enrolment. RRSO uptake was self-reported every three years during systematic follow-up. Of 2,859 women, 571 were eligible. Mean age was 43.3 years; 62 women (10.9 %) had RRSO a median of two years after cohort entry. Factors predicting RRSO were: being parous (OR 3.3, p = 0.015); knowing one's mutation positive status (OR 2.9, p < 0.001) and having a mother and/or sister who died from ovarian cancer (OR 2.5, p = 0.013). Psychological variables measured at cohort entry were not associated with RRSO. These results suggest that women at high risk for ovarian cancer make decisions about RRSO based on risk and individual socio-demographic characteristics, rather than in response to psychological factors such as anxiety.

  5. Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset.

    PubMed

    Hostetter, Jason M; Morrison, James J; Morris, Michael; Jeudy, Jean; Wang, Kenneth C; Siegel, Eliot

    2017-11-01

    To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and ≤6 mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P < .0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6 mm were reclassified to longer-term follow-up than recommended by Fleischner. Twenty-seven percent of nodules ≤4 mm were reclassified to shorter-term follow-up. Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and follow-up recommendations. By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  6. Lung cancer risk of airborne particles for Italian population

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Buonanno, G., E-mail: buonanno@unicas.it; International Laboratory for Air Quality and Health, Queensland University of Technology, 2 George Street 2, 4001 Brisbane, Qld.; Giovinco, G., E-mail: giovinco@unicas.it

    Airborne particles, including both ultrafine and supermicrometric particles, contain various carcinogens. Exposure and risk-assessment studies regularly use particle mass concentration as dosimetry parameter, therefore neglecting the potential impact of ultrafine particles due to their negligible mass compared to supermicrometric particles. The main purpose of this study was the characterization of lung cancer risk due to exposure to polycyclic aromatic hydrocarbons and some heavy metals associated with particle inhalation by Italian non-smoking people. A risk-assessment scheme, modified from an existing risk model, was applied to estimate the cancer risk contribution from both ultrafine and supermicrometric particles. Exposure assessment was carried outmore » on the basis of particle number distributions measured in 25 smoke-free microenvironments in Italy. The predicted lung cancer risk was then compared to the cancer incidence rate in Italy to assess the number of lung cancer cases attributed to airborne particle inhalation, which represents one of the main causes of lung cancer, apart from smoking. Ultrafine particles are associated with a much higher risk than supermicrometric particles, and the modified risk-assessment scheme provided a more accurate estimate than the conventional scheme. Great attention has to be paid to indoor microenvironments and, in particular, to cooking and eating times, which represent the major contributors to lung cancer incidence in the Italian population. The modified risk assessment scheme can serve as a tool for assessing environmental quality, as well as setting up exposure standards for particulate matter. - Highlights: • Lung cancer risk for non-smoking Italian population due to particle inhalation. • The average lung cancer risk for Italian population is equal to 1.90×10{sup −2}. • Ultrafine particle is the aerosol metric mostly contributing to lung cancer risk. • B(a)P is the main (particle-bounded) compound

  7. A Model to Predict the Risk of Keratinocyte Carcinomas.

    PubMed

    Whiteman, David C; Thompson, Bridie S; Thrift, Aaron P; Hughes, Maria-Celia; Muranushi, Chiho; Neale, Rachel E; Green, Adele C; Olsen, Catherine M

    2016-06-01

    Basal cell and squamous cell carcinomas of the skin are the commonest cancers in humans, yet no validated tools exist to estimate future risks of developing keratinocyte carcinomas. To develop a prediction tool, we used baseline data from a prospective cohort study (n = 38,726) in Queensland, Australia, and used data linkage to capture all surgically excised keratinocyte carcinomas arising within the cohort. Predictive factors were identified through stepwise logistic regression models. In secondary analyses, we derived separate models within strata of prior skin cancer history, age, and sex. The primary model included terms for 10 items. Factors with the strongest effects were >20 prior skin cancers excised (odds ratio 8.57, 95% confidence interval [95% CI] 6.73-10.91), >50 skin lesions destroyed (odds ratio 3.37, 95% CI 2.85-3.99), age ≥ 70 years (odds ratio 3.47, 95% CI 2.53-4.77), and fair skin color (odds ratio 1.75, 95% CI 1.42-2.15). Discrimination in the validation dataset was high (area under the receiver operator characteristic curve 0.80, 95% CI 0.79-0.81) and the model appeared well calibrated. Among those reporting no prior history of skin cancer, a similar model with 10 factors predicted keratinocyte carcinoma events with reasonable discrimination (area under the receiver operator characteristic curve 0.72, 95% CI 0.70-0.75). Algorithms using self-reported patient data have high accuracy for predicting risks of keratinocyte carcinomas. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Cardiopulmonary exercise testing for the prediction of morbidity risk after rectal cancer surgery.

    PubMed

    West, M A; Parry, M G; Lythgoe, D; Barben, C P; Kemp, G J; Grocott, M P W; Jack, S

    2014-08-01

    This study investigated the relationship between objectively measured physical fitness variables derived by cardiopulmonary exercise testing (CPET) and in-hospital morbidity after rectal cancer surgery. Patients scheduled for rectal cancer surgery underwent preoperative CPET (reported blind to patient characteristics) with recording of morbidity (recorded blind to CPET variables). Non-parametric receiver operating characteristic (ROC) curves and logistic regression were used to assess the relationship between CPET variables and postoperative morbidity. Of 105 patients assessed, 95 (72 men) were included; ten patients had no surgery and were excluded (3 by choice, 7 owing to unresectable metastasis). Sixty-eight patients had received neoadjuvant treatment. ROC curve analysis of oxygen uptake (V˙o2 ) at estimated lactate threshold (θ^L ) and peak V˙o2 gave an area under the ROC curve of 0·87 (95 per cent confidence interval 0·78 to 0·95; P < 0·001) and 0·85 (0·77 to 0·93; P < 0·001) respectively, indicating that they can help discriminate patients at risk of postoperative morbidity. The optimal cut-off points identified were 10·6 and 18·6 ml per kg per min for V˙o2 at θ^L and peak respectively. CPET can help predict morbidity after rectal cancer surgery. © 2014 BJS Society Ltd. Published by John Wiley & Sons Ltd.

  9. Living in the context of poverty and trajectories of breast cancer worry, knowledge, and perceived risk after a breast cancer risk education session.

    PubMed

    Bartle-Haring, Suzanne

    2010-01-01

    The purpose of this paper was to demonstrate how living in neighborhoods with high levels of poverty (while controlling for personal income) impacts personal characteristics, which in turn impacts retention of breast cancer risk knowledge and changes in worry and perceived risk. The data from this project come from a larger, National Cancer Institute-funded study that included a pretest, a breast cancer risk education session, a posttest, the option of an individualized risk assessment via the Gail Model and three follow-up phone calls over the next 9 months. The percent of individuals living below poverty in the community in which the participant resided was predictive of the personal characteristics assessed, and these characteristics were predictive of changes in breast cancer worry and knowledge across time. Differentiation of self and monitoring, two of the individual characteristics that seem to allow people to process and use information to make "rational" decisions about health care, seem to be impacted by the necessity for adaptation to a culture of poverty. Thus, as a health care community, we need to tailor our messages and our recommendations with an understanding of the complex intersection of poverty and health care decision making. Copyright © 2010 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

  10. TGFbeta1 (Leu10Pro), p53 (Arg72Pro) can predict for increased risk for breast cancer in south Indian women and TGFbeta1 Pro (Leu10Pro) allele predicts response to neo-adjuvant chemo-radiotherapy.

    PubMed

    Rajkumar, Thangarajan; Samson, Mani; Rama, Ranganathan; Sridevi, Veluswami; Mahji, Urmila; Swaminathan, Rajaraman; Nancy, Nirmala K

    2008-11-01

    The breast cancer incidence has been increasing in the south Indian women. A case (n=250)-control (n=500) study was undertaken to investigate the role of Single Nucleotide Polymorphisms (SNP's) in GSTM1 (Present/Null); GSTP1 (Ile105Val), p53 (Arg72Pro), TGFbeta1 (Leu10Pro), c-erbB2 (Ile655Val), and GSTT1 (Null/Present) in breast cancer. In addition, the value of the SNP's in predicting primary tumor's pathologic response following neo-adjuvant chemo-radiotherapy was assessed. Genotyping was done using PCR (GSTM1, GSTT1), Taqman Allelic discrimination assay (GSTP1, c-erbB2) and PCR-CTPP (p53 and TGFbeta1). None of the gene SNP's studied were associated with a statistically significant increased risk for the breast cancer. However, combined analysis of the SNP's showed that p53 (Arg/Arg and Arg/Pro) with TGFbeta1 (Pro/Pro and Leu/Pro) were associated with greater than 2 fold increased risk for breast cancer in Univariate (P=0.01) and Multivariate (P=0.003) analysis. There was no statistically significant association for the GST family members with the breast cancer risk. TGFbeta1 (Pro/Pro) allele was found to predict complete pathologic response in the primary tumour following neo-adjuvant chemo-radiotherapy (OR=6.53 and 10.53 in Univariate and Multivariate analysis respectively) (P=0.004) and was independent of stage. This study suggests that SNP's can help predict breast cancer risk in south Indian women and that TGFbeta1 (Pro/Pro) allele is associated with a better pCR in the primary tumour.

  11. Mammographic density and breast cancer risk: current understanding and future prospects

    PubMed Central

    2011-01-01

    Variations in percent mammographic density (PMD) reflect variations in the amounts of collagen and number of epithelial and non-epithelial cells in the breast. Extensive PMD is associated with a markedly increased risk of invasive breast cancer. The PMD phenotype is important in the context of breast cancer prevention because extensive PMD is common in the population, is strongly associated with risk of the disease, and, unlike most breast cancer risk factors, can be changed. Work now in progress makes it likely that measurement of PMD will be improved in the near future and that understanding of the genetics and biological basis of the association of PMD with breast cancer risk will also improve. Future prospects for the application of PMD include mammographic screening, risk prediction in individuals, breast cancer prevention research, and clinical decision making. PMID:22114898

  12. The role of risk, efficacy, and anxiety in smokers' cancer information seeking.

    PubMed

    Zhao, Xiaoquan; Cai, Xiaomei

    2009-04-01

    Using the risk perception attitude (RPA) framework and the 2005 Health Information National Trends Survey data, this research investigated the role of perceived personal risk, perceived comparative risk, response efficacy, communication efficacy, and anxiety in smokers' active cancer information seeking. The RPA predictions on the interactions between perceived personal risk and the two efficacy measures were not supported. Perceived personal risk and response efficacy were associated with cancer information seeking both directly and through the mediation of anxiety. Optimistic comparative risk perceptions were associated with less anxiety and were found to moderate the relationship between perceived personal risk and cancer information seeking. Surprisingly, communication efficacy emerged as a negative predictor of cancer information seeking. Theoretical and practical implications of these findings are discussed.

  13. Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42,103 individuals

    PubMed Central

    Dunlop, Malcolm G.; Tenesa, Albert; Farrington, Susan M.; Ballereau, Stephane; Brewster, David H.; Pharoah, Paul DP.; Schafmayer, Clemens; Hampe, Jochen; Völzke, Henry; Chang-Claude, Jenny; Hoffmeister, Michael; Brenner, Hermann; von Holst, Susanna; Picelli, Simone; Lindblom, Annika; Jenkins, Mark A.; Hopper, John L.; Casey, Graham; Duggan, David; Newcomb, Polly; Abulí, Anna; Bessa, Xavier; Ruiz-Ponte, Clara; Castellví-Bel, Sergi; Niittymäki, Iina; Tuupanen, Sari; Karhu, Auli; Aaltonen, Lauri; Zanke, Brent W.; Hudson, Thomas J.; Gallinger, Steven; Barclay, Ella; Martin, Lynn; Gorman, Maggie; Carvajal-Carmona, Luis; Walther, Axel; Kerr, David; Lubbe, Steven; Broderick, Peter; Chandler, Ian; Pittman, Alan; Penegar, Steven; Campbell, Harry; Tomlinson, Ian; Houlston, Richard S.

    2016-01-01

    Objective Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. In a large, multi-population study, we set out to assess the feasibility of CRC risk prediction using common genetic variant data, combined with other risk factors. We built a risk prediction model and applied it to the Scottish population using available data. Design Nine populations of European descent were studied to develop and validate colorectal cancer risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence colorectal cancer risk. Risk models were generated from case-control data incorporating genotypes alone (n=39,266), and in combination with gender, age and family history (n=11,324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4,187 independent samples. 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. Results Median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10−16), confirmed in external validation sets (Sweden p=1.2×10−6, Finland p=2×10−5). Mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05–1.13). Discriminative performance was poor across the risk spectrum (area under curve (AUC) for genotypes alone - 0.57; AUC for genotype/age/gender/FH - 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. Conclusion We show that genotype data provides additional information that complements age, gender and FH as risk factors. However, individualized genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential, since it

  14. Risk Assessment Among Prostate Cancer Patients Receiving Primary Androgen Deprivation Therapy

    PubMed Central

    Cooperberg, Matthew R.; Hinotsu, Shiro; Namiki, Mikio; Ito, Kazuto; Broering, Jeanette; Carroll, Peter R.; Akaza, Hideyuki

    2009-01-01

    Purpose Prostate cancer epidemiology has been marked overall by a downward risk migration over time. However, in some populations, both in the United States and abroad, many men are still diagnosed with high-risk and/or advanced disease. Primary androgen deprivation therapy (PADT) is frequently offered to these patients, and disease risk prediction is not well-established in this context. We compared risk features between large disease registries from the United States and Japan, and aimed to build and validate a risk prediction model applicable to PADT patients. Methods Data were analyzed from 13,740 men in the United States community-based Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry and 19,265 men in the Japan Study Group of Prostate Cancer (J-CaP) database, a national Japanese registry of men receiving androgen deprivation therapy. Risk distribution was compared between the two datasets using three well-described multivariable instruments. A novel instrument (Japan Cancer of the Prostate Risk Assessment [J-CAPRA]) was designed and validated to be specifically applicable to PADT patients, and more relevant to high-risk patients than existing instruments. Results J-CaP patients are more likely than CaPSURE patients to be diagnosed with high-risk features; 43% of J-CaP versus 5% of CaPSURE patients had locally advanced or metastatic disease that could not be stratified with the standard risk assessment tools. J-CAPRA—scored 0 to 12 based on Gleason score, prostate-specific antigen level, and clinical stage—predicts progression-free survival among PADT patients in J-CaP with a c-index of 0.71, and cancer-specific survival among PADT patients in CaPSURE with a c-index of 0.84. Conclusion The novel J-CAPRA is the first risk instrument developed and validated for patients undergoing PADT. It is applicable to those with both localized and advanced disease, and performs well in diverse populations. PMID:19667269

  15. Avoiding Cancer Risk Information

    PubMed Central

    Emanuel, Amber S.; Kiviniemi, Marc T.; Howell, Jennifer L.; Hay, Jennifer L.; Waters, Erika A.; Orom, Heather; Shepperd, James A.

    2015-01-01

    RATIONALE Perceived risk for health problems such as cancer is a central construct in many models of health decision making and a target for behavior change interventions. However, some portion of the population actively avoids cancer risk information. The prevalence of, explanations for, and consequences of such avoidance are not well understood. OBJECTIVE We examined the prevalence and demographic and psychosocial correlates of cancer risk information avoidance preference in a nationally representative sample. We also examined whether avoidance of cancer risk information corresponds with avoidance of cancer screening. RESULTS Based on our representative sample, 39% of the population indicated that they agreed or strongly agreed that they would “rather not know [their] chance of getting cancer.” This preference was stronger among older participants, female participants, and participants with lower levels of education. Preferring to avoid cancer risk information was stronger among participants who agreed with the beliefs that everything causes cancer, that there’s not much one can do to prevent cancer, and that there are too many recommendations to follow. Finally, the preference to avoid cancer risk information was associated with lower levels of screening for colon cancer. CONCLUSION These findings suggest that cancer risk information avoidance is a multi-determined phenomenon that is associated with demographic characteristics and psychosocial individual differences and also relates to engagement in cancer screening. PMID:26560410

  16. Genes associated with metabolic syndrome predict disease-free survival in stage II colorectal cancer patients. A novel link between metabolic dysregulation and colorectal cancer.

    PubMed

    Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Ramos, Ricardo; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B; Reglero, Guillermo; Feliu, Jaime; Ramírez de Molina, Ana

    2014-12-01

    Studies have recently suggested that metabolic syndrome and its components increase the risk of colorectal cancer. Both diseases are increasing in most countries, and the genetic association between them has not been fully elucidated. The objective of this study was to assess the association between genetic risk factors of metabolic syndrome or related conditions (obesity, hyperlipidaemia, diabetes mellitus type 2) and clinical outcome in stage II colorectal cancer patients. Expression levels of several genes related to metabolic syndrome and associated alterations were analysed by real-time qPCR in two equivalent but independent sets of stage II colorectal cancer patients. Using logistic regression models and cross-validation analysis with all tumour samples, we developed a metabolic syndrome-related gene expression profile to predict clinical outcome in stage II colorectal cancer patients. The results showed that a gene expression profile constituted by genes previously related to metabolic syndrome was significantly associated with clinical outcome of stage II colorectal cancer patients. This metabolic profile was able to identify patients with a low risk and high risk of relapse. Its predictive value was validated using an independent set of stage II colorectal cancer patients. The identification of a set of genes related to metabolic syndrome that predict survival in intermediate-stage colorectal cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy and avoid the toxic and unnecessary chemotherapy in patients classified as low risk. Our results also confirm the linkage between metabolic disorder and colorectal cancer and suggest the potential for cancer prevention and/or treatment by targeting these genes. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  17. Performance characteristics of prostate-specific antigen density and biopsy core details to predict oncological outcome in patients with intermediate to high-risk prostate cancer underwent robot-assisted radical prostatectomy.

    PubMed

    Yashi, Masahiro; Nukui, Akinori; Tokura, Yuumi; Takei, Kohei; Suzuki, Issei; Sakamoto, Kazumasa; Yuki, Hideo; Kambara, Tsunehito; Betsunoh, Hironori; Abe, Hideyuki; Fukabori, Yoshitatsu; Nakazato, Yoshimasa; Kaji, Yasushi; Kamai, Takao

    2017-06-23

    Many urologic surgeons refer to biopsy core details for decision making in cases of localized prostate cancer (PCa) to determine whether an extended resection and/or lymph node dissection should be performed. Furthermore, recent reports emphasize the predictive value of prostate-specific antigen density (PSAD) for further risk stratification, not only for low-risk PCa, but also for intermediate- and high-risk PCa. This study focused on these parameters and compared respective predictive impact on oncologic outcomes in Japanese PCa patients. Two-hundred and fifty patients with intermediate- and high-risk PCa according to the National Comprehensive Cancer Network (NCCN) classification, that underwent robot-assisted radical prostatectomy at a single institution, and with observation periods of longer than 6 months were enrolled. None of the patients received hormonal treatments including antiandrogens, luteinizing hormone-releasing hormone analogues, or 5-alpha reductase inhibitors preoperatively. PSAD and biopsy core details, including the percentage of positive cores and the maximum percentage of cancer extent in each positive core, were analyzed in association with unfavorable pathologic results of prostatectomy specimens, and further with biochemical recurrence. The cut-off values of potential predictive factors were set through receiver-operating characteristic curve analyses. In the entire cohort, a higher PSAD, the percentage of positive cores, and maximum percentage of cancer extent in each positive core were independently associated with advanced tumor stage ≥ pT3 and an increased index tumor volume > 0.718 ml. NCCN classification showed an association with a tumor stage ≥ pT3 and a Gleason score ≥8, and the attribution of biochemical recurrence was also sustained. In each NCCN risk group, these preoperative factors showed various associations with unfavorable pathological results. In the intermediate-risk group, the percentage of positive cores showed

  18. Application of the Rosner-Wei Risk-Prediction Model to Estimate Sexual Orientation Patterns in Colon Cancer Risk in a Prospective Cohort of U.S. Women

    PubMed Central

    Austin, S. Bryn; Pazaris, Mathew J.; Wei, Esther K.; Rosner, Bernard; Kennedy, Grace A.; Bowen, Deborah; Spiegelman, Donna

    2014-01-01

    Purpose We examined whether lesbian and bisexual women may be at greater risk of colon cancer (CC) than heterosexual women. Methods Working with a large cohort of U.S. women ages 25-64 years, we analyzed 20 years of prospective data to estimate CC incidence, based on known risk factors by applying the Rosner-Wei CC risk-prediction model. Comparing to heterosexual women, we calculated for lesbian and bisexual women the predicted one-year incidence rate (IR) per 100,000 person-years and estimated incidence rate ratios (IRR) and 95% confidence intervals (CI), based on each woman’s comprehensive risk factor profile. Results Analyses included 1,373,817 person-years of data from 66,257 women. For each sexual orientation group, mean predicted one-year CC IR per 100,000 person-years was slightly over 12 cases for each of the sexual orientation groups. After controlling for confounders in fully adjusted models and compared to heterosexuals, no significant differences in IRR were observed for lesbians (IRR 1.01; 95% CI 0.99, 1.04) or bisexuals (IRR 1.01; 95% CI 0.98, 1.04). Conclusions CC risk is similar across all sexual orientation subgroups, with all groups comparably affected. Health professionals must ensure that prevention, screening, and treatment programs are adequately reaching each of these communities. PMID:24852207

  19. Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort

    PubMed Central

    Yoon, Sungroh; Park, Man Sik; Choi, Hoon; Bae, Jae Hyun; Moon, Du Geon; Hong, Sung Kyu; Lee, Sang Eun; Park, Chanwang

    2017-01-01

    Purpose We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort. Materials and Methods Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots. Results PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed. Conclusions KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy

  20. G6PD as a predictive marker for glioma risk, prognosis and chemosensitivity.

    PubMed

    Yang, Chin-An; Huang, Hsi-Yuan; Lin, Cheng-Li; Chang, Jan-Gowth

    2018-05-29

    Glucose-6-phosphate dehydrogenase (G6PD) is a key enzyme preventing cells from oxidative damage and has been reported to have tumor-promoting roles. This study aims to comprehensively evaluate the predictive values of G6PD on brain tumor risk, prognosis and chemo-resistance. A retrospective 13-year cohort study analyzing cancer risk using the Taiwan National Health Insurance Research Database (4066 G6PD deficiency patients and 16,264 controls) was conducted. Furthermore, RNAseq and clinical data of grade II-III glioma (LGG, n = 515) and glioblastoma (GBM, n = 155) were downloaded from The Cancer Genome Atlas (TCGA) and analyzed. Bioinformatics methods were applied to build a glioma prognostication model and to predict response to chemotherapy based on tumor G6PD-related gene expressions. The predicted results were validated in another glioma cohort GSE 16011 and in KALS1 cell line. G6PD-dificient patients were found to have an increased risk for cancers, especially for brain tumor (adjusted hazard ratio (HR) 10.5, 95% CI 1.03-7.60). Furthermore, higher tumor G6PD expression was associated with poor patient survival in LGG, but not in GBM. A prognostication model using expression levels of G6PD and 9 related genes (PSMA2, PSMB8, SHFM1, GSS, GSTK1, MGST2, POLD3, MSH2, MSH6) could independently predict LGG patient survival. Boosted decision tree analysis on 213 cancer cell line database revealed predictive values of G6PD expression on response to gemcitabine and bortezomib. Knockdown of G6PD in KALS1 cell line enhanced its sensitivity to both chemotherapeutic agents. Our study suggests that G6PD could be a marker predicting glioma risk, prognosis and chemo-sensitivity.

  1. Faecal haemoglobin concentration influences risk prediction of interval cancers resulting from inadequate colonoscopy quality: analysis of the Taiwanese Nationwide Colorectal Cancer Screening Program

    PubMed Central

    Chiu, Sherry Yueh-Hsia; Chuang, Shu-Ling; Chen, Sam Li-Sheng; Yen, Amy Ming-Fang; Fann, Jean Ching-Yuan; Chang, Dun-Cheng; Lee, Yi-Chia; Wu, Ming-Shiang; Chou, Chu-Kuang; Hsu, Wen-Feng; Chiou, Shu-Ti; Chiu, Han-Mo

    2017-01-01

    Objectives Interval colorectal cancer (CRC) after colonoscopy may affect effectiveness and cost-effectiveness of screening programmes. We aimed to investigate whether and how faecal haemoglobin concentration (FHbC) of faecal immunochemical testing (FIT) affected the risk prediction of interval cancer (IC) caused by inadequate colonoscopy quality in a FIT-based population screening programme. Design From 2004 to 2009, 29 969 subjects underwent complete colonoscopy after positive FIT in the Taiwanese Nationwide CRC Screening Program. The IC rate was traced until the end of 2012. The incidence of IC was calculated in relation to patient characteristics, endoscopy-related factors (such adenoma detection rate (ADR)) and FHbC. Poisson regression analysis was performed to assess the potential risk factors for colonoscopy IC. Results One hundred and sixty-two ICs developed after an index colonoscopy and the estimated incidence was 1.14 per 1000 person-years of observation for the entire cohort. Increased risk of IC was most remarkable in the uptake of colonoscopy in settings with ADR lower than 15% (adjusted relative risk (aRR)=3.09, 95% CI 1.55 to 6.18) and then higher FHbC (μg Hb/g faeces) (100–149: aRR=2.55, 95% CI 1.52 to 4.29, ≥150: aRR=2.74, 95% CI 1.84 to 4.09) with adjustment for older age and colorectal neoplasm detected at baseline colonoscopy. Similar findings were observed for subjects with negative index colonoscopy. Conclusions Colonoscopy ICs arising from FIT-based population screening programmes were mainly influenced by inadequate colonoscopy quality and independently predicted by FHbC that is associated with a priori chance of advanced neoplasm. This finding is helpful for future modification of screening logistics based on FHbC. PMID:26515543

  2. The Cost-Effectiveness of High-Risk Lung Cancer Screening and Drivers of Program Efficiency.

    PubMed

    Cressman, Sonya; Peacock, Stuart J; Tammemägi, Martin C; Evans, William K; Leighl, Natasha B; Goffin, John R; Tremblay, Alain; Liu, Geoffrey; Manos, Daria; MacEachern, Paul; Bhatia, Rick; Puksa, Serge; Nicholas, Garth; McWilliams, Annette; Mayo, John R; Yee, John; English, John C; Pataky, Reka; McPherson, Emily; Atkar-Khattra, Sukhinder; Johnston, Michael R; Schmidt, Heidi; Shepherd, Frances A; Soghrati, Kam; Amjadi, Kayvan; Burrowes, Paul; Couture, Christian; Sekhon, Harmanjatinder S; Yasufuku, Kazuhiro; Goss, Glenwood; Ionescu, Diana N; Hwang, David M; Martel, Simon; Sin, Don D; Tan, Wan C; Urbanski, Stefan; Xu, Zhaolin; Tsao, Ming-Sound; Lam, Stephen

    2017-08-01

    Lung cancer risk prediction models have the potential to make programs more affordable; however, the economic evidence is limited. Participants in the National Lung Cancer Screening Trial (NLST) were retrospectively identified with the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. The high-risk subgroup was assessed for lung cancer incidence and demographic characteristics compared with those in the low-risk subgroup and the Pan-Canadian Early Detection of Lung Cancer Study (PanCan), which is an observational study that was high-risk-selected in Canada. A comparison of high-risk screening versus standard care was made with a decision-analytic model using data from the NLST with Canadian cost data from screening and treatment in the PanCan study. Probabilistic and deterministic sensitivity analyses were undertaken to assess uncertainty and identify drivers of program efficiency. Use of the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial with a threshold set at 2% over 6 years would have reduced the number of individuals who needed to be screened in the NLST by 81%. High-risk screening participants in the NLST had more adverse demographic characteristics than their counterparts in the PanCan study. High-risk screening would cost $20,724 (in 2015 Canadian dollars) per quality-adjusted life-year gained and would be considered cost-effective at a willingness-to-pay threshold of $100,000 in Canadian dollars per quality-adjusted life-year gained with a probability of 0.62. Cost-effectiveness was driven primarily by non-lung cancer outcomes. Higher noncurative drug costs or current costs for immunotherapy and targeted therapies in the United States would render lung cancer screening a cost-saving intervention. Non-lung cancer outcomes drive screening efficiency in diverse, tobacco-exposed populations. Use of risk selection can reduce the budget impact, and

  3. Development and external multicenter validation of Chinese Prostate Cancer Consortium prostate cancer risk calculator for initial prostate biopsy.

    PubMed

    Chen, Rui; Xie, Liping; Xue, Wei; Ye, Zhangqun; Ma, Lulin; Gao, Xu; Ren, Shancheng; Wang, Fubo; Zhao, Lin; Xu, Chuanliang; Sun, Yinghao

    2016-09-01

    Substantial differences exist in the relationship of prostate cancer (PCa) detection rate and prostate-specific antigen (PSA) level between Western and Asian populations. Classic Western risk calculators, European Randomized Study for Screening of Prostate Cancer Risk Calculator, and Prostate Cancer Prevention Trial Risk Calculator, were shown to be not applicable in Asian populations. We aimed to develop and validate a risk calculator for predicting the probability of PCa and high-grade PCa (defined as Gleason Score sum 7 or higher) at initial prostate biopsy in Chinese men. Urology outpatients who underwent initial prostate biopsy according to the inclusion criteria were included. The multivariate logistic regression-based Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) was constructed with cases from 2 hospitals in Shanghai. Discriminative ability, calibration and decision curve analysis were externally validated in 3 CPCC member hospitals. Of the 1,835 patients involved, PCa was identified in 338/924 (36.6%) and 294/911 (32.3%) men in the development and validation cohort, respectively. Multivariate logistic regression analyses showed that 5 predictors (age, logPSA, logPV, free PSA ratio, and digital rectal examination) were associated with PCa (Model 1) or high-grade PCa (Model 2), respectively. The area under the curve of Model 1 and Model 2 was 0.801 (95% CI: 0.771-0.831) and 0.826 (95% CI: 0.796-0.857), respectively. Both models illustrated good calibration and substantial improvement in decision curve analyses than any single predictors at all threshold probabilities. Higher predicting accuracy, better calibration, and greater clinical benefit were achieved by CPCC-RC, compared with European Randomized Study for Screening of Prostate Cancer Risk Calculator and Prostate Cancer Prevention Trial Risk Calculator in predicting PCa. CPCC-RC performed well in discrimination and calibration and decision curve analysis in external validation compared

  4. Does folic acid supplementation prevent or promote colorectal cancer? Results from model-based predictions.

    PubMed

    Luebeck, E Georg; Moolgavkar, Suresh H; Liu, Amy Y; Boynton, Alanna; Ulrich, Cornelia M

    2008-06-01

    Folate is essential for nucleotide synthesis, DNA replication, and methyl group supply. Low-folate status has been associated with increased risks of several cancer types, suggesting a chemopreventive role of folate. However, recent findings on giving folic acid to patients with a history of colorectal polyps raise concerns about the efficacy and safety of folate supplementation and the long-term health effects of folate fortification. Results suggest that undetected precursor lesions may progress under folic acid supplementation, consistent with the role of folate role in nucleotide synthesis and cell proliferation. To better understand the possible trade-offs between the protective effects due to decreased mutation rates and possibly concomitant detrimental effects due to increased cell proliferation of folic acid, we used a biologically based mathematical model of colorectal carcinogenesis. We predict changes in cancer risk based on timing of treatment start and the potential effect of folic acid on cell proliferation and mutation rates. Changes in colorectal cancer risk in response to folic acid supplementation are likely a complex function of treatment start, duration, and effect on cell proliferation and mutations rates. Predicted colorectal cancer incidence rates under supplementation are mostly higher than rates without folic acid supplementation unless supplementation is initiated early in life (before age 20 years). To the extent to which this model predicts reality, it indicates that the effect on cancer risk when starting folic acid supplementation late in life is small, yet mostly detrimental. Experimental studies are needed to provide direct evidence for this dual role of folate in colorectal cancer and to validate and improve the model predictions.

  5. Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.

    PubMed

    Uttam, Shikhar; Pham, Hoa V; LaFace, Justin; Leibowitz, Brian; Yu, Jian; Brand, Randall E; Hartman, Douglas J; Liu, Yang

    2015-11-15

    Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here, we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict "future" cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 of 15 patients who eventually developed colon cancer and 15 of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk and suggest that further validation in a multicenter study with larger cohorts may eventually advance this method to become a routine clinical test. ©2015 American Association for Cancer Research.

  6. Geriatric Assessment and Tools for Predicting Treatment Toxicity in Older Adults With Cancer.

    PubMed

    Li, Daneng; Soto-Perez-de-Celis, Enrique; Hurria, Arti

    Cancer is a disease of older adults, and the majority of new cancer cases and deaths occur in people 65 years or older. However, fewer data are available regarding the risks and benefits of cancer treatment in older adults, and commonly used assessments in oncology fail to adequately evaluate factors that affect treatment efficacy and outcomes in the older patients. The geriatric assessment is a multidisciplinary evaluation that provides detailed information about a patient's functional status, comorbidities, psychological state, social support, nutritional status, and cognitive function. Among older patients with cancer, geriatric assessment has been shown to identify patients at risk of poorer overall survival, and geriatric assessment-based tools are significantly more effective in predicting chemotherapy toxicity than other currently utilized measures. In this review, we summarize the components of the geriatric assessment and provide information about existing tools used to predict treatment toxicity in older patients with cancer.

  7. Risk Analysis of Prostate Cancer in PRACTICAL, a Multinational Consortium, Using 25 Known Prostate Cancer Susceptibility Loci.

    PubMed

    Amin Al Olama, Ali; Benlloch, Sara; Antoniou, Antonis C; Giles, Graham G; Severi, Gianluca; Neal, David E; Hamdy, Freddie C; Donovan, Jenny L; Muir, Kenneth; Schleutker, Johanna; Henderson, Brian E; Haiman, Christopher A; Schumacher, Fredrick R; Pashayan, Nora; Pharoah, Paul D P; Ostrander, Elaine A; Stanford, Janet L; Batra, Jyotsna; Clements, Judith A; Chambers, Suzanne K; Weischer, Maren; Nordestgaard, Børge G; Ingles, Sue A; Sorensen, Karina D; Orntoft, Torben F; Park, Jong Y; Cybulski, Cezary; Maier, Christiane; Doerk, Thilo; Dickinson, Joanne L; Cannon-Albright, Lisa; Brenner, Hermann; Rebbeck, Timothy R; Zeigler-Johnson, Charnita; Habuchi, Tomonori; Thibodeau, Stephen N; Cooney, Kathleen A; Chappuis, Pierre O; Hutter, Pierre; Kaneva, Radka P; Foulkes, William D; Zeegers, Maurice P; Lu, Yong-Jie; Zhang, Hong-Wei; Stephenson, Robert; Cox, Angela; Southey, Melissa C; Spurdle, Amanda B; FitzGerald, Liesel; Leongamornlert, Daniel; Saunders, Edward; Tymrakiewicz, Malgorzata; Guy, Michelle; Dadaev, Tokhir; Little, Sarah J; Govindasami, Koveela; Sawyer, Emma; Wilkinson, Rosemary; Herkommer, Kathleen; Hopper, John L; Lophatonanon, Aritaya; Rinckleb, Antje E; Kote-Jarai, Zsofia; Eeles, Rosalind A; Easton, Douglas F

    2015-07-01

    Genome-wide association studies have identified multiple genetic variants associated with prostate cancer risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of prostate cancer. We genotyped 25 prostate cancer susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical odds ratios (OR) for prostate cancer associated with different risk strata defined by PRS and derived age-specific absolute risks of developing prostate cancer by PRS stratum and family history. The prostate cancer risk for men in the top 1% of the PRS distribution was 30.6 (95% CI, 16.4-57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI, 3.2-5.5) fold compared with the median risk. The absolute risk of prostate cancer by age of 85 years was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation = 0.09). Risk profiling can identify men at substantially increased or reduced risk of prostate cancer. The effect size, measured by OR per unit PRS, was higher in men at younger ages and in men with family history of prostate cancer. Incorporating additional newly identified loci into a PRS should improve the predictive value of risk profiles. We demonstrate that the risk profiling based on SNPs can identify men at substantially increased or reduced risk that could have useful implications for targeted prevention and screening programs. ©2015 American Association for Cancer Research.

  8. Health literacy, numeracy, and interpretation of graphical breast cancer risk estimates.

    PubMed

    Brown, Sandra M; Culver, Julie O; Osann, Kathryn E; MacDonald, Deborah J; Sand, Sharon; Thornton, Andrea A; Grant, Marcia; Bowen, Deborah J; Metcalfe, Kelly A; Burke, Harry B; Robson, Mark E; Friedman, Susan; Weitzel, Jeffrey N

    2011-04-01

    Health literacy and numeracy are necessary to understand health information and to make informed medical decisions. This study explored the relationships among health literacy, numeracy, and ability to accurately interpret graphical representations of breast cancer risk. Participants (N=120) were recruited from the Facing Our Risk of Cancer Empowered (FORCE) membership. Health literacy and numeracy were assessed. Participants interpreted graphs depicting breast cancer risk, made hypothetical treatment decisions, and rated preference of graphs. Most participants were Caucasian (98%) and had completed at least one year of college (93%). Fifty-two percent had breast cancer, 86% had a family history of breast cancer, and 57% had a deleterious BRCA gene mutation. Mean health literacy score was 65/66; mean numeracy score was 4/6; and mean graphicacy score was 9/12. Education and numeracy were significantly associated with accurate graph interpretation (r=0.42, p<0.001 and r=0.65, p<0.001, respectively). However, after adjusting for numeracy in multivariate linear regression, education added little to the prediction of graphicacy (r(2)=0.41 versus 0.42, respectively). In our highly health-literate population, numeracy was predictive of graphicacy. Effective risk communication strategies should consider the impact of numeracy on graphicacy and patient understanding. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  9. Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women.

    PubMed

    Wang, Yuan; Gao, Ying; Battsend, Munkhzul; Chen, Kexin; Lu, Wenli; Wang, Yaogang

    2014-11-01

    The optimal approach regarding breast cancer screening for Chinese women is unclear due to the relative low incidence rate. A risk assessment tool may be useful for selection of high-risk subsets of population for mammography screening in low-incidence and resource-limited developing country. The odd ratios for six main risk factors of breast cancer were pooled by review manager after a systematic research of literature. Health risk appraisal (HRA) model was developed to predict an individual's risk of developing breast cancer in the next 5 years from current age. The performance of this HRA model was assessed based on a first-round screening database. Estimated risk of breast cancer increased with age. Increases in the 5-year risk of developing breast cancer were found with the existence of any of included risk factors. When individuals who had risk above median risk (3.3‰) were selected from the validation database, the sensitivity is 60.0% and the specificity is 47.8%. The unweighted area under the curve (AUC) was 0.64 (95% CI = 0.50-0.78). The risk-prediction model reported in this article is based on a combination of risk factors and shows good overall predictive power, but it is still weak at predicting which particular women will develop the disease. It would be very helpful for the improvement of a current model if more population-based prospective follow-up studies were used for the validation.

  10. Association analysis identifies 65 new breast cancer risk loci

    PubMed Central

    Lemaçon, Audrey; Soucy, Penny; Glubb, Dylan; Rostamianfar, Asha; Bolla, Manjeet K.; Wang, Qin; Tyrer, Jonathan; Dicks, Ed; Lee, Andrew; Wang, Zhaoming; Allen, Jamie; Keeman, Renske; Eilber, Ursula; French, Juliet D.; Chen, Xiao Qing; Fachal, Laura; McCue, Karen; McCart Reed, Amy E.; Ghoussaini, Maya; Carroll, Jason; Jiang, Xia; Finucane, Hilary; Adams, Marcia; Adank, Muriel A.; Ahsan, Habibul; Aittomäki, Kristiina; Anton-Culver, Hoda; Antonenkova, Natalia N.; Arndt, Volker; Aronson, Kristan J.; Arun, Banu; Auer, Paul L.; Bacot, François; Barrdahl, Myrto; Baynes, Caroline; Beckmann, Matthias W.; Behrens, Sabine; Benitez, Javier; Bermisheva, Marina; Bernstein, Leslie; Blomqvist, Carl; Bogdanova, Natalia V.; Bojesen, Stig E.; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith S.; Brauch, Hiltrud; Brennan, Paul; Brenner, Hermann; Brinton, Louise; Broberg, Per; Brock, Ian W.; Broeks, Annegien; Brooks-Wilson, Angela; Brucker, Sara Y.; Brüning, Thomas; Burwinkel, Barbara; Butterbach, Katja; Cai, Qiuyin; Cai, Hui; Caldés, Trinidad; Canzian, Federico; Carracedo, Angel; Carter, Brian D.; Castelao, Jose E.; Chan, Tsun L.; Cheng, Ting-Yuan David; Chia, Kee Seng; Choi, Ji-Yeob; Christiansen, Hans; Clarke, Christine L.; Collée, Margriet; Conroy, Don M.; Cordina-Duverger, Emilie; Cornelissen, Sten; Cox, David G; Cox, Angela; Cross, Simon S.; Cunningham, Julie M.; Czene, Kamila; Daly, Mary B.; Devilee, Peter; Doheny, Kimberly F.; Dörk, Thilo; dos-Santos-Silva, Isabel; Dumont, Martine; Durcan, Lorraine; Dwek, Miriam; Eccles, Diana M.; Ekici, Arif B.; Eliassen, A. Heather; Ellberg, Carolina; Elvira, Mingajeva; Engel, Christoph; Eriksson, Mikael; Fasching, Peter A.; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fritschi, Lin; Gaborieau, Valerie; Gabrielson, Marike; Gago-Dominguez, Manuela; Gao, Yu-Tang; Gapstur, Susan M.; García-Sáenz, José A.; Gaudet, Mia M.; Georgoulias, Vassilios; Giles, Graham G.; Glendon, Gord; Goldberg, Mark S.; Goldgar, David E.; González-Neira, Anna; Grenaker Alnæs, Grethe I.; Grip, Mervi; Gronwald, Jacek; Grundy, Anne; Guénel, Pascal; Haeberle, Lothar; Hahnen, Eric; Haiman, Christopher A.; Håkansson, Niclas; Hamann, Ute; Hamel, Nathalie; Hankinson, Susan; Harrington, Patricia; Hart, Steven N.; Hartikainen, Jaana M.; Hartman, Mikael; Hein, Alexander; Heyworth, Jane; Hicks, Belynda; Hillemanns, Peter; Ho, Dona N.; Hollestelle, Antoinette; Hooning, Maartje J.; Hoover, Robert N.; Hopper, John L.; Hou, Ming-Feng; Hsiung, Chia-Ni; Huang, Guanmengqian; Humphreys, Keith; Ishiguro, Junko; Ito, Hidemi; Iwasaki, Motoki; Iwata, Hiroji; Jakubowska, Anna; Janni, Wolfgang; John, Esther M.; Johnson, Nichola; Jones, Kristine; Jones, Michael; Jukkola-Vuorinen, Arja; Kaaks, Rudolf; Kabisch, Maria; Kaczmarek, Katarzyna; Kang, Daehee; Kasuga, Yoshio; Kerin, Michael J.; Khan, Sofia; Khusnutdinova, Elza; Kiiski, Johanna I.; Kim, Sung-Won; Knight, Julia A.; Kosma, Veli-Matti; Kristensen, Vessela N.; Krüger, Ute; Kwong, Ava; Lambrechts, Diether; Marchand, Loic Le; Lee, Eunjung; Lee, Min Hyuk; Lee, Jong Won; Lee, Chuen Neng; Lejbkowicz, Flavio; Li, Jingmei; Lilyquist, Jenna; Lindblom, Annika; Lissowska, Jolanta; Lo, Wing-Yee; Loibl, Sibylle; Long, Jirong; Lophatananon, Artitaya; Lubinski, Jan; Luccarini, Craig; Lux, Michael P.; Ma, Edmond S.K.; MacInnis, Robert J.; Maishman, Tom; Makalic, Enes; Malone, Kathleen E; Kostovska, Ivana Maleva; Mannermaa, Arto; Manoukian, Siranoush; Manson, JoAnn E.; Margolin, Sara; Mariapun, Shivaani; Martinez, Maria Elena; Matsuo, Keitaro; Mavroudis, Dimitrios; McKay, James; McLean, Catriona; Meijers-Heijboer, Hanne; Meindl, Alfons; Menéndez, Primitiva; Menon, Usha; Meyer, Jeffery; Miao, Hui; Miller, Nicola; Mohd Taib, Nur Aishah; Muir, Kenneth; Mulligan, Anna Marie; Mulot, Claire; Neuhausen, Susan L.; Nevanlinna, Heli; Neven, Patrick; Nielsen, Sune F.; Noh, Dong-Young; Nordestgaard, Børge G.; Norman, Aaron; Olopade, Olufunmilayo I.; Olson, Janet E.; Olsson, Håkan; Olswold, Curtis; Orr, Nick; Pankratz, V. Shane; Park, Sue K.; Park-Simon, Tjoung-Won; Lloyd, Rachel; Perez, Jose I.A.; Peterlongo, Paolo; Peto, Julian; Phillips, Kelly-Anne; Pinchev, Mila; Plaseska-Karanfilska, Dijana; Prentice, Ross; Presneau, Nadege; Prokofieva, Darya; Pugh, Elizabeth; Pylkäs, Katri; Rack, Brigitte; Radice, Paolo; Rahman, Nazneen; Rennert, Gadi; Rennert, Hedy S.; Rhenius, Valerie; Romero, Atocha; Romm, Jane; Ruddy, Kathryn J; Rüdiger, Thomas; Rudolph, Anja; Ruebner, Matthias; Rutgers, Emiel J. Th.; Saloustros, Emmanouil; Sandler, Dale P.; Sangrajrang, Suleeporn; Sawyer, Elinor J.; Schmidt, Daniel F.; Schmutzler, Rita K.; Schneeweiss, Andreas; Schoemaker, Minouk J.; Schumacher, Fredrick; Schürmann, Peter; Scott, Rodney J.; Scott, Christopher; Seal, Sheila; Seynaeve, Caroline; Shah, Mitul; Sharma, Priyanka; Shen, Chen-Yang; Sheng, Grace; Sherman, Mark E.; Shrubsole, Martha J.; Shu, Xiao-Ou; Smeets, Ann; Sohn, Christof; Southey, Melissa C.; Spinelli, John J.; Stegmaier, Christa; Stewart-Brown, Sarah; Stone, Jennifer; Stram, Daniel O.; Surowy, Harald; Swerdlow, Anthony; Tamimi, Rulla; Taylor, Jack A.; Tengström, Maria; Teo, Soo H.; Terry, Mary Beth; Tessier, Daniel C.; Thanasitthichai, Somchai; Thöne, Kathrin; Tollenaar, Rob A.E.M.; Tomlinson, Ian; Tong, Ling; Torres, Diana; Truong, Thérèse; Tseng, Chiu-chen; Tsugane, Shoichiro; Ulmer, Hans-Ulrich; Ursin, Giske; Untch, Michael; Vachon, Celine; van Asperen, Christi J.; Van Den Berg, David; van den Ouweland, Ans M.W.; van der Kolk, Lizet; van der Luijt, Rob B.; Vincent, Daniel; Vollenweider, Jason; Waisfisz, Quinten; Wang-Gohrke, Shan; Weinberg, Clarice R.; Wendt, Camilla; Whittemore, Alice S.; Wildiers, Hans; Willett, Walter; Winqvist, Robert; Wolk, Alicja; Wu, Anna H.; Xia, Lucy; Yamaji, Taiki; Yang, Xiaohong R.; Yip, Cheng Har; Yoo, Keun-Young; Yu, Jyh-Cherng; Zheng, Wei; Zheng, Ying; Zhu, Bin; Ziogas, Argyrios; Ziv, Elad; Lakhani, Sunil R.; Antoniou, Antonis C.; Droit, Arnaud; Andrulis, Irene L.; Amos, Christopher I.; Couch, Fergus J.; Pharoah, Paul D.P.; Chang-Claude, Jenny; Hall, Per; Hunter, David J.; Milne, Roger L.; García-Closas, Montserrat; Schmidt, Marjanka K.; Chanock, Stephen J.; Dunning, Alison M.; Edwards, Stacey L.; Bader, Gary D.; Chenevix-Trench, Georgia; Simard, Jacques; Kraft, Peter; Easton, Douglas F.

    2017-01-01

    Breast cancer risk is influenced by rare coding variants in susceptibility genes such as BRCA1 and many common, mainly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. We report results from a genome-wide association study (GWAS) of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry1. We identified 65 new loci associated with overall breast cancer at p<5x10-8. The majority of credible risk SNPs in the new loci fall in distal regulatory elements, and by integrating in-silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all SNPs in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the utility of genetic risk scores for individualized screening and prevention. PMID:29059683

  11. A link prediction approach to cancer drug sensitivity prediction.

    PubMed

    Turki, Turki; Wei, Zhi

    2017-10-03

    Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

  12. Association analysis identifies 65 new breast cancer risk loci.

    PubMed

    Michailidou, Kyriaki; Lindström, Sara; Dennis, Joe; Beesley, Jonathan; Hui, Shirley; Kar, Siddhartha; Lemaçon, Audrey; Soucy, Penny; Glubb, Dylan; Rostamianfar, Asha; Bolla, Manjeet K; Wang, Qin; Tyrer, Jonathan; Dicks, Ed; Lee, Andrew; Wang, Zhaoming; Allen, Jamie; Keeman, Renske; Eilber, Ursula; French, Juliet D; Qing Chen, Xiao; Fachal, Laura; McCue, Karen; McCart Reed, Amy E; Ghoussaini, Maya; Carroll, Jason S; Jiang, Xia; Finucane, Hilary; Adams, Marcia; Adank, Muriel A; Ahsan, Habibul; Aittomäki, Kristiina; Anton-Culver, Hoda; Antonenkova, Natalia N; Arndt, Volker; Aronson, Kristan J; Arun, Banu; Auer, Paul L; Bacot, François; Barrdahl, Myrto; Baynes, Caroline; Beckmann, Matthias W; Behrens, Sabine; Benitez, Javier; Bermisheva, Marina; Bernstein, Leslie; Blomqvist, Carl; Bogdanova, Natalia V; Bojesen, Stig E; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith S; Brauch, Hiltrud; Brennan, Paul; Brenner, Hermann; Brinton, Louise; Broberg, Per; Brock, Ian W; Broeks, Annegien; Brooks-Wilson, Angela; Brucker, Sara Y; Brüning, Thomas; Burwinkel, Barbara; Butterbach, Katja; Cai, Qiuyin; Cai, Hui; Caldés, Trinidad; Canzian, Federico; Carracedo, Angel; Carter, Brian D; Castelao, Jose E; Chan, Tsun L; David Cheng, Ting-Yuan; Seng Chia, Kee; Choi, Ji-Yeob; Christiansen, Hans; Clarke, Christine L; Collée, Margriet; Conroy, Don M; Cordina-Duverger, Emilie; Cornelissen, Sten; Cox, David G; Cox, Angela; Cross, Simon S; Cunningham, Julie M; Czene, Kamila; Daly, Mary B; Devilee, Peter; Doheny, Kimberly F; Dörk, Thilo; Dos-Santos-Silva, Isabel; Dumont, Martine; Durcan, Lorraine; Dwek, Miriam; Eccles, Diana M; Ekici, Arif B; Eliassen, A Heather; Ellberg, Carolina; Elvira, Mingajeva; Engel, Christoph; Eriksson, Mikael; Fasching, Peter A; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fritschi, Lin; Gaborieau, Valerie; Gabrielson, Marike; Gago-Dominguez, Manuela; Gao, Yu-Tang; Gapstur, Susan M; García-Sáenz, José A; Gaudet, Mia M; Georgoulias, Vassilios; Giles, Graham G; Glendon, Gord; Goldberg, Mark S; Goldgar, David E; González-Neira, Anna; Grenaker Alnæs, Grethe I; Grip, Mervi; Gronwald, Jacek; Grundy, Anne; Guénel, Pascal; Haeberle, Lothar; Hahnen, Eric; Haiman, Christopher A; Håkansson, Niclas; Hamann, Ute; Hamel, Nathalie; Hankinson, Susan; Harrington, Patricia; Hart, Steven N; Hartikainen, Jaana M; Hartman, Mikael; Hein, Alexander; Heyworth, Jane; Hicks, Belynda; Hillemanns, Peter; Ho, Dona N; Hollestelle, Antoinette; Hooning, Maartje J; Hoover, Robert N; Hopper, John L; Hou, Ming-Feng; Hsiung, Chia-Ni; Huang, Guanmengqian; Humphreys, Keith; Ishiguro, Junko; Ito, Hidemi; Iwasaki, Motoki; Iwata, Hiroji; Jakubowska, Anna; Janni, Wolfgang; John, Esther M; Johnson, Nichola; Jones, Kristine; Jones, Michael; Jukkola-Vuorinen, Arja; Kaaks, Rudolf; Kabisch, Maria; Kaczmarek, Katarzyna; Kang, Daehee; Kasuga, Yoshio; Kerin, Michael J; Khan, Sofia; Khusnutdinova, Elza; Kiiski, Johanna I; Kim, Sung-Won; Knight, Julia A; Kosma, Veli-Matti; Kristensen, Vessela N; Krüger, Ute; Kwong, Ava; Lambrechts, Diether; Le Marchand, Loic; Lee, Eunjung; Lee, Min Hyuk; Lee, Jong Won; Neng Lee, Chuen; Lejbkowicz, Flavio; Li, Jingmei; Lilyquist, Jenna; Lindblom, Annika; Lissowska, Jolanta; Lo, Wing-Yee; Loibl, Sibylle; Long, Jirong; Lophatananon, Artitaya; Lubinski, Jan; Luccarini, Craig; Lux, Michael P; Ma, Edmond S K; MacInnis, Robert J; Maishman, Tom; Makalic, Enes; Malone, Kathleen E; Kostovska, Ivana Maleva; Mannermaa, Arto; Manoukian, Siranoush; Manson, JoAnn E; Margolin, Sara; Mariapun, Shivaani; Martinez, Maria Elena; Matsuo, Keitaro; Mavroudis, Dimitrios; McKay, James; McLean, Catriona; Meijers-Heijboer, Hanne; Meindl, Alfons; Menéndez, Primitiva; Menon, Usha; Meyer, Jeffery; Miao, Hui; Miller, Nicola; Taib, Nur Aishah Mohd; Muir, Kenneth; Mulligan, Anna Marie; Mulot, Claire; Neuhausen, Susan L; Nevanlinna, Heli; Neven, Patrick; Nielsen, Sune F; Noh, Dong-Young; Nordestgaard, Børge G; Norman, Aaron; Olopade, Olufunmilayo I; Olson, Janet E; Olsson, Håkan; Olswold, Curtis; Orr, Nick; Pankratz, V Shane; Park, Sue K; Park-Simon, Tjoung-Won; Lloyd, Rachel; Perez, Jose I A; Peterlongo, Paolo; Peto, Julian; Phillips, Kelly-Anne; Pinchev, Mila; Plaseska-Karanfilska, Dijana; Prentice, Ross; Presneau, Nadege; Prokofyeva, Darya; Pugh, Elizabeth; Pylkäs, Katri; Rack, Brigitte; Radice, Paolo; Rahman, Nazneen; Rennert, Gadi; Rennert, Hedy S; Rhenius, Valerie; Romero, Atocha; Romm, Jane; Ruddy, Kathryn J; Rüdiger, Thomas; Rudolph, Anja; Ruebner, Matthias; Rutgers, Emiel J T; Saloustros, Emmanouil; Sandler, Dale P; Sangrajrang, Suleeporn; Sawyer, Elinor J; Schmidt, Daniel F; Schmutzler, Rita K; Schneeweiss, Andreas; Schoemaker, Minouk J; Schumacher, Fredrick; Schürmann, Peter; Scott, Rodney J; Scott, Christopher; Seal, Sheila; Seynaeve, Caroline; Shah, Mitul; Sharma, Priyanka; Shen, Chen-Yang; Sheng, Grace; Sherman, Mark E; Shrubsole, Martha J; Shu, Xiao-Ou; Smeets, Ann; Sohn, Christof; Southey, Melissa C; Spinelli, John J; Stegmaier, Christa; Stewart-Brown, Sarah; Stone, Jennifer; Stram, Daniel O; Surowy, Harald; Swerdlow, Anthony; Tamimi, Rulla; Taylor, Jack A; Tengström, Maria; Teo, Soo H; Beth Terry, Mary; Tessier, Daniel C; Thanasitthichai, Somchai; Thöne, Kathrin; Tollenaar, Rob A E M; Tomlinson, Ian; Tong, Ling; Torres, Diana; Truong, Thérèse; Tseng, Chiu-Chen; Tsugane, Shoichiro; Ulmer, Hans-Ulrich; Ursin, Giske; Untch, Michael; Vachon, Celine; van Asperen, Christi J; Van Den Berg, David; van den Ouweland, Ans M W; van der Kolk, Lizet; van der Luijt, Rob B; Vincent, Daniel; Vollenweider, Jason; Waisfisz, Quinten; Wang-Gohrke, Shan; Weinberg, Clarice R; Wendt, Camilla; Whittemore, Alice S; Wildiers, Hans; Willett, Walter; Winqvist, Robert; Wolk, Alicja; Wu, Anna H; Xia, Lucy; Yamaji, Taiki; Yang, Xiaohong R; Har Yip, Cheng; Yoo, Keun-Young; Yu, Jyh-Cherng; Zheng, Wei; Zheng, Ying; Zhu, Bin; Ziogas, Argyrios; Ziv, Elad; Lakhani, Sunil R; Antoniou, Antonis C; Droit, Arnaud; Andrulis, Irene L; Amos, Christopher I; Couch, Fergus J; Pharoah, Paul D P; Chang-Claude, Jenny; Hall, Per; Hunter, David J; Milne, Roger L; García-Closas, Montserrat; Schmidt, Marjanka K; Chanock, Stephen J; Dunning, Alison M; Edwards, Stacey L; Bader, Gary D; Chenevix-Trench, Georgia; Simard, Jacques; Kraft, Peter; Easton, Douglas F

    2017-11-02

    Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10 -8 . The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

  13. A New Scoring System to Predict the Risk for High-risk Adenoma and Comparison of Existing Risk Calculators.

    PubMed

    Murchie, Brent; Tandon, Kanwarpreet; Hakim, Seifeldin; Shah, Kinchit; O'Rourke, Colin; Castro, Fernando J

    2017-04-01

    Colorectal cancer (CRC) screening guidelines likely over-generalizes CRC risk, 35% of Americans are not up to date with screening, and there is growing incidence of CRC in younger patients. We developed a practical prediction model for high-risk colon adenomas in an average-risk population, including an expanded definition of high-risk polyps (≥3 nonadvanced adenomas), exposing higher than average-risk patients. We also compared results with previously created calculators. Patients aged 40 to 59 years, undergoing first-time average-risk screening or diagnostic colonoscopies were evaluated. Risk calculators for advanced adenomas and high-risk adenomas were created based on age, body mass index, sex, race, and smoking history. Previously established calculators with similar risk factors were selected for comparison of concordance statistic (c-statistic) and external validation. A total of 5063 patients were included. Advanced adenomas, and high-risk adenomas were seen in 5.7% and 7.4% of the patient population, respectively. The c-statistic for our calculator was 0.639 for the prediction of advanced adenomas, and 0.650 for high-risk adenomas. When applied to our population, all previous models had lower c-statistic results although one performed similarly. Our model compares favorably to previously established prediction models. Age and body mass index were used as continuous variables, likely improving the c-statistic. It also reports absolute predictive probabilities of advanced and high-risk polyps, allowing for more individualized risk assessment of CRC.

  14. Characterization of SNPs Associated with Prostate Cancer in Men of Ashkenazic Descent from the Set of GWAS Identified SNPs: Impact of Cancer Family History and Cumulative SNP Risk Prediction

    PubMed Central

    Agalliu, Ilir; Wang, Zhaoming; Wang, Tao; Dunn, Anne; Parikh, Hemang; Myers, Timothy

    2013-01-01

    Background Genome-wide association studies (GWAS) have identified multiple SNPs associated with prostate cancer (PrCa). Population isolates may have different sets of risk alleles for PrCa constituting unique population and individual risk profiles. Methods To test this hypothesis, associations between 31 GWAS SNPs of PrCa were examined among 979 PrCa cases and 1,251 controls of Ashkenazic descent using logistic regression. We also investigated risks by age at diagnosis, pathological features of PrCa, and family history of cancer. Moreover, we examined associations between cumulative number of risk alleles and PrCa and assessed the utility of risk alleles in PrCa risk prediction by comparing the area under the curve (AUC) for different logistic models. Results Of the 31 genotyped SNPs, 8 were associated with PrCa at p≤0.002 (corrected p-value threshold) with odds ratios (ORs) ranging from 1.22 to 1.42 per risk allele. Four SNPs were associated with aggressive PrCa, while three other SNPs showed potential interactions for PrCa by family history of PrCa (rs8102476; 19q13), lung cancer (rs17021918; 4q22), and breast cancer (rs10896449; 11q13). Men in the highest vs. lowest quartile of cumulative number of risk alleles had ORs of 3.70 (95% CI 2.76–4.97); 3.76 (95% CI 2.57–5.50), and 5.20 (95% CI 2.94–9.19) for overall PrCa, aggressive cancer and younger age at diagnosis, respectively. The addition of cumulative risk alleles to the model containing age at diagnosis and family history of PrCa yielded a slightly higher AUC (0.69 vs. 0.64). Conclusion These data define a set of risk alleles associated with PrCa in men of Ashkenazic descent and indicate possible genetic differences for PrCa between populations of European and Ashkenazic ancestry. Use of genetic markers might provide an opportunity to identify men at highest risk for younger age of onset PrCa; however, their clinical utility in identifying men at highest risk for aggressive cancer remains limited

  15. A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients.

    PubMed

    Damasco, Christian; Lembo, Antonio; Somma, Maria Patrizia; Gatti, Maurizio; Di Cunto, Ferdinando; Provero, Paolo

    2011-02-28

    The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures. We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes). The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis. This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.

  16. Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

    NASA Astrophysics Data System (ADS)

    Kawata, Y.; Niki, N.; Ohmatsu, H.; Satake, M.; Kusumoto, M.; Tsuchida, T.; Aokage, K.; Eguchi, K.; Kaneko, M.; Moriyama, N.

    2014-03-01

    In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.

  17. Predicting Scheduling and Attending for an Oral Cancer Examination

    PubMed Central

    Shepperd, James A.; Emanuel, Amber S.; Howell, Jennifer L.; Logan, Henrietta L.

    2015-01-01

    Background Oral and pharyngeal cancer is highly treatable if diagnosed early, yet late diagnosis is commonplace apparently because of delays in undergoing an oral cancer examination. Purpose We explored predictors of scheduling and attending an oral cancer examination among a sample of Black and White men who were at high risk for oral cancer because they smoked. Methods During an in-person interview, participants (N = 315) from rural Florida learned about oral and pharyngeal cancer, completed survey measures, and were offered a free examination in the next week. Later, participants received a follow-up phone call to explore why they did or did not attend their examination. Results Consistent with the notion that scheduling and attending an oral cancer exam represent distinct decisions, we found that the two outcomes had different predictors. Defensive avoidance and exam efficacy predicted scheduling an examination; exam efficacy and having coping resources, time, and transportation predicted attending the examination. Open-ended responses revealed that the dominant reasons participants offered for missing a scheduled examination was conflicting obligations, forgetting, and confusion or misunderstanding about the examination. Conclusions The results suggest interventions to increase scheduling and attending an oral cancer examination. PMID:26152644

  18. Comparison of risk classification between EndoPredict and MammaPrint in ER-positive/HER2-negative primary invasive breast cancer

    PubMed Central

    Peláez-García, Alberto; Yébenes, Laura; Berjón, Alberto; Angulo, Antonia; Zamora, Pilar; Sánchez-Méndez, José Ignacio; Espinosa, Enrique; Redondo, Andrés; Heredia-Soto, Victoria; Mendiola, Marta; Feliú, Jaime

    2017-01-01

    Purpose To compare the concordance in risk classification between the EndoPredict and the MammaPrint scores obtained for the same cancer samples on 40 estrogen-receptor positive/HER2-negative breast carcinomas. Methods Formalin-fixed, paraffin-embedded invasive breast carcinoma tissues that were previously analyzed with MammaPrint as part of routine care of the patients, and were classified as high-risk (20 patients) and low-risk (20 patients), were selected to be analyzed by the EndoPredict assay, a second generation gene expression test that combines expression of 8 genes (EP score) with two clinicopathological variables (tumor size and nodal status, EPclin score). Results The EP score classified 15 patients as low-risk and 25 patients as high-risk. EPclin re-classified 5 of the 25 EP high-risk patients into low-risk, resulting in a total of 20 high-risk and 20 low-risk tumors. EP score and MammaPrint score were significantly correlated (p = 0.008). Twelve of 20 samples classified as low-risk by MammaPrint were also low-risk by EP score (60%). 17 of 20 MammaPrint high-risk tumors were also high-risk by EP score. The overall concordance between EP score and MammaPrint was 72.5% (κ = 0.45, (95% CI, 0.182 to 0.718)). EPclin score also correlated with MammaPrint results (p = 0.004). Discrepancies between both tests occurred in 10 cases: 5 MammaPrint low-risk patients were classified as EPclin high-risk and 5 high-risk MammaPrint were classified as low-risk by EPclin and overall concordance of 75% (κ = 0.5, (95% CI, 0.232 to 0.768)). Conclusions This pilot study demonstrates a limited concordance between MammaPrint and EndoPredict. Differences in results could be explained by the inclusion of different gene sets in each platform, the use of different methodology, and the inclusion of clinicopathological parameters, such as tumor size and nodal status, in the EndoPredict test. PMID:28886093

  19. Melanoma Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. The influence of family history on cognitive heuristics, risk perceptions, and prostate cancer screening behavior.

    PubMed

    McDowell, Michelle E; Occhipinti, Stefano; Chambers, Suzanne K

    2013-11-01

    To examine how family history of prostate cancer, risk perceptions, and heuristic decision strategies influence prostate cancer screening behavior. Men with a first-degree family history of prostate cancer (FDRs; n = 207) and men without a family history (PM; n = 239) completed a Computer Assisted Telephone Interview (CATI) examining prostate cancer risk perceptions, PSA testing behaviors, perceptions of similarity to the typical man who gets prostate cancer (representativeness heuristic), and availability of information about prostate cancer (availability heuristic). A path model explored family history as influencing the availability of information about prostate cancer (number of acquaintances with prostate cancer and number of recent discussions about prostate cancer) to mediate judgments of risk and to predict PSA testing behaviors and family history as a moderator of the relationship between representativeness (perceived similarity) and risk perceptions. FDRs reported greater risk perceptions and a greater number of PSA tests than did PM. Risk perceptions predicted increased PSA testing only in path models and was significant only for PM in multi-Group SEM analyses. Family history moderated the relationship between similarity perceptions and risk perceptions such that the relationship between these variables was significant only for FDRs. Recent discussions about prostate cancer mediated the relationships between family history and risk perceptions, and the number of acquaintances men knew with prostate cancer mediated the relationship between family history and PSA testing behavior. Family history interacts with the individuals' broader social environment to influence risk perceptions and screening behavior. Research into how risk perceptions develop and what primes behavior change is crucial to underpin psychological or public health intervention that seeks to influence health decision making.

  1. Development and validation of risk models and molecular diagnostics to permit personalized management of cancer.

    PubMed

    Pu, Xia; Ye, Yuanqing; Wu, Xifeng

    2014-01-01

    Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation. © 2013 American Cancer Society.

  2. Quantitative assessment of background parenchymal enhancement in breast magnetic resonance images predicts the risk of breast cancer.

    PubMed

    Hu, Xiaoxin; Jiang, Luan; Li, Qiang; Gu, Yajia

    2017-02-07

    The objective of this study was to evaluate the association betweenthe quantitative assessment of background parenchymal enhancement rate (BPER) and breast cancer. From 14,033 consecutive patients who underwent breast MRI in our center, we randomly selected 101 normal controls. Then, we selected 101 women with benign breast lesions and 101 women with breast cancer who were matched for age and menstruation status. We evaluated BPER at early (2 minutes), medium (4 minutes) and late (6 minutes) enhanced time phases of breast MRI for quantitative assessment. Odds ratios (ORs) for risk of breast cancer were calculated using the receiver operating curve. The BPER increased in a time-dependent manner after enhancement in both premenopausal and postmenopausal women. Premenopausal women had higher BPER than postmenopausal women at early, medium and late enhanced phases. In the normal population, the OR for probability of breast cancer for premenopausal women with high BPER was 4.1 (95% CI: 1.7-9.7) and 4.6 (95% CI: 1.7-12.0) for postmenopausal women. The OR of breast cancer morbidity in premenopausal women with high BPER was 2.6 (95% CI: 1.1-6.4) and 2.8 (95% CI: 1.2-6.1) for postmenopausal women. The BPER was found to be a predictive factor of breast cancer morbidity. Different time phases should be used to assess BPER in premenopausal and postmenopausal women.

  3. Living in the context of poverty and trajectories of breast cancer worry, knowledge and perceived risk after a breast cancer risk education session

    PubMed Central

    Bartle-Haring, Suzanne

    2010-01-01

    Objectives The purpose of this paper was to demonstrate how living in neighborhoods with high levels of poverty (while controlling for personal income) impacts personal characteristics which in turn impacts retention of breast cancer risk knowledge and changes in worry and perceived risk. Methods The data from this project come from a larger NCI funded study that included a pre-test, a breast cancer risk education session, a post-test, the option of an individualized risk assessment via the Gail Model and three follow-up phone calls over the next nine months. Results The percent of individuals living below poverty in the community in which the participant resided was predictive of the personal characteristics assessed, and these characteristics were predictive of changes in breast cancer worry, and knowledge across time. Conclusions Differentiation of self and monitoring, two of the individual characteristics that appear to allow people to process and use information to make “rational” decisions about health care, appear to be impacted by the necessity for adaptation to a culture of poverty. Thus, as a health care community, we need to tailor our messages and our recommendations with an understanding of the complex intersection of poverty and health care decision making. PMID:20688528

  4. Development and external validation of new ultrasound-based mathematical models for preoperative prediction of high-risk endometrial cancer.

    PubMed

    Van Holsbeke, C; Ameye, L; Testa, A C; Mascilini, F; Lindqvist, P; Fischerova, D; Frühauf, F; Fransis, S; de Jonge, E; Timmerman, D; Epstein, E

    2014-05-01

    To develop and validate strategies, using new ultrasound-based mathematical models, for the prediction of high-risk endometrial cancer and compare them with strategies using previously developed models or the use of preoperative grading only. Women with endometrial cancer were prospectively examined using two-dimensional (2D) and three-dimensional (3D) gray-scale and color Doppler ultrasound imaging. More than 25 ultrasound, demographic and histological variables were analyzed. Two logistic regression models were developed: one 'objective' model using mainly objective variables; and one 'subjective' model including subjective variables (i.e. subjective impression of myometrial and cervical invasion, preoperative grade and demographic variables). The following strategies were validated: a one-step strategy using only preoperative grading and two-step strategies using preoperative grading as the first step and one of the new models, subjective assessment or previously developed models as a second step. One-hundred and twenty-five patients were included in the development set and 211 were included in the validation set. The 'objective' model retained preoperative grade and minimal tumor-free myometrium as variables. The 'subjective' model retained preoperative grade and subjective assessment of myometrial invasion. On external validation, the performance of the new models was similar to that on the development set. Sensitivity for the two-step strategy with the 'objective' model was 78% (95% CI, 69-84%) at a cut-off of 0.50, 82% (95% CI, 74-88%) for the strategy with the 'subjective' model and 83% (95% CI, 75-88%) for that with subjective assessment. Specificity was 68% (95% CI, 58-77%), 72% (95% CI, 62-80%) and 71% (95% CI, 61-79%) respectively. The two-step strategies detected up to twice as many high-risk cases as preoperative grading only. The new models had a significantly higher sensitivity than did previously developed models, at the same specificity. Two

  5. Assessing the Risk of Occult Cancer and 30-day Morbidity in Women Undergoing Risk-reducing Surgery: A Prospective Experience.

    PubMed

    Bogani, Giorgio; Tagliabue, Elena; Signorelli, Mauro; Chiappa, Valentina; Carcangiu, Maria Luisa; Paolini, Biagio; Casarin, Jvan; Scaffa, Cono; Gennaro, Massimiliano; Martinelli, Fabio; Borghi, Chiara; Ditto, Antonino; Lorusso, Domenica; Raspagliesi, Francesco

    To investigate the incidence and predictive factors of 30-day surgery-related morbidity and occult precancerous and cancerous conditions for women undergoing risk-reducing surgery. A prospective study (Canadian Task Force classification II-1). A gynecologic oncology referral center. Breast-related cancer antigen (BRCA) mutation carriers and BRCAX patients (those with a significant family history of breast and ovarian cancer). Minimally invasive risk-reduction surgery. Overall, 85 women underwent risk-reducing surgery: 30 (35%) and 55 (65%) had hysterectomy plus bilateral salpingo-oophorectomy (BSO) and BSO alone, respectively. Overall, in 6 (7%) patients, the final pathology revealed unexpected cancer: 3 early-stage ovarian/fallopian tube cancers, 2 advanced-stage ovarian cancers (stage IIIA and IIIB), and 1 serous endometrial carcinoma. Additionally, 3 (3.6%) patients had incidental finding of serous tubal intraepithelial carcinoma. Four (4.7%) postoperative complications within 30 days from surgery were registered, including fever (n = 3) and postoperative ileus (n = 1); no severe (grade 3 or more) complications were observed. All complications were managed conservatively. The presence of occult cancer was the only factor predicting the development of postoperative complications (p = .02). Minimally invasive risk-reducing surgery is a safe and effective strategy to manage BRCA mutation carriers. Patients should benefit from an appropriate counseling about the high prevalence of undiagnosed cancers observed at the time of surgery. Copyright © 2017 AAGL. Published by Elsevier Inc. All rights reserved.

  6. Identification of Patients at Risk for Hereditary Colorectal Cancer

    PubMed Central

    Mishra, Nitin; Hall, Jason

    2012-01-01

    Diagnosis of hereditary colorectal cancer syndromes requires clinical suspicion and knowledge of such syndromes. Lynch syndrome is the most common cause of hereditary colorectal cancer. Other less common causes include familial adenomatous polyposis (FAP), Peutz-Jeghers syndrome (PJS), juvenile polyposis syndrome, and others. There have been a growing number of clinical and molecular tools used to screen and test at risk individuals. Screening tools include diagnostic clinical criteria, family history, genetic prediction models, and tumor testing. Patients who are high risk based on screening should be referred for genetic testing. PMID:23730221

  7. Ovarian Cancer Risk Factors by Histologic Subtype: An Analysis From the Ovarian Cancer Cohort Consortium.

    PubMed

    Wentzensen, Nicolas; Poole, Elizabeth M; Trabert, Britton; White, Emily; Arslan, Alan A; Patel, Alpa V; Setiawan, V Wendy; Visvanathan, Kala; Weiderpass, Elisabete; Adami, Hans-Olov; Black, Amanda; Bernstein, Leslie; Brinton, Louise A; Buring, Julie; Butler, Lesley M; Chamosa, Saioa; Clendenen, Tess V; Dossus, Laure; Fortner, Renee; Gapstur, Susan M; Gaudet, Mia M; Gram, Inger T; Hartge, Patricia; Hoffman-Bolton, Judith; Idahl, Annika; Jones, Michael; Kaaks, Rudolf; Kirsh, Victoria; Koh, Woon-Puay; Lacey, James V; Lee, I-Min; Lundin, Eva; Merritt, Melissa A; Onland-Moret, N Charlotte; Peters, Ulrike; Poynter, Jenny N; Rinaldi, Sabina; Robien, Kim; Rohan, Thomas; Sandler, Dale P; Schairer, Catherine; Schouten, Leo J; Sjöholm, Louise K; Sieri, Sabina; Swerdlow, Anthony; Tjonneland, Anna; Travis, Ruth; Trichopoulou, Antonia; van den Brandt, Piet A; Wilkens, Lynne; Wolk, Alicja; Yang, Hannah P; Zeleniuch-Jacquotte, Anne; Tworoger, Shelley S

    2016-08-20

    An understanding of the etiologic heterogeneity of ovarian cancer is important for improving prevention, early detection, and therapeutic approaches. We evaluated 14 hormonal, reproductive, and lifestyle factors by histologic subtype in the Ovarian Cancer Cohort Consortium (OC3). Among 1.3 million women from 21 studies, 5,584 invasive epithelial ovarian cancers were identified (3,378 serous, 606 endometrioid, 331 mucinous, 269 clear cell, 1,000 other). By using competing-risks Cox proportional hazards regression stratified by study and birth year and adjusted for age, parity, and oral contraceptive use, we assessed associations for all invasive cancers by histology. Heterogeneity was evaluated by likelihood ratio test. Most risk factors exhibited significant heterogeneity by histology. Higher parity was most strongly associated with endometrioid (relative risk [RR] per birth, 0.78; 95% CI, 0.74 to 0.83) and clear cell (RR, 0.68; 95% CI, 0.61 to 0.76) carcinomas (P value for heterogeneity [P-het] < .001). Similarly, age at menopause, endometriosis, and tubal ligation were only associated with endometrioid and clear cell tumors (P-het ≤ .01). Family history of breast cancer (P-het = .008) had modest heterogeneity. Smoking was associated with an increased risk of mucinous (RR per 20 pack-years, 1.26; 95% CI, 1.08 to 1.46) but a decreased risk of clear cell (RR, 0.72; 95% CI, 0.55 to 0.94) tumors (P-het = .004). Unsupervised clustering by risk factors separated endometrioid, clear cell, and low-grade serous carcinomas from high-grade serous and mucinous carcinomas. The heterogeneous associations of risk factors with ovarian cancer subtypes emphasize the importance of conducting etiologic studies by ovarian cancer subtypes. Most established risk factors were more strongly associated with nonserous carcinomas, which demonstrate challenges for risk prediction of serous cancers, the most fatal subtype. © 2016 by American Society of Clinical Oncology.

  8. Ovarian Cancer Risk Factors by Histologic Subtype: An Analysis From the Ovarian Cancer Cohort Consortium

    PubMed Central

    Poole, Elizabeth M.; Trabert, Britton; White, Emily; Arslan, Alan A.; Patel, Alpa V.; Setiawan, V. Wendy; Visvanathan, Kala; Weiderpass, Elisabete; Adami, Hans-Olov; Black, Amanda; Bernstein, Leslie; Brinton, Louise A.; Buring, Julie; Butler, Lesley M.; Chamosa, Saioa; Clendenen, Tess V.; Dossus, Laure; Fortner, Renee; Gapstur, Susan M.; Gaudet, Mia M.; Gram, Inger T.; Hartge, Patricia; Hoffman-Bolton, Judith; Idahl, Annika; Jones, Michael; Kaaks, Rudolf; Kirsh, Victoria; Koh, Woon-Puay; Lacey, James V.; Lee, I-Min; Lundin, Eva; Merritt, Melissa A.; Onland-Moret, N. Charlotte; Peters, Ulrike; Poynter, Jenny N.; Rinaldi, Sabina; Robien, Kim; Rohan, Thomas; Sandler, Dale P.; Schairer, Catherine; Schouten, Leo J.; Sjöholm, Louise K.; Sieri, Sabina; Swerdlow, Anthony; Tjonneland, Anna; Travis, Ruth; Trichopoulou, Antonia; van den Brandt, Piet A.; Wilkens, Lynne; Wolk, Alicja; Yang, Hannah P.; Zeleniuch-Jacquotte, Anne; Tworoger, Shelley S.

    2016-01-01

    Purpose An understanding of the etiologic heterogeneity of ovarian cancer is important for improving prevention, early detection, and therapeutic approaches. We evaluated 14 hormonal, reproductive, and lifestyle factors by histologic subtype in the Ovarian Cancer Cohort Consortium (OC3). Patients and Methods Among 1.3 million women from 21 studies, 5,584 invasive epithelial ovarian cancers were identified (3,378 serous, 606 endometrioid, 331 mucinous, 269 clear cell, 1,000 other). By using competing-risks Cox proportional hazards regression stratified by study and birth year and adjusted for age, parity, and oral contraceptive use, we assessed associations for all invasive cancers by histology. Heterogeneity was evaluated by likelihood ratio test. Results Most risk factors exhibited significant heterogeneity by histology. Higher parity was most strongly associated with endometrioid (relative risk [RR] per birth, 0.78; 95% CI, 0.74 to 0.83) and clear cell (RR, 0.68; 95% CI, 0.61 to 0.76) carcinomas (P value for heterogeneity [P-het] < .001). Similarly, age at menopause, endometriosis, and tubal ligation were only associated with endometrioid and clear cell tumors (P-het ≤ .01). Family history of breast cancer (P-het = .008) had modest heterogeneity. Smoking was associated with an increased risk of mucinous (RR per 20 pack-years, 1.26; 95% CI, 1.08 to 1.46) but a decreased risk of clear cell (RR, 0.72; 95% CI, 0.55 to 0.94) tumors (P-het = .004). Unsupervised clustering by risk factors separated endometrioid, clear cell, and low-grade serous carcinomas from high-grade serous and mucinous carcinomas. Conclusion The heterogeneous associations of risk factors with ovarian cancer subtypes emphasize the importance of conducting etiologic studies by ovarian cancer subtypes. Most established risk factors were more strongly associated with nonserous carcinomas, which demonstrate challenges for risk prediction of serous cancers, the most fatal subtype. PMID:27325851

  9. Which risk models perform best in selecting ever-smokers for lung cancer screening?

    Cancer.gov

    A new analysis by scientists at NCI evaluates nine different individualized lung cancer risk prediction models based on their selections of ever-smokers for computed tomography (CT) lung cancer screening.

  10. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

    PubMed

    Zhao, Di; Weng, Chunhua

    2011-10-01

    In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  11. Combining PubMed Knowledge and EHR Data to Develop a Weighted Bayesian Network for Pancreatic Cancer Prediction

    PubMed Central

    Zhao, Di; Weng, Chunhua

    2011-01-01

    In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. PMID:21642013

  12. Prediction of Ischemic Heart Disease and Stroke in Survivors of Childhood Cancer.

    PubMed

    Chow, Eric J; Chen, Yan; Hudson, Melissa M; Feijen, Elizabeth A M; Kremer, Leontien C; Border, William L; Green, Daniel M; Meacham, Lillian R; Mulrooney, Daniel A; Ness, Kirsten K; Oeffinger, Kevin C; Ronckers, Cécile M; Sklar, Charles A; Stovall, Marilyn; van der Pal, Helena J; van Dijk, Irma W E M; van Leeuwen, Flora E; Weathers, Rita E; Robison, Leslie L; Armstrong, Gregory T; Yasui, Yutaka

    2018-01-01

    Purpose We aimed to predict individual risk of ischemic heart disease and stroke in 5-year survivors of childhood cancer. Patients and Methods Participants in the Childhood Cancer Survivor Study (CCSS; n = 13,060) were observed through age 50 years for the development of ischemic heart disease and stroke. Siblings (n = 4,023) established the baseline population risk. Piecewise exponential models with backward selection estimated the relationships between potential predictors and each outcome. The St Jude Lifetime Cohort Study (n = 1,842) and the Emma Children's Hospital cohort (n = 1,362) were used to validate the CCSS models. Results Ischemic heart disease and stroke occurred in 265 and 295 CCSS participants, respectively. Risk scores based on a standard prediction model that included sex, chemotherapy, and radiotherapy (cranial, neck, and chest) exposures achieved an area under the curve and concordance statistic of 0.70 and 0.70 for ischemic heart disease and 0.63 and 0.66 for stroke, respectively. Validation cohort area under the curve and concordance statistics ranged from 0.66 to 0.67 for ischemic heart disease and 0.68 to 0.72 for stroke. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups. The cumulative incidences at age 50 years among CCSS low-risk groups were < 5%, compared with approximately 20% for high-risk groups ( P < .001); cumulative incidence was only 1% for siblings ( P < .001 v low-risk survivors). Conclusion Information available to clinicians soon after completion of childhood cancer therapy can predict individual risk for subsequent ischemic heart disease and stroke with reasonable accuracy and discrimination through age 50 years. These models provide a framework on which to base future screening strategies and interventions.

  13. ["Screening" in special situations. Assessing predictive genetic screening for hereditary breast and colorectal cancer].

    PubMed

    Jonas, Susanna; Wild, Claudia; Schamberger, Chantal

    2003-02-01

    The aim of this health technology assessment was to analyse the current scientific and genetic counselling on predictive genetic testing for hereditary breast and colorectal cancer. Predictive genetic testing will be available for several common diseases in the future and questions related to financial issues and quality standards will be raised. This report is based on a systematic/nonsystematic literature search in several databases (e.g. EmBase, Medline, Cochrane Library) and on a specific health technology assessment report (CCOHTA) and review (American Gastroenterological Ass.), respectively. Laboratory test methods, early detection methods and the benefit from prophylactic interventions were analysed and social consequences interpreted. Breast and colorectal cancer are counted among the most frequently cancer diseases. Most of them are based on random accumulation of risk factors, 5-10% show a familial determination. A hereditary modified gene is responsible for the increased cancer risk. In these families, high tumour frequency, young age at diagnosis and multiple primary tumours are remarkable. GENETIC DIAGNOSIS: Sequence analysis is the gold standard. Denaturing high performance liquid chromatography is a quick alternative method. The identification of the responsible gene defect in an affected family member is important. If the test result is positive there is an uncertainty whether the disease will develop or not, when and in which degree, which is founded in the geno-/phenotype correlation. The individual risk estimation is based upon empirical evidence. The test results affect the whole family. Currently, primary prevention is possible for familial adenomatous polyposis (celecoxib, prophylactic colectomy) and for hereditary mamma carcinoma (prophylactic mastectomy). The so-called preventive medical check-ups are early detection examinations. The evidence about early detection methods for colorectal cancer is better than for breast cancer. Prophylactic

  14. The Distinct Role of Comparative Risk Perceptions in a Breast Cancer Prevention Program

    PubMed Central

    Dillard, Amanda J.; Ubel, Peter A.; Smith, Dylan M.; Zikmund-Fisher, Brian J.; Nair, Vijay; Derry, Holly A.; Zhang, Aijun; Pitsch, Rosemarie K.; Alford, Sharon Hensley; McClure, Jennifer B.; Fagerlin, Angela

    2013-01-01

    Background Comparative risk perceptions may rival other types of information in terms of effects on health behavior decisions. Purpose We examined associations between comparative risk perceptions, affect, and behavior while controlling for absolute risk perceptions and actual risk. Methods Women at an increased risk of breast cancer participated in a program to learn about tamoxifen which can reduce the risk of breast cancer. Women reported comparative risk perceptions of breast cancer and completed measures of anxiety, knowledge, and tamoxifen-related behavior intentions. Three months later, women reported their behavior. Results Comparative risk perceptions were positively correlated with anxiety, knowledge, intentions, and behavior three months later. After controlling for participants’ actual risk of breast cancer and absolute risk perceptions, comparative risk perceptions predicted anxiety and knowledge, but not intentions or behavior. Conclusions Comparative risk perceptions can affect patient outcomes like anxiety and knowledge independently of absolute risk perceptions and actual risk information. PMID:21698518

  15. Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

    PubMed

    Li, Xiaohong; Blount, Patricia L; Vaughan, Thomas L; Reid, Brian J

    2011-02-01

    Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

  16. Nutrition-Related Cancer Prevention Cognitions and Behavioral Intentions: Testing the Risk Perception Attitude Framework

    ERIC Educational Resources Information Center

    Sullivan, Helen W.; Beckjord, Ellen Burke; Finney Rutten, Lila J.; Hesse, Bradford W.

    2008-01-01

    This study tested whether the risk perception attitude framework predicted nutrition-related cancer prevention cognitions and behavioral intentions. Data from the 2003 Health Information National Trends Survey were analyzed to assess respondents' reported likelihood of developing cancer (risk) and perceptions of whether they could lower their…

  17. Thinking through cancer risk: characterizing smokers' process of risk determination.

    PubMed

    Hay, Jennifer; Shuk, Elyse; Cruz, Gustavo; Ostroff, Jamie

    2005-10-01

    The perception of cancer risk motivates cancer risk reduction behaviors. However, common measurement strategies for cancer risk perceptions, which involve numerical likelihood estimates, do not adequately capture individuals' thoughts and feelings about cancer risk. To guide the development of novel measurement strategies, the authors used semistructured interviews to examine the thought processes used by smokers (N = 15) as they considered their cancer risk. They used grounded theory to guide systematic data coding and develop a heuristic model describing smokers' risk perception process that includes a cognitive, primarily rational process whereby salient personal risk factors for cancer are considered and combined, and an affective/attitudinal process, which shifts risk perceptions either up or down. The model provides a tentative explanation concerning how people hold cancer risk perceptions that diverge from rational assessment of their risks and will be useful in guiding the development of non-numerical measurements strategies for cancer risk perceptions.

  18. Age at exposure and attained age variations of cancer risk in the Japanese A-bomb and radiotherapy cohorts.

    PubMed

    Schneider, Uwe; Walsh, Linda

    2015-08-01

    Phenomenological risk models for radiation-induced cancer are frequently applied to estimate the risk of radiation-induced cancers at radiotherapy doses. Such models often include the effect modification, of the main risk to radiation dose response, by age at exposure and attained age. The aim of this paper is to compare the patterns in risk effect modification by age, between models obtained from the Japanese atomic-bomb (A-bomb) survivor data and models for cancer risks previously reported for radiotherapy patients. Patterns in risk effect modification by age from the epidemiological studies of radiotherapy patients were also used to refine and extend the risk effect modification by age obtained from the A-bomb survivor data, so that more universal models can be presented here. Simple log-linear and power functions of age for the risk effect modification applied in models of the A-bomb survivor data are compared to risks from epidemiological studies of second cancers after radiotherapy. These functions of age were also refined and fitted to radiotherapy risks. The resulting age models provide a refined and extended functional dependence of risk with age at exposure and attained age especially beyond 40 and 65 yr, respectively, and provide a better representation than the currently available simple age functions. It was found that the A-bomb models predict risk similarly to the outcomes of testicular cancer survivors. The survivors of Hodgkin's disease show steeper variations of risk with both age at exposure and attained age. The extended models predict solid cancer risk increase as a function of age at exposure beyond 40 yr and the risk decrease as a function of attained age beyond 65 yr better than the simple models. The standard functions for risk effect modification by age, based on the A-bomb survivor data, predict second cancer risk in radiotherapy patients for ages at exposure prior to 40 yr and attained ages before 55 yr reasonably well. However, for

  19. Space Radiation Cancer Risk Projections and Uncertainties - 2010

    NASA Technical Reports Server (NTRS)

    Cucinotta, Francis A.; Kim, Myung-Hee Y.; Chappell, Lori J.

    2011-01-01

    Uncertainties in estimating health risks from galactic cosmic rays greatly limit space mission lengths and potential risk mitigation evaluations. NASA limits astronaut exposures to a 3% risk of exposure-induced death and protects against uncertainties using an assessment of 95% confidence intervals in the projection model. Revisions to this model for lifetime cancer risks from space radiation and new estimates of model uncertainties are described here. We review models of space environments and transport code predictions of organ exposures, and characterize uncertainties in these descriptions. We summarize recent analysis of low linear energy transfer radio-epidemiology data, including revision to Japanese A-bomb survivor dosimetry, longer follow-up of exposed cohorts, and reassessments of dose and dose-rate reduction effectiveness factors. We compare these projections and uncertainties with earlier estimates. Current understanding of radiation quality effects and recent data on factors of relative biological effectiveness and particle track structure are reviewed. Recent radiobiology experiment results provide new information on solid cancer and leukemia risks from heavy ions. We also consider deviations from the paradigm of linearity at low doses of heavy ions motivated by non-targeted effects models. New findings and knowledge are used to revise the NASA risk projection model for space radiation cancer risks.

  20. Sun Protection Motivational Stages and Behavior: Skin Cancer Risk Profiles

    ERIC Educational Resources Information Center

    Pagoto, Sherry L.; McChargue, Dennis E.; Schneider, Kristin; Cook, Jessica Werth

    2004-01-01

    Objective: To create skin cancer risk profiles that could be used to predict sun protection among Midwest beachgoers. Method: Cluster analysis was used with study participants (N=239), who provided information about sun protection motivation and behavior, perceived risk, burn potential, and tan importance. Participants were clustered according to…

  1. ATM, radiation, and the risk of second primary breast cancer.

    PubMed

    Bernstein, Jonine L; Concannon, Patrick

    2017-10-01

    It was first suggested more than 40 years ago that heterozygous carriers for the human autosomal recessive disorder Ataxia-Telangiectasia (A-T) might also be at increased risk for cancer. Subsequent studies have identified the responsible gene, Ataxia-Telangiectasia Mutated (ATM), characterized genetic variation at this locus in A-T and a variety of different cancers, and described the functions of the ATM protein with regard to cellular DNA damage responses. However, an overall model of how ATM contributes to cancer risk, and in particular, the role of DNA damage in this process, remains lacking. This review considers these questions in the context of contralateral breast cancer (CBC). Heterozygous carriers of loss of function mutations in ATM that are A-T causing, are at increased risk of breast cancer. However, examination of a range of genetic variants, both rare and common, across multiple cancers, suggests that ATM may have additional effects on cancer risk that are allele-dependent. In the case of CBC, selected common alleles at ATM are associated with a reduced incidence of CBC, while other rare and predicted deleterious variants may act jointly with radiation exposure to increase risk. Further studies that characterize germline and somatic ATM mutations in breast cancer and relate the detected genetic changes to functional outcomes, particularly with regard to radiation responses, are needed to gain a complete picture of the complex relationship between ATM, radiation and breast cancer.

  2. Cancer risks in naval divers with multiple exposures to carcinogens.

    PubMed Central

    Richter, Elihu D; Friedman, Lee S; Tamir, Yuval; Berman, Tamar; Levy, Or; Westin, Jerome B; Peretz, Tamar

    2003-01-01

    We investigated risks for cancer and the case for a cause-effect relationship in five successive cohorts of naval commando divers (n = 682) with prolonged underwater exposures (skin, gastrointestinal tract, and airways) to many toxic compounds in the Kishon River, Israel's most polluted waterway, from 1948 to 1995. Releases of industrial, ship, and agricultural effluents in the river increased substantially, fish yields decreased, and toxic damage to marine organisms increased. Among the divers (16,343 person-years follow-up from 18 years of age to year 2000), the observed/expected ratio for all tumors was 2.29 (p<0.01). Risks increased in cohorts first diving after 1960 compared to risks in earlier cohorts, notably for hematolymphopoietic, central nervous system, gastrointestinal, and skin cancer; induction periods were often brief. The findings suggest that the increases in risk for cancer and short induction periods resulted from direct contact with and absorption of multiple toxic compounds. Early toxic effects in marine life predicted later risks for cancer in divers. PMID:12676624

  3. Predictive models in cancer management: A guide for clinicians.

    PubMed

    Kazem, Mohammed Ali

    2017-04-01

    Predictive tools in cancer management are used to predict different outcomes including survival probability or risk of recurrence. The uptake of these tools by clinicians involved in cancer management has not been as common as other clinical tools, which may be due to the complexity of some of these tools or a lack of understanding of how they can aid decision-making in particular clinical situations. The aim of this article is to improve clinicians' knowledge and understanding of predictive tools used in cancer management, including how they are built, how they can be applied to medical practice, and what their limitations may be. Literature review was conducted to investigate the role of predictive tools in cancer management. All predictive models share similar characteristics, but depending on the type of the tool its ability to predict an outcome will differ. Each type has its own pros and cons, and its generalisability will depend on the cohort used to build the tool. These factors will affect the clinician's decision whether to apply the model to their cohort or not. Before a model is used in clinical practice, it is important to appreciate how the model is constructed, what its use may add over and above traditional decision-making tools, and what problems or limitations may be associated with it. Understanding all the above is an important step for any clinician who wants to decide whether or not use predictive tools in their practice. Copyright © 2016 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

  4. Sparse feature selection for classification and prediction of metastasis in endometrial cancer.

    PubMed

    Ahsen, Mehmet Eren; Boren, Todd P; Singh, Nitin K; Misganaw, Burook; Mutch, David G; Moore, Kathleen N; Backes, Floor J; McCourt, Carolyn K; Lea, Jayanthi S; Miller, David S; White, Michael A; Vidyasagar, Mathukumalli

    2017-03-27

    Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.

  5. A joint model of persistent human papillomavirus infection and cervical cancer risk: Implications for cervical cancer screening

    PubMed Central

    Katki, Hormuzd A.; Cheung, Li C.; Fetterman, Barbara; Castle, Philip E.; Sundaram, Rajeshwari

    2014-01-01

    Summary New cervical cancer screening guidelines in the US and many European countries recommend that women get tested for human papillomavirus (HPV). To inform decisions about screening intervals, we calculate the increase in precancer/cancer risk per year of continued HPV infection. However, both time to onset of precancer/cancer and time to HPV clearance are interval-censored, and onset of precancer/cancer strongly informatively censors HPV clearance. We analyze this bivariate informatively interval-censored data by developing a novel joint model for time to clearance of HPV and time to precancer/cancer using shared random-effects, where the estimated mean duration of each woman’s HPV infection is a covariate in the submodel for time to precancer/cancer. The model was fit to data on 9,553 HPV-positive/Pap-negative women undergoing cervical cancer screening at Kaiser Permanente Northern California, data that were pivotal to the development of US screening guidelines. We compare the implications for screening intervals of this joint model to those from population-average marginal models of precancer/cancer risk. In particular, after 2 years the marginal population-average precancer/cancer risk was 5%, suggesting a 2-year interval to control population-average risk at 5%. In contrast, the joint model reveals that almost all women exceeding 5% individual risk in 2 years also exceeded 5% in 1 year, suggesting that a 1-year interval is better to control individual risk at 5%. The example suggests that sophisticated risk models capable of predicting individual risk may have different implications than population-average risk models that are currently used for informing medical guideline development. PMID:26556961

  6. A joint model of persistent human papillomavirus infection and cervical cancer risk: Implications for cervical cancer screening.

    PubMed

    Katki, Hormuzd A; Cheung, Li C; Fetterman, Barbara; Castle, Philip E; Sundaram, Rajeshwari

    2015-10-01

    New cervical cancer screening guidelines in the US and many European countries recommend that women get tested for human papillomavirus (HPV). To inform decisions about screening intervals, we calculate the increase in precancer/cancer risk per year of continued HPV infection. However, both time to onset of precancer/cancer and time to HPV clearance are interval-censored, and onset of precancer/cancer strongly informatively censors HPV clearance. We analyze this bivariate informatively interval-censored data by developing a novel joint model for time to clearance of HPV and time to precancer/cancer using shared random-effects, where the estimated mean duration of each woman's HPV infection is a covariate in the submodel for time to precancer/cancer. The model was fit to data on 9,553 HPV-positive/Pap-negative women undergoing cervical cancer screening at Kaiser Permanente Northern California, data that were pivotal to the development of US screening guidelines. We compare the implications for screening intervals of this joint model to those from population-average marginal models of precancer/cancer risk. In particular, after 2 years the marginal population-average precancer/cancer risk was 5%, suggesting a 2-year interval to control population-average risk at 5%. In contrast, the joint model reveals that almost all women exceeding 5% individual risk in 2 years also exceeded 5% in 1 year, suggesting that a 1-year interval is better to control individual risk at 5%. The example suggests that sophisticated risk models capable of predicting individual risk may have different implications than population-average risk models that are currently used for informing medical guideline development.

  7. Physical activity and the risk of colorectal cancer in Lynch syndrome.

    PubMed

    Dashti, S Ghazaleh; Win, Aung Ko; Hardikar, Sheetal S; Glombicki, Stephen E; Mallenahalli, Sheila; Thirumurthi, Selvi; Peterson, Susan K; You, Y Nancy; Buchanan, Daniel D; Figueiredo, Jane C; Campbell, Peter T; Gallinger, Steven; Newcomb, Polly A; Potter, John D; Lindor, Noralane M; Le Marchand, Loic; Haile, Robert W; Hopper, John L; Jenkins, Mark A; Basen-Engquist, Karen M; Lynch, Patrick M; Pande, Mala

    2018-06-14

    Greater physical activity is associated with a decrease in risk of colorectal cancer for the general population; however, little is known about its relationship with colorectal cancer risk for people with Lynch syndrome, carriers of inherited pathogenic mutations in genes affecting DNA mismatch repair (MMR). We studied a cohort of 2,042 MMR gene mutations carriers (n=807, diagnosed with colorectal cancer), from the Colon Cancer Family Registry. Self-reported physical activity in three age-periods (20-29, 30-49, and ≥50 years) was summarized as average metabolic equivalent of task hours per week (MET-h/week) during the age-period of cancer diagnosis or censoring (near-term exposure), and across all age-periods preceding cancer diagnosis or censoring (long-term exposure). Weighted Cox regression was used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) for the association between physical activity and colorectal cancer risk. Near-term physical activity was associated with a small reduction in the risk of colorectal cancer (HR ≥35 vs. <3.5 MET-h/week, 0.71; 95% CI, 0.53 - 0.96). The strength and direction of associations were similar for long-term physical activity, although the associations were not nominally significant. Our results suggest that physical activity is inversely associated with the risk of colorectal cancer for people with Lynch syndrome, however, further confirmation is warranted. The potential modifying effect of physical activity on colorectal cancer risk for people with Lynch syndrome could be useful for risk prediction and support counseling advice for lifestyle modification to reduce cancer risk. This article is protected by copyright. All rights reserved. © 2018 UICC.

  8. Reader performance in visual assessment of breast density using visual analogue scales: Are some readers more predictive of breast cancer?

    NASA Astrophysics Data System (ADS)

    Rayner, Millicent; Harkness, Elaine F.; Foden, Philip; Wilson, Mary; Gadde, Soujanya; Beetles, Ursula; Lim, Yit Y.; Jain, Anil; Bundred, Sally; Barr, Nicky; Evans, D. Gareth; Howell, Anthony; Maxwell, Anthony; Astley, Susan M.

    2018-03-01

    Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

  9. Predicting general and cancer-related distress in women with newly diagnosed breast cancer.

    PubMed

    Gibbons, Andrea; Groarke, AnnMarie; Sweeney, Karl

    2016-12-03

    Psychological distress can impact medical outcomes such as recovery from surgery and experience of side effects during treatment. Identifying the factors that explain variability in distress would guide future interventions aimed at decreasing distress. Two factors that have been implicated in distress are illness perceptions and coping, and are part of the Self-Regulatory Model of Illness Behaviour (SRM). The model suggests that coping mediates the relationship between illness perceptions and distress. Despite this; very little research has assessed this relationship with cancer-related distress, and none have examined women with screen-detected breast cancer. This study is the first to examine the relative contribution of illness perceptions and coping on general and cancer-related distress in women with screen-detected breast cancer. Women recently diagnosed with breast cancer (N = 94) who had yet to receive treatment completed measures of illness perceptions (Revised Illness Perception Questionnaire), cancer-specific coping (Mental Adjustment to Cancer Scale), general anxiety and depression (Hospital Anxiety and Depression scale), and cancer-related distress. Hierarchical regression analyses revealed that medical variables, illness perceptions and coping predicted 50% of the variance in depression, 42% in general anxiety, and 40% in cancer-related distress. Believing in more emotional causes to breast cancer (β = .22, p = .021), more illness identity (β = .25, p = .004), greater anxious preoccupation (β = .23, p = .030), and less fighting spirit (β = -.31, p = .001) predicted greater depression. Greater illness coherence predicted less cancer-related distress (β = -.20, p = .043). Greater anxious preoccupation also led to greater general anxiety (β = .44, p < .001) and cancer-related distress (β = .37, p = .001). Mediation analyses revealed that holding greater beliefs in a chronic timeline

  10. A New View of Radiation-Induced Cancer: Integrating Short- and Long-Term Processes. Part II: Second Cancer Risk Estimation

    NASA Technical Reports Server (NTRS)

    Shuryak, Igor; Brenner, David J.; Hahnfeldt, Philip; Hlatky, Lynn; Sachs, Rainer K.

    2009-01-01

    As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

  11. A new view of radiation-induced cancer: integrating short- and long-term processes. Part II: second cancer risk estimation.

    PubMed

    Shuryak, Igor; Hahnfeldt, Philip; Hlatky, Lynn; Sachs, Rainer K; Brenner, David J

    2009-08-01

    As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

  12. A prospectively validated nomogram for predicting the risk of chemotherapy-induced febrile neutropenia: a multicenter study.

    PubMed

    Bozcuk, H; Yıldız, M; Artaç, M; Kocer, M; Kaya, Ç; Ulukal, E; Ay, S; Kılıç, M P; Şimşek, E H; Kılıçkaya, P; Uçar, S; Coskun, H S; Savas, B

    2015-06-01

    There is clinical need to predict risk of febrile neutropenia before a specific cycle of chemotherapy in cancer patients. Data on 3882 chemotherapy cycles in 1089 consecutive patients with lung, breast, and colon cancer from four teaching hospitals were used to construct a predictive model for febrile neutropenia. A final nomogram derived from the multivariate predictive model was prospectively confirmed in a second cohort of 960 consecutive cases and 1444 cycles. The following factors were used to construct the nomogram: previous history of febrile neutropenia, pre-cycle lymphocyte count, type of cancer, cycle of current chemotherapy, and patient age. The predictive model had a concordance index of 0.95 (95 % confidence interval (CI) = 0.91-0.99) in the derivation cohort and 0.85 (95 % CI = 0.80-0.91) in the external validation cohort. A threshold of 15 % for the risk of febrile neutropenia in the derivation cohort was associated with a sensitivity of 0.76 and specificity of 0.98. These figures were 1.00 and 0.49 in the validation cohort if a risk threshold of 50 % was chosen. This nomogram is helpful in the prediction of febrile neutropenia after chemotherapy in patients with lung, breast, and colon cancer. Usage of this nomogram may help decrease the morbidity and mortality associated with febrile neutropenia and deserves further validation.

  13. Familial Risk and Heritability of Cancer Among Twins in Nordic Countries

    PubMed Central

    Mucci, Lorelei A.; Hjelmborg, Jacob B.; Harris, Jennifer R.; Czene, Kamila; Havelick, David J.; Scheike, Thomas; Graff, Rebecca E.; Holst, Klaus; Möller, Sören; Unger, Robert H.; McIntosh, Christina; Nuttall, Elizabeth; Brandt, Ingunn; Penney, Kathryn L.; Hartman, Mikael; Kraft, Peter; Parmigiani, Giovanni; Christensen, Kaare; Koskenvuo, Markku; Holm, Niels V.; Heikkilä, Kauko; Pukkala, Eero; Skytthe, Axel; Adami, Hans-Olov; Kaprio, Jaakko

    2017-01-01

    Importance Estimates of familial cancer risk from population-based studies are essential components of cancer risk prediction. Objective To estimate familial risk and heritability of cancer types in a large twin cohort. Design, Setting, and Participants Prospective study of 80 309 monozygotic and 123 382 same-sex dizygotic twin individuals (N = 203 691) within the population-based registers of Denmark, Finland, Norway, and Sweden. Twins were followed up a median of 32 years between 1943 and 2010. There were 50 990 individuals who died of any cause, and 3804 who emigrated and were lost to follow-up. Exposures Shared environmental and heritable risk factors among pairs of twins. Main Outcomes and Measures The main outcome was incident cancer. Time-to-event analyses were used to estimate familial risk (risk of cancer in an individual given a twin's development of cancer) and heritability (proportion of variance in cancer risk due to interindividual genetic differences) with follow-up via cancer registries. Statistical models adjusted for age and follow-up time, and accounted for censoring and competing risk of death. Results A total of 27 156 incident cancers were diagnosed in 23 980 individuals, translating to a cumulative incidence of 32%. Cancer was diagnosed in both twins among 1383 monozygotic (2766 individuals) and 1933 dizygotic (2866 individuals) pairs. Of these, 38% of monozygotic and 26% of dizygotic pairs were diagnosed with the same cancer type. There was an excess cancer risk in twins whose co-twin was diagnosed with cancer, with estimated cumulative risks that were an absolute 5% (95% CI, 4%-6%) higher in dizygotic (37%; 95% CI, 36%-38%) and an absolute 14% (95% CI, 12%-16%) higher in monozygotic twins (46%; 95% CI, 44%-48%) whose twin also developed cancer compared with the cumulative risk in the overall cohort (32%). For most cancer types, there were significant familial risks and the cumulative risks were higher in monozygotic than dizygotic twins

  14. Integration of second cancer risk calculations in a radiotherapy treatment planning system

    NASA Astrophysics Data System (ADS)

    Hartmann, M.; Schneider, U.

    2014-03-01

    Second cancer risk in patients, in particular in children, who were treated with radiotherapy is an important side effect. It should be minimized by selecting an appropriate treatment plan for the patient. The objectives of this study were to integrate a risk model for radiation induced cancer into a treatment planning system which allows to judge different treatment plans with regard to second cancer induction and to quantify the potential reduction in predicted risk. A model for radiation induced cancer including fractionation effects which is valid for doses in the radiotherapy range was integrated into a treatment planning system. From the three-dimensional (3D) dose distribution the 3D-risk equivalent dose (RED) was calculated on an organ specific basis. In addition to RED further risk coefficients like OED (organ equivalent dose), EAR (excess absolute risk) and LAR (lifetime attributable risk) are computed. A risk model for radiation induced cancer was successfully integrated in a treatment planning system. Several risk coefficients can be viewed and used to obtain critical situations were a plan can be optimised. Risk-volume-histograms and organ specific risks were calculated for different treatment plans and were used in combination with NTCP estimates for plan evaluation. It is concluded that the integration of second cancer risk estimates in a commercial treatment planning system is feasible. It can be used in addition to NTCP modelling for optimising treatment plans which result in the lowest possible second cancer risk for a patient.

  15. [Establishment of risk evaluation model of peritoneal metastasis in gastric cancer and its predictive value].

    PubMed

    Zhao, Junjie; Zhou, Rongjian; Zhang, Qi; Shu, Ping; Li, Haojie; Wang, Xuefei; Shen, Zhenbin; Liu, Fenglin; Chen, Weidong; Qin, Jing; Sun, Yihong

    2017-01-25

    To establish an evaluation model of peritoneal metastasis in gastric cancer, and to assess its clinical significance. Clinical and pathologic data of the consecutive cases of gastric cancer admitted between April 2015 and December 2015 in Department of General Surgery, Zhongshan Hospital of Fudan University were analyzed retrospectively. A total of 710 patients were enrolled in the study after 18 patients with other distant metastasis were excluded. The correlations between peritoneal metastasis and different factors were studied through univariate (Pearson's test or Fisher's exact test) and multivariate analyses (Binary Logistic regression). Independent predictable factors for peritoneal metastasis were combined to establish a risk evaluation model (nomogram). The nomogram was created with R software using the 'rms' package. In the nomogram, each factor had different scores, and every patient could have a total score by adding all the scores of each factor. A higher total score represented higher risk of peritoneal metastasis. Receiver operating characteristic (ROC) curve analysis was used to compare the sensitivity and specificity of the established nomogram. Delong. Delong. Clarke-Pearson test was used to compare the difference of the area under the curve (AUC). The cut-off value was determined by the AUC, when the ROC curve had the biggest AUC, the model had the best sensitivity and specificity. Among 710 patients, 47 patients had peritoneal metastasis (6.6%), including 30 male (30/506, 5.9%) and 17 female (17/204, 8.3%); 31 were ≥ 60 years old (31/429, 7.2%); 38 had tumor ≥ 3 cm(38/461, 8.2%). Lauren classification indicated that 2 patients were intestinal type(2/245, 0.8%), 8 patients were mixed type(8/208, 3.8%), 11 patients were diffuse type(11/142, 7.7%), and others had no associated data. CA19-9 of 13 patients was ≥ 37 kU/L(13/61, 21.3%); CA125 of 11 patients was ≥ 35 kU/L(11/36, 30.6%); CA72-4 of 11 patients was ≥ 10 kU/L(11/39, 28

  16. A proposal for a comprehensive risk scoring system for predicting postoperative complications in octogenarian patients with medically operable lung cancer: JACS1303.

    PubMed

    Saji, Hisashi; Ueno, Takahiko; Nakamura, Hiroshige; Okumura, Norihito; Tsuchida, Masanori; Sonobe, Makoto; Miyazaki, Takuro; Aokage, Keiju; Nakao, Masayuki; Haruki, Tomohiro; Ito, Hiroyuki; Kataoka, Kazuhiko; Okabe, Kazunori; Tomizawa, Kenji; Yoshimoto, Kentaro; Horio, Hirotoshi; Sugio, Kenji; Ode, Yasuhisa; Takao, Motoshi; Okada, Morihito; Chida, Masayuki

    2018-04-01

    Although some retrospective studies have reported clinicopathological scoring systems for predicting postoperative complications and survival outcomes for elderly lung cancer patients, optimized scoring systems remain controversial. The Japanese Association for Chest Surgery (JACS) conducted a nationwide multicentre prospective cohort and enrolled a total of 1019 octogenarians with medically operable lung cancer. Details of the clinical factors, comorbidities and comprehensive geriatric assessment were recorded for 895 patients to develop a comprehensive risk scoring (RS) system capable of predicting severe complications. Operative (30 days) and hospital mortality rates were 1.0% and 1.6%, respectively. Complications were observed in 308 (34%) patients, of whom 81 (8.4%) had Grade 3-4 severe complications. Pneumonia was the most common severe complication, observed in 27 (3.0%) patients. Five predictive factors, gender, comprehensive geriatric assessment75: memory and Simplified Comorbidity Score (SCS): diabetes mellitus, albumin and percentage vital capacity, were identified as independent predictive factors for severe postoperative complications (odds ratio = 2.73, 1.86, 1.54, 1.66 and 1.61, respectively) through univariate and multivariate analyses. A 5-fold cross-validation was performed as an internal validation to reconfirm these 5 predictive factors (average area under the curve 0.70). We developed a simplified RS system as follows: RS = 3 (gender: male) + 2 (comprehensive geriatric assessment 75: memory: yes) + 2 (albumin: <3.8 ng/ml) + 1 (percentage vital capacity: ≤90) + 1 (SCS: diabetes mellitus: yes). The current series shows that octogenarians can be successfully treated for lung cancer with surgical resection with an acceptable rate of severe complications and mortality. We propose a simplified RS system to predict severe complications in octogenarian patients with medically operative lung cancer. JACS1303 (UMIN000016756).

  17. A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development.

    PubMed

    Wang, Na-Na; Yang, Zheng-Jun; Wang, Xue; Chen, Li-Xuan; Zhao, Hong-Meng; Cao, Wen-Feng; Zhang, Bin

    2018-04-25

    Molecular subtype of breast cancer is associated with sentinel lymph node status. We sought to establish a mathematical prediction model that included breast cancer molecular subtype for risk of positive non-sentinel lymph nodes in breast cancer patients with sentinel lymph node metastasis and further validate the model in a separate validation cohort. We reviewed the clinicopathologic data of breast cancer patients with sentinel lymph node metastasis who underwent axillary lymph node dissection between June 16, 2014 and November 16, 2017 at our hospital. Sentinel lymph node biopsy was performed and patients with pathologically proven sentinel lymph node metastasis underwent axillary lymph node dissection. Independent risks for non-sentinel lymph node metastasis were assessed in a training cohort by multivariate analysis and incorporated into a mathematical prediction model. The model was further validated in a separate validation cohort, and a nomogram was developed and evaluated for diagnostic performance in predicting the risk of non-sentinel lymph node metastasis. Moreover, we assessed the performance of five different models in predicting non-sentinel lymph node metastasis in training cohort. Totally, 495 cases were eligible for the study, including 291 patients in the training cohort and 204 in the validation cohort. Non-sentinel lymph node metastasis was observed in 33.3% (97/291) patients in the training cohort. The AUC of MSKCC, Tenon, MDA, Ljubljana, and Louisville models in training cohort were 0.7613, 0.7142, 0.7076, 0.7483, and 0.671, respectively. Multivariate regression analysis indicated that tumor size (OR = 1.439; 95% CI 1.025-2.021; P = 0.036), sentinel lymph node macro-metastasis versus micro-metastasis (OR = 5.063; 95% CI 1.111-23.074; P = 0.036), the number of positive sentinel lymph nodes (OR = 2.583, 95% CI 1.714-3.892; P < 0.001), and the number of negative sentinel lymph nodes (OR = 0.686, 95% CI 0

  18. Salivary Gland Cancer: Risk Factors

    MedlinePlus

    ... Cancer: Risk Factors Request Permissions Salivary Gland Cancer: Risk Factors Approved by the Cancer.Net Editorial Board , ... To see other pages, use the menu. A risk factor is anything that increases a person’s chance ...

  19. Delayed risk stratification, to include the response to initial treatment (surgery and radioiodine ablation), has better outcome predictivity in differentiated thyroid cancer patients.

    PubMed

    Castagna, Maria Grazia; Maino, Fabio; Cipri, Claudia; Belardini, Valentina; Theodoropoulou, Alexandra; Cevenini, Gabriele; Pacini, Furio

    2011-09-01

    After initial treatment, differentiated thyroid cancer (DTC) patients are stratified as low and high risk based on clinical/pathological features. Recently, a risk stratification based on additional clinical data accumulated during follow-up has been proposed. To evaluate the predictive value of delayed risk stratification (DRS) obtained at the time of the first diagnostic control (8-12 months after initial treatment). We reviewed 512 patients with DTC whose risk assessment was initially defined according to the American (ATA) and European Thyroid Association (ETA) guidelines. At the time of the first control, 8-12 months after initial treatment, patients were re-stratified according to their clinical status: DRS. Using DRS, about 50% of ATA/ETA intermediate/high-risk patients moved to DRS low-risk category, while about 10% of ATA/ETA low-risk patients moved to DRS high-risk category. The ability of the DRS to predict the final outcome was superior to that of ATA and ETA. Positive and negative predictive values for both ATA (39.2 and 90.6% respectively) and ETA (38.4 and 91.3% respectively) were significantly lower than that observed with the DRS (72.8 and 96.3% respectively, P<0.05). The observed variance in predicting final outcome was 25.4% for ATA, 19.1% for ETA, and 62.1% for DRS. Delaying the risk stratification of DTC patients at a time when the response to surgery and radioiodine ablation is evident allows to better define individual risk and to better modulate the subsequent follow-up.

  20. Evaluating biomarkers to model cancer risk post cosmic ray exposure

    PubMed Central

    Sridhara, Deepa M.; Asaithamby, Aroumougame; Blattnig, Steve R.; Costes, Sylvain V.; Doetsch, Paul W.; Dynan, William S.; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D.; Peterson, Leif E.; Plante, Ianik; Ponomarev, Artem L.; Saha, Janapriya; Snijders, Antoine M.; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M.

    2017-01-01

    Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing

  1. Evaluating biomarkers to model cancer risk post cosmic ray exposure

    NASA Astrophysics Data System (ADS)

    Sridharan, Deepa M.; Asaithamby, Aroumougame; Blattnig, Steve R.; Costes, Sylvain V.; Doetsch, Paul W.; Dynan, William S.; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D.; Peterson, Leif E.; Plante, Ianik; Ponomarev, Artem L.; Saha, Janapriya; Snijders, Antoine M.; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M.

    2016-06-01

    Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing

  2. SOX9 expression predicts relapse of stage II colon cancer patients.

    PubMed

    Marcker Espersen, Maiken Lise; Linnemann, Dorte; Christensen, Ib Jarle; Alamili, Mahdi; Troelsen, Jesper T; Høgdall, Estrid

    2016-06-01

    The aim of this study was to investigate if the protein expression of sex-determining region y-box 9 (SOX9) in primary tumors could predict relapse of stage II colon cancer patients. One hundred forty-four patients with stage II primary colon cancer were retrospectively enrolled in the study. SOX9 expression was evaluated by immunohistochemistry, and mismatch repair status was assessed by both immunohistochemistry and promoter hypermethylation assay. High SOX9 expression at the invasive front was significantly associated with lower risk of relapse when including the SOX9 expression as a continuous variable (from low to high expression) in univariate (hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.56-0.94; P = .01) and multivariate Cox proportional hazards analyses (HR, 0.75; 95% CI, 0.58-0.96; P = .02), adjusting for mismatch repair deficiency and histopathologic risk factors. Conversely, low SOX9 expression at the invasive front was significantly associated with high risk of relapse, when including SOX9 expression as a dichotomous variable, in univariate (HR, 2.32; 95% CI, 1.14-4.69; P = .02) and multivariate analyses (HR, 2.32; 95% CI, 1.14-4.69; P = .02), adjusting for histopathologic risk factors and mismatch repair deficiency. In conclusion, high levels of SOX9 of primary stage II colon tumors predict low risk of relapse, whereas low levels of SOX9 predict high risk of relapse. SOX9 may have an important value as a biomarker when evaluating risk of relapse for personalized treatment. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Sleep Disturbance, Inflammation and Depression Risk in Cancer Survivors

    PubMed Central

    Irwin, Michael R.; Olmstead, Richard E.; Ganz, Patricia A.; Haque, Reina

    2012-01-01

    Over two-thirds of the 11.4 million cancer survivors in the United States can expect long-term survival, with many others living with cancer as a chronic disease controlled by ongoing therapy. However, behavioral co-morbidities often arise during treatment and persist long-term to complicate survival and reduce quality of life. In this review, the inter-relationships between cancer, depression, and sleep disturbance are described, with a focus on the role of sleep disturbance as a risk factor for depression. Increasing evidence also links alterations in inflammatory biology dynamics to these long-term effects of cancer diagnosis and treatment, and the hypothesis that sleep disturbance drives inflammation, which together contribute to depression, is discussed. Better understanding of the associations between inflammation and behavioral co-morbidities has the potential to refine prediction of risk and development of strategies for the prevention and treatment of sleep disturbance and depression in cancer survivors. PMID:22634367

  4. Development and validation of a risk assessment tool for gastric cancer in a general Japanese population.

    PubMed

    Iida, Masahiro; Ikeda, Fumie; Hata, Jun; Hirakawa, Yoichiro; Ohara, Tomoyuki; Mukai, Naoko; Yoshida, Daigo; Yonemoto, Koji; Esaki, Motohiro; Kitazono, Takanari; Kiyohara, Yutaka; Ninomiya, Toshiharu

    2018-05-01

    There have been very few reports of risk score models for the development of gastric cancer. The aim of this study was to develop and validate a risk assessment tool for discerning future gastric cancer risk in Japanese. A total of 2444 subjects aged 40 years or over were followed up for 14 years from 1988 (derivation cohort), and 3204 subjects of the same age group were followed up for 5 years from 2002 (validation cohort). The weighting (risk score) of each risk factor for predicting future gastric cancer in the risk assessment tool was determined based on the coefficients of a Cox proportional hazards model in the derivation cohort. The goodness of fit of the established risk assessment tool was assessed using the c-statistic and the Hosmer-Lemeshow test in the validation cohort. During the follow-up, gastric cancer developed in 90 subjects in the derivation cohort and 35 subjects in the validation cohort. In the derivation cohort, the risk prediction model for gastric cancer was established using significant risk factors: age, sex, the combination of Helicobacter pylori antibody and pepsinogen status, hemoglobin A1c level, and smoking status. The incidence of gastric cancer increased significantly as the sum of risk scores increased (P trend < 0.001). The risk assessment tool was validated internally and showed good discrimination (c-statistic = 0.76) and calibration (Hosmer-Lemeshow test P = 0.43) in the validation cohort. We developed a risk assessment tool for gastric cancer that provides a useful guide for stratifying an individual's risk of future gastric cancer.

  5. Serum Trimethylamine N-oxide, Carnitine, Choline, and Betaine in Relation to Colorectal Cancer Risk in the Alpha Tocopherol, Beta Carotene Cancer Prevention Study.

    PubMed

    Guertin, Kristin A; Li, Xinmin S; Graubard, Barry I; Albanes, Demetrius; Weinstein, Stephanie J; Goedert, James J; Wang, Zeneng; Hazen, Stanley L; Sinha, Rashmi

    2017-06-01

    Background: Trimethylamine N-oxide (TMAO), a choline-derived metabolite produced by gut microbiota, and its biomarker precursors have not been adequately evaluated in relation to colorectal cancer risk. Methods: We investigated the relationship between serum concentrations of TMAO and its biomarker precursors (choline, carnitine, and betaine) and incident colorectal cancer risk in a nested case-control study of male smokers in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. We measured biomarker concentrations in baseline fasting serum samples from 644 incident colorectal cancer cases and 644 controls using LC/MS-MS. Logistic regression models estimated the ORs and 95% confidence interval (CI) for colorectal cancer by quartile (Q) of serum TMAO, choline, carnitine, and betaine concentrations. Results: Men with higher serum choline at ATBC baseline had approximately 3-fold greater risk of developing colorectal cancer over the ensuing (median ± IQR) 14 ± 10 years (in fully adjusted models, Q4 vs. Q1, OR, 3.22; 95% CI, 2.24-4.61; P trend < 0.0001). The prognostic value of serum choline for prediction of incident colorectal cancer was similarly robust for proximal, distal, and rectal colon cancers (all P < 0.0001). The association between serum TMAO, carnitine, or betaine and colorectal cancer risk was not statistically significant ( P = 0.25, 0.71, and 0.61, respectively). Conclusions: Higher serum choline concentration (but not TMAO, carnitine, or betaine) was associated with increased risk of colorectal cancer. Impact: Serum choline levels showed strong prognostic value for prediction of incident colorectal cancer risk across all anatomical subsites, suggesting a role of altered choline metabolism in colorectal cancer pathogenesis. Cancer Epidemiol Biomarkers Prev; 26(6); 945-52. ©2017 AACR . ©2017 American Association for Cancer Research.

  6. Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature.

    PubMed

    Kluth, Luis A; Black, Peter C; Bochner, Bernard H; Catto, James; Lerner, Seth P; Stenzl, Arnulf; Sylvester, Richard; Vickers, Andrew J; Xylinas, Evanguelos; Shariat, Shahrokh F

    2015-08-01

    This review focuses on risk assessment and prediction tools for bladder cancer (BCa). To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa. A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool. Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non-muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa. Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care. We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve

  7. Intestinal metaplasia in Barrett's oesophagus: An essential factor to predict the risk of dysplasia and cancer development.

    PubMed

    Salemme, Marianna; Villanacci, Vincenzo; Cengia, Gianpaolo; Cestari, Renzo; Missale, Guido; Bassotti, Gabrio

    2016-02-01

    To date, there is still uncertainty on the role of specialized intestinal metaplasia in the carcinogenic process of Barrett's oesophagus (BE); this fact seems of importance for planning adequate surveillance programs. To predict the risk of progression towards dysplasia/cancer based on typical morphological features by evaluating the importance of intestinal metaplasia in BE patients. 647 cases with a histological diagnosis of BE, referred to the Endoscopy Unit of a tertiary centre between 2000 and 2012 were retrospectively identified, and divided into two groups according to the presence/absence of intestinal metaplasia. For each patient, all histological reports performed during a follow-up of 4-8 years were analyzed. Overall, 537 cases (83%) with intestinal metaplasia and 110 cases (17%) without intestinal metaplasia were included. During the follow-up period, none of the patients without intestinal metaplasia developed dysplasia/cancer nor progressed to metaplasia, whereas 72 patients with intestinal metaplasia (13.4%) showed histological progression of the disease. The histological identification of intestinal metaplasia seems to be an essential factor for the progression towards dysplasia and cancer in BE patients. Copyright © 2015 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

  8. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Hemphill, Geralyn M.

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to bemore » an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.« less

  9. Predictive features of breast cancer on Mexican screening mammography patients

    NASA Astrophysics Data System (ADS)

    Rodriguez-Rojas, Juan; Garza-Montemayor, Margarita; Trevino-Alvarado, Victor; Tamez-Pena, José Gerardo

    2013-02-01

    Breast cancer is the most common type of cancer worldwide. In response, breast cancer screening programs are becoming common around the world and public programs now serve millions of women worldwide. These programs are expensive, requiring many specialized radiologists to examine all images. Nevertheless, there is a lack of trained radiologists in many countries as in Mexico, which is a barrier towards decreasing breast cancer mortality, pointing at the need of a triaging system that prioritizes high risk cases for prompt interpretation. Therefore we explored in an image database of Mexican patients whether high risk cases can be distinguished using image features. We collected a set of 200 digital screening mammography cases from a hospital in Mexico, and assigned low or high risk labels according to its BIRADS score. Breast tissue segmentation was performed using an automatic procedure. Image features were obtained considering only the segmented region on each view and comparing the bilateral di erences of the obtained features. Predictive combinations of features were chosen using a genetic algorithms based feature selection procedure. The best model found was able to classify low-risk and high-risk cases with an area under the ROC curve of 0.88 on a 150-fold cross-validation test. The features selected were associated to the differences of signal distribution and tissue shape on bilateral views. The model found can be used to automatically identify high risk cases and trigger the necessary measures to provide prompt treatment.

  10. Dietary flavonoids and cancer risk in the Zutphen Elderly Study.

    PubMed

    Hertog, M G; Feskens, E J; Hollman, P C; Katan, M B; Kromhout, D

    1994-01-01

    Flavonoids are polyphenolic antioxidants naturally present in vegetable foods. Some flavonoids, such as quercetin, inhibit carcinogenesis in rodents, but their effect in humans is unknown. We measured the flavonoids quercetin, kaempferol, myricetin, apigenin, and luteolin in foods and assessed flavonoid intake in 1985 by dietary history in 738 men aged 65-84 years without a history of cancer, who were then followed for five years. Mean flavonoid intake was 25.9 mg/day. The major sources of flavonoid intake were tea at 61% and vegetables and fruits (mainly onions, kale, endive, and apples) at 38%. Between 1985 and 1990, 75 men developed cancer (all sites) and 34 men died from cancer. Flavonoid intake in 1985 was not associated with incidence of all-cause cancer (p for trend = 0.54) or with mortality from all-cause cancer (p for trend = 0.51). Flavonoid intake was also not associated with risk of cancers of the alimentary and respiratory tract (p for trend = 0.92). Adjustment for age, body mass index, smoking, physical activity, and vitamin C, vitamin E, beta-carotene, and dietary fiber intake did not change the relative risks. A high intake of flavonoids from vegetables and fruits only was inversely associated with risk of cancer of the alimentary and respiratory tract (relative risk of highest vs. lowest tertile = 0.51, 95% confidence interval 0.25-1.05); these results suggest the presence of other nonvitamin components with anticarcinogenic potential in these foods. We conclude that intake of flavonoids, mainly from tea, apples, and onions, does not predict a reduced risk of all-cause cancer or of cancer of the alimentary and respiratory tract in elderly men. The effect of flavonoids on risk of cancer at specific sites needs further investigation in prospective cohort studies.

  11. Age at exposure and attained age variations of cancer risk in the Japanese A-bomb and radiotherapy cohorts

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Schneider, Uwe, E-mail: uwe.schneider@uzh.ch; Walsh, Linda

    Purpose: Phenomenological risk models for radiation-induced cancer are frequently applied to estimate the risk of radiation-induced cancers at radiotherapy doses. Such models often include the effect modification, of the main risk to radiation dose response, by age at exposure and attained age. The aim of this paper is to compare the patterns in risk effect modification by age, between models obtained from the Japanese atomic-bomb (A-bomb) survivor data and models for cancer risks previously reported for radiotherapy patients. Patterns in risk effect modification by age from the epidemiological studies of radiotherapy patients were also used to refine and extend themore » risk effect modification by age obtained from the A-bomb survivor data, so that more universal models can be presented here. Methods: Simple log-linear and power functions of age for the risk effect modification applied in models of the A-bomb survivor data are compared to risks from epidemiological studies of second cancers after radiotherapy. These functions of age were also refined and fitted to radiotherapy risks. The resulting age models provide a refined and extended functional dependence of risk with age at exposure and attained age especially beyond 40 and 65 yr, respectively, and provide a better representation than the currently available simple age functions. Results: It was found that the A-bomb models predict risk similarly to the outcomes of testicular cancer survivors. The survivors of Hodgkin’s disease show steeper variations of risk with both age at exposure and attained age. The extended models predict solid cancer risk increase as a function of age at exposure beyond 40 yr and the risk decrease as a function of attained age beyond 65 yr better than the simple models. Conclusions: The standard functions for risk effect modification by age, based on the A-bomb survivor data, predict second cancer risk in radiotherapy patients for ages at exposure prior to 40 yr and attained

  12. A multiparametric magnetic resonance imaging-based risk model to determine the risk of significant prostate cancer prior to biopsy.

    PubMed

    van Leeuwen, Pim J; Hayen, Andrew; Thompson, James E; Moses, Daniel; Shnier, Ron; Böhm, Maret; Abuodha, Magdaline; Haynes, Anne-Maree; Ting, Francis; Barentsz, Jelle; Roobol, Monique; Vass, Justin; Rasiah, Krishan; Delprado, Warick; Stricker, Phillip D

    2017-12-01

    To develop and externally validate a predictive model for detection of significant prostate cancer. Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer. Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over-detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

  13. Infective Endocarditis and Cancer Risk

    PubMed Central

    Sun, Li-Min; Wu, Jung-Nan; Lin, Cheng-Li; Day, Jen-Der; Liang, Ji-An; Liou, Li-Ren; Kao, Chia-Hung

    2016-01-01

    Abstract This study investigated the possible relationship between endocarditis and overall and individual cancer risk among study participants in Taiwan. We used data from the National Health Insurance program of Taiwan to conduct a population-based, observational, and retrospective cohort study. The case group consisted of 14,534 patients who were diagnosed with endocarditis between January 1, 2000 and December 31, 2010. For the control group, 4 patients without endocarditis were frequency matched to each endocarditis patient according to age, sex, and index year. Competing risks regression analysis was conducted to determine the effect of endocarditis on cancer risk. A large difference was noted in Charlson comorbidity index between endocarditis and nonendocarditis patients. In patients with endocarditis, the risk for developing overall cancer was significant and 119% higher than in patients without endocarditis (adjusted subhazard ratio = 2.19, 95% confidence interval = 1.98–2.42). Regarding individual cancers, in addition to head and neck, uterus, female breast and hematological malignancies, the risks of developing colorectal cancer, and some digestive tract cancers were significantly higher. Additional analyses determined that the association of cancer with endocarditis is stronger within the 1st 5 years after endocarditis diagnosis. This population-based cohort study found that patients with endocarditis are at a higher risk for colorectal cancer and other cancers in Taiwan. The risk was even higher within the 1st 5 years after endocarditis diagnosis. It suggested that endocarditis is an early marker of colorectal cancer and other cancers. The underlying mechanisms must still be explored and may account for a shared risk factor of infection in both endocarditis and malignancy. PMID:27015220

  14. An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer

    PubMed Central

    Cosgrove, Casey M; Tritchler, David L; Cohn, David E; Mutch, David G; Rush, Craig M; Lankes, Heather A; Creasman, William T.; Miller, David S; Ramirez, Nilsa C; Geller, Melissa A; Powell, Matthew A; Backes, Floor J; Landrum, Lisa M; Timmers, Cynthia; Suarez, Adrian A; Zaino, Richard J; Pearl, Michael L; DiSilvestro, Paul A; Lele, Shashikant B; Goodfellow, Paul J

    2017-01-01

    Objectives The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. Methods Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. Results Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53–3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10–7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04–4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. Conclusions A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system. PMID:29132872

  15. Risk of treatment-related esophageal cancer among breast cancer survivors.

    PubMed

    Morton, L M; Gilbert, E S; Hall, P; Andersson, M; Joensuu, H; Vaalavirta, L; Dores, G M; Stovall, M; Holowaty, E J; Lynch, C F; Curtis, R E; Smith, S A; Kleinerman, R A; Kaijser, M; Storm, H H; Pukkala, E; Weathers, R E; Linet, M S; Rajaraman, P; Fraumeni, J F; Brown, L M; van Leeuwen, F E; Fossa, S D; Johannesen, T B; Langmark, F; Lamart, S; Travis, L B; Aleman, B M P

    2012-12-01

    Radiotherapy for breast cancer may expose the esophagus to ionizing radiation, but no study has evaluated esophageal cancer risk after breast cancer associated with radiation dose or systemic therapy use. Nested case-control study of esophageal cancer among 289 748 ≥5-year survivors of female breast cancer from five population-based cancer registries (252 cases, 488 individually matched controls), with individualized radiation dosimetry and information abstracted from medical records. The largest contributors to esophageal radiation exposure were supraclavicular and internal mammary chain treatments. Esophageal cancer risk increased with increasing radiation dose to the esophageal tumor location (P(trend )< 0.001), with doses of ≥35 Gy associated with an odds ratio (OR) of 8.3 [95% confidence interval (CI) 2.7-28]. Patients with hormonal therapy ≤5 years preceding esophageal cancer diagnosis had lower risk (OR = 0.4, 95% CI 0.2-0.8). Based on few cases, alkylating agent chemotherapy did not appear to affect risk. Our data were consistent with a multiplicative effect of radiation and other esophageal cancer risk factors (e.g. smoking). Esophageal cancer is a radiation dose-related complication of radiotherapy for breast cancer, but absolute risk is low. At higher esophageal doses, the risk warrants consideration in radiotherapy risk assessment and long-term follow-up.

  16. Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

    PubMed

    Kerlikowske, Karla; Scott, Christopher G; Mahmoudzadeh, Amir P; Ma, Lin; Winham, Stacey; Jensen, Matthew R; Wu, Fang Fang; Malkov, Serghei; Pankratz, V Shane; Cummings, Steven R; Shepherd, John A; Brandt, Kathleen R; Miglioretti, Diana L; Vachon, Celine M

    2018-06-05

    assessed on tomosynthesis, an emerging breast screening method. Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density. National Cancer Institute.

  17. Predicting Brain Metastasis in Breast Cancer Patients: Stage Versus Biology.

    PubMed

    Azim, Hamdy A; Abdel-Malek, Raafat; Kassem, Loay

    2018-04-01

    Brain metastasis (BM) is a life-threatening event in breast cancer patients. Identifying patients at a high risk for BM can help to adopt screening programs and test preventive interventions. We tried to identify the incidence of BM in different stages and subtypes of breast cancer. We reviewed the clinical records of 2193 consecutive breast cancer patients who presented between January 1999 and December 2010. We explored the incidence of BM in relation to standard clinicopathological factors, and determined the cumulative risk of BM according to the disease stage and phenotype. Of the 2193 included women, 160 (7.3%) developed BM at a median follow-up of 5.8 years. Age younger than 60 years (P = .015), larger tumors (P = .004), lymph node (LN) positivity (P < .001), high tumor grade (P = .012), and HER2 positivity (P < .001) were associated with higher incidence of BM in the whole population. In patients who presented with locoregional disease, 3 factors independently predicted BM: large tumors (hazard ratio [HR], 3.60; 95% confidence interval [CI], 1.54-8.38; P = .003), axillary LN metastasis (HR, 4.03; 95% CI, 1.91-8.52; P < .001), and HER2 positivity (HR, 1.89; 95% CI, 1.0-3.41; P = .049). A Brain Relapse Index was formulated using those 3 factors, with 5-year cumulative incidence of BM of 19.2% in those having the 2 or 3 risk factors versus 2.5% in those with no or 1 risk factor (P < .001). In metastatic patients, 3 factors were associated with higher risk of BM: HER2 positivity (P = .007), shorter relapse-free interval (P < .001), and lung metastasis (P < .001). Disease stage and biological subtypes predict the risk for BM and subsequent treatment outcome. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Frailty Models for Familial Risk with Application to Breast Cancer.

    PubMed

    Gorfine, Malka; Hsu, Li; Parmigiani, Giovanni

    2013-12-01

    In evaluating familial risk for disease we have two main statistical tasks: assessing the probability of carrying an inherited genetic mutation conferring higher risk; and predicting the absolute risk of developing diseases over time, for those individuals whose mutation status is known. Despite substantial progress, much remains unknown about the role of genetic and environmental risk factors, about the sources of variation in risk among families that carry high-risk mutations, and about the sources of familial aggregation beyond major Mendelian effects. These sources of heterogeneity contribute substantial variation in risk across families. In this paper we present simple and efficient methods for accounting for this variation in familial risk assessment. Our methods are based on frailty models. We implemented them in the context of generalizing Mendelian models of cancer risk, and compared our approaches to others that do not consider heterogeneity across families. Our extensive simulation study demonstrates that when predicting the risk of developing a disease over time conditional on carrier status, accounting for heterogeneity results in a substantial improvement in the area under the curve of the receiver operating characteristic. On the other hand, the improvement for carriership probability estimation is more limited. We illustrate the utility of the proposed approach through the analysis of BRCA1 and BRCA2 mutation carriers in the Washington Ashkenazi Kin-Cohort Study of Breast Cancer.

  19. Predictive accuracy of combined genetic and environmental risk scores.

    PubMed

    Dudbridge, Frank; Pashayan, Nora; Yang, Jian

    2018-02-01

    The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. © 2017 WILEY PERIODICALS, INC.

  20. Predictive accuracy of combined genetic and environmental risk scores

    PubMed Central

    Pashayan, Nora; Yang, Jian

    2017-01-01

    ABSTRACT The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. PMID:29178508

  1. Developmental windows of breast cancer risk provide opportunities for targeted chemoprevention

    PubMed Central

    Martinson, Holly A.; Lyons, Traci R.; Giles, Erin D.; Borges, Virginia F.; Schedin, Pepper

    2014-01-01

    The magnitude of the breast cancer problem implores researchers to aggressively investigate prevention strategies. However, several barriers currently reduce the feasibility of breast cancer prevention. These barriers include the inability to accurately predict future breast cancer diagnosis at the individual level, the need for improved understanding of when to implement interventions, uncertainty with respect to optimal duration of treatment, and negative side effects associated with currently approved chemoprevention therapies. None-the-less, the unique biology of the mammary gland, with its postnatal development and conditional terminal differentiation, may permit the resolution of many of these barriers. Specifically, lifecycle-specific windows of breast cancer risk have been identified that may be amenable to risk-reducing strategies. Here, we argue for prevention research focused on two of these lifecycle windows of risk: postpartum mammary gland involution and peri-menopause. We provide evidence that these windows are highly amenable to targeted, limited duration treatments. Such approaches could result in the prevention of postpartum and postmenopausal breast cancers, correspondingly. PMID:23664839

  2. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.

    PubMed

    Paik, Soonmyung; Shak, Steven; Tang, Gong; Kim, Chungyeul; Baker, Joffre; Cronin, Maureen; Baehner, Frederick L; Walker, Michael G; Watson, Drew; Park, Taesung; Hiller, William; Fisher, Edwin R; Wickerham, D Lawrence; Bryant, John; Wolmark, Norman

    2004-12-30

    The likelihood of distant recurrence in patients with breast cancer who have no involved lymph nodes and estrogen-receptor-positive tumors is poorly defined by clinical and histopathological measures. We tested whether the results of a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of 21 prospectively selected genes in paraffin-embedded tumor tissue would correlate with the likelihood of distant recurrence in patients with node-negative, tamoxifen-treated breast cancer who were enrolled in the National Surgical Adjuvant Breast and Bowel Project clinical trial B-14. The levels of expression of 16 cancer-related genes and 5 reference genes were used in a prospectively defined algorithm to calculate a recurrence score and to determine a risk group (low, intermediate, or high) for each patient. Adequate RT-PCR profiles were obtained in 668 of 675 tumor blocks. The proportions of patients categorized as having a low, intermediate, or high risk by the RT-PCR assay were 51, 22, and 27 percent, respectively. The Kaplan-Meier estimates of the rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8 percent (95 percent confidence interval, 4.0 to 9.6), 14.3 percent (95 percent confidence interval, 8.3 to 20.3), and 30.5 percent (95 percent confidence interval, 23.6 to 37.4). The rate in the low-risk group was significantly lower than that in the high-risk group (P<0.001). In a multivariate Cox model, the recurrence score provided significant predictive power that was independent of age and tumor size (P<0.001). The recurrence score was also predictive of overall survival (P<0.001) and could be used as a continuous function to predict distant recurrence in individual patients. The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor-positive breast cancer. Copyright 2004 Massachusetts Medical Society.

  3. Adult height is associated with increased risk of ovarian cancer: a Mendelian randomisation study.

    PubMed

    Dixon-Suen, Suzanne C; Nagle, Christina M; Thrift, Aaron P; Pharoah, Paul D P; Ewing, Ailith; Pearce, Celeste Leigh; Zheng, Wei; Chenevix-Trench, Georgia; Fasching, Peter A; Beckmann, Matthias W; Lambrechts, Diether; Vergote, Ignace; Lambrechts, Sandrina; Van Nieuwenhuysen, Els; Rossing, Mary Anne; Doherty, Jennifer A; Wicklund, Kristine G; Chang-Claude, Jenny; Jung, Audrey Y; Moysich, Kirsten B; Odunsi, Kunle; Goodman, Marc T; Wilkens, Lynne R; Thompson, Pamela J; Shvetsov, Yurii B; Dörk, Thilo; Park-Simon, Tjoung-Won; Hillemanns, Peter; Bogdanova, Natalia; Butzow, Ralf; Nevanlinna, Heli; Pelttari, Liisa M; Leminen, Arto; Modugno, Francesmary; Ness, Roberta B; Edwards, Robert P; Kelley, Joseph L; Heitz, Florian; du Bois, Andreas; Harter, Philipp; Schwaab, Ira; Karlan, Beth Y; Lester, Jenny; Orsulic, Sandra; Rimel, Bobbie J; Kjær, Susanne K; Høgdall, Estrid; Jensen, Allan; Goode, Ellen L; Fridley, Brooke L; Cunningham, Julie M; Winham, Stacey J; Giles, Graham G; Bruinsma, Fiona; Milne, Roger L; Southey, Melissa C; Hildebrandt, Michelle A T; Wu, Xifeng; Lu, Karen H; Liang, Dong; Levine, Douglas A; Bisogna, Maria; Schildkraut, Joellen M; Berchuck, Andrew; Cramer, Daniel W; Terry, Kathryn L; Bandera, Elisa V; Olson, Sara H; Salvesen, Helga B; Thomsen, Liv Cecilie Vestrheim; Kopperud, Reidun K; Bjorge, Line; Kiemeney, Lambertus A; Massuger, Leon F A G; Pejovic, Tanja; Bruegl, Amanda; Cook, Linda S; Le, Nhu D; Swenerton, Kenneth D; Brooks-Wilson, Angela; Kelemen, Linda E; Lubiński, Jan; Huzarski, Tomasz; Gronwald, Jacek; Menkiszak, Janusz; Wentzensen, Nicolas; Brinton, Louise; Yang, Hannah; Lissowska, Jolanta; Høgdall, Claus K; Lundvall, Lene; Song, Honglin; Tyrer, Jonathan P; Campbell, Ian; Eccles, Diana; Paul, James; Glasspool, Rosalind; Siddiqui, Nadeem; Whittemore, Alice S; Sieh, Weiva; McGuire, Valerie; Rothstein, Joseph H; Narod, Steven A; Phelan, Catherine; Risch, Harvey A; McLaughlin, John R; Anton-Culver, Hoda; Ziogas, Argyrios; Menon, Usha; Gayther, Simon A; Ramus, Susan J; Gentry-Maharaj, Aleksandra; Wu, Anna H; Pike, Malcolm C; Tseng, Chiu-Chen; Kupryjanczyk, Jolanta; Dansonka-Mieszkowska, Agnieszka; Budzilowska, Agnieszka; Rzepecka, Iwona K; Webb, Penelope M

    2018-04-01

    Observational studies suggest greater height is associated with increased ovarian cancer risk, but cannot exclude bias and/or confounding as explanations for this. Mendelian randomisation (MR) can provide evidence which may be less prone to bias. We pooled data from 39 Ovarian Cancer Association Consortium studies (16,395 cases; 23,003 controls). We applied two-stage predictor-substitution MR, using a weighted genetic risk score combining 609 single-nucleotide polymorphisms. Study-specific odds ratios (OR) and 95% confidence intervals (CI) for the association between genetically predicted height and risk were pooled using random-effects meta-analysis. Greater genetically predicted height was associated with increased ovarian cancer risk overall (pooled-OR (pOR) = 1.06; 95% CI: 1.01-1.11 per 5 cm increase in height), and separately for invasive (pOR = 1.06; 95% CI: 1.01-1.11) and borderline (pOR = 1.15; 95% CI: 1.02-1.29) tumours. Women with a genetic propensity to being taller have increased risk of ovarian cancer. This suggests genes influencing height are involved in pathways promoting ovarian carcinogenesis.

  4. Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones.

    PubMed

    Shieh, Yiwey; Hu, Donglei; Ma, Lin; Huntsman, Scott; Gard, Charlotte C; Leung, Jessica W T; Tice, Jeffrey A; Ziv, Elad; Kerlikowske, Karla; Cummings, Steven R

    2017-11-01

    Models that predict the risk of estrogen receptor (ER)-positive breast cancers may improve our ability to target chemoprevention. We investigated the contributions of sex hormones to the discrimination of the Breast Cancer Surveillance Consortium (BCSC) risk model and a polygenic risk score comprised of 83 single nucleotide polymorphisms. We conducted a nested case-control study of 110 women with ER-positive breast cancers and 214 matched controls within a mammography screening cohort. Participants were postmenopausal and not on hormonal therapy. The associations of estradiol, estrone, testosterone, and sex hormone binding globulin with ER-positive breast cancer were evaluated using conditional logistic regression. We assessed the individual and combined discrimination of estradiol, the BCSC risk score, and polygenic risk score using the area under the receiver operating characteristic curve (AUROC). Of the sex hormones assessed, estradiol (OR 3.64, 95% CI 1.64-8.06 for top vs bottom quartile), and to a lesser degree estrone, was most strongly associated with ER-positive breast cancer in unadjusted analysis. The BCSC risk score (OR 1.32, 95% CI 1.00-1.75 per 1% increase) and polygenic risk score (OR 1.58, 95% CI 1.06-2.36 per standard deviation) were also associated with ER-positive cancers. A model containing the BCSC risk score, polygenic risk score, and estradiol levels showed good discrimination for ER-positive cancers (AUROC 0.72, 95% CI 0.65-0.79), representing a significant improvement over the BCSC risk score (AUROC 0.58, 95% CI 0.50-0.65). Adding estradiol and a polygenic risk score to a clinical risk model improves discrimination for postmenopausal ER-positive breast cancers.

  5. Simple criteria to predict margin involvement after chemoradiotherapy and sphincter-sparing for low rectal cancer.

    PubMed

    Dumont, F; Tilly, C; Dartigues, P; Goéré, D; Honoré, C; Elias, D

    2015-09-01

    Low rectal cancers carry a high risk of circumferential margin involvement (CRM+). The anatomy of the lower part of the rectum and a long course of chemoradiotherapy (CRT) limit the accuracy of imaging to predict the CRM+. Additional criteria are required. Eighty six patients undergoing rectal resection with a sphincter-sparing procedure after CRT for low rectal cancer between 2000 and 2013 were retrospectively reviewed. Risk factors of CRM+ and the cut-off number of risk factors required to accurately predict the CRM+ were analyzed. The CRM+ rate was 9.3% and in the multivariate analysis, the significant risk factors were a tumor size exceeding 3 cm, poor response to CRT and a fixed tumor. The best cut-off to predict CRM+ was the presence of 2 risk factors. Patients with 0-1 and 2-3 risk factors had a CRM+ respectively in 1.3% and 50% of cases and a 3-year recurrence rate of 7% and 35% after a median follow-up of 50 months. Poor response, a residual tumor greater than 3 cm and a fixed tumor are predictive of CRM+. Sphincter sparing is an oncological safety procedure for patients with 0-1 criteria but not for patients with 2-3 criteria. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Risk stratification of prostate cancer: integrating multiparametric MRI, nomograms and biomarkers

    PubMed Central

    Watson, Matthew J; George, Arvin K; Maruf, Mahir; Frye, Thomas P; Muthigi, Akhil; Kongnyuy, Michael; Valayil, Subin G; Pinto, Peter A

    2016-01-01

    Accurate risk stratification of prostate cancer is achieved with a number of existing tools to ensure the identification of at-risk patients, characterization of disease aggressiveness, prediction of cancer burden and extrapolation of treatment outcomes for appropriate management of the disease. Statistical tables and nomograms using classic clinicopathological variables have long been the standard of care. However, the introduction of multiparametric MRI, along with fusion-guided targeted prostate biopsy and novel biomarkers, are being assimilated into clinical practice. The majority of studies to date present the outcomes of each in isolation. The current review offers a critical and objective assessment regarding the integration of multiparametric MRI and fusion-guided prostate biopsy with novel biomarkers and predictive nomograms in contemporary clinical practice. PMID:27400645

  7. Epigenetic Biomarkers of Breast Cancer Risk: Across the Breast Cancer Prevention Continuum.

    PubMed

    Terry, Mary Beth; McDonald, Jasmine A; Wu, Hui Chen; Eng, Sybil; Santella, Regina M

    2016-01-01

    Epigenetic biomarkers, such as DNA methylation, can increase cancer risk through altering gene expression. The Cancer Genome Atlas (TCGA) Network has demonstrated breast cancer-specific DNA methylation signatures. DNA methylation signatures measured at the time of diagnosis may prove important for treatment options and in predicting disease-free and overall survival (tertiary prevention). DNA methylation measurement in cell free DNA may also be useful in improving early detection by measuring tumor DNA released into the blood (secondary prevention). Most evidence evaluating the use of DNA methylation markers in tertiary and secondary prevention efforts for breast cancer comes from studies that are cross-sectional or retrospective with limited corresponding epidemiologic data, raising concerns about temporality. Few prospective studies exist that are large enough to address whether DNA methylation markers add to the prediction of tertiary and secondary outcomes over and beyond standard clinical measures. Determining the role of epigenetic biomarkers in primary prevention can help in identifying modifiable pathways for targeting interventions and reducing disease incidence. The potential is great for DNA methylation markers to improve cancer outcomes across the prevention continuum. Large, prospective epidemiological studies will provide essential evidence of the overall utility of adding these markers to primary prevention efforts, screening, and clinical care.

  8. Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer: a study of gene expression in the DBCG82bc cohort.

    PubMed

    Tramm, Trine; Mohammed, Hayat; Myhre, Simen; Kyndi, Marianne; Alsner, Jan; Børresen-Dale, Anne-Lise; Sørlie, Therese; Frigessi, Arnoldo; Overgaard, Jens

    2014-10-15

    To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into "high LRR risk" and "low LRR risk" groups. PMRT significantly reduced risk of LRR in "high LRR risk" patients, whereas "low LRR risk" patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. ©2014 American Association for Cancer Research.

  9. Household-level disparities in cancer risks from vehicular air pollution in Miami

    NASA Astrophysics Data System (ADS)

    Collins, Timothy W.; Grineski, Sara E.; Chakraborty, Jayajit

    2015-09-01

    Environmental justice (EJ) research has relied on ecological analyses of socio-demographic data from areal units to determine if particular populations are disproportionately burdened by toxic risks. This article advances quantitative EJ research by (a) examining whether statistical associations found for geographic units translate to relationships at the household level; (b) testing alternative explanations for distributional injustices never before investigated; and (c) applying a novel statistical technique appropriate for geographically-clustered data. Our study makes these advances by using generalized estimating equations to examine distributive environmental inequities in the Miami (Florida) metropolitan area, based on primary household-level survey data and census block-level cancer risk estimates of hazardous air pollutant (HAP) exposure from on-road mobile emission sources. In addition to modeling determinants of on-road HAP cancer risk among all survey participants, two subgroup models are estimated to examine whether determinants of risk differ based on disadvantaged minority (Hispanic and non-Hispanic Black) versus non-Hispanic white racial/ethnic status. Results reveal multiple determinants of risk exposure disparities. In the model including all survey participants, renter-occupancy, Hispanic and non-Hispanic black race/ethnicity, the desire to live close to work/urban services or public transportation, and higher risk perception are associated with greater on-road HAP cancer risk; the desire to live in an amenity-rich environment is associated with less risk. Divergent subgroup model results shed light on the previously unexamined role of racial/ethnic status in shaping determinants of risk exposures. While lower socioeconomic status and higher risk perception predict significantly greater on-road HAP cancer risk among disadvantaged minorities, the desire to live near work/urban services or public transport predict significantly greater risk among

  10. Risk of treatment-related esophageal cancer among breast cancer survivors

    PubMed Central

    Morton, L. M.; Gilbert, E. S.; Hall, P.; Andersson, M.; Joensuu, H.; Vaalavirta, L.; Dores, G. M.; Stovall, M.; Holowaty, E. J.; Lynch, C. F.; Curtis, R. E.; Smith, S. A.; Kleinerman, R. A.; Kaijser, M.; Storm, H. H.; Pukkala, E.; Weathers, R. E.; Linet, M. S.; Rajaraman, P.; Fraumeni, J. F.; Brown, L. M.; van Leeuwen, F. E.; Fossa, S. D.; Johannesen, T. B.; Langmark, F.; Lamart, S.; Travis, L. B.; Aleman, B. M. P.

    2012-01-01

    Background Radiotherapy for breast cancer may expose the esophagus to ionizing radiation, but no study has evaluated esophageal cancer risk after breast cancer associated with radiation dose or systemic therapy use. Design Nested case–control study of esophageal cancer among 289 748 ≥5-year survivors of female breast cancer from five population-based cancer registries (252 cases, 488 individually matched controls), with individualized radiation dosimetry and information abstracted from medical records. Results The largest contributors to esophageal radiation exposure were supraclavicular and internal mammary chain treatments. Esophageal cancer risk increased with increasing radiation dose to the esophageal tumor location (Ptrend < 0.001), with doses of ≥35 Gy associated with an odds ratio (OR) of 8.3 [95% confidence interval (CI) 2.7–28]. Patients with hormonal therapy ≤5 years preceding esophageal cancer diagnosis had lower risk (OR = 0.4, 95% CI 0.2–0.8). Based on few cases, alkylating agent chemotherapy did not appear to affect risk. Our data were consistent with a multiplicative effect of radiation and other esophageal cancer risk factors (e.g. smoking). Conclusions Esophageal cancer is a radiation dose-related complication of radiotherapy for breast cancer, but absolute risk is low. At higher esophageal doses, the risk warrants consideration in radiotherapy risk assessment and long-term follow-up. PMID:22745217

  11. Evaluation of BRCA1 and BRCA2 mutations and risk-prediction models in a typical Asian country (Malaysia) with a relatively low incidence of breast cancer.

    PubMed

    Thirthagiri, E; Lee, S Y; Kang, P; Lee, D S; Toh, G T; Selamat, S; Yoon, S-Y; Taib, N A Mohd; Thong, M K; Yip, C H; Teo, S H

    2008-01-01

    The cost of genetic testing and the limited knowledge about the BRCA1 and BRCA2 genes in different ethnic groups has limited its availability in medium- and low-resource countries, including Malaysia. In addition, the applicability of many risk-assessment tools, such as the Manchester Scoring System and BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) which were developed based on mutation rates observed primarily in Caucasian populations using data from multiplex families, and in populations where the rate of breast cancer is higher, has not been widely tested in Asia or in Asians living elsewhere. Here, we report the results of genetic testing for mutations in the BRCA1 or BRCA2 genes in a series of families with breast cancer in the multi-ethnic population (Malay, Chinese and Indian) of Malaysia. A total of 187 breast cancer patients with either early-onset breast cancer (at age cancer were comprehensively tested by full sequencing of both BRCA1 and BRCA2. Two algorithms to predict the presence of mutations, the Manchester Scoring System and BOADICEA, were evaluated. Twenty-seven deleterious mutations were detected (14 in BRCA1 and 13 in BRCA2), only one of which was found in two unrelated individuals (BRCA2 490 delCT). In addition, 47 variants of uncertain clinical significance were identified (16 in BRCA1 and 31 in BRCA2). Notably, many mutations are novel (13 of the 30 BRCA1 mutations and 24 of the 44 BRCA2). We report that while there were an equal proportion of BRCA1 and BRCA2 mutations in the Chinese population in our study, there were significantly more BRCA2 mutations among the Malays. In addition, we show that the predictive power of the BOADICEA risk-prediction model and the Manchester Scoring System was significantly better for BRCA1 than BRCA2, but that the overall sensitivity, specificity and positive-predictive value was lower in this

  12. How accurate is our clinical prediction of "minimal prostate cancer"?

    PubMed

    Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

    2013-07-01

    Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

  13. Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.

    PubMed

    Vuong, Kylie; Armstrong, Bruce K; Weiderpass, Elisabete; Lund, Eiliv; Adami, Hans-Olov; Veierod, Marit B; Barrett, Jennifer H; Davies, John R; Bishop, D Timothy; Whiteman, David C; Olsen, Catherine M; Hopper, John L; Mann, Graham J; Cust, Anne E; McGeechan, Kevin

    2016-08-01

    Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies. To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors. We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women's Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma). We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women's Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness. The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women's Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction

  14. The PER (Preoperative Esophagectomy Risk) Score: A Simple Risk Score to Predict Short-Term and Long-Term Outcome in Patients with Surgically Treated Esophageal Cancer.

    PubMed

    Reeh, Matthias; Metze, Johannes; Uzunoglu, Faik G; Nentwich, Michael; Ghadban, Tarik; Wellner, Ullrich; Bockhorn, Maximilian; Kluge, Stefan; Izbicki, Jakob R; Vashist, Yogesh K

    2016-02-01

    Esophageal resection in patients with esophageal cancer (EC) is still associated with high mortality and morbidity rates. We aimed to develop a simple preoperative risk score for the prediction of short-term and long-term outcomes for patients with EC treated by esophageal resection. In total, 498 patients suffering from esophageal carcinoma, who underwent esophageal resection, were included in this retrospective cohort study. Three preoperative esophagectomy risk (PER) groups were defined based on preoperative functional evaluation of different organ systems by validated tools (revised cardiac risk index, model for end-stage liver disease score, and pulmonary function test). Clinicopathological parameters, morbidity, and mortality as well as disease-free survival (DFS) and overall survival (OS) were correlated to the PER score. The PER score significantly predicted the short-term outcome of patients with EC who underwent esophageal resection. PER 2 and PER 3 patients had at least double the risk of morbidity and mortality compared to PER 1 patients. Furthermore, a higher PER score was associated with shorter DFS (P < 0.001) and OS (P < 0.001). The PER score was identified as an independent predictor of tumor recurrence (hazard ratio [HR] 2.1; P < 0.001) and OS (HR 2.2; P < 0.001). The PER score allows preoperative objective allocation of patients with EC into different risk categories for morbidity, mortality, and long-term outcomes. Thus, multicenter studies are needed for independent validation of the PER score.

  15. Predictive cytogenetic biomarkers for colorectal neoplasia in medium risk patients.

    PubMed

    Ionescu, E M; Nicolaie, T; Ionescu, M A; Becheanu, G; Andrei, F; Diculescu, M; Ciocirlan, M

    2015-01-01

    DNA damage and chromosomal alterations in peripheral lymphocytes parallels DNA mutations in tumor tissues. The aim of our study was to predict the presence of neoplastic colorectal lesions by specific biomarkers in "medium risk" individuals (age 50 to 75, with no personal or family of any colorectal neoplasia). We designed a prospective cohort observational study including patients undergoing diagnostic or opportunistic screening colonoscopy. Specific biomarkers were analyzed for each patient in peripheral lymphocytes - presence of micronuclei (MN), nucleoplasmic bridges (NPB) and the Nuclear Division Index (NDI) by the cytokinesis-blocked micronucleus assay (CBMN). Of 98 patients included, 57 were "medium risk" individuals. MN frequency and NPB presence were not significantly different in patients with neoplastic lesions compared to controls. In "medium risk" individuals, mean NDI was significantly lower for patients with any neoplastic lesions (adenomas and adenocarcinomas, AUROC 0.668, p 00.5), for patients with advanced neoplasia (advanced adenoma and adenocarcinoma, AUROC 0.636 p 0.029) as well as for patients with adenocarcinoma (AUROC 0.650, p 0.048), for each comparison with the rest of the population. For a cut-off of 1.8, in "medium risk" individuals, an NDI inferior to that value may predict any neoplastic lesion with a sensitivity of 97.7%, an advanced neoplastic lesion with a sensitivity of 97% and adenocarcinoma with a sensitivity of 94.4%. NDI score may have a role as a colorectal cancer-screening test in "medium risk" individuals. DNA = deoxyribonucleic acid; CRC = colorectal cancer; EU = European Union; WHO = World Health Organization; FOBT = fecal occult blood test; CBMN = cytokinesis-blocked micronucleus assay; MN = micronuclei; NPB = nucleoplasmic bridges; NDI = Nuclear Division Index; FAP = familial adenomatous polyposis; HNPCC = hereditary non-polypoid colorectal cancer; IBD = inflammatory bowel diseases; ROC = receiver operating

  16. Multi-walled carbon nanotube-induced gene signatures in the mouse lung: potential predictive value for human lung cancer risk and prognosis

    PubMed Central

    Guo, Nancy L; Wan, Ying-Wooi; Denvir, James; Porter, Dale W; Pacurari, Maricica; Wolfarth, Michael G; Castranova, Vincent; Qian, Yong

    2012-01-01

    Concerns over the potential for multi-walled carbon nanotubes (MWCNT) to induce lung carcinogenesis have emerged. This study sought to (1) identify gene expression signatures in the mouse lungs following pharyngeal aspiration of well-dispersed MWCNT and (2) determine if these genes were associated with human lung cancer risk and progression. Genome-wide mRNA expression profiles were analyzed in mouse lungs (n=160) exposed to 0, 10, 20, 40, or 80 µg of MWCNT by pharyngeal aspiration at 1, 7, 28, and 56 days post-exposure. By using pairwise-Statistical Analysis of Microarray (SAM) and linear modeling, 24 genes were selected, which have significant changes in at least two time points, have a more than 1.5 fold change at all doses, and are significant in the linear model for the dose or the interaction of time and dose. Additionally, a 38-gene set was identified as related to cancer from 330 genes differentially expressed at day 56 post-exposure in functional pathway analysis. Using the expression profiles of the cancer-related gene set in 8 mice at day 56 post-exposure to 10 µg of MWCNT, a nearest centroid classification accurately predicts human lung cancer survival with a significant hazard ratio in training set (n=256) and test set (n=186). Furthermore, both gene signatures were associated with human lung cancer risk (n=164) with significant odds ratios. These results may lead to development of a surveillance approach for early detection of lung cancer and prognosis associated with MWCNT in the workplace. PMID:22891886

  17. Lifetime and 5 years risk of breast cancer and attributable risk factor according to Gail model in Iranian women

    PubMed Central

    Mohammadbeigi, Abolfazl; Mohammadsalehi, Narges; Valizadeh, Razieh; Momtaheni, Zeinab; Mokhtari, Mohsen; Ansari, Hossein

    2015-01-01

    Introduction: Breast cancer is the most commonly diagnosed cancers in women worldwide and in Iran. It is expected to account for 29% of all new cancers in women at 2015. This study aimed to assess the 5 years and lifetime risk of breast cancer according to Gail model, and to evaluate the effect of other additional risk factors on the Gail risk. Materials and Methods: A cross sectional study conducted on 296 women aged more than 34-year-old in Qom, Center of Iran. Breast Cancer Risk Assessment Tool calculated the Gail risk for each subject. Data were analyzed by paired t-test, independent t-test, and analysis of variance in bivariate approach to evaluate the effect of each factor on Gail risk. Multiple linear regression models with stepwise method were used to predict the effect of each variable on the Gail risk. Results: The mean age of the participants was 47.8 ± 8.8-year-old and 47% have Fars ethnicity. The 5 years and lifetime risk was 0.37 ± 0.18 and 4.48 ± 0.925%, respectively. It was lower than the average risk in same race and age women (P < 0.001). Being single, positive family history of breast cancer, positive history of biopsy, and radiotherapy as well as using nonhormonal contraceptives were related to higher lifetime risk (P < 0.05). Moreover, a significant direct correlation observed between lifetime risk and body mass index, age of first live birth, and menarche age. While an inversely correlation observed between lifetimes risk of breast cancer and total month of breast feeding duration and age. Conclusion: Based on our results, the 5 years and lifetime risk of breast cancer according to Gail model was lower than the same race and age. Moreover, by comparison with national epidemiologic indicators about morbidity and mortality of breast cancer, it seems that the Gail model overestimate the risk of breast cancer in Iranian women. PMID:26229355

  18. Prostate Cancer Probability Prediction By Machine Learning Technique.

    PubMed

    Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

    2017-11-26

    The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

  19. Risk assessment and remedial policy evaluation using predictive modeling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Linkov, L.; Schell, W.R.

    1996-06-01

    As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forestmore » compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment.« less

  20. Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis.

    PubMed

    Spyropoulos, Evangelos; Kotsiris, Dimitrios; Spyropoulos, Katherine; Panagopoulos, Aggelos; Galanakis, Ioannis; Mavrikos, Stamatios

    2017-02-01

    We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis. A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R 2 ), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ 2 test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P < .05). The biopsy findings were positive for PCa in 167 patients (45.1%) and negative in 164 (44.2%). The PCRD index was positively signed in 89.82% positive PCa cases and negative in 91.46% negative PCa cases (χ 2 test; P < .001; relative risk, 8.98). The sensitivity was 89.8%, specificity was 91.5%, positive predictive value was 91.5%, negative predictive value was 89.8%, positive likelihood ratio was 10.5, negative likelihood ratio was 0.11, and accuracy was 90.6%. Multiple logistic regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of

  1. Factors Influencing Cancer Risk Perception in High Risk Populations: A Systematic Review

    PubMed Central

    2011-01-01

    Background Patients at higher than average risk of heritable cancer may process risk information differently than the general population. However, little is known about clinical, demographic, or psychosocial predictors that may impact risk perception in these groups. The objective of this study was to characterize factors associated with perceived risk of developing cancer in groups at high risk for cancer based on genetics or family history. Methods We searched Ovid MEDLINE, Ovid Embase, Ovid PsycInfo, and Scopus from inception through April 2009 for English-language, original investigations in humans using core concepts of "risk" and "cancer." We abstracted key information and then further restricted articles dealing with perceived risk of developing cancer due to inherited risk. Results Of 1028 titles identified, 53 articles met our criteria. Most (92%) used an observational design and focused on women (70%) with a family history of or contemplating genetic testing for breast cancer. Of the 53 studies, 36 focused on patients who had not had genetic testing for cancer risk, 17 included studies of patients who had undergone genetic testing for cancer risk. Family history of cancer, previous prophylactic tests and treatments, and younger age were associated with cancer risk perception. In addition, beliefs about the preventability and severity of cancer, personality factors such as "monitoring" personality, the ability to process numerical information, as well as distress/worry also were associated with cancer risk perception. Few studies addressed non-breast cancer or risk perception in specific demographic groups (e.g. elderly or minority groups) and few employed theory-driven analytic strategies to decipher interrelationships of factors. Conclusions Several factors influence cancer risk perception in patients at elevated risk for cancer. The science of characterizing and improving risk perception in cancer for high risk groups, although evolving, is still

  2. Comparison of risk of radiogenic second cancer following photon and proton craniospinal irradiation for a pediatric medulloblastoma patient

    NASA Astrophysics Data System (ADS)

    Zhang, Rui; Howell, Rebecca M.; Giebeler, Annelise; Taddei, Phillip J.; Mahajan, Anita; Newhauser, Wayne D.

    2013-02-01

    Pediatric patients who received radiation therapy are at risk of developing side effects such as radiogenic second cancer. We compared proton and photon therapies in terms of the predicted risk of second cancers for a 4 year old medulloblastoma patient receiving craniospinal irradiation (CSI). Two CSI treatment plans with 23.4 Gy or Gy (RBE) prescribed dose were computed: a three-field 6 MV photon therapy plan and a four-field proton therapy plan. The primary doses for both plans were determined using a commercial treatment planning system. Stray radiation doses for proton therapy were determined from Monte Carlo simulations, and stray radiation doses for photon therapy were determined from measured data. Dose-risk models based on the Biological Effects of Ionization Radiation VII report were used to estimate the risk of second cancer in eight tissues/organs. Baseline predictions of the relative risk for each organ were always less for proton CSI than for photon CSI at all attained ages. The total lifetime attributable risk of the incidence of second cancer considered after proton CSI was much lower than that after photon CSI, and the ratio of lifetime risk was 0.18. Uncertainty analysis revealed that the qualitative findings of this study were insensitive to any plausible changes of dose-risk models and mean radiation weighting factor for neutrons. Proton therapy confers lower predicted risk of second cancer than photon therapy for the pediatric medulloblastoma patient.

  3. Age and Cancer Risk

    PubMed Central

    White, Mary C.; Holman, Dawn M.; Boehm, Jennifer E.; Peipins, Lucy A.; Grossman, Melissa; Henley, S. Jane

    2015-01-01

    This article challenges the idea that cancer cannot be prevented among older adults by examining different aspects of the relationship between age and cancer. Although the sequential patterns of aging cannot be changed, several age-related factors that contribute to disease risk can be. For most adults, age is coincidentally associated with preventable chronic conditions, avoidable exposures, and modifiable risk behaviors that are causally associated with cancer. Midlife is a period of life when the prevalence of multiple cancer risk factors is high and incidence rates begin to increase for many types of cancer. However, current evidence suggests that for most adults, cancer does not have to be an inevitable consequence of growing older. Interventions that support healthy environments, help people manage chronic conditions, and promote healthy behaviors may help people make a healthier transition from midlife to older age and reduce the likelihood of developing cancer. Because the number of adults reaching older ages is increasing rapidly, the number of new cancer cases will also increase if current incidence rates remain unchanged. Thus, the need to translate the available research into practice to promote cancer prevention, especially for adults at midlife, has never been greater. PMID:24512933

  4. Quality of life in pediatric cancer survivors: contributions of parental distress and psychosocial family risk.

    PubMed

    Racine, N M; Khu, M; Reynolds, K; Guilcher, G M T; Schulte, F S M

    2018-02-01

    Pediatric survivors of childhood cancer are at increased risk of poor quality of life and social-emotional outcomes following treatment. The relationship between parent psychological distress and child adjustment in pediatric cancer survivors has been well established. However, limited research has examined the factors that may buffer this association. The current study examined the associations between psychosocial family risk factors, parental psychological distress, and health-related quality of life (hrql) in pediatric cancer survivors. Fifty-two pediatric cancer survivors (34 males, 18 females, mean age = 11.92) and their parents were recruited from a long-term cancer survivor clinic. Children and their parents who consented to participate completed the Pediatric Quality of Life Inventory 4.0. Parents completed a demographic information form, the Psychosocial Assessment Tool (pat 2.0) and the Brief Symptom Inventory (bsi). The Intensity of Treatment Rating (itr-3) was evaluated by the research team. Multiple regression analyses revealed that parental psychological distress negatively predicted parent-reported hrql, while treatment intensity, gender, and psychosocial risk negatively predicted parent and child-reported hrql. Psychosocial risk moderated the association between parent psychological distress and parent-reported child hrql ( p = 0.03), whereby parents with high psychological distress but low levels of psychosocial risk reported their children to have higher hrql. Low levels of family psychosocial risk buffer the impact of parent psychological distress on child hrql in pediatric cancer survivors. The findings highlight the importance of identifying parents and families with at-risk psychological distress and psychosocial risk in order to provide targeted support interventions to mitigate the impact on hrql.

  5. Combination antiretroviral therapy and cancer risk.

    PubMed

    Borges, Álvaro H

    2017-01-01

    To review the newest research about the effects of combination antiretroviral therapy (cART) on cancer risk. HIV+ persons are at increased risk of cancer. As this risk is higher for malignancies driven by viral and bacterial coinfections, classifying malignancies into infection-related and infection-unrelated has been an emerging trend. Cohorts have detected major reductions in the incidence of Kaposi sarcoma and non-Hodgkin lymphoma (NHL) following cART initiation among immunosuppressed HIV+ persons. However, recent randomized data indicate that cART reduces risk of Kaposi sarcoma and NHL also during early HIV infection before overt immunosuppression occurs. Long-term effects of cART exposure on cancer risk are not well defined; according to basic and epidemiological research, there might be specific associations of each cART class with distinct patterns of cancer risk. The relationship between cART exposure and cancer risk is complex and nuanced. It is an intriguing fact that, whether initiated during severe immunosuppression or not, cART reduces risk of Kaposi sarcoma and NHL. Further research should identify mediators of the benefit of immediate cART initiation in reducing cancer risk, understand the relationship between long-term cART exposure and cancer incidence and assess whether adjuvant anti-inflammatory therapies can reduce cancer risk during treated HIV infection.

  6. Microarray-based cancer prediction using soft computing approach.

    PubMed

    Wang, Xiaosheng; Gotoh, Osamu

    2009-05-26

    One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

  7. Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers.

    PubMed

    Klimov, Sergey; Rida, Padmashree Cg; Aleskandarany, Mohammed A; Green, Andrew R; Ellis, Ian O; Janssen, Emiel Am; Rakha, Emad A; Aneja, Ritu

    2017-09-05

    Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most common underlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis. Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiple variables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed at identifying a biomarker signature to predict particular sites of DM in TNBC. A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, to develop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasis to each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Cox univariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariable analyses. Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher risk of developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predicting site-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status. Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specific sites of metastasis, and potentially unravel biomarkers previously unknown in site tropism.

  8. Perceived Versus Objective Breast Cancer, Breast Cancer Risk in Diverse Women

    PubMed Central

    Fehniger, Julia; Livaudais-Toman, Jennifer; Karliner, Leah; Kerlikowske, Karla; Tice, Jeffrey A.; Quinn, Jessica; Ozanne, Elissa

    2014-01-01

    Abstract Background: Prior research suggests that women do not accurately estimate their risk for breast cancer. Estimating and informing women of their risk is essential for tailoring appropriate screening and risk reduction strategies. Methods: Data were collected for BreastCARE, a randomized controlled trial designed to evaluate a PC-tablet based intervention providing multiethnic women and their primary care physicians with tailored information about breast cancer risk. We included women ages 40–74 visiting general internal medicine primary care clinics at one academic practice and one safety net practice who spoke English, Spanish, or Cantonese, and had no personal history of breast cancer. We collected baseline information regarding risk perception and concern. Women were categorized as high risk (vs. average risk) if their family history met criteria for referral to genetic counseling or if they were in the top 5% of risk for their age based on the Gail or Breast Cancer Surveillance Consortium Model (BCSC) breast cancer risk model. Results: Of 1,261 participants, 25% (N=314) were classified as high risk. More average risk than high risk women had correct risk perception (72% vs. 18%); 25% of both average and high risk women reported being very concerned about breast cancer. Average risk women with correct risk perception were less likely to be concerned about breast cancer (odds ratio [OR]=0.3; 95% confidence interval [CI]=0.2–0.4) while high risk women with correct risk perception were more likely to be concerned about breast cancer (OR=5.1; 95%CI=2.7–9.6). Conclusions: Many women did not accurately perceive their risk for breast cancer. Women with accurate risk perception had an appropriate level of concern about breast cancer. Improved methods of assessing and informing women of their breast cancer risk could motivate high risk women to apply appropriate prevention strategies and allay unnecessary concern among average risk women. PMID:24372085

  9. Menopausal hormone therapy and cancer risk: An overestimated risk?

    PubMed

    Simin, Johanna; Tamimi, Rulla; Lagergren, Jesper; Adami, Hans-Olov; Brusselaers, Nele

    2017-10-01

    We aimed to assess the overall cancer risk among contemporary menopausal hormone therapy (MHT) users in Sweden and the risk for different cancer types. A nationwide Swedish population-based cohort study including all 290,186 women aged ≥ 40 years having used systemic MHT during the study period (July 2005 and December 2012), compared with the Swedish female background population. MHT ever-use (all MHT, oestrogen-only MHT [E-MHT] and oestrogen plus progestin MHT [EP-MHT]) was based on the nationwide Prescribed Drug Registry. Cancer diagnoses were grouped into 16 different anatomical locations, for which standardised incidence ratios (SIRs) and 95% confidence intervals (CIs) were calculated. The SIR of any cancer was 1.09 (95% CI: 1.07-1.11) following ever MHT, 1.04 (95% CI: 1.01-1.06) for E-MHT and 1.14 (95% CI: 1.12-1.17) for EP-MHT. The highest SIR was found for EP-MHT among users aged ≥70 years (SIR = 1.33, 95% CI: 1.26-1.40). The risk for invasive breast, endometrial or ovarian cancer combined was increased for any MHT (SIR = 1.31, 95% CI: 1.28-1.34). The risk of invasive breast cancer was increased following MHT and increased with age for EP-MHT users. The risk of gastrointestinal cancers combined was decreased (SIR = 0.90, 95% CI: 0.86-0.94), particularly the oesophagus (SIR = 0.81, 95% CI: 0.64-1.00), liver (SIR = 0.81, 95% CI: 0.65-0.99) and colon (SIR = 0.90, 95% CI: 0.84-0.95). MHT, notably EP-MHT, was associated with a limited increase in overall cancer risk. The increased risk of female reproductive organ cancers was almost balanced by a decreased risk of gastrointestinal cancers. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Levels of beta-microseminoprotein in blood and risk of prostate cancer in multiple populations.

    PubMed

    Haiman, Christopher A; Stram, Daniel O; Vickers, Andrew J; Wilkens, Lynne R; Braun, Katharina; Valtonen-André, Camilla; Peltola, Mari; Pettersson, Kim; Waters, Kevin M; Marchand, Loic Le; Kolonel, Laurence N; Henderson, Brian E; Lilja, Hans

    2013-02-06

    A common genetic variant (rs10993994) in the 5' region of the gene encoding β-microseminoprotein (MSP) is associated with circulating levels of MSP and prostate cancer risk. Whether MSP levels are predictive of prostate cancer risk has not been evaluated. We investigated the prospective relationship between circulating plasma levels of MSP and prostate cancer risk in a nested case-control study of 1503 case subjects and 1503 control subjects among black, Latino, Japanese, Native Hawaiian, and white men from the Multiethnic Cohort study. We also examined the ability of MSP to serve as a biomarker for discriminating prostate cancer case subjects from control subjects. All statistical tests are two-sided. In all racial and ethnic groups, men with lower MSP levels were at greater risk of developing prostate cancer (odds ratio = 1.02 per one unit decrease in MSP, P < .001 in the prostate-specific antigen [PSA]-adjusted analysis). Compared with men in the highest decile of MSP, the multivariable PSA-adjusted odds ratio was 3.64 (95% confidence interval = 2.41 to 5.49) for men in the lowest decile. The positive association with lower MSP levels was observed consistently across racial and ethnic populations, by disease stage and Gleason score, for men with both high and low levels of PSA and across all genotype classes of rs10993994. However, we did not detect strong evidence of MSP levels in improving prostate cancer prediction beyond that of PSA. Regardless of race and ethnicity or rs10993994 genotype, men with low blood levels of MSP have increased risk of prostate cancer.

  11. Evaluating biomarkers to model cancer risk post cosmic ray exposure.

    PubMed

    Sridharan, Deepa M; Asaithamby, Aroumougame; Blattnig, Steve R; Costes, Sylvain V; Doetsch, Paul W; Dynan, William S; Hahnfeldt, Philip; Hlatky, Lynn; Kidane, Yared; Kronenberg, Amy; Naidu, Mamta D; Peterson, Leif E; Plante, Ianik; Ponomarev, Artem L; Saha, Janapriya; Snijders, Antoine M; Srinivasan, Kalayarasan; Tang, Jonathan; Werner, Erica; Pluth, Janice M

    2016-06-01

    Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing

  12. Prediction of mesothelioma and lung cancer in a cohort of asbestos exposed workers.

    PubMed

    Gasparrini, Antonio; Pizzo, Anna Maria; Gorini, Giuseppe; Seniori Costantini, Adele; Silvestri, Stefano; Ciapini, Cesare; Innocenti, Andrea; Berry, Geoffrey

    2008-01-01

    Several papers have reported state-wide projections of mesothelioma deaths, but few have computed these predictions in selected exposed groups. To predict the future deaths attributable to asbestos in a cohort of railway rolling stock workers. The future mortality of the 1,146 living workers has been computed in term of individual probability of dying for three different risks: baseline mortality, lung cancer excess, mesothelioma mortality. Lung cancer mortality attributable to asbestos was calculated assuming the excess risk as stable or with a decrease after a period of time since first exposure. Mesothelioma mortality was based on cumulative exposure and time since first exposure, with the inclusion of a term for clearance of asbestos fibres from the lung. The most likely range of the number of deaths attributable to asbestos in the period 2005-2050 was 15-30 for excess of lung cancer, and 23-35 for mesothelioma. This study provides predictions of asbestos-related mortality even in a selected cohort of exposed subjects, using previous knowledge about exposure-response relationship. The inclusion of individual information in the projection model helps reduce misclassification and improves the results. The method could be extended in other selected cohorts.

  13. Computed tomography screening for lung cancer: results of ten years of annual screening and validation of cosmos prediction model.

    PubMed

    Veronesi, G; Maisonneuve, P; Rampinelli, C; Bertolotti, R; Petrella, F; Spaggiari, L; Bellomi, M

    2013-12-01

    It is unclear how long low-dose computed tomographic (LDCT) screening should continue in populations at high risk of lung cancer. We assessed outcomes and the predictive ability of the COSMOS prediction model in volunteers screened for 10 years. Smokers and former smokers (>20 pack-years), >50 years, were enrolled over one year (2000-2001), receiving annual LDCT for 10 years. The frequency of screening-detected lung cancers was compared with COSMOS and Bach risk model estimates. Among 1035 recruited volunteers (71% men, mean age 58 years) compliance was 65% at study end. Seventy-one (6.95%) lung cancers were diagnosed, 12 at baseline. Disease stage was: IA in 48 (66.6%); IB in 6; IIA in 5; IIB in 2; IIIA in 5; IIIB in 1; IV in 5; and limited small cell cancer in 3. Five- and ten-year survival were 64% and 57%, respectively, 84% and 65% for stage I. Ten (12.1%) received surgery for a benign lesion. The number of lung cancers detected during the first two screening rounds was close to that predicted by the COSMOS model, while the Bach model accurately predicted frequency from the third year on. Neither cancer frequency nor proportion at stage I decreased over 10 years, indicating that screening should not be discontinued. Most cancers were early stage, and overall survival was high. Only a limited number of invasive procedures for benign disease were performed. The Bach model - designed to predict symptomatic cancers - accurately predicted cancer frequency from the third year, suggesting that overdiagnosis is a minor problem in lung cancer screening. The COSMOS model - designed to estimate screening-detected lung cancers - accurately predicted cancer frequency at baseline and second screening round. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  14. Understanding your colon cancer risk

    MedlinePlus

    Colon cancer - prevention; Colon cancer - screening ... We do not know what causes colon cancer, but we do know some of the things that may increase the risk of getting it, such as: Age. Your risk increases after ...

  15. Cognitive and emotional factors predicting decisional conflict among high-risk breast cancer survivors who receive uninformative BRCA1/2 results.

    PubMed

    Rini, Christine; O'Neill, Suzanne C; Valdimarsdottir, Heiddis; Goldsmith, Rachel E; Jandorf, Lina; Brown, Karen; DeMarco, Tiffani A; Peshkin, Beth N; Schwartz, Marc D

    2009-09-01

    To investigate high-risk breast cancer survivors' risk reduction decision making and decisional conflict after an uninformative BRCA1/2 test. Prospective, longitudinal study of 182 probands undergoing BRCA1/2 testing, with assessments 1-, 6-, and 12-months postdisclosure. Primary predictors were health beliefs and emotional responses to testing assessed 1-month postdisclosure. Main outcomes included women's perception of whether they had made a final risk management decision (decision status) and decisional conflict related to this issue. There were four patterns of decision making, depending on how long it took women to make a final decision and the stability of their decision status across assessments. Late decision makers and nondecision makers reported the highest decisional conflict; however, substantial numbers of women--even early and intermediate decision makers--reported elevated decisional conflict. Analyses predicting decisional conflict 1- and 12-months postdisclosure found that, after accounting for control variables and decision status, health beliefs and emotional factors predicted decisional conflict at different timepoints, with health beliefs more important 1 month after test disclosure and emotional factors more important 1 year later. Many of these women may benefit from decision making assistance. Copyright 2009 APA, all rights reserved.

  16. Causes of Mortality After Dose-Escalated Radiation Therapy and Androgen Deprivation for High-Risk Prostate Cancer

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Tendulkar, Rahul D., E-mail: tendulr@ccf.org; Hunter, Grant K.; Reddy, Chandana A.

    Purpose: Men with high-risk prostate cancer have other competing causes of mortality; however, current risk stratification schema do not account for comorbidities. We aim to identify the causes of death and factors predictive for mortality in this population. Methods and Materials: A total of 660 patients with high-risk prostate cancer were treated with definitive high-dose external beam radiation therapy (≥74 Gy) and androgen deprivation (AD) between 1996 and 2009 at a single institution. Cox proportional hazards regression analysis was conducted to determine factors predictive of survival. Results: The median radiation dose was 78 Gy, median duration of AD was 6more » months, and median follow-up was 74 months. The 10-year overall survival (OS) was 60.6%. Prostate cancer was the leading single cause of death, with 10-year mortality of 14.1% (95% CI 10.7-17.6), compared with other cancers (8.4%, 95% CI 5.7-11.1), cardiovascular disease (7.3%, 95% CI 4.7-9.9), and all other causes (10.4%, 95% CI 7.2-13.6). On multivariate analysis, older age (HR 1.55, P=.002) and Charlson comorbidity index score (CS) ≥1 (HR 2.20, P<.0001) were significant factors predictive of OS, whereas Gleason score, T stage, prostate-specific antigen, duration of AD, radiation dose, smoking history, and body mass index were not. Men younger than 70 years of age with CS = 0 were more likely to die of prostate cancer than any other cause, whereas older men or those with CS ≥1 more commonly suffered non-prostate cancer death. The cumulative incidences of prostate cancer-specific mortality were similar regardless of age or comorbidities (P=.60). Conclusions: Men with high-risk prostate cancer are more likely to die of causes other than prostate cancer, except for the subgroup of men younger than 70 years of age without comorbidities. Only older age and presence of comorbidities significantly predicted for OS, whereas prostate cancer- and treatment-related factors did not.« less

  17. Necrosis predicts benefit from hypoxia-modifying therapy in patients with high risk bladder cancer enrolled in a phase III randomised trial☆

    PubMed Central

    Eustace, Amanda; Irlam, Joely J.; Taylor, Janet; Denley, Helen; Agrawal, Shailesh; Choudhury, Ananya; Ryder, David; Ord, Jonathan J.; Harris, Adrian L.; Rojas, Ana M.; Hoskin, Peter J.; West, Catharine M.L.

    2013-01-01

    Background and purpose Addition of carbogen and nicotinamide (hypoxia-modifying agents) to radiotherapy improves the survival of patients with high risk bladder cancer. The study investigated whether histopathological tumour features and putative hypoxia markers predicted benefit from hypoxia modification. Materials and methods Samples were available from 231 patients with high grade and invasive bladder carcinoma from the BCON phase III trial of radiotherapy (RT) alone or with carbogen and nicotinamide (RT + CON). Histopathological tumour features examined were: necrosis, growth pattern, growing margin, and tumour/stroma ratio. Hypoxia markers carbonic anhydrase-IX and glucose transporter-1 were examined using tissue microarrays. Results Necrosis was the only independent prognostic indicator (P = 0.04). Necrosis also predicted benefit from hypoxia modification. Five-year overall survival was 48% (RT) versus 39% (RT + CON) (P = 0.32) in patients without necrosis and 34% (RT) versus 56% (RT + CON) (P = 0.004) in patients with necrosis. There was a significant treatment by necrosis strata interaction (P = 0.001 adjusted). Necrosis was an independent predictor of benefit from RT + CON versus RT (hazard ratio [HR]: 0.43, 95% CI 0.25–0.73, P = 0.002). This trend was not observed when there was no necrosis (HR: 1.64, 95% CI 0.95–2.85, P = 0.08). Conclusions Necrosis predicts benefit from hypoxia modification in patients with high risk bladder cancer and should be used to select patients; it is simple to identify and easy to incorporate into routine histopathological examination. PMID:23773411

  18. Northeast Regional Cancer Institute's Cancer Surveillance and Risk Factor Program

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lesko, Samuel M.

    2007-07-31

    OBJECTIVES The Northeast Regional Cancer Institute is conducting a program of ongoing epidemiologic research to address cancer disparities in northeast Pennsylvania. Of particular concern are disparities in the incidence of, stage at diagnosis, and mortality from colorectal cancer. In northeast Pennsylvania, age-adjusted incidence and mortality rates for colorectal cancer are higher, and a significantly smaller proportion of new colorectal cancer cases are diagnosed with local stage disease than is observed in comparable national data. Further, estimates of the prevalence of colorectal cancer screening in northeast Pennsylvania are lower than the US average. The Northeast Regional Cancer Institute’s research program supportsmore » surveillance of common cancers, investigations of cancer risk factors and screening behaviors, and the development of resources to further cancer research in this community. This project has the following specific objectives: I. To conduct cancer surveillance in northeast Pennsylvania. a. To monitor incidence and mortality for all common cancers, and colorectal cancer, in particular, and b. To document changes in the stage at diagnosis of colorectal cancer in this high-risk, underserved community. II. To conduct a population-based study of cancer risk factors and screening behavior in a six county region of northeast Pennsylvania. a. To monitor and document changes in colorectal cancer screening rates, and b. To document the prevalence of cancer risk factors (especially factors that increase the risk of colorectal cancer) and to identify those risk factors that are unusually common in this community. APPROACH Cancer surveillance was conducted using data from the Northeast Regional Cancer Institute’s population-based Regional Cancer Registry, the Pennsylvania Cancer Registry, and NCI’s SEER program. For common cancers, incidence and mortality were examined by county within the region and compared to data for similar populations in

  19. [Risk Prediction Using Routine Data: Development and Validation of Multivariable Models Predicting 30- and 90-day Mortality after Surgical Treatment of Colorectal Cancer].

    PubMed

    Crispin, Alexander; Strahwald, Brigitte; Cheney, Catherine; Mansmann, Ulrich

    2018-06-04

    Quality control, benchmarking, and pay for performance (P4P) require valid indicators and statistical models allowing adjustment for differences in risk profiles of the patient populations of the respective institutions. Using hospital remuneration data for measuring quality and modelling patient risks has been criticized by clinicians. Here we explore the potential of prediction models for 30- and 90-day mortality after colorectal cancer surgery based on routine data. Full census of a major statutory health insurer. Surgical departments throughout the Federal Republic of Germany. 4283 and 4124 insurants with major surgery for treatment of colorectal cancer during 2013 and 2014, respectively. Age, sex, primary and secondary diagnoses as well as tumor locations as recorded in the hospital remuneration data according to §301 SGB V. 30- and 90-day mortality. Elixhauser comorbidities, Charlson conditions, and Charlson scores were generated from the ICD-10 diagnoses. Multivariable prediction models were developed using a penalized logistic regression approach (logistic ridge regression) in a derivation set (patients treated in 2013). Calibration and discrimination of the models were assessed in an internal validation sample (patients treated in 2014) using calibration curves, Brier scores, receiver operating characteristic curves (ROC curves) and the areas under the ROC curves (AUC). 30- and 90-day mortality rates in the learning-sample were 5.7 and 8.4%, respectively. The corresponding values in the validation sample were 5.9% and once more 8.4%. Models based on Elixhauser comorbidities exhibited the highest discriminatory power with AUC values of 0.804 (95% CI: 0.776 -0.832) and 0.805 (95% CI: 0.782-0.828) for 30- and 90-day mortality. The Brier scores for these models were 0.050 (95% CI: 0.044-0.056) and 0.067 (95% CI: 0.060-0.074) and similar to the models based on Charlson conditions. Regardless of the model, low predicted probabilities were well calibrated, while

  20. Background risk of breast cancer and the association between physical activity and mammographic density.

    PubMed

    Trinh, Thang; Eriksson, Mikael; Darabi, Hatef; Bonn, Stephanie E; Brand, Judith S; Cuzick, Jack; Czene, Kamila; Sjölander, Arvid; Bälter, Katarina; Hall, Per

    2015-04-02

    High physical activity has been shown to decrease the risk of breast cancer, potentially by a mechanism that also reduces mammographic density. We tested the hypothesis that the risk of developing breast cancer in the next 10 years according to the Tyrer-Cuzick prediction model influences the association between physical activity and mammographic density. We conducted a population-based cross-sectional study of 38,913 Swedish women aged 40-74 years. Physical activity was assessed using the validated web-questionnaire Active-Q and mammographic density was measured by the fully automated volumetric Volpara method. The 10-year risk of breast cancer was estimated using the Tyrer-Cuzick (TC) prediction model. Linear regression analyses were performed to assess the association between physical activity and volumetric mammographic density and the potential interaction with the TC breast cancer risk. Overall, high physical activity was associated with lower absolute dense volume. As compared to women with the lowest total activity level (<40 metabolic equivalent hours [MET-h] per day), women with the highest total activity level (≥50 MET-h/day) had an estimated 3.4 cm(3) (95% confidence interval, 2.3-4.7) lower absolute dense volume. The inverse association was seen for any type of physical activity among women with <3.0% TC 10-year risk, but only for total and vigorous activities among women with 3.0-4.9% TC risk, and only for vigorous activity among women with ≥5.0% TC risk. The association between total activity and absolute dense volume was modified by the TC breast cancer risk (P interaction = 0.05). As anticipated, high physical activity was also associated with lower non-dense volume. No consistent association was found between physical activity and percent dense volume. Our results suggest that physical activity may decrease breast cancer risk through reducing mammographic density, and that the physical activity needed to reduce mammographic density may depend

  1. Oestrogen exposure and breast cancer risk

    PubMed Central

    Travis, Ruth C; Key, Timothy J

    2003-01-01

    Epidemiological and experimental evidence implicates oestrogens in the aetiology of breast cancer. Most established risk factors for breast cancer in humans probably act through hormone-related pathways, and increased concentrations of circulating oestrogens have been found to be strongly associated with increased risk for breast cancer in postmenopausal women. This article explores the evidence for the hypothesis that oestrogen exposure is a major determinant of risk for breast cancer. We review recent data on oestrogens and breast cancer risk, consider oestrogen-related risk factors and examine possible mechanisms that might account for the effects of oestrogen. Finally, we discuss how these advances might influence strategies for reducing the incidence of breast cancer. PMID:12927032

  2. University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk etiologic models.

    PubMed

    Beck, J D; Weintraub, J A; Disney, J A; Graves, R C; Stamm, J W; Kaste, L M; Bohannan, H M

    1992-12-01

    The purpose of this analysis is to compare three different statistical models for predicting children likely to be at risk of developing dental caries over a 3-yr period. Data are based on 4117 children who participated in the University of North Carolina Caries Risk Assessment Study, a longitudinal study conducted in the Aiken, South Carolina, and Portland, Maine areas. The three models differed with respect to either the types of variables included or the definition of disease outcome. The two "Prediction" models included both risk factor variables thought to cause dental caries and indicator variables that are associated with dental caries, but are not thought to be causal for the disease. The "Etiologic" model included only etiologic factors as variables. A dichotomous outcome measure--none or any 3-yr increment, was used in the "Any Risk Etiologic model" and the "Any Risk Prediction Model". Another outcome, based on a gradient measure of disease, was used in the "High Risk Prediction Model". The variables that are significant in these models vary across grades and sites, but are more consistent among the Etiologic model than the Predictor models. However, among the three sets of models, the Any Risk Prediction Models have the highest sensitivity and positive predictive values, whereas the High Risk Prediction Models have the highest specificity and negative predictive values. Considerations in determining model preference are discussed.

  3. Prediction of the 10-year probability of gastric cancer occurrence in the Japanese population: the JPHC study cohort II.

    PubMed

    Charvat, Hadrien; Sasazuki, Shizuka; Inoue, Manami; Iwasaki, Motoki; Sawada, Norie; Shimazu, Taichi; Yamaji, Taiki; Tsugane, Shoichiro

    2016-01-15

    Gastric cancer is a particularly important issue in Japan, where incidence rates are among the highest observed. In this work, we provide a risk prediction model allowing the estimation of the 10-year cumulative probability of gastric cancer occurrence. The study population consisted of 19,028 individuals from the Japanese Public Health Center cohort II who were followed-up from 1993 to 2009. A parametric survival model was used to assess the impact on the probability of gastric cancer of clinical and lifestyle-related risk factors in combination with serum anti-Helicobacter pylori antibody titres and pepsinogen I and pepsinogen II levels. Based on the resulting model, cumulative probability estimates were calculated and a simple risk scoring system was developed. A total of 412 cases of gastric cancer occurred during 270,854 person-years of follow-up. The final model included (besides the biological markers) age, gender, smoking status, family history of gastric cancer and consumption of highly salted food. The developed prediction model showed good predictive performance in terms of discrimination (optimism-corrected c-index: 0.768) and calibration (Nam and d'Agostino's χ(2) test: 14.78; p values = 0.06). Estimates of the 10-year probability of gastric cancer occurrence ranged from 0.04% (0.02, 0.1) to 14.87% (8.96, 24.14) for men and from 0.03% (0.02, 0.07) to 4.91% (2.71, 8.81) for women. In conclusion, we developed a risk prediction model for gastric cancer that combines clinical and biological markers. It might prompt individuals to modify their lifestyle habits, attend regular check-up visits or participate in screening programmes. © 2015 UICC.

  4. Germline BRCA testing is moving from cancer risk assessment to a predictive biomarker for targeting cancer therapeutics.

    PubMed

    Moreno, L; Linossi, C; Esteban, I; Gadea, N; Carrasco, E; Bonache, S; Gutiérrez-Enríquez, S; Cruz, C; Díez, O; Balmaña, J

    2016-10-01

    Originally, BRCA testing was used for risk assessment and prevention strategies for breast and ovarian cancer. Nowadays, BRCA status may influence therapeutic decision making at cancer diagnosis. Our objective was to analyze whether the medical advances have changed the burden and pattern of referral, and the pathogenic mutation detection rate. We included 969 probands from our hereditary cancer registry who undertook a full BRCA analysis between 2006 and 2014. Chi-square tests were used to compare categorical variables. The number of genetic tests have raised from 28 to 170, representing a sixfold increase. In 2006, we tested 1.6 relatives/proband while this proportion was four in 2014. Overall, 20 % harbored a deleterious mutation and 11 % had a variant of unknown significance (VUS). There has been a downward trend in the detection rate of VUS. Testing patients with breast cancer during neoadjuvancy has raised from 4 to 25 % (p = 0.002), while testing them during remission has decreased from 79 to 29 % (p < 0.001). The proportion of patients assessed during the first 6 months after their cancer diagnosis has increased from 3 to 34 % (p = 0.001). Risk reducing mastectomy and salpingoophorectomy have raised from 0 to 24 %, and from 36 to 65 %, respectively. BRCA testing has experienced a sixfold increase, the number of relatives being tested has doubled, and the test is being performed at earlier phases of the disease. It is necessary to adequate the health resources to preserve the BRCA genetic counseling quality while incorporating BRCA testing for therapeutic decision making.

  5. Breast Cancer Risk in American Women

    MedlinePlus

    ... September 7, 2012. Related Resources BRCA Mutations: Cancer Risk and Genetic Testing Breast Cancer Prevention (PDQ®)–Patient Version Diethylstilbestrol (DES) and Cancer Genetics of Breast and Gynecologic Cancers (PDQ®)–Health Professional Version Mammograms Reproductive History and Cancer Risk ...

  6. Risks of Prostate Cancer Screening

    MedlinePlus

    ... decrease the risk of dying from cancer. Scientists study screening tests to find those with the fewest risks and ... or routine screening test for prostate cancer. Screening tests for prostate cancer are under study, and there are screening clinical trials taking place ...

  7. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer

    PubMed Central

    Milne, Roger L; Kuchenbaecker, Karoline B; Michailidou, Kyriaki; Beesley, Jonathan; Kar, Siddhartha; Lindström, Sara; Hui, Shirley; Lemaçon, Audrey; Soucy, Penny; Dennis, Joe; Jiang, Xia; Rostamianfar, Asha; Finucane, Hilary; Bolla, Manjeet K; McGuffog, Lesley; Wang, Qin; Aalfs, Cora M; Adams, Marcia; Adlard, Julian; Agata, Simona; Ahmed, Shahana; Ahsan, Habibul; Aittomäki, Kristiina; Al-Ejeh, Fares; Allen, Jamie; Ambrosone, Christine B; Amos, Christopher I; Andrulis, Irene L; Anton-Culver, Hoda; Antonenkova, Natalia N; Arndt, Volker; Arnold, Norbert; Aronson, Kristan J; Auber, Bernd; Auer, Paul L; Ausems, Margreet G E M; Azzollini, Jacopo; Bacot, François; Balmaña, Judith; Barile, Monica; Barjhoux, Laure; Barkardottir, Rosa B; Barrdahl, Myrto; Barnes, Daniel; Barrowdale, Daniel; Baynes, Caroline; Beckmann, Matthias W; Benitez, Javier; Bermisheva, Marina; Bernstein, Leslie; Bignon, Yves-Jean; Blazer, Kathleen R; Blok, Marinus J; Blomqvist, Carl; Blot, William; Bobolis, Kristie; Boeckx, Bram; Bogdanova, Natalia V; Bojesen, Anders; Bojesen, Stig E; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Bozsik, Aniko; Bradbury, Angela R; Brand, Judith S; Brauch, Hiltrud; Brenner, Hermann; Bressac-de Paillerets, Brigitte; Brewer, Carole; Brinton, Louise; Broberg, Per; Brooks-Wilson, Angela; Brunet, Joan; Brüning, Thomas; Burwinkel, Barbara; Buys, Saundra S; Byun, Jinyoung; Cai, Qiuyin; Caldés, Trinidad; Caligo, Maria A; Campbell, Ian; Canzian, Federico; Caron, Olivier; Carracedo, Angel; Carter, Brian D; Castelao, J Esteban; Castera, Laurent; Caux-Moncoutier, Virginie; Chan, Salina B; Chang-Claude, Jenny; Chanock, Stephen J; Chen, Xiaoqing; Cheng, Ting-Yuan David; Chiquette, Jocelyne; Christiansen, Hans; Claes, Kathleen B M; Clarke, Christine L; Conner, Thomas; Conroy, Don M; Cook, Jackie; Cordina-Duverger, Emilie; Cornelissen, Sten; Coupier, Isabelle; Cox, Angela; Cox, David G; Cross, Simon S; Cuk, Katarina; Cunningham, Julie M; Czene, Kamila; Daly, Mary B; Damiola, Francesca; Darabi, Hatef; Davidson, Rosemarie; De Leeneer, Kim; Devilee, Peter; Dicks, Ed; Diez, Orland; Ding, Yuan Chun; Ditsch, Nina; Doheny, Kimberly F; Domchek, Susan M; Dorfling, Cecilia M; Dörk, Thilo; dos-Santos-Silva, Isabel; Dubois, Stéphane; Dugué, Pierre-Antoine; Dumont, Martine; Dunning, Alison M; Durcan, Lorraine; Dwek, Miriam; Dworniczak, Bernd; Eccles, Diana; Eeles, Ros; Ehrencrona, Hans; Eilber, Ursula; Ejlertsen, Bent; Ekici, Arif B; Engel, Christoph; Eriksson, Mikael; Fachal, Laura; Faivre, Laurence; Fasching, Peter A; Faust, Ulrike; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Foulkes, William D; Friedman, Eitan; Fritschi, Lin; Frost, Debra; Gabrielson, Marike; Gaddam, Pragna; Gammon, Marilie D; Ganz, Patricia A; Gapstur, Susan M; Garber, Judy; Garcia-Barberan, Vanesa; García-Sáenz, José A; Gaudet, Mia M; Gauthier-Villars, Marion; Gehrig, Andrea; Georgoulias, Vassilios; Gerdes, Anne-Marie; Giles, Graham G; Glendon, Gord; Godwin, Andrew K; Goldberg, Mark S; Goldgar, David E; González-Neira, Anna; Goodfellow, Paul; Greene, Mark H; Grip, Mervi; Gronwald, Jacek; Grundy, Anne; Gschwantler-Kaulich, Daphne; Guénel, Pascal; Guo, Qi; Haeberle, Lothar; Hahnen, Eric; Haiman, Christopher A; Håkansson, Niclas; Hallberg, Emily; Hamann, Ute; Hamel, Nathalie; Hankinson, Susan; Hansen, Thomas V O; Harrington, Patricia; Hart, Steven N; Hartikainen, Jaana M; Healey, Catherine S; Hein, Alexander; Helbig, Sonja; Henderson, Alex; Heyworth, Jane; Hicks, Belynda; Hillemanns, Peter; Hodgson, Shirley; Hogervorst, Frans B; Hollestelle, Antoinette; Hooning, Maartje J; Hoover, Bob; Hopper, John L; Hu, Chunling; Huang, Guanmengqian; Hulick, Peter J; Humphreys, Keith; Hunter, David J; Imyanitov, Evgeny N; Isaacs, Claudine; Iwasaki, Motoki; Izatt, Louise; Jakubowska, Anna; James, Paul; Janavicius, Ramunas; Janni, Wolfgang; Jensen, Uffe Birk; John, Esther M; Johnson, Nichola; Jones, Kristine; Jones, Michael; Jukkola-Vuorinen, Arja; Kaaks, Rudolf; Kabisch, Maria; Kaczmarek, Katarzyna; Kang, Daehee; Kast, Karin; Keeman, Renske; Kerin, Michael J; Kets, Carolien M; Keupers, Machteld; Khan, Sofia; Khusnutdinova, Elza; Kiiski, Johanna I; Kim, Sung-Won; Knight, Julia A; Konstantopoulou, Irene; Kosma, Veli-Matti; Kristensen, Vessela N; Kruse, Torben A; Kwong, Ava; Lænkholm, Anne-Vibeke; Laitman, Yael; Lalloo, Fiona; Lambrechts, Diether; Landsman, Keren; Lasset, Christine; Lazaro, Conxi; Le Marchand, Loic; Lecarpentier, Julie; Lee, Andrew; Lee, Eunjung; Lee, Jong Won; Lee, Min Hyuk; Lejbkowicz, Flavio; Lesueur, Fabienne; Li, Jingmei; Lilyquist, Jenna; Lincoln, Anne; Lindblom, Annika; Lissowska, Jolanta; Lo, Wing-Yee; Loibl, Sibylle; Long, Jirong; Loud, Jennifer T; Lubinski, Jan; Luccarini, Craig; Lush, Michael; MacInnis, Robert J; Maishman, Tom; Makalic, Enes; Kostovska, Ivana Maleva; Malone, Kathleen E; Manoukian, Siranoush; Manson, JoAnn E; Margolin, Sara; Martens, John W M; Martinez, Maria Elena; Matsuo, Keitaro; Mavroudis, Dimitrios; Mazoyer, Sylvie; McLean, Catriona; Meijers-Heijboer, Hanne; Menéndez, Primitiva; Meyer, Jeffery; Miao, Hui; Miller, Austin; Miller, Nicola; Mitchell, Gillian; Montagna, Marco; Muir, Kenneth; Mulligan, Anna Marie; Mulot, Claire; Nadesan, Sue; Nathanson, Katherine L; Neuhausen, Susan L; Nevanlinna, Heli; Nevelsteen, Ines; Niederacher, Dieter; Nielsen, Sune F; Nordestgaard, Børge G; Norman, Aaron; Nussbaum, Robert L; Olah, Edith; Olopade, Olufunmilayo I; Olson, Janet E; Olswold, Curtis; Ong, Kai-ren; Oosterwijk, Jan C; Orr, Nick; Osorio, Ana; Pankratz, V Shane; Papi, Laura; Park-Simon, Tjoung-Won; Paulsson-Karlsson, Ylva; Lloyd, Rachel; Pedersen, Inge Søkilde; Peissel, Bernard; Peixoto, Ana; Perez, Jose I A; Peterlongo, Paolo; Peto, Julian; Pfeiler, Georg; Phelan, Catherine M; Pinchev, Mila; Plaseska-Karanfilska, Dijana; Poppe, Bruce; Porteous, Mary E; Prentice, Ross; Presneau, Nadege; Prokofieva, Darya; Pugh, Elizabeth; Pujana, Miquel Angel; Pylkäs, Katri; Rack, Brigitte; Radice, Paolo; Rahman, Nazneen; Rantala, Johanna; Rappaport-Fuerhauser, Christine; Rennert, Gad; Rennert, Hedy S; Rhenius, Valerie; Rhiem, Kerstin; Richardson, Andrea; Rodriguez, Gustavo C; Romero, Atocha; Romm, Jane; Rookus, Matti A; Rudolph, Anja; Ruediger, Thomas; Saloustros, Emmanouil; Sanders, Joyce; Sandler, Dale P; Sangrajrang, Suleeporn; Sawyer, Elinor J; Schmidt, Daniel F; Schoemaker, Minouk J; Schumacher, Fredrick; Schürmann, Peter; Schwentner, Lukas; Scott, Christopher; Scott, Rodney J; Seal, Sheila; Senter, Leigha; Seynaeve, Caroline; Shah, Mitul; Sharma, Priyanka; Shen, Chen-Yang; Sheng, Xin; Shimelis, Hermela; Shrubsole, Martha J; Shu, Xiao-Ou; Side, Lucy E; Singer, Christian F; Sohn, Christof; Southey, Melissa C; Spinelli, John J; Spurdle, Amanda B; Stegmaier, Christa; Stoppa-Lyonnet, Dominique; Sukiennicki, Grzegorz; Surowy, Harald; Sutter, Christian; Swerdlow, Anthony; Szabo, Csilla I; Tamimi, Rulla M; Tan, Yen Y; Taylor, Jack A; Tejada, Maria-Isabel; Tengström, Maria; Teo, Soo H; Terry, Mary B; Tessier, Daniel C; Teulé, Alex; Thöne, Kathrin; Thull, Darcy L; Tibiletti, Maria Grazia; Tihomirova, Laima; Tischkowitz, Marc; Toland, Amanda E; Tollenaar, Rob A E M; Tomlinson, Ian; Tong, Ling; Torres, Diana; Tranchant, Martine; Truong, Thérèse; Tucker, Kathy; Tung, Nadine; Tyrer, Jonathan; Ulmer, Hans-Ulrich; Vachon, Celine; van Asperen, Christi J; Van Den Berg, David; van den Ouweland, Ans M W; van Rensburg, Elizabeth J; Varesco, Liliana; Varon-Mateeva, Raymonda; Vega, Ana; Viel, Alessandra; Vijai, Joseph; Vincent, Daniel; Vollenweider, Jason; Walker, Lisa; Wang, Zhaoming; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Weinberg, Clarice R; Weitzel, Jeffrey N; Wendt, Camilla; Wesseling, Jelle; Whittemore, Alice S; Wijnen, Juul T; Willett, Walter; Winqvist, Robert; Wolk, Alicja; Wu, Anna H; Xia, Lucy; Yang, Xiaohong R; Yannoukakos, Drakoulis; Zaffaroni, Daniela; Zheng, Wei; Zhu, Bin; Ziogas, Argyrios; Ziv, Elad; Zorn, Kristin K; Gago-Dominguez, Manuela; Mannermaa, Arto; Olsson, Håkan; Teixeira, Manuel R; Stone, Jennifer; Offit, Kenneth; Ottini, Laura; Park, Sue K; Thomassen, Mads; Hall, Per; Meindl, Alfons; Schmutzler, Rita K; Droit, Arnaud; Bader, Gary D; Pharoah, Paul D P; Couch, Fergus J; Easton, Douglas F; Kraft, Peter; Chenevix-Trench, Georgia; García-Closas, Montserrat; Schmidt, Marjanka K; Antoniou, Antonis C; Simard, Jacques

    2018-01-01

    Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease1. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10−8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 14% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer. PMID:29058716

  8. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.

    PubMed

    Milne, Roger L; Kuchenbaecker, Karoline B; Michailidou, Kyriaki; Beesley, Jonathan; Kar, Siddhartha; Lindström, Sara; Hui, Shirley; Lemaçon, Audrey; Soucy, Penny; Dennis, Joe; Jiang, Xia; Rostamianfar, Asha; Finucane, Hilary; Bolla, Manjeet K; McGuffog, Lesley; Wang, Qin; Aalfs, Cora M; Adams, Marcia; Adlard, Julian; Agata, Simona; Ahmed, Shahana; Ahsan, Habibul; Aittomäki, Kristiina; Al-Ejeh, Fares; Allen, Jamie; Ambrosone, Christine B; Amos, Christopher I; Andrulis, Irene L; Anton-Culver, Hoda; Antonenkova, Natalia N; Arndt, Volker; Arnold, Norbert; Aronson, Kristan J; Auber, Bernd; Auer, Paul L; Ausems, Margreet G E M; Azzollini, Jacopo; Bacot, François; Balmaña, Judith; Barile, Monica; Barjhoux, Laure; Barkardottir, Rosa B; Barrdahl, Myrto; Barnes, Daniel; Barrowdale, Daniel; Baynes, Caroline; Beckmann, Matthias W; Benitez, Javier; Bermisheva, Marina; Bernstein, Leslie; Bignon, Yves-Jean; Blazer, Kathleen R; Blok, Marinus J; Blomqvist, Carl; Blot, William; Bobolis, Kristie; Boeckx, Bram; Bogdanova, Natalia V; Bojesen, Anders; Bojesen, Stig E; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Bozsik, Aniko; Bradbury, Angela R; Brand, Judith S; Brauch, Hiltrud; Brenner, Hermann; Bressac-de Paillerets, Brigitte; Brewer, Carole; Brinton, Louise; Broberg, Per; Brooks-Wilson, Angela; Brunet, Joan; Brüning, Thomas; Burwinkel, Barbara; Buys, Saundra S; Byun, Jinyoung; Cai, Qiuyin; Caldés, Trinidad; Caligo, Maria A; Campbell, Ian; Canzian, Federico; Caron, Olivier; Carracedo, Angel; Carter, Brian D; Castelao, J Esteban; Castera, Laurent; Caux-Moncoutier, Virginie; Chan, Salina B; Chang-Claude, Jenny; Chanock, Stephen J; Chen, Xiaoqing; Cheng, Ting-Yuan David; Chiquette, Jocelyne; Christiansen, Hans; Claes, Kathleen B M; Clarke, Christine L; Conner, Thomas; Conroy, Don M; Cook, Jackie; Cordina-Duverger, Emilie; Cornelissen, Sten; Coupier, Isabelle; Cox, Angela; Cox, David G; Cross, Simon S; Cuk, Katarina; Cunningham, Julie M; Czene, Kamila; Daly, Mary B; Damiola, Francesca; Darabi, Hatef; Davidson, Rosemarie; De Leeneer, Kim; Devilee, Peter; Dicks, Ed; Diez, Orland; Ding, Yuan Chun; Ditsch, Nina; Doheny, Kimberly F; Domchek, Susan M; Dorfling, Cecilia M; Dörk, Thilo; Dos-Santos-Silva, Isabel; Dubois, Stéphane; Dugué, Pierre-Antoine; Dumont, Martine; Dunning, Alison M; Durcan, Lorraine; Dwek, Miriam; Dworniczak, Bernd; Eccles, Diana; Eeles, Ros; Ehrencrona, Hans; Eilber, Ursula; Ejlertsen, Bent; Ekici, Arif B; Eliassen, A Heather; Engel, Christoph; Eriksson, Mikael; Fachal, Laura; Faivre, Laurence; Fasching, Peter A; Faust, Ulrike; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Foulkes, William D; Friedman, Eitan; Fritschi, Lin; Frost, Debra; Gabrielson, Marike; Gaddam, Pragna; Gammon, Marilie D; Ganz, Patricia A; Gapstur, Susan M; Garber, Judy; Garcia-Barberan, Vanesa; García-Sáenz, José A; Gaudet, Mia M; Gauthier-Villars, Marion; Gehrig, Andrea; Georgoulias, Vassilios; Gerdes, Anne-Marie; Giles, Graham G; Glendon, Gord; Godwin, Andrew K; Goldberg, Mark S; Goldgar, David E; González-Neira, Anna; Goodfellow, Paul; Greene, Mark H; Alnæs, Grethe I Grenaker; Grip, Mervi; Gronwald, Jacek; Grundy, Anne; Gschwantler-Kaulich, Daphne; Guénel, Pascal; Guo, Qi; Haeberle, Lothar; Hahnen, Eric; Haiman, Christopher A; Håkansson, Niclas; Hallberg, Emily; Hamann, Ute; Hamel, Nathalie; Hankinson, Susan; Hansen, Thomas V O; Harrington, Patricia; Hart, Steven N; Hartikainen, Jaana M; Healey, Catherine S; Hein, Alexander; Helbig, Sonja; Henderson, Alex; Heyworth, Jane; Hicks, Belynda; Hillemanns, Peter; Hodgson, Shirley; Hogervorst, Frans B; Hollestelle, Antoinette; Hooning, Maartje J; Hoover, Bob; Hopper, John L; Hu, Chunling; Huang, Guanmengqian; Hulick, Peter J; Humphreys, Keith; Hunter, David J; Imyanitov, Evgeny N; Isaacs, Claudine; Iwasaki, Motoki; Izatt, Louise; Jakubowska, Anna; James, Paul; Janavicius, Ramunas; Janni, Wolfgang; Jensen, Uffe Birk; John, Esther M; Johnson, Nichola; Jones, Kristine; Jones, Michael; Jukkola-Vuorinen, Arja; Kaaks, Rudolf; Kabisch, Maria; Kaczmarek, Katarzyna; Kang, Daehee; Kast, Karin; Keeman, Renske; Kerin, Michael J; Kets, Carolien M; Keupers, Machteld; Khan, Sofia; Khusnutdinova, Elza; Kiiski, Johanna I; Kim, Sung-Won; Knight, Julia A; Konstantopoulou, Irene; Kosma, Veli-Matti; Kristensen, Vessela N; Kruse, Torben A; Kwong, Ava; Lænkholm, Anne-Vibeke; Laitman, Yael; Lalloo, Fiona; Lambrechts, Diether; Landsman, Keren; Lasset, Christine; Lazaro, Conxi; Le Marchand, Loic; Lecarpentier, Julie; Lee, Andrew; Lee, Eunjung; Lee, Jong Won; Lee, Min Hyuk; Lejbkowicz, Flavio; Lesueur, Fabienne; Li, Jingmei; Lilyquist, Jenna; Lincoln, Anne; Lindblom, Annika; Lissowska, Jolanta; Lo, Wing-Yee; Loibl, Sibylle; Long, Jirong; Loud, Jennifer T; Lubinski, Jan; Luccarini, Craig; Lush, Michael; MacInnis, Robert J; Maishman, Tom; Makalic, Enes; Kostovska, Ivana Maleva; Malone, Kathleen E; Manoukian, Siranoush; Manson, JoAnn E; Margolin, Sara; Martens, John W M; Martinez, Maria Elena; Matsuo, Keitaro; Mavroudis, Dimitrios; Mazoyer, Sylvie; McLean, Catriona; Meijers-Heijboer, Hanne; Menéndez, Primitiva; Meyer, Jeffery; Miao, Hui; Miller, Austin; Miller, Nicola; Mitchell, Gillian; Montagna, Marco; Muir, Kenneth; Mulligan, Anna Marie; Mulot, Claire; Nadesan, Sue; Nathanson, Katherine L; Neuhausen, Susan L; Nevanlinna, Heli; Nevelsteen, Ines; Niederacher, Dieter; Nielsen, Sune F; Nordestgaard, Børge G; Norman, Aaron; Nussbaum, Robert L; Olah, Edith; Olopade, Olufunmilayo I; Olson, Janet E; Olswold, Curtis; Ong, Kai-Ren; Oosterwijk, Jan C; Orr, Nick; Osorio, Ana; Pankratz, V Shane; Papi, Laura; Park-Simon, Tjoung-Won; Paulsson-Karlsson, Ylva; Lloyd, Rachel; Pedersen, Inge Søkilde; Peissel, Bernard; Peixoto, Ana; Perez, Jose I A; Peterlongo, Paolo; Peto, Julian; Pfeiler, Georg; Phelan, Catherine M; Pinchev, Mila; Plaseska-Karanfilska, Dijana; Poppe, Bruce; Porteous, Mary E; Prentice, Ross; Presneau, Nadege; Prokofieva, Darya; Pugh, Elizabeth; Pujana, Miquel Angel; Pylkäs, Katri; Rack, Brigitte; Radice, Paolo; Rahman, Nazneen; Rantala, Johanna; Rappaport-Fuerhauser, Christine; Rennert, Gad; Rennert, Hedy S; Rhenius, Valerie; Rhiem, Kerstin; Richardson, Andrea; Rodriguez, Gustavo C; Romero, Atocha; Romm, Jane; Rookus, Matti A; Rudolph, Anja; Ruediger, Thomas; Saloustros, Emmanouil; Sanders, Joyce; Sandler, Dale P; Sangrajrang, Suleeporn; Sawyer, Elinor J; Schmidt, Daniel F; Schoemaker, Minouk J; Schumacher, Fredrick; Schürmann, Peter; Schwentner, Lukas; Scott, Christopher; Scott, Rodney J; Seal, Sheila; Senter, Leigha; Seynaeve, Caroline; Shah, Mitul; Sharma, Priyanka; Shen, Chen-Yang; Sheng, Xin; Shimelis, Hermela; Shrubsole, Martha J; Shu, Xiao-Ou; Side, Lucy E; Singer, Christian F; Sohn, Christof; Southey, Melissa C; Spinelli, John J; Spurdle, Amanda B; Stegmaier, Christa; Stoppa-Lyonnet, Dominique; Sukiennicki, Grzegorz; Surowy, Harald; Sutter, Christian; Swerdlow, Anthony; Szabo, Csilla I; Tamimi, Rulla M; Tan, Yen Y; Taylor, Jack A; Tejada, Maria-Isabel; Tengström, Maria; Teo, Soo H; Terry, Mary B; Tessier, Daniel C; Teulé, Alex; Thöne, Kathrin; Thull, Darcy L; Tibiletti, Maria Grazia; Tihomirova, Laima; Tischkowitz, Marc; Toland, Amanda E; Tollenaar, Rob A E M; Tomlinson, Ian; Tong, Ling; Torres, Diana; Tranchant, Martine; Truong, Thérèse; Tucker, Kathy; Tung, Nadine; Tyrer, Jonathan; Ulmer, Hans-Ulrich; Vachon, Celine; van Asperen, Christi J; Van Den Berg, David; van den Ouweland, Ans M W; van Rensburg, Elizabeth J; Varesco, Liliana; Varon-Mateeva, Raymonda; Vega, Ana; Viel, Alessandra; Vijai, Joseph; Vincent, Daniel; Vollenweider, Jason; Walker, Lisa; Wang, Zhaoming; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Weinberg, Clarice R; Weitzel, Jeffrey N; Wendt, Camilla; Wesseling, Jelle; Whittemore, Alice S; Wijnen, Juul T; Willett, Walter; Winqvist, Robert; Wolk, Alicja; Wu, Anna H; Xia, Lucy; Yang, Xiaohong R; Yannoukakos, Drakoulis; Zaffaroni, Daniela; Zheng, Wei; Zhu, Bin; Ziogas, Argyrios; Ziv, Elad; Zorn, Kristin K; Gago-Dominguez, Manuela; Mannermaa, Arto; Olsson, Håkan; Teixeira, Manuel R; Stone, Jennifer; Offit, Kenneth; Ottini, Laura; Park, Sue K; Thomassen, Mads; Hall, Per; Meindl, Alfons; Schmutzler, Rita K; Droit, Arnaud; Bader, Gary D; Pharoah, Paul D P; Couch, Fergus J; Easton, Douglas F; Kraft, Peter; Chenevix-Trench, Georgia; García-Closas, Montserrat; Schmidt, Marjanka K; Antoniou, Antonis C; Simard, Jacques

    2017-12-01

    Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10 -8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

  9. Association between long non-coding RNA polymorphisms and cancer risk: a meta-analysis.

    PubMed

    Huang, Xin; Zhang, Weiyue; Shao, Zengwu

    2018-05-25

    Several studies have suggested that long non-coding RNA (lncRNA) gene polymorphisms are associated with cancer risk. In the present study, we conducted a meta-analysis related to studies on the association between lncRNA single-nucleotide polymorphisms (SNPs) and the overall risk of cancer. A total 12 SNPs in five common lncRNA genes were finally included in the meta-analysis. In the lncRNA antisense noncoding RNA in the INK4 locus (ANRIL), the rs1333048 A/C, rs4977574 A/G, and rs10757278 A/G polymorphisms, but not rs1333045 C/T, were correlated with overall cancer risk. Our study also demonstrated that other SNPs were correlated with overall cancer risk, namely, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1, rs619586 A/G), HOXA distal transcript antisense RNA (HOTTIP, rs1859168 A/C) and highly up-regulated in liver cancer (HULC, rs7763881 A/C). Moreover, four prostate cancer‑associated non‑coding RNA 1 (PRNCR1, rs16901946 G/A, rs13252298 G/A, rs1016343 T/C, and rs1456315 G/A) SNPs were in association with cancer risk. No association was found between the PRNCR1 (rs7007694 C/T) SNP and the risk of cancer. In conclusion, our results suggest that several studied lncRNA SNPs are associated with overall cancer risk. Therefore, they might be potential predictive biomarkers for the risk of cancer. More studies based on larger sample sizes and more lncRNA SNPs are warranted to confirm these findings. ©2018 The Author(s).

  10. Increased Cancer Risks in Myotonic Dystrophy

    PubMed Central

    Win, Aung Ko; Perattur, Promilla G.; Pulido, Jose S.; Pulido, Christine M.; Lindor, Noralane M.

    2012-01-01

    Objective To estimate cancer risks for patients with myotonic dystrophy, given that increased risks for neoplasms in association with myotonic dystrophy type 1 and type 2 have been suggested in several studies but the risks of cancers have not been quantified. Patients and Methods A cohort of 307 patients with myotonic dystrophy identified from medical records of Mayo Clinic in Rochester, MN, from January 1, l993, through May 28, 2010, was retrospectively analyzed. We estimated standardized incidence ratios (SIRs) of specific cancers for patients with myotonic dystrophy compared with age- and sex-specific cancer incidences of the general population. Age-dependent cumulative risks were calculated using the Kaplan-Meier method. Results A total of 53 cancers were observed at a median age at diagnosis of 55 years. Patients with myotonic dystrophy had an increased risk of thyroid cancer (SIR, 5.54; 95% confidence interval [CI], 1.80-12.93; P=.001) and choroidal melanoma (SIR, 27.54; 95% CI, 3.34-99.49; P<.001). They may also have an increased risk of testicular cancer (SIR, 5.09; 95% CI, 0.62-18.38; P=.06) and prostate cancer (SIR, 2.21; 95% CI, 0.95-4.35; P=.05). The estimated cumulative risks at age 50 years were 1.72% (95% CI, 0.64%-4.55%) for thyroid cancer and 1.00% (95% CI, 0.25%-3.92%) for choroidal melanoma. There was no statistical evidence of an increased risk of brain, breast, colorectal, lung, renal, bladder, endometrial, or ovarian cancer; lymphoma; leukemia; or multiple myeloma. Conclusion Patients with myotonic dystrophy may have an increased risk of thyroid cancer and choroidal melanoma and, possibly, testicular and prostate cancers. PMID:22237010

  11. Deep learning based tissue analysis predicts outcome in colorectal cancer.

    PubMed

    Bychkov, Dmitrii; Linder, Nina; Turkki, Riku; Nordling, Stig; Kovanen, Panu E; Verrill, Clare; Walliander, Margarita; Lundin, Mikael; Haglund, Caj; Lundin, Johan

    2018-02-21

    Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.

  12. Cancer morbidity among methyl isocyanate exposed long- term survivors and their offspring: a hospital-based five year descriptive study (2006 - 2011) and future directions to predict cancer risk in the affected population.

    PubMed

    Senthilkumar, Chinnu Sugavanam; Malla, Tahir Mohi-ud-Din; Sah, Nand Kishore; Ganesh, Narayanan

    2011-01-01

    The purpose of this study was to update both researchers and clinicians about the cancer incidence in methyl isocyanate (MIC) exposed long-term survivors and in their offspring, focusing on the etiological plausibility. In the time period 2006-2011, cancer morbidity was evaluated in the population surviving after exposure to (MIC) on December 3rd, 1984, in Bhopal. This descriptive study is based on hospital registration of 1261 cancer patients those are MIC gas victims and their subsequently born offspring. Morbidity status was studied on the basis of gender, age, organ and site with relative percentages. Cancers on specific sites, with special reference to breast (n=231) (18.31%), lung (n=103) (8.16%), tongue (n=103) (8.16%), buccal mucosa (n=94) (7.45%), cervix (n=72) (5.70%), and esophagus (n=68) (5.39%) were found in high proportions. Ovary (n=43) (3.40%), brain (n=42) (3.33%), larynx (n=40) (3.17%), non-Hodgkin's (n=31) (2.45%), gallbladder (n=29) (2.29%), stomach (n=28) (2.22%), head and neck (n=28) (2.22%), liver (n=27) (2.14%), acute lymphoid leukemia (n=24) (1.90%), rectum (n=20) (1.58%), colon (n=20) (1.58%), chronic myeloid leukemia (n=17) (1.34%), alveolus (n=17) (1.34%), Hodgkin's (n=14) (1.11%), uterus (n=14) (1.11%), multiple myeloma (n=14) (1.11%), and prostate (n=11) (0.87%) lesions were observed less frequently. Remarkably, gradual increase of cancers on different organs and sites were observed in the long- term survivors and their offspring. The present study observed some cancers which were not previously reported in this population. In addition, we also present the future research directions with systematic approaches to predict cancer risk in long-term survivors and their future generations. On the basis of this morbidity report, we suggest the need of biological surveillance through immune system biomonitoring and cytogenetic screening to predict the cancer risk in the MIC exposed population and their offspring.

  13. Classification of TP53 Mutations and HPV Predict Survival in Advanced Larynx Cancer

    PubMed Central

    Scheel, Adam; Bellile, Emily; McHugh, Jonathan B.; Walline, Heather M.; Prince, Mark E.; Urba, Susan; Wolf, Gregory T.; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E.; Bradford, Carol

    2016-01-01

    OBJECTIVE Assess TP53 functional mutations in the context of other biomarkers in advanced larynx cancer. STUDY DESIGN Prospective analysis of pretreatment tumor TP53, HPV, Bcl-xL and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. METHODS TP53 exons 4-9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl and cyclin D1 expression. RESULTS TP53 Mutations were found in 22/58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13/58 (22.4%) patients, nonsense mutations in 4/58 (6.9%), and deletions in 5/58 (8.6%). High risk HPV was found in 20/52 (38.5%) tumors. A classification based on crystal Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low risk mutations (p=0.0315). A model including this TP53 classification, HPV status, cyclin D1 and Bcl-xL staining significantly predicts survival (p=0.0017). CONCLUSION EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. PMID:27345657

  14. Lifetime attributable risk as an alternative to effective dose to describe the risk of cancer for patients in diagnostic and therapeutic nuclear medicine.

    PubMed

    Andersson, Martin; Eckerman, Keith; Mattsson, Sören

    2017-11-21

    The aim of this study is to implement lifetime attributable risk (LAR) predictions of cancer for patients of various age and gender, undergoing diagnostic investigations or treatments in nuclear medicine and to compare the outcome with a population risk estimate using effective dose and the International Commission on Radiological Protection risk coefficients. The radiation induced risk of cancer occurrence (incidence) or death from four nuclear medicine procedures are estimated for both male and female between 0 and 120 years. Estimations of cancer risk are performed using recommended administered activities for two diagnostic ( 18 F-FDG and 99m Tc-phosphonate complex) and two therapeutic ( 131 I-iodide and 223 Ra-dichloride) radiopharmaceuticals to illustrate the use of cancer risk estimations in nuclear medicine. For 18 F-FDG, the cancer incidence for a male of 5, 25, 50 and 75 years at exposure is 0.0021, 0.0010, 0.0008 and 0.0003, respectively. For 99m Tc phosphonates complex the corresponding values are 0.000 59, 0.000 34, 0.000 27 and 0.000 13, respectively. For an 131 I-iodide treatment with 3.7 GBq and 1% uptake 24 h after administration, the cancer incidence for a male of 25, 50 and 75 years at exposure is 0.041, 0.029 and 0.012, respectively. For 223 Ra-dichloride with an administration of 21.9 MBq the cancer incidence for a male of 25, 50 and 75 years is 0.31, 0.21 and 0.09, respectively. The LAR estimations are more suitable in health care situations involving individual patients or specific groups of patients than the health detriment based on effective dose, which represents a population average. The detriment consideration in effective dose adjusts the cancer incidence for suffering of non-lethal cancers while LAR predicts morbidity (incidence) or mortality (cancer). The advantages of these LARs are that they are gender and age specific, allowing risk estimations for specific patients or subgroups thus better representing individuals in

  15. Lifetime attributable risk as an alternative to effective dose to describe the risk of cancer for patients in diagnostic and therapeutic nuclear medicine

    NASA Astrophysics Data System (ADS)

    Andersson, Martin; Eckerman, Keith; Mattsson, Sören

    2017-12-01

    The aim of this study is to implement lifetime attributable risk (LAR) predictions of cancer for patients of various age and gender, undergoing diagnostic investigations or treatments in nuclear medicine and to compare the outcome with a population risk estimate using effective dose and the International Commission on Radiological Protection risk coefficients. The radiation induced risk of cancer occurrence (incidence) or death from four nuclear medicine procedures are estimated for both male and female between 0 and 120 years. Estimations of cancer risk are performed using recommended administered activities for two diagnostic (18F-FDG and 99mTc-phosphonate complex) and two therapeutic (131I-iodide and 223Ra-dichloride) radiopharmaceuticals to illustrate the use of cancer risk estimations in nuclear medicine. For 18F-FDG, the cancer incidence for a male of 5, 25, 50 and 75 years at exposure is 0.0021, 0.0010, 0.0008 and 0.0003, respectively. For 99mTc phosphonates complex the corresponding values are 0.000 59, 0.000 34, 0.000 27 and 0.000 13, respectively. For an 131I-iodide treatment with 3.7 GBq and 1% uptake 24 h after administration, the cancer incidence for a male of 25, 50 and 75 years at exposure is 0.041, 0.029 and 0.012, respectively. For 223Ra-dichloride with an administration of 21.9 MBq the cancer incidence for a male of 25, 50 and 75 years is 0.31, 0.21 and 0.09, respectively. The LAR estimations are more suitable in health care situations involving individual patients or specific groups of patients than the health detriment based on effective dose, which represents a population average. The detriment consideration in effective dose adjusts the cancer incidence for suffering of non-lethal cancers while LAR predicts morbidity (incidence) or mortality (cancer). The advantages of these LARs are that they are gender and age specific, allowing risk estimations for specific patients or subgroups thus better representing individuals in health care

  16. Cancer-related symptoms predict psychological wellbeing among prostate cancer survivors: results from the PiCTure study.

    PubMed

    Sharp, Linda; O'Leary, Eamonn; Kinnear, Heather; Gavin, Anna; Drummond, Frances J

    2016-03-01

    Prostate cancer treatments are associated with a range of symptoms and physical side-effects. Cancer can also adversely impact on psychological wellbeing. Because many prostate cancer-related symptoms and side-effects are potentially modifiable, we investigated associations between symptoms and psychological wellbeing among prostate cancer survivors. Postal questionnaires were distributed to men diagnosed with prostate cancer 2-18 years previously identified through cancer registries. General and prostate cancer-specific symptoms were assessed using the EORTC QLQ-C30 and QLQ-PR25, with higher symptom scores indicating more/worse symptomatology. Psychological wellbeing was assessed by the DASS-21. Associations between symptoms and each outcome were investigated using multivariate logistic regression, controlling for socio-demographic and clinical factors. A total 3348 men participated (response rate = 54%). Seventeen percent (95%CI 15.2%-17.9%), 16% (95%CI 15.1%-17.8%) and 11% (95%CI 9.5%-11.8%) of survivors scored in the range for depression, anxiety and distress on the DASS scales, respectively. In multivariate models, risk of depression on the DASS scale was significantly higher in men with higher urinary and androgen deprivation therapy (ADT)-related symptoms, and higher scores for fatigue, insomnia and financial difficulties. Risk of anxiety on the DASS scale was higher in men with higher scores for urinary, bowel and ADT-related symptoms and fatigue, dyspnoea and financial difficulties. Risk of distress on the DASS scale was positively associated with urinary, bowel and ADT-related symptoms, fatigue, insomnia and financial difficulties. Cancer-related symptoms significantly predict psychological wellbeing among prostate cancer survivors. Greater use of interventions and medications and to alleviate symptoms might improve psychological wellbeing of prostate cancer survivors. Copyright © 2015 John Wiley & Sons, Ltd.

  17. Predictive Factors for Developing Venous Thrombosis during Cisplatin-Based Chemotherapy in Testicular Cancer.

    PubMed

    Heidegger, Isabel; Porres, Daniel; Veek, Nica; Heidenreich, Axel; Pfister, David

    2017-01-01

    Malignancies and cisplatin-based chemotherapy are both known to correlate with a high risk of venous thrombotic events (VTT). In testicular cancer, the information regarding the incidence and reason of VTT in patients undergoing cisplatin-based chemotherapy is still discussed controversially. Moreover, no risk factors for developing a VTT during cisplatin-based chemotherapy have been elucidated so far. We retrospectively analyzed 153 patients with testicular cancer undergoing cisplatin-based chemotherapy at our institution for the development of a VTT during or after chemotherapy. Clinical and pathological parameters for identifying possible risk factors for VTT were analyzed. The Khorana risk score was used to calculate the risk of VTT. Student t test was applied for calculating the statistical significance of differences between the treatment groups. Twenty-six out of 153 patients (17%) developed a VTT during chemotherapy. When we analyzed the risk factors for developing a VTT, we found that Lugano stage ≥IIc was significantly (p = 0.0006) correlated with the risk of developing a VTT during chemotherapy. On calculating the VTT risk using the Khorana risk score model, we found that only 2 out of 26 patients (7.7%) were in the high-risk Khorana group (≥3). Patients with testicular cancer with a high tumor volume have a significant risk of developing a VTT with cisplatin-based chemotherapy. The Khorana risk score is not an accurate tool for predicting VTT in testicular cancer. © 2017 S. Karger AG, Basel.

  18. Skin Cancer: Biology, Risk Factors & Treatment

    MedlinePlus

    ... turn Javascript on. Feature: Skin Cancer Skin Cancer: Biology, Risk Factors & Treatment Past Issues / Summer 2013 Table ... Articles Skin Cancer Can Strike Anyone / Skin Cancer: Biology, Risk Factors & Treatment / Timely Healthcare Checkup Catches Melanoma ...

  19. Quality of life in pediatric cancer survivors: contributions of parental distress and psychosocial family risk

    PubMed Central

    Racine, N.M.; Khu, M.; Reynolds, K.; Guilcher, G.M.T.; Schulte, F.S.M.

    2018-01-01

    Background Pediatric survivors of childhood cancer are at increased risk of poor quality of life and social-emotional outcomes following treatment. The relationship between parent psychological distress and child adjustment in pediatric cancer survivors has been well established. However, limited research has examined the factors that may buffer this association. The current study examined the associations between psychosocial family risk factors, parental psychological distress, and health-related quality of life (hrql) in pediatric cancer survivors. Methods Fifty-two pediatric cancer survivors (34 males, 18 females, mean age = 11.92) and their parents were recruited from a long-term cancer survivor clinic. Children and their parents who consented to participate completed the Pediatric Quality of Life Inventory 4.0. Parents completed a demographic information form, the Psychosocial Assessment Tool (pat 2.0) and the Brief Symptom Inventory (bsi). The Intensity of Treatment Rating (itr-3) was evaluated by the research team. Results Multiple regression analyses revealed that parental psychological distress negatively predicted parent-reported hrql, while treatment intensity, gender, and psychosocial risk negatively predicted parent and child-reported hrql. Psychosocial risk moderated the association between parent psychological distress and parent-reported child hrql (p = 0.03), whereby parents with high psychological distress but low levels of psychosocial risk reported their children to have higher hrql. Conclusion Low levels of family psychosocial risk buffer the impact of parent psychological distress on child hrql in pediatric cancer survivors. The findings highlight the importance of identifying parents and families with at-risk psychological distress and psychosocial risk in order to provide targeted support interventions to mitigate the impact on hrql. PMID:29507482

  20. Using multiscale texture and density features for near-term breast cancer risk analysis

    PubMed Central

    Sun, Wenqing; Tseng, Tzu-Liang (Bill); Qian, Wei; Zhang, Jianying; Saltzstein, Edward C.; Zheng, Bin; Lure, Fleming; Yu, Hui; Zhou, Shi

    2015-01-01

    Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors’ dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the “prior” screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. PMID:26127038

  1. Psychological impact of providing women with personalised 10-year breast cancer risk estimates.

    PubMed

    French, David P; Southworth, Jake; Howell, Anthony; Harvie, Michelle; Stavrinos, Paula; Watterson, Donna; Sampson, Sarah; Evans, D Gareth; Donnelly, Louise S

    2018-05-08

    The Predicting Risk of Cancer at Screening (PROCAS) study estimated 10-year breast cancer risk for 53,596 women attending NHS Breast Screening Programme. The present study, nested within the PROCAS study, aimed to assess the psychological impact of receiving breast cancer risk estimates, based on: (a) the Tyrer-Cuzick (T-C) algorithm including breast density or (b) T-C including breast density plus single-nucleotide polymorphisms (SNPs), versus (c) comparison women awaiting results. A sample of 2138 women from the PROCAS study was stratified by testing groups: T-C only, T-C(+SNPs) and comparison women; and by 10-year risk estimates received: 'moderate' (5-7.99%), 'average' (2-4.99%) or 'below average' (<1.99%) risk. Postal questionnaires were returned by 765 (36%) women. Overall state anxiety and cancer worry were low, and similar for women in T-C only and T-C(+SNPs) groups. Women in both T-C only and T-C(+SNPs) groups showed lower-state anxiety but slightly higher cancer worry than comparison women awaiting results. Risk information had no consistent effects on intentions to change behaviour. Most women were satisfied with information provided. There was considerable variation in understanding. No major harms of providing women with 10-year breast cancer risk estimates were detected. Research to establish the feasibility of risk-stratified breast screening is warranted.

  2. Expression profiles of loneliness-associated genes for survival prediction in cancer patients.

    PubMed

    You, Liang-Fu; Yeh, Jia-Rong; Su, Mu-Chun

    2014-01-01

    Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for high- lonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Loneliness- associated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.

  3. Risks of Esophageal Cancer Screening

    MedlinePlus

    ... decrease the risk of dying from cancer. Scientists study screening tests to find those with the fewest risks and ... stage . There is no standard or routine screening test for esophageal cancer. Screening for esophageal cancer is under study with screening clinical trials taking place in many ...

  4. Risks of Endometrial Cancer Screening

    MedlinePlus

    ... decrease the risk of dying from cancer. Scientists study screening tests to find those with the fewest risks and ... recovery. There is no standard or routine screening test for endometrial cancer. Screening for endometrial cancer is under study and there are screening clinical trials taking place ...

  5. Early-life family structure and microbially induced cancer risk.

    PubMed

    Blaser, Martin J; Nomura, Abraham; Lee, James; Stemmerman, Grant N; Perez-Perez, Guillermo I

    2007-01-01

    Cancer may follow exposure to an environmental agent after many decades. The bacterium Helicobacter pylori, known to be acquired early in life, increases risk for gastric adenocarcinoma, but other factors are also important. In this study, we considered whether early-life family structure affects the risk of later developing gastric cancer among H. pylori+ men. We examined a long-term cohort of Japanese-American men followed for 28 y, and performed a nested case-control study among those carrying H. pylori or the subset carrying the most virulent cagA+ H. pylori strains to address whether family structure predicted cancer development. We found that among the men who were H. pylori+ and/or cagA+ (it is possible to be cagA+ and H. pylori- if the H. pylori test is falsely negative), belonging to a large sibship or higher birth order was associated with a significantly increased risk of developing gastric adenocarcinoma late in life. For those with cagA+ strains, the risk of developing gastric cancer was more than twice as high (odds ratio 2.2; 95% confidence interval 1.2-4.0) among those in a sibship of seven or more individuals than in a sibship of between one and three persons. These results provide evidence that early-life social environment plays a significant role in risk of microbially induced malignancies expressing five to eight decades later, and these findings lead to new models to explain these interactions.

  6. A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients.

    PubMed

    Sun, Jie; Chen, Xihai; Wang, Zhenzhen; Guo, Maoni; Shi, Hongbo; Wang, Xiaojun; Cheng, Liang; Zhou, Meng

    2015-11-09

    Long non-coding RNAs (lncRNAs) have been implicated in a variety of biological processes, and dysregulated lncRNAs have demonstrated potential roles as biomarkers and therapeutic targets for cancer prognosis and treatment. In this study, by repurposing microarray probes, we analyzed lncRNA expression profiles of 916 breast cancer patients from the Gene Expression Omnibus (GEO). Nine lncRNAs were identified to be significantly associated with metastasis-free survival (MFS) in the training dataset of 254 patients using the Cox proportional hazards regression model. These nine lncRNAs were then combined to form a single prognostic signature for predicting metastatic risk in breast cancer patients that was able to classify patients in the training dataset into high- and low-risk subgroups with significantly different MFSs (median 2.4 years versus 3.0 years, log-rank test p < 0.001). This nine-lncRNA signature was similarly effective for prognosis in a testing dataset and two independent datasets. Further analysis showed that the predictive ability of the signature was independent of clinical variables, including age, ER status, ESR1 status and ERBB2 status. Our results indicated that lncRNA signature could be a useful prognostic marker to predict metastatic risk in breast cancer patients and may improve upon our understanding of the molecular mechanisms underlying breast cancer metastasis.

  7. Metabolic Syndrome and Breast Cancer Risk.

    PubMed

    Wani, Burhan; Aziz, Shiekh Aejaz; Ganaie, Mohammad Ashraf; Mir, Mohammad Hussain

    2017-01-01

    The study was meant to estimate the prevalence of metabolic syndrome in patients with breast cancer and to establish its role as an independent risk factor on occurrence of breast cancer. Fifty women aged between 40 and 80 years with breast cancer and fifty controls of similar age were assessed for metabolic syndrome prevalence and breast cancer risk factors, including age at menarche, reproductive status, live births, breastfeeding, and family history of breast cancer, age at diagnosis of breast cancer, body mass index, and metabolic syndrome parameters. Metabolic syndrome prevalence was found in 40.0% of breast cancer patients, and 18.0% of those in control group ( P = 0.02). An independent and positive association was seen between metabolic syndrome and breast cancer risk (odds ratio = 3.037; 95% confidence interval 1.214-7.597). Metabolic syndrome is more prevalent in breast cancer patients and is an independent risk factor for breast cancer.

  8. The influence of narrative risk communication on feelings of cancer risk.

    PubMed

    Janssen, Eva; van Osch, Liesbeth; de Vries, Hein; Lechner, Lilian

    2013-05-01

    Evidence is accumulating for the importance of feelings of risk in explaining cancer preventive behaviours, but best practices for influencing these feelings are limited. The aim of this experimental study was to compare the effects of narrative and non-narrative risk communication about sunbed use on ease of imagination and feelings of cancer risk. A total of 233 female sunbed users in the general Dutch population were randomly assigned to one of three conditions: a narrative message (i.e., personal testimonial), a non-narrative cognitive message (i.e., factual risk information using cognitive-laden words), or a non-narrative affective message (i.e., factual risk information using affective-laden words). Ease of imagination and feelings of risk were assessed directly after the risk information was given (T1). Three weeks after the baseline session, feelings of risk were measured again (T2). The results revealed that sunbed users who were exposed to narrative risk information could better imagine themselves developing skin cancer and reported higher feelings of skin cancer risk at T1. Moreover, ease of imagination mediated the effects of message type on feelings of risk at T1 and T2. The findings provide support for the effects of narrative risk communication in influencing feelings of cancer risk through ease of imagination. Cancer prevention programmes may therefore benefit from including narrative risk information. Future research is important to investigate other mechanisms of narrative information and their most effective content and format. What is already known on this subject? Evidence is growing for the importance of feelings of risk in explaining cancer preventive behaviours. Narratives have increasingly been considered as an effective format for persuasive risk messages and studies have shown narrative risk communication to be effective in influencing cognitive risk beliefs. What does this study add? Increasing understanding of how feelings of cancer

  9. European cancer mortality predictions for the year 2018 with focus on colorectal cancer.

    PubMed

    Malvezzi, M; Carioli, G; Bertuccio, P; Boffetta, P; Levi, F; La Vecchia, C; Negri, E

    2018-04-01

    We projected cancer mortality statistics for 2018 for the European Union (EU) and its six more populous countries, using the most recent available data. We focused on colorectal cancer. We obtained cancer death certification data from stomach, colorectum, pancreas, lung, breast, uterus, ovary, prostate, bladder, leukaemia, and total cancers from the World Health Organisation database and projected population data from Eurostat. We derived figures for France, Germany, Italy, Poland, Spain, the UK, and the EU in 1970-2012. We predicted death numbers by age group and age-standardized (world population) rates for 2018 through joinpoint regression models. EU total cancer mortality rates are predicted to decline by 10.3% in men between 2012 and 2018, reaching a predicted rate of 128.9/100 000, and by 5.0% in women with a rate of 83.6. The predicted total number of cancer deaths is 1 382 000 when compared with 1 333 362 in 2012 (+3.6%). We confirmed a further fall in male lung cancer, but an unfavourable trend in females, with a rate of 14.7/100 000 for 2018 (13.9 in 2012, +5.8%) and 94 500 expected deaths, higher than the rate of 13.7 and 92 700 deaths from breast cancer. Colorectal cancer predicted rates are 15.8/100 000 men (-6.7%) and 9.2 in women (-7.5%); declines are expected in all age groups. Pancreatic cancer is stable in men, but in women it rose +2.8% since 2012. Ovarian, uterine and bladder cancer rates are predicted to decline further. In 2018 alone, about 392 300 cancer deaths were avoided compared with peak rates in the late 1980s. We predicted continuing falls in mortality rates from major cancer sites in the EU and its major countries to 2018. Exceptions are pancreatic cancer and lung cancer in women. Improved treatment and-above age 50 years-organized screening may account for recent favourable colorectal cancer trends.

  10. Cancer risk estimation in Belarussian children due to thyroid irradiation as a consequence of the Chernobyl nuclear accident

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Buglova, E.; Kenigsberg, J.E.; Sergeeva, N.V.

    1996-07-01

    The thyroid doses received by the juvenile population of Belarus following the Chernobyl accident ranged up to about 10 Gy. The thyroid cancer risk estimate recommended in NCRP Report No. 80 was used to predict the number of thyroid cancer cases among children during 1990-1992 in selected Belarussian regions and cities. The results obtained using this risk estimate show an excess of thyroid cancer cases being registered vs. the predicted cases. Thyroid cancer incidence rate among boys under investigation is higher than among girls in the postaccident period. The excess of the observed over the expected incidence in the generalmore » juvenile population is caused by the high thyroid cancer incidence rate among boys. These results, which can be considered part of the first stage of a thorough thyroid cancer risk estimation after the Chernobyl accident, demonstrate the critical need to complete these studies in depth. 6 refs., 5 figs., 3 tabs.« less

  11. Maternal lung cancer and testicular cancer risk in the offspring.

    PubMed

    Kaijser, Magnus; Akre, Olof; Cnattingius, Sven; Ekbom, Anders

    2003-07-01

    It has been hypothesized that smoking during pregnancy could increase the offspring's risk for testicular cancer. This hypothesis is indirectly supported by both ecological studies and studies of cancer aggregations within families. However, results from analytical epidemiological studies are not consistent, possibly due to methodological difficulties. To further study the association between smoking during pregnancy and testicular cancer, we did a population-based cohort study on cancer risk among offspring of women diagnosed with lung cancer. Through the use of the Swedish Cancer Register and the Swedish Second-Generation Register, we identified 8,430 women who developed lung cancer between 1958 and 1997 and delivered sons between 1941 and 1979. Cancer cases among the male offspring were then identified through the Swedish Cancer Register. Standardized incidence ratios were computed, using 95% confidence intervals. We identified 12,592 male offspring of mothers with a subsequent diagnosis of lung cancer, and there were 40 cases of testicular cancer (standardized incidence ratio, 1.90; 95% confidence interval, 1.35-2.58). The association was independent of maternal lung cancer subtype, and the risk of testicular cancer increased stepwise with decreasing time interval between birth and maternal lung cancer diagnosis. Our results support the hypothesis that exposure to cigarette smoking in utero increases the risk of testicular cancer.

  12. Can one blood draw replace transrectal ultrasonography-estimated prostate volume to predict prostate cancer risk?

    PubMed

    Carlsson, Sigrid V; Peltola, Mari T; Sjoberg, Daniel; Schröder, Fritz H; Hugosson, Jonas; Pettersson, Kim; Scardino, Peter T; Vickers, Andrew J; Lilja, Hans; Roobol, Monique J

    2013-09-01

    To explore whether a panel of kallikrein markers in blood: total, free and intact prostate-specific antigen (PSA) and kallikrein-related peptidase 2, could be used as a non-invasive alternative for predicting prostate cancer on biopsy in a screening setting. The study cohort comprised previously unscreened men who underwent sextant biopsy owing to elevated PSA (≥3 ng/mL) in two different centres of the European Randomized Study of Screening for Prostate Cancer, Rotterdam (n = 2914) and Göteborg (n = 740). A statistical model, based on kallikrein markers, was compared with one based on established clinical factors for the prediction of biopsy outcome. The clinical tests were found to be no better than blood markers, with an area under the curve in favour of the blood measurements of 0.766 vs. 0.763 in Rotterdam and 0.809 vs. 0.774 in Göteborg. Adding digital rectal examination (DRE) or DRE plus transrectal ultrasonography (TRUS) volume to the markers improved discrimination, although the increases were small. Results were similar for predicting high-grade cancer. There was a strong correlation between the blood measurements and TRUS-estimated prostate volume (Spearman's correlation 0.60 in Rotterdam and 0.57 in Göteborg). In previously unscreened men, each with indication for biopsy, a statistical model based on kallikrein levels was similar to a clinical model in predicting prostate cancer in a screening setting, outside the day-to-day clinical practice. Whether a clinical approach can be replaced by laboratory analyses or used in combination with decision models (nomograms) is a clinical judgment that may vary from clinician to clinician depending on how they weigh the different advantages and disadvantages (harms, costs, time, invasiveness) of both approaches. © 2013 BJU International.

  13. High intra-tumoral stromal content defines Reactive breast cancer as a low-risk breast cancer subtype

    PubMed Central

    Dennison, Jennifer B.; Shahmoradgoli, Maria; Liu, Wenbin; Ju, Zhenlin; Meric-Bernstam, Funda; Perou, Charles M.; Sahin, Aysegul A.; Welm, Alana; Oesterreich, Steffi; Sikora, Matthew J.; Brown, Robert E.; Mills, Gordon B.

    2016-01-01

    Purpose The current study evaluated associative effects of breast cancer cells with the tumor microenvironment and its influence on tumor behavior. Experimental design Formalin-fixed paraffin embedded tissue and matched protein lysates were evaluated from two independent breast cancer patient data sets (TCGA and MD Anderson). Reverse-phase protein arrays (RPPA) were utilized to create a proteomics signature to define breast tumor subtypes. Expression patterns of cell lines and normal breast tissues were utilized to determine markers that were differentially expressed in stroma and cancer cells. Protein localization and stromal contents were evaluated for matched cases by imaging. Results A subtype of breast cancers designated “Reactive,” previously identified by RPPA that was not predicted by mRNA profiling, was extensively characterized. These tumors were primarily estrogen receptor (ER)-positive/human epidermal growth factor receptor (HER)2-negative, low-risk cancers as determined by enrichment of low-grade nuclei, lobular or tubular histopathology, and the luminal A subtype by PAM50. Reactive breast cancers contained high numbers of stromal cells and the highest extracellular matrix content typically without infiltration of immune cells. For ER-positive/HER2-negative cancers, the Reactive classification predicted favorable clinical outcomes in the TCGA cohort (HR = 0.36, P < 0.05). Conclusions A protein stromal signature in breast cancers is associated with a highly differentiated phenotype. The stromal compartment content and proteins are an extended phenotype not predicted by mRNA expression that could be utilized to sub-classify ER-positive/HER2-negative breast cancers. PMID:27172895

  14. A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method.

    PubMed

    Papageorgiou, Elpiniki I; Jayashree Subramanian; Karmegam, Akila; Papandrianos, Nikolaos

    2015-11-01

    Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40-49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC). Besides, as each individual woman possesses different levels of risk of developing breast cancer depending on their family history, genetic predispositions and personal medical history, individualized care setting mechanism needs to be identified so that appropriate risk assessment, counseling, screening, and prevention options can be determined by the health care professionals. The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map (FCM) that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women. The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95% diagnostic accuracy. The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer-Cuzick model for 38/40 patient cases (95%). Besides, the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM-based risk prediction methods for BC. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  15. Risk stratification in cervical cancer screening by complete screening history: Applying bioinformatics to a general screening population.

    PubMed

    Baltzer, Nicholas; Sundström, Karin; Nygård, Jan F; Dillner, Joakim; Komorowski, Jan

    2017-07-01

    Women screened for cervical cancer in Sweden are currently treated under a one-size-fits-all programme, which has been successful in reducing the incidence of cervical cancer but does not use all of the participants' available medical information. This study aimed to use women's complete cervical screening histories to identify diagnostic patterns that may indicate an increased risk of developing cervical cancer. A nationwide case-control study was performed where cervical cancer screening data from 125,476 women with a maximum follow-up of 10 years were evaluated for patterns of SNOMED diagnoses. The cancer development risk was estimated for a number of different screening history patterns and expressed as Odds Ratios (OR), with a history of 4 benign cervical tests as reference, using logistic regression. The overall performance of the model was moderate (64% accuracy, 71% area under curve) with 61-62% of the study population showing no specific patterns associated with risk. However, predictions for high-risk groups as defined by screening history patterns were highly discriminatory with ORs ranging from 8 to 36. The model for computing risk performed consistently across different screening history lengths, and several patterns predicted cancer outcomes. The results show the presence of risk-increasing and risk-decreasing factors in the screening history. Thus it is feasible to identify subgroups based on their complete screening histories. Several high-risk subgroups identified might benefit from an increased screening density. Some low-risk subgroups identified could likely have a moderately reduced screening density without additional risk. © 2017 UICC.

  16. Evaluation of BRCA1 and BRCA2 mutation prevalence, risk prediction models and a multistep testing approach in French‐Canadian families with high risk of breast and ovarian cancer

    PubMed Central

    Simard, Jacques; Dumont, Martine; Moisan, Anne‐Marie; Gaborieau, Valérie; Vézina, Hélène; Durocher, Francine; Chiquette, Jocelyne; Plante, Marie; Avard, Denise; Bessette, Paul; Brousseau, Claire; Dorval, Michel; Godard, Béatrice; Houde, Louis; Joly, Yann; Lajoie, Marie‐Andrée; Leblanc, Gilles; Lépine, Jean; Lespérance, Bernard; Malouin, Hélène; Parboosingh, Jillian; Pichette, Roxane; Provencher, Louise; Rhéaume, Josée; Sinnett, Daniel; Samson, Carolle; Simard, Jean‐Claude; Tranchant, Martine; Voyer, Patricia; BRCAs, INHERIT; Easton, Douglas; Tavtigian, Sean V; Knoppers, Bartha‐Maria; Laframboise, Rachel; Bridge, Peter; Goldgar, David

    2007-01-01

    Background and objective In clinical settings with fixed resources allocated to predictive genetic testing for high‐risk cancer predisposition genes, optimal strategies for mutation screening programmes are critically important. These depend on the mutation spectrum found in the population under consideration and the frequency of mutations detected as a function of the personal and family history of cancer, which are both affected by the presence of founder mutations and demographic characteristics of the underlying population. The results of multistep genetic testing for mutations in BRCA1 or BRCA2 in a large series of families with breast cancer in the French‐Canadian population of Quebec, Canada are reported. Methods A total of 256 high‐risk families were ascertained from regional familial cancer clinics throughout the province of Quebec. Initially, families were tested for a panel of specific mutations known to occur in this population. Families in which no mutation was identified were then comprehensively tested. Three algorithms to predict the presence of mutations were evaluated, including the prevalence tables provided by Myriad Genetics Laboratories, the Manchester Scoring System and a logistic regression approach based on the data from this study. Results 8 of the 15 distinct mutations found in 62 BRCA1/BRCA2‐positive families had never been previously reported in this population, whereas 82% carried 1 of the 4 mutations currently observed in ⩾2 families. In the subset of 191 families in which at least 1 affected individual was tested, 29% carried a mutation. Of these 27 BRCA1‐positive and 29 BRCA2‐positive families, 48 (86%) were found to harbour a mutation detected by the initial test. Among the remaining 143 inconclusive families, all 8 families found to have a mutation after complete sequencing had Manchester Scores ⩾18. The logistic regression and Manchester Scores provided equal predictive power, and both were significantly better

  17. Body Fat and Breast Cancer Risk in Postmenopausal Women: A Longitudinal Study

    PubMed Central

    Rohan, Thomas E.; Heo, Moonseong; Choi, Lydia; Freudenheim, Jo L.; Kamensky, Victor; Ochs-Balcom, Heather M.; Thomson, Cynthia A.; Vitolins, Mara Z.; Wassertheil-Smoller, Sylvia; Kabat, Geoffrey C.

    2013-01-01

    Associations between anthropometric indices of obesity and breast cancer risk may fail to capture the true relationship between excess body fat and risk. We used dual-energy-X-ray-absorptiometry- (DXA-) derived measures of body fat obtained in the Women's Health Initiative to examine the association between body fat and breast cancer risk; we compared these risk estimates with those for conventional anthropometric measurements. The study included 10,960 postmenopausal women aged 50–79 years at recruitment, with baseline DXA measurements and no history of breast cancer. During followup (median: 12.9 years), 503 incident breast cancer cases were diagnosed. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox proportional hazards models. All baseline DXA-derived body fat measures showed strong positive associations with breast cancer risk. The multivariable-adjusted HR for the uppermost quintile level (versus lowest) ranged from 1.53 (95% CI 1.14–2.07) for fat mass of the right leg to 2.05 (1.50–2.79) for fat mass of the trunk. Anthropometric indices (categorized by quintiles) of obesity (BMI (1.97, 1.45–2.68), waist circumference (1.97, 1.46–2.65), and waist : hip ratio (1.91, 1.41–2.58)) were all strongly, positively associated with risk and did not differ from DXA-derived measures in prediction of risk. PMID:23690776

  18. Association between family cancer history and risk of pancreatic cancer.

    PubMed

    Schulte, Annaka; Pandeya, Nirmala; Fawcett, Jonathan; Fritschi, Lin; Klein, Kerenaftali; Risch, Harvey A; Webb, Penelope M; Whiteman, David C; Neale, Rachel E

    2016-12-01

    Family history of pancreatic adenocarcinoma is an established risk factor for the disease. However, associations of pancreatic cancer with other familial cancers are less clear. We analyzed data from the Queensland Pancreatic Cancer Study (QPCS), an Australian population-based case-control study, to investigate associations between family history of various cancer types and risk of pancreatic cancer. Our study included 591 pancreatic cancer patients and 646 controls, all of whom self-reported the histories of cancer in their first-degree relatives. We used logistic regression to estimate adjusted odds ratios (ORs) and their 95% confidence intervals (CIs). Based on our results, we conducted a systematic literature review using the Medline (OVID) database to identify articles pertaining to the association between family history of melanoma and risk of pancreatic cancer. A meta-analysis including associations in five published studies, unpublished results from a study co-author and the QPCS results was then performed using the DerSimonian and Laird random-effects model. Cases were more likely than controls to report a family history of pancreatic cancer (OR 2.20, 95% CI 1.16-4.19) and melanoma (OR 1.74, 95% CI 1.03-2.95), but not of breast, ovarian, respiratory, other gastrointestinal or prostate cancer. Meta-analysis of melanoma family history and pancreatic cancer risk yielded an OR of 1.22 (95% CI 1.00-1.51). Our results yield further evidence of increased risk of pancreatic cancer in those with family histories of the disease. We also provide suggestive evidence of an association between family history of melanoma and risk of pancreatic cancer. Copyright © 2016. Published by Elsevier Ltd.

  19. Cancer stem cell marker Musashi-1 rs2522137 genotype is associated with an increased risk of lung cancer.

    PubMed

    Wang, Xu; Hu, Ji-Fan; Tan, Yehui; Cui, Jiuwei; Wang, Guanjun; Mrsny, Randall J; Li, Wei

    2014-01-01

    Gene single nucleotide polymorphisms (SNPs) have been extensively studied in association with development and prognosis of various malignancies. However, the potential role of genetic polymorphisms of cancer stem cell (CSC) marker genes with respect to cancer risk has not been examined. We conducted a case-control study involving a total of 1000 subjects (500 lung cancer patients and 500 age-matched cancer-free controls) from northeastern China. Lung cancer risk was analyzed in a logistic regression model in association with genotypes of four lung CSC marker genes (CD133, ALDH1, Musashi-1, and EpCAM). Using univariate analysis, the Musashi-1 rs2522137 GG genotype was found to be associated with a higher incidence of lung cancer compared with the TT genotype. No significant associations were observed for gene variants of CD133, ALDH1, or EpCAM. In multivariate analysis, Musashi-1 rs2522137 was still significantly associated with lung cancer when environmental and lifestyle factors were incorporated in the model, including lower BMI; family history of cancer; prior diagnosis of chronic obstructive pulmonary disease, pneumonia, or pulmonary tuberculosis; occupational exposure to pesticide; occupational exposure to gasoline or diesel fuel; heavier smoking; and exposure to heavy cooking emissions. The value of the area under the receiver-operating characteristic (ROC) curve (AUC) was 0.7686. To our knowledge, this is the first report to show an association between a Musashi-1 genotype and lung cancer risk. Further, the prediction model in this study may be useful in determining individuals with high risk of lung cancer.

  20. Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification

    PubMed Central

    Kamps, Rick; Brandão, Rita D.; van den Bosch, Bianca J.; Paulussen, Aimee D. C.; Xanthoulea, Sofia; Blok, Marinus J.; Romano, Andrea

    2017-01-01

    Next-generation sequencing (NGS) technology has expanded in the last decades with significant improvements in the reliability, sequencing chemistry, pipeline analyses, data interpretation and costs. Such advances make the use of NGS feasible in clinical practice today. This review describes the recent technological developments in NGS applied to the field of oncology. A number of clinical applications are reviewed, i.e., mutation detection in inherited cancer syndromes based on DNA-sequencing, detection of spliceogenic variants based on RNA-sequencing, DNA-sequencing to identify risk modifiers and application for pre-implantation genetic diagnosis, cancer somatic mutation analysis, pharmacogenetics and liquid biopsy. Conclusive remarks, clinical limitations, implications and ethical considerations that relate to the different applications are provided. PMID:28146134

  1. Cognitive and Emotional Factors Predicting Decisional Conflict among High-Risk Breast Cancer Survivors Who Receive Uninformative BRCA1/2 Results

    PubMed Central

    Rini, Christine; O’Neill, Suzanne C.; Valdimarsdottir, Heiddis; Goldsmith, Rachel E.; DeMarco, Tiffani A.; Peshkin, Beth N.; Schwartz, Marc D.

    2012-01-01

    Objective To investigate high-risk breast cancer survivors’ risk reduction decision making and decisional conflict after an uninformative BRCA1/2 test. Design Prospective, longitudinal study of 182 probands undergoing BRCA1/2 testing, with assessments 1-, 6-, and 12-months post-disclosure. Measures Primary predictors were health beliefs and emotional responses to testing assessed 1-month post-disclosure. Main outcomes included women’s perception of whether they had made a final risk management decision (decision status) and decisional conflict related to this issue. Results There were four patterns of decision making, depending on how long it took women to make a final decision and the stability of their decision status across assessments. Late decision makers and non-decision makers reported the highest decisional conflict; however, substantial numbers of women—even early and intermediate decision makers—reported elevated decisional conflict. Analyses predicting decisional conflict 1- and 12-months post-disclosure found that, after accounting for controls and decision status, health beliefs and emotional factors predicted decisional conflict at different timepoints, with health beliefs more important one month after test disclosure and health beliefs more important one year later. Conclusion Many of these women may benefit from decision making assistance. PMID:19751083

  2. A germline mutation in the BRCA1 3'UTR predicts Stage IV breast cancer.

    PubMed

    Dorairaj, Jemima J; Salzman, David W; Wall, Deirdre; Rounds, Tiffany; Preskill, Carina; Sullivan, Catherine A W; Lindner, Robert; Curran, Catherine; Lezon-Geyda, Kim; McVeigh, Terri; Harris, Lyndsay; Newell, John; Kerin, Michael J; Wood, Marie; Miller, Nicola; Weidhaas, Joanne B

    2014-06-10

    A germline, variant in the BRCA1 3'UTR (rs8176318) was previously shown to predict breast and ovarian cancer risk in women from high-risk families, as well as increased risk of triple negative breast cancer. Here, we tested the hypothesis that this variant predicts tumor biology, like other 3'UTR mutations in cancer. The impact of the BRCA1-3'UTR-variant on BRCA1 gene expression, and altered response to external stimuli was tested in vitro using a luciferase reporter assay. Gene expression was further tested in vivo by immunoflourescence staining on breast tumor tissue, comparing triple negative patient samples with the variant (TG or TT) or non-variant (GG) BRCA1 3'UTR. To determine the significance of the variant on clinically relevant endpoints, a comprehensive collection of West-Irish breast cancer patients were tested for the variant. Finally, an association of the variant with breast screening clinical phenotypes was evaluated using a cohort of women from the High Risk Breast Program at the University of Vermont. Luciferase reporters with the BRCA1-3'UTR-variant (T allele) displayed significantly lower gene expression, as well as altered response to external hormonal stimuli, compared to the non-variant 3'UTR (G allele) in breast cancer cell lines. This was confirmed clinically by the finding of reduced BRCA1 gene expression in triple negative samples from patients carrying the homozygous TT variant, compared to non-variant patients. The BRCA1-3'UTR-variant (TG or TT) also associated with a modest increased risk for developing breast cancer in the West-Irish cohort (OR=1.4, 95% CI 1.1-1.8, p=0.033). More importantly, patients with the BRCA1-3'UTR-variant had a 4-fold increased risk of presenting with Stage IV disease (p=0.018, OR=3.37, 95% CI 1.3-11.0). Supporting that this finding is due to tumor biology, and not difficulty screening, obese women with the BRCA1-3'UTR-variant had significantly less dense breasts (p=0.0398) in the Vermont cohort. A variant in

  3. Adherence to cancer prevention guidelines and risk of breast cancer.

    PubMed

    Catsburg, Chelsea; Miller, Anthony B; Rohan, Thomas E

    2014-11-15

    Healthy eating patterns and keeping physically active are potentially more important for chronic disease prevention than intake or exclusion of specific food items or nutrients. To this end, many health organizations routinely publish dietary and lifestyle recommendations aimed at preventing chronic disease. Using data from the Canadian National Breast Screening Study, we investigated the association between breast cancer risk and adherence to two sets of guidelines specific for cancer prevention, namely the American Cancer Society (ACS) Guidelines and the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Recommendations. At baseline, 49,613 women completed dietary and lifestyle questionnaires and height and weight measurements were taken. During a mean follow-up of 16.6 years, 2,503 incident cases of breast cancer were ascertained. Cox proportional hazard models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association of meeting each guideline, and number of guidelines met, with breast cancer risk. The two sets of guidelines yielded similar results. Specifically, adherence to all six ACS guidelines was associated with a 31% reduction in breast cancer risk when compared to subjects adhering to at most one guideline (HR=0.69; 95% CI=0.49-0.97); similarly, adherence to six or seven of the WCRF/AICR guidelines was also associated with a 31% reduction in breast cancer risk (HR=0.69; 95% CI=0.47-1.00). Under either classification, meeting each additional guideline was associated with a 4-6% reduction in breast cancer risk. These results suggest that adherence to cancer prevention guidelines is associated with a reduced risk of breast cancer. © 2014 UICC.

  4. Combining quantitative and qualitative breast density measures to assess breast cancer risk.

    PubMed

    Kerlikowske, Karla; Ma, Lin; Scott, Christopher G; Mahmoudzadeh, Amir P; Jensen, Matthew R; Sprague, Brian L; Henderson, Louise M; Pankratz, V Shane; Cummings, Steven R; Miglioretti, Diana L; Vachon, Celine M; Shepherd, John A

    2017-08-22

    Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk. We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume. Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women

  5. Signal enhancement ratio (SER) quantified from breast DCE-MRI and breast cancer risk

    NASA Astrophysics Data System (ADS)

    Wu, Shandong; Kurland, Brenda F.; Berg, Wendie A.; Zuley, Margarita L.; Jankowitz, Rachel C.; Sumkin, Jules; Gur, David

    2015-03-01

    Breast magnetic resonance imaging (MRI) is recommended as an adjunct to mammography for women who are considered at elevated risk of developing breast cancer. As a key component of breast MRI, dynamic contrast-enhanced MRI (DCE-MRI) uses a contrast agent to provide high intensity contrast between breast tissues, making it sensitive to tissue composition and vascularity. Breast DCE-MRI characterizes certain physiologic properties of breast tissue that are potentially related to breast cancer risk. Studies have shown that increased background parenchymal enhancement (BPE), which is the contrast enhancement occurring in normal cancer-unaffected breast tissues in post-contrast sequences, predicts increased breast cancer risk. Signal enhancement ratio (SER) computed from pre-contrast and post-contrast sequences in DCE-MRI measures change in signal intensity due to contrast uptake over time and is a measure of contrast enhancement kinetics. SER quantified in breast tumor has been shown potential as a biomarker for characterizing tumor response to treatments. In this work we investigated the relationship between quantitative measures of SER and breast cancer risk. A pilot retrospective case-control study was performed using a cohort of 102 women, consisting of 51 women who had diagnosed with unilateral breast cancer and 51 matched controls (by age and MRI date) with a unilateral biopsy-proven benign lesion. SER was quantified using fully-automated computerized algorithms and three SER-derived quantitative volume measures were compared between the cancer cases and controls using logistic regression analysis. Our preliminary results showed that SER is associated with breast cancer risk, after adjustment for the Breast Imaging Reporting and Data System (BI-RADS)-based mammographic breast density measures. This pilot study indicated that SER has potential for use as a risk factor for breast cancer risk assessment in women at elevated risk of developing breast cancer.

  6. Breast Cancer Risk Reduction, Version 2.2015.

    PubMed

    Bevers, Therese B; Ward, John H; Arun, Banu K; Colditz, Graham A; Cowan, Kenneth H; Daly, Mary B; Garber, Judy E; Gemignani, Mary L; Gradishar, William J; Jordan, Judith A; Korde, Larissa A; Kounalakis, Nicole; Krontiras, Helen; Kumar, Shicha; Kurian, Allison; Laronga, Christine; Layman, Rachel M; Loftus, Loretta S; Mahoney, Martin C; Merajver, Sofia D; Meszoely, Ingrid M; Mortimer, Joanne; Newman, Lisa; Pritchard, Elizabeth; Pruthi, Sandhya; Seewaldt, Victoria; Specht, Michelle C; Visvanathan, Kala; Wallace, Anne; Bergman, Mary Ann; Kumar, Rashmi

    2015-07-01

    Breast cancer is the most frequently diagnosed malignancy in women in the United States and is second only to lung cancer as a cause of cancer death. To assist women who are at increased risk of developing breast cancer and their physicians in the application of individualized strategies to reduce breast cancer risk, NCCN has developed these guidelines for breast cancer risk reduction. Copyright © 2015 by the National Comprehensive Cancer Network.

  7. Pleiotropic analysis of cancer risk loci on esophageal adenocarcinoma risk

    PubMed Central

    Lee, Eunjung; Stram, Daniel O.; Ek, Weronica E.; Onstad, Lynn E; MacGregor, Stuart; Gharahkhani, Puya; Ye, Weimin; Lagergren, Jesper; Shaheen, Nicholas J.; Murray, Liam J.; Hardie, Laura J; Gammon, Marilie D.; Chow, Wong-Ho; Risch, Harvey A.; Corley, Douglas A.; Levine, David M; Whiteman, David C.; Bernstein, Leslie; Bird, Nigel C.; Vaughan, Thomas L.; Wu, Anna H.

    2015-01-01

    Background Several cancer-associated loci identified from genome-wide association studies (GWAS) have been associated with risks of multiple cancer sites, suggesting pleiotropic effects. We investigated whether GWAS-identified risk variants for other common cancers are associated with risk of esophageal adenocarcinoma (EA) or its precursor, Barrett's esophagus (BE). Methods We examined the associations between risks of EA and BE and 387 single nucleotide polymorphisms (SNPs) that have been associated with risks of other cancers, by using genotype imputation data on 2,163 control participants and 3,885 (1,501 EA and 2,384 BE) case patients from the Barrett's and Esophageal Adenocarcinoma Genetic Susceptibility Study, and investigated effect modification by smoking history, body mass index (BMI), and reflux/heartburn. Results After correcting for multiple testing, none of the tested 387 SNPs were statistically significantly associated with risk of EA or BE. No evidence of effect modification by smoking, BMI, or reflux/heartburn was observed. Conclusions Genetic risk variants for common cancers identified from GWAS appear not to be associated with risks of EA or BE. Impact To our knowledge, this is the first investigation of pleiotropic genetic associations with risks of EA and BE. PMID:26364162

  8. Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer.

    PubMed

    Hsu, Jia-Lien; Hung, Ping-Cheng; Lin, Hung-Yen; Hsieh, Chung-Ho

    2015-04-01

    Breast cancer is one of the most common cause of cancer mortality. Early detection through mammography screening could significantly reduce mortality from breast cancer. However, most of screening methods may consume large amount of resources. We propose a computational model, which is solely based on personal health information, for breast cancer risk assessment. Our model can be served as a pre-screening program in the low-cost setting. In our study, the data set, consisting of 3976 records, is collected from Taipei City Hospital starting from 2008.1.1 to 2008.12.31. Based on the dataset, we first apply the sampling techniques and dimension reduction method to preprocess the testing data. Then, we construct various kinds of classifiers (including basic classifiers, ensemble methods, and cost-sensitive methods) to predict the risk. The cost-sensitive method with random forest classifier is able to achieve recall (or sensitivity) as 100 %. At the recall of 100 %, the precision (positive predictive value, PPV), and specificity of cost-sensitive method with random forest classifier was 2.9 % and 14.87 %, respectively. In our study, we build a breast cancer risk assessment model by using the data mining techniques. Our model has the potential to be served as an assisting tool in the breast cancer screening.

  9. Breast cancer risk from different mammography screening practices.

    PubMed

    Bijwaard, Harmen; Brenner, Alina; Dekkers, Fieke; van Dillen, Teun; Land, Charles E; Boice, John D

    2010-09-01

    Mammography screening is an accepted procedure for early detection of breast tumors among asymptomatic women. Since this procedure involves the use of X rays, it is itself potentially carcinogenic. Although there is general consensus about the benefit of screening for older women, screening practices differ between countries. In this paper radiation risks for these different practices are estimated using a new approach. We model breast cancer induction by ionizing radiation in a cohort of patients exposed to frequent X-ray examinations. The biologically based, mechanistic model provides a better foundation for the extrapolation of risks to different mammography screening practices than empirical models do. The model predicts that the excess relative risk (ERR) doubles when screening starts at age 40 instead of 50 and that a continuation of screening at ages 75 and higher carries little extra risk. The number of induced fatal breast cancers is estimated to be considerably lower than derived from epidemiological studies and from internationally accepted radiation protection risks. The present findings, if used in a risk-benefit analysis for mammography screening, would be more favorable to screening than estimates currently recommended for radiation protection. This has implications for the screening ages that are currently being reconsidered in several countries.

  10. Identifying risk factors for refractory febrile neutropenia in patients with lung cancer.

    PubMed

    Fujita, Masaki; Tokunaga, Shoji; Ikegame, Satoshi; Harada, Eiji; Matsumoto, Takemasa; Uchino, Junji; Watanabe, Kentaro; Nakanishi, Yoichi

    2012-02-01

    Information about the development of febrile neutropenia in patients with solid tumors remains insufficient. In this study, we tried to identify the risk factors for refractory febrile neutropenia in patients with lung cancer. A total of 59 neutropenic fever episodes associated with anti-tumor chemotherapy for lung cancer were retrospectively analyzed. We compared patient characteristics according to their initial response to treatment with antibiotics. For 34 of 59 (58%) episodes a response to initial antibiotics was obtained whereas 25 of 59 (42%) were refractory to treatment. Multivariate analysis demonstrated independent risk factors for refractory febrile neutropenia with lung cancer. These risk factors were the severity of febrile neutropenia (odds ratio (OR) 6.11; 95% confidence interval (CI) 1.85-20.14) and C-reactive protein more than 10 mg/dl (OR 4.39; 95% CI 1.22-15.74). These factors could predict outcome for patients with lung cancer who develop refractory febrile neutropenia.

  11. Obesity, physical activity and cancer risks: Results from the Cancer, Lifestyle and Evaluation of Risk Study (CLEAR).

    PubMed

    Nunez, Carlos; Bauman, Adrian; Egger, Sam; Sitas, Freddy; Nair-Shalliker, Visalini

    2017-04-01

    Physical activity (PA) has been associated with lower risk of cardiovascular diseases, but the evidence linking PA with lower cancer risk is inconclusive. We examined the independent and interactive effects of PA and obesity using body mass index (BMI) as a proxy for obesity, on the risk of developing prostate (PC), postmenopausal breast (BC), colorectal (CRC), ovarian (OC) and uterine (UC) cancers. We estimated odds ratios (OR) and 95% confidence intervals (CI), adjusting for cancer specific confounders, in 6831 self-reported cancer cases and 1992 self-reported cancer-free controls from the Cancer Lifestyle and Evaluation of Risk Study, using unconditional logistic regression. For women, BMI was positively associated with UC risk; specifically, obese women (BMI≥30kg/m 2 ) had nearly twice the risk of developing UC compared to women with healthy-BMI-range (<25kg/m 2 ) (OR=1.99;CI:1.31-3.03). For men, BMI was also positively associated with the risk of developing any cancer type, CRC and PC. In particular, obese men had 37% (OR=1.37;CI:1.11-1.70), 113% (OR=2.13;CI:1.55-2.91) and 51% (OR=1.51;CI:1.17-1.94) higher risks of developing any cancer, CRC and PC respectively, when compared to men with healthy-BMI-range (BMI<25kg/m 2 ). Among women, PA was inversely associated with the risks of CRC, UC and BC. In particular, the highest level of PA (versus nil activity) was associated with reduced risks of CRC (OR=0.60;CI:0.44-0.84) and UC (OR=0.47;CI:0.27-0.80). Reduced risks of BC were associated with low (OR=0.66;CI:0.51-0.86) and moderate (OR=0.72;CI:0.57-0.91) levels of PA. There was no association between PA levels and cancer risks for men. We found no evidence of an interaction between BMI and PA in the CLEAR study. These findings suggest that PA and obesity are independent cancer risk factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Classification of TP53 mutations and HPV predict survival in advanced larynx cancer.

    PubMed

    Scheel, Adam; Bellile, Emily; McHugh, Jonathan B; Walline, Heather M; Prince, Mark E; Urba, Susan; Wolf, Gregory T; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E; Bradford, Carol

    2016-09-01

    Assess tumor suppressor p53 (TP53) functional mutations in the context of other biomarkers in advanced larynx cancer. Prospective analysis of pretreatment tumor TP53, human papillomavirus (HPV), Bcl-xL, and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. TP53 exons 4 through 9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl, and cyclin D1 expression. TP53 mutations were found in 22 of 58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13 of 58 (22.4%) patients, nonsense mutations in four of 58 (6.9%), and deletions in five of 58 (8.6%). High-risk HPV was found in 20 of 52 (38.5%) tumors. A classification based on Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low-risk mutations (P = 0.0315). A model including this TP53 classification, HPV status, cyclin D1, and Bcl-xL staining significantly predicts survival (P = 0.0017). EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. NA. Laryngoscope, 126:E292-E299, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

  13. Validation of a predictive model that identifies patients at high risk of developing febrile neutropaenia following chemotherapy for breast cancer.

    PubMed

    Jenkins, P; Scaife, J; Freeman, S

    2012-07-01

    We have previously developed a predictive model that identifies patients at increased risk of febrile neutropaenia (FN) following chemotherapy, based on pretreatment haematological indices. This study was designed to validate our earlier findings in a separate cohort of patients undergoing more myelosuppressive chemotherapy supported by growth factors. We conducted a retrospective analysis of 263 patients who had been treated with adjuvant docetaxel, adriamycin and cyclophosphamide (TAC) chemotherapy for breast cancer. All patients received prophylactic pegfilgrastim and the majority also received prophylactic antibiotics. Thirty-one patients (12%) developed FN. Using our previous model, patients in the highest risk group (pretreatment absolute neutrophil count≤3.1 10(9)/l and absolute lymphocyte count≤1.5 10(9)/l) comprised 8% of the total population and had a 33% risk of developing FN. Compared with the rest of the cohort, this group had a 3.4-fold increased risk of developing FN (P=0.001) and a 5.2-fold increased risk of cycle 1 FN (P<0.001). A simple model based on pretreatment differential white blood cell count can be applied to pegfilgrastim-supported patients to identify those who are at higher risk of FN.

  14. Preoperative CA125 and fibrinogen in patients with endometrial cancer: a risk model for predicting lymphovascular space invasion

    PubMed Central

    2017-01-01

    Objective The aim of this study was to build a model to predict the risk of lymphovascular space invasion (LVSI) in women with endometrial cancer (EC). Methods From December 2010 to June 2013, 211 patients with EC undergoing surgery at Shanghai First Maternity and Infant Hospital were enrolled in this retrospective study. Those patients were divided into a positive LVSI group and a negative LVSI group. The clinical and pathological characteristics were compared between the two groups; logistic regression was used to explore risk factors associated with LVSI occurrence. The threshold values of significant factors were calculated to build a risk model and predict LVSI. Results There were 190 patients who were negative for LVSI and 21 patients were positive for LVSI out of 211 patients with EC. It was found that tumor grade, depth of myometrial invasion, number of pelvic lymph nodes, and International Federation of Gynecology and Obstetrics (FIGO) stage (p<0.05) were associated with LVSI occurrence. However, cervical involvement and age (p>0.05) were not associated with LVSI. Receiver operating characteristic (ROC) curves revealed that the threshold values of the following factors were correlated with positive LVSI: 28.1 U/mL of CA19-9, 21.2 U/mL of CA125, 2.58 mg/dL of fibrinogen (Fn), 1.84 U/mL of carcinoembryonic antigen (CEA) and (6.35×109)/L of white blood cell (WBC). Logistic regression analysis indicated that CA125 ≥21.2 (p=0.032) and Fn ≥2.58 mg/dL (p=0.014) were significantly associated with LVSI. Conclusion Positive LVSI could be predicted by CA125 ≥21.2 U/mL and Fn ≥2.58 mg/dL in women with EC. It could help gynecologists better adapt surgical staging and adjuvant therapies. PMID:27894164

  15. Risk factors for deep venous thrombosis in women with ovarian cancer

    PubMed Central

    Ebina, Yasuhiko; Uchiyama, Mihoko; Imafuku, Hitomi; Suzuki, Kaho; Miyahara, Yoshiya; Yamada, Hideto

    2018-01-01

    Abstract We aim to clarify the incidence of deep venous thrombosis (DVT) before treatment in women with ovarian cancer and identify risk factors for DVT. In this prospective study, 110 women underwent venous ultrasonography before cancer treatment and D-dimer levels were measured. We investigated factors predicting DVT by logistic regression. DVT was detected in 25 of 110 women (22.7%) and pulmonary thromboembolism was coexisted in 2 women (1.8%). A total of 21 women (84.4%) with DVT were asymptomatic. D-dimer levels in women with DVT (median, 10.9; range, <0.5–98.2 μg/mL) were significantly higher than those in women without DVT (2.0; <0.5–60.8 μg/mL; P < .01). When 10.9 μg/mL was used as a cutoff value for D-dimer levels to predict DVT, specificity, sensitivity, and positive and negative predictive values were 92.9%, 52.0%, 68.4%, and 86.8%, respectively. The multivariate analysis demonstrated that D-dimer level (odds ratio [OR], 19.7; 95% confidence interval [CI], 5.89–76.76) and clear cell histology (OR, 7.1; 95% CI, 2.12–25.67) were independent factors predicting DVT. Asymptomatic DVT occurred with great frequency before treatment in patients with ovarian cancer. High D-dimer level and clear cell pathology is associated with a higher DVT risk. PMID:29879062

  16. A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

    NASA Astrophysics Data System (ADS)

    Sun, Wenqing; Tseng, Tzu-Liang B.; Zheng, Bin; Zhang, Jianying; Qian, Wei

    2015-03-01

    A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.

  17. Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE.

    PubMed

    Mavaddat, Nasim; Peock, Susan; Frost, Debra; Ellis, Steve; Platte, Radka; Fineberg, Elena; Evans, D Gareth; Izatt, Louise; Eeles, Rosalind A; Adlard, Julian; Davidson, Rosemarie; Eccles, Diana; Cole, Trevor; Cook, Jackie; Brewer, Carole; Tischkowitz, Marc; Douglas, Fiona; Hodgson, Shirley; Walker, Lisa; Porteous, Mary E; Morrison, Patrick J; Side, Lucy E; Kennedy, M John; Houghton, Catherine; Donaldson, Alan; Rogers, Mark T; Dorkins, Huw; Miedzybrodzka, Zosia; Gregory, Helen; Eason, Jacqueline; Barwell, Julian; McCann, Emma; Murray, Alex; Antoniou, Antonis C; Easton, Douglas F

    2013-06-05

    Reliable estimates of cancer risk are critical for guiding management of BRCA1 and BRCA2 mutation carriers. The aims of this study were to derive penetrance estimates for breast cancer, ovarian cancer, and contralateral breast cancer in a prospective series of mutation carriers and to assess how these risks are modified by common breast cancer susceptibility alleles. Prospective cancer risks were estimated using a cohort of 978 BRCA1 and 909 BRCA2 carriers from the United Kingdom. Nine hundred eighty-eight women had no breast or ovarian cancer diagnosis at baseline, 1509 women were unaffected by ovarian cancer, and 651 had been diagnosed with unilateral breast cancer. Cumulative risks were obtained using Kaplan-Meier estimates. Associations between cancer risk and covariables of interest were evaluated using Cox regression. All statistical tests were two-sided. The average cumulative risks by age 70 years for BRCA1 carriers were estimated to be 60% (95% confidence interval [CI] = 44% to 75%) for breast cancer, 59% (95% CI = 43% to 76%) for ovarian cancer, and 83% (95% CI = 69% to 94%) for contralateral breast cancer. For BRCA2 carriers, the corresponding risks were 55% (95% CI = 41% to 70%) for breast cancer, 16.5% (95% CI = 7.5% to 34%) for ovarian cancer, and 62% (95% CI = 44% to 79.5%) for contralateral breast cancer. BRCA2 carriers in the highest tertile of risk, defined by the joint genotype distribution of seven single nucleotide polymorphisms associated with breast cancer risk, were at statistically significantly higher risk of developing breast cancer than those in the lowest tertile (hazard ratio = 4.1, 95% CI = 1.2 to 14.5; P = .02). Prospective risk estimates confirm that BRCA1 and BRCA2 carriers are at high risk of developing breast, ovarian, and contralateral breast cancer. Our results confirm findings from retrospective studies that common breast cancer susceptibility alleles in combination are predictive of breast cancer risk for BRCA2 carriers.

  18. Status of estrogen receptor 1 (ESR1) gene in mastopathy predicts subsequent development of breast cancer.

    PubMed

    Soysal, Savas D; Kilic, Incken B; Regenbrecht, Christian R A; Schneider, Sandra; Muenst, Simone; Kilic, Nerbil; Güth, Uwe; Dietel, Manfred; Terracciano, Luigi M; Kilic, Ergin

    2015-06-01

    Mastopathy is a common disease of the breast likely associated with elevated estrogen levels and a putative risk factor for breast cancer. The role of estrogen receptor alpha (ESR1) in mastopathy has not been investigated previously. Here, we investigated the prevalence of ESR1 gene amplification in mastopathy and its prediction for breast cancer. Paraffin-embedded tissues from 58 women with invasive breast cancer were analyzed. For all women, tissues with mastopathy taken at least 1.5 years before first diagnosis of breast cancer were available. Tissue from 46 women with mastopathy without a diagnosis of breast carcinoma in the observed time frame (12-18 years) was used as control. Fluorescence in situ hybridization analysis revealed that ESR1 was amplified in nine of 58 (15.5 %) breast cancers. All ESR1-amplified breast cancers were strongly positive for estrogen receptor with ER immunohistochemistry. Interestingly, in women with ESR1 amplification in breast cancer, the amplification was detectable in mastopathic tissues prior to the first diagnosis of breast cancer but was absent in tissues from women with mastopathy who did not develop breast cancer. Our study suggests that ESR1 gene amplification is an early event in breast pathology and might be a helpful predictive marker to identify patients at high risk of developing breast cancer.

  19. Metabolic Syndrome and Risk of Cancer

    PubMed Central

    Esposito, Katherine; Chiodini, Paolo; Colao, Annamaria; Lenzi, Andrea; Giugliano, Dario

    2012-01-01

    OBJECTIVE Available evidence supports the emerging hypothesis that metabolic syndrome may be associated with the risk of some common cancers. We did a systematic review and meta-analysis to assess the association between metabolic syndrome and risk of cancer at different sites. RESEARCH DESIGN AND METHODS We conducted an electronic search for articles published through October 2011 without restrictions and by reviewing reference lists from retrieved articles. Every included study was to report risk estimates with 95% CIs for the association between metabolic syndrome and cancer. RESULTS We analyzed 116 datasets from 43 articles, including 38,940 cases of cancer. In cohort studies in men, the presence of metabolic syndrome was associated with liver (relative risk 1.43, P < 0.0001), colorectal (1.25, P < 0.001), and bladder cancer (1.10, P = 0.013). In cohort studies in women, the presence of metabolic syndrome was associated with endometrial (1.61, P = 0.001), pancreatic (1.58, P < 0.0001), breast postmenopausal (1.56, P = 0.017), rectal (1.52, P = 0.005), and colorectal (1.34, P = 0.006) cancers. Associations with metabolic syndrome were stronger in women than in men for pancreatic (P = 0.01) and rectal (P = 0.01) cancers. Associations were different between ethnic groups: we recorded stronger associations in Asia populations for liver cancer (P = 0.002), in European populations for colorectal cancer in women (P = 0.004), and in U.S. populations (whites) for prostate cancer (P = 0.001). CONCLUSIONS Metabolic syndrome is associated with increased risk of common cancers; for some cancers, the risk differs betweens sexes, populations, and definitions of metabolic syndrome. PMID:23093685

  20. Telomere length, ATM mutation status and cancer risk in Ataxia-Telangiectasia families.

    PubMed

    Renault, Anne-Laure; Mebirouk, Noura; Cavaciuti, Eve; Le Gal, Dorothée; Lecarpentier, Julie; d'Enghien, Catherine Dubois; Laugé, Anthony; Dondon, Marie-Gabrielle; Labbé, Martine; Lesca, Gaetan; Leroux, Dominique; Gladieff, Laurence; Adenis, Claude; Faivre, Laurence; Gilbert-Dussardier, Brigitte; Lortholary, Alain; Fricker, Jean-Pierre; Dahan, Karin; Bay, Jacques-Olivier; Longy, Michel; Buecher, Bruno; Janin, Nicolas; Zattara, Hélène; Berthet, Pascaline; Combès, Audrey; Coupier, Isabelle; Hall, Janet; Stoppa-Lyonnet, Dominique; Andrieu, Nadine; Lesueur, Fabienne

    2017-10-01

    Recent studies have linked constitutive telomere length (TL) to aging-related diseases including cancer at different sites. ATM participates in the signaling of telomere erosion, and inherited mutations in ATM have been associated with increased risk of cancer, particularly breast cancer. The goal of this study was to investigate whether carriage of an ATM mutation and TL interplay to modify cancer risk in ataxia-telangiectasia (A-T) families.The study population consisted of 284 heterozygous ATM mutation carriers (HetAT) and 174 non-carriers (non-HetAT) from 103 A-T families. Forty-eight HetAT and 14 non-HetAT individuals had cancer, among them 25 HetAT and 6 non-HetAT were diagnosed after blood sample collection. We measured mean TL using a quantitative PCR assay and genotyped seven single-nucleotide polymorphisms (SNPs) recurrently associated with TL in large population-based studies.HetAT individuals were at increased risk of cancer (OR = 2.3, 95%CI = 1.2-4.4, P = 0.01), and particularly of breast cancer for women (OR = 2.9, 95%CI = 1.2-7.1, P = 0.02), in comparison to their non-HetAT relatives. HetAT individuals had longer telomeres than non-HetAT individuals (P = 0.0008) but TL was not associated with cancer risk, and no significant interaction was observed between ATM mutation status and TL. Furthermore, rs9257445 (ZNF311) was associated with TL in HetAT subjects and rs6060627 (BCL2L1) modified cancer risk in HetAT and non-HetAT women.Our findings suggest that carriage of an ATM mutation impacts on the age-related TL shortening and that TL per se is not related to cancer risk in ATM carriers. TL measurement alone is not a good marker for predicting cancer risk in A-T families. © The Author 2017. Published by Oxford University Press.

  1. Lung cancer risk due to residential radon exposures: estimation and prevention.

    PubMed

    Truta, L A; Hofmann, W; Cosma, C

    2014-07-01

    Epidemiological studies proved that cumulative exposure to radon is the second leading cause of lung cancer, the world's most common cancer. The objectives of the present study are (i) to analyse lung cancer risk for chronic, low radon exposures based on the transformation frequency-tissue response (TF-TR) model formulated in terms of alpha particle hits in cell nuclei; (ii) to assess the percentage of attributable lung cancers in six areas of Transylvania where the radon concentration was measured and (iii) to point out the most efficient remediation measures tested on a pilot house in Stei, Romania. Simulations performed with the TF-TR model exhibit a linear dose-effect relationship for chronic, residential radon exposures. The fraction of lung cancer cases attributed to radon ranged from 9 to 28% for the investigated areas. Model predictions may represent a useful tool to complement epidemiological studies on lung cancer risk and to establish reasonable radiation protection regulations for human safety. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. A New Formula for Prostate Cancer Lymph Node Risk

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yu, James B., E-mail: james.b.yu@yale.ed; Yale Cancer Center, New Haven, CT; Makarov, Danil V.

    2011-05-01

    Introduction: The successful treatment of prostate cancer depends on the accurate estimation of the risk of regional lymph node (LN) involvement. The Roach formula (RF) has been criticized as overestimating LN risk. A modification of the RF has been attempted by other investigators using simplified adjustment ratios: the Nguyen formula (NF). Methods and Materials: The National Cancer Institute Surveillance, Epidemiology, and End Results database was investigated for patients treated in 2004 through 2006 for whom at least 10 LN were examined at radical prostatectomy, cT1c or cT2 disease, and prostate-specific antigen (PSA) <26 ng/ml (N = 2,930). The Yale formulamore » (YF) was derived from half of the sample (n = 1,460), and validated in the other half (n = 1,470). Results: We identified 2,930 patients. Only 4.6% of patients had LN+, and 72.6% had cT1c disease. Gleason (GS) 8-10 histology was found in 14.4% of patients. The YF for prediction of %LN+ risk is [GS - 5]x [PSA/3 + 1.5 x T], where T = 0, 1, and 2 for cT1c, cT2a, and cT2b/cT2c. Within each strata of predicted %LN+ risk, the actual %LN+ was closest to the YF. Using a >15% risk as an indicator of high-risk disease, the YF had increased sensitivity (39.0% vs. 13.6%) compared with the NF, without a significant reduction in specificity (94.9% vs. 98.8%). The NF was overly restrictive of the high-risk group, with only 2% of patients having a >15% risk of LN+ by that formula. Conclusion: The YF performed better than the RF and NF and was best at differentiating patients at high risk for LN+ disease.« less

  3. Diet Quality Scores and Prediction of All-Cause, Cardiovascular and Cancer Mortality in a Pan-European Cohort Study

    PubMed Central

    Lassale, Camille; Gunter, Marc J.; Romaguera, Dora; Peelen, Linda M.; Van der Schouw, Yvonne T.; Beulens, Joline W. J.; Freisling, Heinz; Muller, David C.; Ferrari, Pietro; Huybrechts, Inge; Fagherazzi, Guy; Boutron-Ruault, Marie-Christine; Affret, Aurélie; Overvad, Kim; Dahm, Christina C.; Olsen, Anja; Roswall, Nina; Tsilidis, Konstantinos K.; Katzke, Verena A.; Kühn, Tilman; Buijsse, Brian; Quirós, José-Ramón; Sánchez-Cantalejo, Emilio; Etxezarreta, Nerea; Huerta, José María; Barricarte, Aurelio; Bonet, Catalina; Khaw, Kay-Tee; Key, Timothy J.; Trichopoulou, Antonia; Bamia, Christina; Lagiou, Pagona; Palli, Domenico; Agnoli, Claudia; Tumino, Rosario; Fasanelli, Francesca; Panico, Salvatore; Bueno-de-Mesquita, H. Bas; Boer, Jolanda M. A.; Sonestedt, Emily; Nilsson, Lena Maria; Renström, Frida; Weiderpass, Elisabete; Skeie, Guri; Lund, Eiliv; Moons, Karel G. M.; Riboli, Elio; Tzoulaki, Ioanna

    2016-01-01

    Scores of overall diet quality have received increasing attention in relation to disease aetiology; however, their value in risk prediction has been little examined. The objective was to assess and compare the association and predictive performance of 10 diet quality scores on 10-year risk of all-cause, CVD and cancer mortality in 451,256 healthy participants to the European Prospective Investigation into Cancer and Nutrition, followed-up for a median of 12.8y. All dietary scores studied showed significant inverse associations with all outcomes. The range of HRs (95% CI) in the top vs. lowest quartile of dietary scores in a composite model including non-invasive factors (age, sex, smoking, body mass index, education, physical activity and study centre) was 0.75 (0.72–0.79) to 0.88 (0.84–0.92) for all-cause, 0.76 (0.69–0.83) to 0.84 (0.76–0.92) for CVD and 0.78 (0.73–0.83) to 0.91 (0.85–0.97) for cancer mortality. Models with dietary scores alone showed low discrimination, but composite models also including age, sex and other non-invasive factors showed good discrimination and calibration, which varied little between different diet scores examined. Mean C-statistic of full models was 0.73, 0.80 and 0.71 for all-cause, CVD and cancer mortality. Dietary scores have poor predictive performance for 10-year mortality risk when used in isolation but display good predictive ability in combination with other non-invasive common risk factors. PMID:27409582

  4. Risk factors, lifetime risk, and age at onset of breast cancer.

    PubMed

    Fraser, G E; Shavlik, D

    1997-08-01

    We evaluated the relationship between exposure variables and both lifetime risk and mean age at diagnosis of breast cancer in subjects from the Adventist Health Study who developed breast cancer before the age of 91 years. Multiple decrement life-table analysis was used. This study provided data from 20,341 women followed for 6 years. In the total population, 30-year-old women with a parental history of any cancer or a maternal history of breast cancer had, respectively, 72% (P < 0.002) and 98% (P < 0.03) higher lifetime risks of breast cancer. Thirty-year-old women who had their first delivery after age 24 years or body mass indices above the 50th percentile had, respectively, 53% (P < 0.007) or 57% (P = 0.01) greater lifetime risk of breast cancer. Women who exercised infrequently had a 27% higher life-time risk (P = 0.09) and an age at diagnosis of breast cancer 6.6 years younger (P < 0.005) than other women. Standard risk factors account for substantial increases in lifetime risk of breast cancer and may be associated with differences in age at diagnosis.

  5. Lung cancer risk associated with Thr495Pro polymorphism of GHR in Chinese population.

    PubMed

    Cao, Guochun; Lu, Hongna; Feng, Jifeng; Shu, Jian; Zheng, Datong; Hou, Yayi

    2008-04-01

    The incidence of lung cancer has been increasing over recent decades. Previous studies showed that polymorphisms of the genes involved in carcinogen-detoxication, DNA repair and cell cycle control comprise risk factors for lung cancer. Recent observations revealed that the growth hormone receptor (GHR) might play important roles in carcinogenesis and Rudd et al. found that the Thr495Pro polymorphism of GHR was strongly associated with lung cancer risk in Caucasians living in the UK (OR = 12.98, P = 0.0019, 95% CI: 1.77-infinity). To test whether this variant of GHR would modify the risk of lung cancer in Chinese population, we compared the polymorphism between 778 lung cancer patients and 781 healthy control subjects. Our results indicate that the frequency of 495Thr (2.8%) allele in cases was significantly higher than in controls (OR = 2.04, P = 0.006, 95% CI: 1.21-3.42) which indicated this allele might be a risk factor for lung cancer. Further analyses revealed Thr495Pro variant was associated with lung cancer in the subpopulation with higher risk for lung cancer: male subpopulation, still-smokers subpopulation and the subpopulation with familial history of cancer. In different histological types of lung cancer, Thr495Pro SNP was significantly associated with small cell and squamous cell lung cancer, but not with adenocarcinoma, which suggested a potential interaction between this polymorphism and metabolic pathways related to smoking. The potential gene-environment interaction on lung cancer risk was evaluated using MDR software. A significant redundant interaction between Thr495Pro polymorphism and smoking dose and familial history of cancer was identified and the combination of genetic factors and smoking status or familial history of cancer barely increased the cancer risk prediction accuracy. In conclusion, our results suggested that the Thr495Pro polymorphism of GHR was associated with the risk of lung cancer in a redundant interaction with smoking and

  6. Risk perception measures' associations with behavior intentions, affect, and cognition following colon cancer screening messages.

    PubMed

    Dillard, Amanda J; Ferrer, Rebecca A; Ubel, Peter A; Fagerlin, Angela

    2012-01-01

    Risk perception is important for motivating health behavior (e.g., Janz & Becker, 1984), but different measures of the construct may change how important that relationship appears. In two studies, we examined associations between four measures of risk perception, health behavior intentions and possible behavioral determinants. Participants in these studies, who were due for colorectal cancer screening, read an online message about the importance of screening to reduce the chance of cancer. We examined bivariate and multivariate associations between risk perception measures, including absolute, comparative, and feelings-of-risk, and behavioral intentions to screen, general worry, and knowledge and attitudes related to screening. Results across the two studies were consistent, with all risk perception measures being correlated with intentions and attitudes. Multivariate analyses revealed that feelings-of-risk was most predictive of all variables, with the exception of general worry, for which comparative measures were the most predictive. Researchers interested in risk perception should assess feelings-of-risk along with more traditional measures. Those interested in influencing health behavior specifically should attempt to increase feelings of vulnerability rather than numerical risk.

  7. Risks of Colorectal Cancer Screening

    MedlinePlus

    ... blood test Sigmoidoscopy Colonoscopy Virtual colonoscopy DNA stool test Studies have shown that screening for colorectal cancer using ... decrease the risk of dying from cancer. Scientists study screening tests to find those with the fewest risks and ...

  8. Left-sided breast cancer and risks of secondary lung cancer and ischemic heart disease : Effects of modern radiotherapy techniques.

    PubMed

    Corradini, Stefanie; Ballhausen, Hendrik; Weingandt, Helmut; Freislederer, Philipp; Schönecker, Stephan; Niyazi, Maximilian; Simonetto, Cristoforo; Eidemüller, Markus; Ganswindt, Ute; Belka, Claus

    2018-03-01

    Modern breast cancer radiotherapy techniques, such as respiratory-gated radiotherapy in deep-inspiration breath-hold (DIBH) or volumetric-modulated arc radiotherapy (VMAT) have been shown to reduce the high dose exposure of the heart in left-sided breast cancer. The aim of the present study was to comparatively estimate the excess relative and absolute risks of radiation-induced secondary lung cancer and ischemic heart disease for different modern radiotherapy techniques. Four different treatment plans were generated for ten computed tomography data sets of patients with left-sided breast cancer, using either three-dimensional conformal radiotherapy (3D-CRT) or VMAT, in free-breathing (FB) or DIBH. Dose-volume histograms were used for organ equivalent dose (OED) calculations using linear, linear-exponential, and plateau models for the lung. A linear model was applied to estimate the long-term risk of ischemic heart disease as motivated by epidemiologic data. Excess relative risk (ERR) and 10-year excess absolute risk (EAR) for radiation-induced secondary lung cancer and ischemic heart disease were estimated for different representative baseline risks. The DIBH maneuver resulted in a significant reduction of the ERR and estimated 10-year excess absolute risk for major coronary events compared to FB in 3D-CRT plans (p = 0.04). In VMAT plans, the mean predicted risk reduction through DIBH was less pronounced and not statistically significant (p = 0.44). The risk of radiation-induced secondary lung cancer was mainly influenced by the radiotherapy technique, with no beneficial effect through DIBH. VMAT plans correlated with an increase in 10-year EAR for radiation-induced lung cancer as compared to 3D-CRT plans (DIBH p = 0.007; FB p = 0.005, respectively). However, the EARs were affected more strongly by nonradiation-associated risk factors, such as smoking, as compared to the choice of treatment technique. The results indicate that 3D-CRT plans in DIBH pose the

  9. Familial risks and estrogen receptor-positive breast cancer in Hong Kong Chinese women.

    PubMed

    Tse, Lap Ah; Li, Mengjie; Chan, Wing-cheong; Kwok, Chi-hei; Leung, Siu-lan; Wu, Cherry; Yu, Ignatius Tak-sun; Yu, Wai-cho; Lao, Xiangqian; Wang, Xiaorong; Wong, Carmen Ka-man; Lee, Priscilla Ming-yi; Wang, Feng; Yang, Xiaohong Rose

    2015-01-01

    The role of family history to the risk of breast cancer was analyzed by incorporating menopausal status in Hong Kong Chinese women, with a particular respect to the estrogen receptor-positive (ER+) type. Seven hundred and forty seven breast cancer incident cases and 781 hospital controls who had completed information on family cancer history in first-degree relatives (nature father, mother, and siblings) were recruited. Odds ratio for breast cancer were calculated by unconditional multiple logistic regression, stratified by menopausal status (a surrogate of endogenous female sex hormone level and age) and type of relative affected with the disease. Further subgroup analysis by tumor type according to ER status was investigated. Altogether 52 (6.96%) breast cancer cases and 23 (2.95%) controls was found that the patients' one or more first-degree relatives had a history of breast cancer, showing an adjusted odds ratio (OR) of 2.41 (95%CI: 1.45-4.02). An excess risk of breast cancer was restricted to the ER+ tumor (OR = 2.43, 95% CI: 1.38-4.28), with a relatively higher risk associated with an affected mother (OR = 3.97, 95%CI: 1.46-10.79) than an affected sister (OR = 2.06, 95%CI: 1.07-3.97), while the relative risk was more prominent in the subgroup of pre-menopausal women. Compared with the breast cancer overall, the familial risks to the ER+ tumor increased progressively with the number of affected first-degree relatives. This study provides new insights on a relationship between family breast cancer history, menopausal status, and the ER+ breast cancer. A separate risk prediction model for ER+ tumor in Asian population is desired.

  10. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

    PubMed

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

    2015-02-01

    specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

  11. Age- and Tumor Subtype–Specific Breast Cancer Risk Estimates for CHEK2*1100delC Carriers

    PubMed Central

    Hogervorst, Frans; van Hien, Richard; Cornelissen, Sten; Broeks, Annegien; Adank, Muriel A.; Meijers, Hanne; Waisfisz, Quinten; Hollestelle, Antoinette; Schutte, Mieke; van den Ouweland, Ans; Hooning, Maartje; Andrulis, Irene L.; Anton-Culver, Hoda; Antonenkova, Natalia N.; Antoniou, Antonis C.; Arndt, Volker; Bermisheva, Marina; Bogdanova, Natalia V.; Bolla, Manjeet K.; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Chang-Claude, Jenny; Chenevix-Trench, Georgia; Couch, Fergus J.; Cox, Angela; Cross, Simon S.; Czene, Kamila; Dunning, Alison M.; Fasching, Peter A.; Figueroa, Jonine; Fletcher, Olivia; Flyger, Henrik; Galle, Eva; García-Closas, Montserrat; Giles, Graham G.; Haeberle, Lothar; Hall, Per; Hillemanns, Peter; Hopper, John L.; Jakubowska, Anna; John, Esther M.; Jones, Michael; Khusnutdinova, Elza; Knight, Julia A.; Kosma, Veli-Matti; Kristensen, Vessela; Lee, Andrew; Lindblom, Annika; Lubinski, Jan; Mannermaa, Arto; Margolin, Sara; Meindl, Alfons; Milne, Roger L.; Muranen, Taru A.; Newcomb, Polly A.; Offit, Kenneth; Park-Simon, Tjoung-Won; Peto, Julian; Pharoah, Paul D.P.; Robson, Mark; Rudolph, Anja; Sawyer, Elinor J.; Schmutzler, Rita K.; Seynaeve, Caroline; Soens, Julie; Southey, Melissa C.; Spurdle, Amanda B.; Surowy, Harald; Swerdlow, Anthony; Tollenaar, Rob A.E.M.; Tomlinson, Ian; Trentham-Dietz, Amy; Vachon, Celine; Wang, Qin; Whittemore, Alice S.; Ziogas, Argyrios; van der Kolk, Lizet; Nevanlinna, Heli; Dörk, Thilo; Bojesen, Stig; Easton, Douglas F.

    2016-01-01

    Purpose CHEK2*1100delC is a well-established breast cancer risk variant that is most prevalent in European populations; however, there are limited data on risk of breast cancer by age and tumor subtype, which limits its usefulness in breast cancer risk prediction. We aimed to generate tumor subtype- and age-specific risk estimates by using data from the Breast Cancer Association Consortium, including 44,777 patients with breast cancer and 42,997 controls from 33 studies genotyped for CHEK2*1100delC. Patients and Methods CHEK2*1100delC genotyping was mostly done by a custom Taqman assay. Breast cancer odds ratios (ORs) for CHEK2*1100delC carriers versus noncarriers were estimated by using logistic regression and adjusted for study (categorical) and age. Main analyses included patients with invasive breast cancer from population- and hospital-based studies. Results Proportions of heterozygous CHEK2*1100delC carriers in controls, in patients with breast cancer from population- and hospital-based studies, and in patients with breast cancer from familial- and clinical genetics center–based studies were 0.5%, 1.3%, and 3.0%, respectively. The estimated OR for invasive breast cancer was 2.26 (95%CI, 1.90 to 2.69; P = 2.3 × 10−20). The OR was higher for estrogen receptor (ER)–positive disease (2.55 [95%CI, 2.10 to 3.10; P = 4.9 × 10−21]) than it was for ER-negative disease (1.32 [95%CI, 0.93 to 1.88; P = .12]; P interaction = 9.9 × 10−4). The OR significantly declined with attained age for breast cancer overall (P = .001) and for ER-positive tumors (P = .001). Estimated cumulative risks for development of ER-positive and ER-negative tumors by age 80 in CHEK2*1100delC carriers were 20% and 3%, respectively, compared with 9% and 2%, respectively, in the general population of the United Kingdom. Conclusion These CHEK2*1100delC breast cancer risk estimates provide a basis for incorporating CHEK2*1100delC into breast cancer risk prediction models and into

  12. Age- and Tumor Subtype-Specific Breast Cancer Risk Estimates for CHEK2*1100delC Carriers.

    PubMed

    Schmidt, Marjanka K; Hogervorst, Frans; van Hien, Richard; Cornelissen, Sten; Broeks, Annegien; Adank, Muriel A; Meijers, Hanne; Waisfisz, Quinten; Hollestelle, Antoinette; Schutte, Mieke; van den Ouweland, Ans; Hooning, Maartje; Andrulis, Irene L; Anton-Culver, Hoda; Antonenkova, Natalia N; Antoniou, Antonis C; Arndt, Volker; Bermisheva, Marina; Bogdanova, Natalia V; Bolla, Manjeet K; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Chang-Claude, Jenny; Chenevix-Trench, Georgia; Couch, Fergus J; Cox, Angela; Cross, Simon S; Czene, Kamila; Dunning, Alison M; Fasching, Peter A; Figueroa, Jonine; Fletcher, Olivia; Flyger, Henrik; Galle, Eva; García-Closas, Montserrat; Giles, Graham G; Haeberle, Lothar; Hall, Per; Hillemanns, Peter; Hopper, John L; Jakubowska, Anna; John, Esther M; Jones, Michael; Khusnutdinova, Elza; Knight, Julia A; Kosma, Veli-Matti; Kristensen, Vessela; Lee, Andrew; Lindblom, Annika; Lubinski, Jan; Mannermaa, Arto; Margolin, Sara; Meindl, Alfons; Milne, Roger L; Muranen, Taru A; Newcomb, Polly A; Offit, Kenneth; Park-Simon, Tjoung-Won; Peto, Julian; Pharoah, Paul D P; Robson, Mark; Rudolph, Anja; Sawyer, Elinor J; Schmutzler, Rita K; Seynaeve, Caroline; Soens, Julie; Southey, Melissa C; Spurdle, Amanda B; Surowy, Harald; Swerdlow, Anthony; Tollenaar, Rob A E M; Tomlinson, Ian; Trentham-Dietz, Amy; Vachon, Celine; Wang, Qin; Whittemore, Alice S; Ziogas, Argyrios; van der Kolk, Lizet; Nevanlinna, Heli; Dörk, Thilo; Bojesen, Stig; Easton, Douglas F

    2016-08-10

    CHEK2*1100delC is a well-established breast cancer risk variant that is most prevalent in European populations; however, there are limited data on risk of breast cancer by age and tumor subtype, which limits its usefulness in breast cancer risk prediction. We aimed to generate tumor subtype- and age-specific risk estimates by using data from the Breast Cancer Association Consortium, including 44,777 patients with breast cancer and 42,997 controls from 33 studies genotyped for CHEK2*1100delC. CHEK2*1100delC genotyping was mostly done by a custom Taqman assay. Breast cancer odds ratios (ORs) for CHEK2*1100delC carriers versus noncarriers were estimated by using logistic regression and adjusted for study (categorical) and age. Main analyses included patients with invasive breast cancer from population- and hospital-based studies. Proportions of heterozygous CHEK2*1100delC carriers in controls, in patients with breast cancer from population- and hospital-based studies, and in patients with breast cancer from familial- and clinical genetics center-based studies were 0.5%, 1.3%, and 3.0%, respectively. The estimated OR for invasive breast cancer was 2.26 (95%CI, 1.90 to 2.69; P = 2.3 × 10(-20)). The OR was higher for estrogen receptor (ER)-positive disease (2.55 [95%CI, 2.10 to 3.10; P = 4.9 × 10(-21)]) than it was for ER-negative disease (1.32 [95%CI, 0.93 to 1.88; P = .12]; P interaction = 9.9 × 10(-4)). The OR significantly declined with attained age for breast cancer overall (P = .001) and for ER-positive tumors (P = .001). Estimated cumulative risks for development of ER-positive and ER-negative tumors by age 80 in CHEK2*1100delC carriers were 20% and 3%, respectively, compared with 9% and 2%, respectively, in the general population of the United Kingdom. These CHEK2*1100delC breast cancer risk estimates provide a basis for incorporating CHEK2*1100delC into breast cancer risk prediction models and into guidelines for intensified screening and follow-up. © 2016

  13. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error.

    PubMed

    Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M; Berman, D M; Blume-Jensen, P

    2014-09-09

    Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively. Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error. Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

  14. External validation of a published nomogram for prediction of brain metastasis in patients with extra-cerebral metastatic breast cancer and risk regression analysis.

    PubMed

    Genre, Ludivine; Roché, Henri; Varela, Léonel; Kanoun, Dorra; Ouali, Monia; Filleron, Thomas; Dalenc, Florence

    2017-02-01

    Survival of patients with metastatic breast cancer (MBC) suffering from brain metastasis (BM) is limited and this event is usually fatal. In 2010, the Graesslin's nomogram was published in order to predict subsequent BM in patients with breast cancer (BC) with extra-cerebral metastatic disease. This model aims to select a patient population at high risk for BM and thus will facilitate the design of prevention strategies and/or the impact of early treatment of BM in prospective clinical studies. Nomogram external validation was retrospectively applied to patients with BC and later BM between January 2005 and December 2012, treated in our institution. Moreover, risk factors of BM appearance were studied by Fine and Gray's competing risk analysis. Among 492 patients with MBC, 116 developed subsequent BM. Seventy of them were included for the nomogram validation. The discrimination is good (area under curve = 0.695 [95% confidence interval, 0.61-0.77]). Risk factors of BM appearance are: human epidermal growth factor receptor 2 (HER2) overexpression/amplification, triple-negative BC and number of extra-cerebral metastatic sites (>1). With a competing risk model, we highlight the nomogram interest for HER2+ tumour subgroup exclusively. Graesslin's nomogram external validation demonstrates exportability and reproducibility. Importantly, the competing risk model analysis provides additional information for the design of prospective trials concerning the early diagnosis of BM and/or preventive treatment on high risk patients with extra-cerebral metastatic BC. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Validation of COLA score for predicting wound infection in patients undergoing surgery for rectal cancer.

    PubMed

    Saylam, Baris; Tez, Mesut; Comcali, Bulent; Vural, Veli; Duzgun, Arife Polat; Ozer, Mehmet Vasfi; Coskun, Faruk

    2017-01-01

    The purpose of our study was to estimate the incidence of SSI (Surgical site infection) and the effect of COLA (contamination, obesity, laparotomy and ASA grade) score on SSI in patients undergoing rectal surgical procedures for rectal cancer. A total of 92 patients who underwent operation for rectum cancer were enrolled in this study. Wound surveillance was performed in all patients by a staff surgeon identified infected wounds during the hospital stay, and collected information for up to 30 days after operation. The overall rate of incisional SSI and organ/space SSI was 22.8% and 7.6% respectively. Surgical site infection rates were 14.2%, 20.58%, 40.7%, 57.1% for COLA 1,2,3 and 4 scores respectively. The area under the receiver/ operator characteristic curve for the score was 0,660. COLA scoring systems predict, with reasonable accuracy, the risk of SSI in rectal cancer patients undergoing elective rectal surgery. COLA score Rectal surgery, Surgical site infection, Risk prediction, Wound infection.

  16. Percentage of Positive Biopsy Cores: A Better Risk Stratification Model for Prostate Cancer?

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Huang Jiayi; Vicini, Frank A.; Williams, Scott G.

    2012-07-15

    Purpose: To assess the prognostic value of the percentage of positive biopsy cores (PPC) and perineural invasion in predicting the clinical outcomes after radiotherapy (RT) for prostate cancer and to explore the possibilities to improve on existing risk-stratification models. Methods and Materials: Between 1993 and 2004, 1,056 patients with clinical Stage T1c-T3N0M0 prostate cancer, who had four or more biopsy cores sampled and complete biopsy core data available, were treated with external beam RT, with or without a high-dose-rate brachytherapy boost at William Beaumont Hospital. The median follow-up was 7.6 years. Multivariate Cox regression analysis was performed with PPC, Gleasonmore » score, pretreatment prostate-specific antigen, T stage, PNI, radiation dose, androgen deprivation, age, prostate-specific antigen frequency, and follow-up duration. A new risk stratification (PPC classification) was empirically devised to incorporate PPC and replace the T stage. Results: On multivariate Cox regression analysis, the PPC was an independent predictor of distant metastasis, cause-specific survival, and overall survival (all p < .05). A PPC >50% was associated with significantly greater distant metastasis (hazard ratio, 4.01; 95% confidence interval, 1.86-8.61), and its independent predictive value remained significant with or without androgen deprivation therapy (all p < .05). In contrast, PNI and T stage were only predictive for locoregional recurrence. Combining the PPC ({<=}50% vs. >50%) with National Comprehensive Cancer Network risk stratification demonstrated added prognostic value of distant metastasis for the intermediate-risk (hazard ratio, 5.44; 95% confidence interval, 1.78-16.6) and high-risk (hazard ratio, 4.39; 95% confidence interval, 1.70-11.3) groups, regardless of the use of androgen deprivation and high-dose RT (all p < .05). The proposed PPC classification appears to provide improved stratification of the clinical outcomes relative to the

  17. Pleiotropic associations of risk variants identified for other cancers with lung cancer risk: the PAGE and TRICL consortia.

    PubMed

    Park, S Lani; Fesinmeyer, Megan D; Timofeeva, Maria; Caberto, Christian P; Kocarnik, Jonathan M; Han, Younghun; Love, Shelly-Ann; Young, Alicia; Dumitrescu, Logan; Lin, Yi; Goodloe, Robert; Wilkens, Lynne R; Hindorff, Lucia; Fowke, Jay H; Carty, Cara; Buyske, Steven; Schumacher, Frederick R; Butler, Anne; Dilks, Holli; Deelman, Ewa; Cote, Michele L; Chen, Wei; Pande, Mala; Christiani, David C; Field, John K; Bickebller, Heike; Risch, Angela; Heinrich, Joachim; Brennan, Paul; Wang, Yufei; Eisen, Timothy; Houlston, Richard S; Thun, Michael; Albanes, Demetrius; Caporaso, Neil; Peters, Ulrike; North, Kari E; Heiss, Gerardo; Crawford, Dana C; Bush, William S; Haiman, Christopher A; Landi, Maria Teresa; Hung, Rayjean J; Kooperberg, Charles; Amos, Christopher I; Le Marchand, Loïc; Cheng, Iona

    2014-04-01

    Genome-wide association studies have identified hundreds of genetic variants associated with specific cancers. A few of these risk regions have been associated with more than one cancer site; however, a systematic evaluation of the associations between risk variants for other cancers and lung cancer risk has yet to be performed. We included 18023 patients with lung cancer and 60543 control subjects from two consortia, Population Architecture using Genomics and Epidemiology (PAGE) and Transdisciplinary Research in Cancer of the Lung (TRICL). We examined 165 single-nucleotide polymorphisms (SNPs) that were previously associated with at least one of 16 non-lung cancer sites. Study-specific logistic regression results underwent meta-analysis, and associations were also examined by race/ethnicity, histological cell type, sex, and smoking status. A Bonferroni-corrected P value of 2.5×10(-5) was used to assign statistical significance. The breast cancer SNP LSP1 rs3817198 was associated with an increased risk of lung cancer (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.05 to 1.14; P = 2.8×10(-6)). This association was strongest for women with adenocarcinoma (P = 1.2×10(-4)) and not statistically significant in men (P = .14) with this cell type (P het by sex = .10). Two glioma risk variants, TERT rs2853676 and CDKN2BAS1 rs4977756, which are located in regions previously associated with lung cancer, were associated with increased risk of adenocarcinoma (OR = 1.16; 95% CI = 1.10 to 1.22; P = 1.1×10(-8)) and squamous cell carcinoma (OR = 1.13; CI = 1.07 to 1.19; P = 2.5×10(-5)), respectively. Our findings demonstrate a novel pleiotropic association between the breast cancer LSP1 risk region marked by variant rs3817198 and lung cancer risk.

  18. Predictive genetic testing for hereditary breast and ovarian cancer: psychological distress and illness representations 1 year following disclosure.

    PubMed

    Claes, E; Evers-Kiebooms, G; Denayer, L; Decruyenaere, M; Boogaerts, A; Philippe, K; Legius, E

    2005-10-01

    This prospective study evaluates emotional functioning and illness representations in 68 unaffected women (34 carriers/34 noncarriers) 1 year after predictive testing for BRCA1/2 mutations when offered within a multidisciplinary approach. Carriers had higher subjective risk perception of breast cancer than noncarriers. Carriers who did not have prophylactic oophorectomy had the highest risk perception of ovarian cancer. No differences were found between carriers and noncarriers regarding perceived seriousness and perceived control of breast and ovarian cancer. Mean levels of distress were within normal ranges. Only few women showed an overall pattern of clinically elevated distress. Cancer-specific distress and state-anxiety significantly decreased in noncarriers from pre- to posttest while general distress remained about the same. There were no significant changes in distress in the group of carriers except for ovarian cancer distress which significantly decreased from pre- to posttest. Our study did not reveal adverse effects of predictive testing when offered in the context of a multidisciplinary approach.

  19. Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk

    PubMed Central

    Nguyen, Tuong L; Aung, Ye K; Evans, Christopher F; Dite, Gillian S; Stone, Jennifer; MacInnis, Robert J; Dowty, James G; Bickerstaffe, Adrian; Aujard, Kelly; Rommens, Johanna M; Song, Yun-Mi; Sung, Joohon; Jenkins, Mark A; Southey, Melissa C; Giles, Graham G; Apicella, Carmel; Hopper, John L

    2017-01-01

    Abstract Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus, and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus, respectively. All measures were Box–Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Results: Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6, respectively). For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus, respectively (all P < 0.001). After adjusting for Altocumulus and Cirrocumulus, Cumulus was not significant (P > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64–2.14] and AUC = 0.68 (0.65–0.71). Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research. PMID:28338721

  20. Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk.

    PubMed

    Nguyen, Tuong L; Aung, Ye K; Evans, Christopher F; Dite, Gillian S; Stone, Jennifer; MacInnis, Robert J; Dowty, James G; Bickerstaffe, Adrian; Aujard, Kelly; Rommens, Johanna M; Song, Yun-Mi; Sung, Joohon; Jenkins, Mark A; Southey, Melissa C; Giles, Graham G; Apicella, Carmel; Hopper, John L

    2017-04-01

    Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus , and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus , respectively. All measures were Box-Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6 , respectively) . For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus , respectively (all P  < 0.001). After adjusting for Altocumulus and Cirrocumulus , Cumulus was not significant ( P  > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64-2.14] and AUC = 0.68 (0.65-0.71). The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association

  1. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

    PubMed

    Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

    2018-01-01

    The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

  2. Risk Belief and Attitude Formation From Translated Scientific Messages About PFOA, an Environmental Risk Associated With Breast Cancer.

    PubMed

    Smith, Sandi W; Hitt, Rose; Russell, Jessica; Nazione, Samantha; Silk, Kami; Atkin, Charles K; Keating, David

    2017-03-01

    Evidence regarding possible environmental causes of breast cancer is advancing. Often, however, the public is not informed about these advances in a manner that is easily understandable. This research translates findings from biologists into messages at two literacy levels about perfluorooctanoic acid (PFOA), a possible environmental contributor to breast cancer. The Heuristic Systematic Model (HSM) was used to investigate how ability, motivation, and systematic and heuristic processing lead to risk beliefs and, ultimately, to negative attitudes for individuals receiving translated scientific messages about PFOA. Participants (N = 1,389) came from the Dr. Susan Love Research Foundation's Army of Women. Findings indicated that ability, in the form of translated messages, predicted systematic processing, operationalized as knowledge gain, which was negatively associated with formation of risk beliefs that led to negative attitudes toward PFOA. Heuristic processing cues, operationalized as perceived message quality and source credibility, were positively associated with risk beliefs, which predicted negative attitudes about PFOA. Overall, more knowledge and lower literacy messages led to lower perceived risk, while greater involvement and ratings of heuristic cues led to greater risk perceptions. This is an example of a research, translation, and dissemination team effort in which biologists created knowledge, communication scholars translated and tested messages, and advocates were participants and those who disseminated messages.

  3. Do proinflammatory cytokine levels predict serious complication risk of infection in pediatric cancer patients?

    PubMed

    Karakurt, Deniz Guven; Demirsoy, Ugur; Corapcioglu, Funda; Oncel, Selim; Karadogan, Meriban; Arisoy, Emin Sami

    2014-08-01

    Determination of risk of severe bacterial infection complication in children with cancer is important to diminish the cost of hospitalization and therapy. In this study, children with cancer (leukemia excluded) were evaluated for risk of severe infection complication, success of therapy and the relation between clinical and inflammatory parameters during neutropenic fever attacks. Children who fulfilled the criteria of neutropenic fever with cancer were enrolled in the study. During admission, together with clinical and laboratory parameters; interleukin-6, interleukin-8, soluble tumor necrosis factor receptor II, and soluble interleukin 2 reseptor ve procalcitonin levels were detected. Empirical therapy was started with piperacillin/tazobactam and relation between the inflammatory cytokine levels and therapy response parameters were evaluated. The study population included 31 children and 50 neutropenic attacks were studied. In 48% of the attacks, absolute neutrophile count was >100/mm(3) and infectious agents were shown microbiologically in 12% of the attacks. In the study group with piperacillin/tazobactam monotherapy, the success rate without modification was 58%. In the therapy modified group mean duration of fever, antibiotherapy and hospitalization were significantly longer than the group without modification. Inflammatory cytokines' levels during admission (interleukin-6, interleukin-8, soluble tumor necrosis factor reseptor II) were higher in patients with fever >3 days and in multiple regression analysis, it has been shown that they have a determinative role on fever control time. Other cytokines did not show any significant relationship with risk of severe bacterial infection complication and success of therapy.

  4. Modifiers of breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers.

    PubMed

    Milne, Roger L; Antoniou, Antonis C

    2016-10-01

    Pathogenic mutations in BRCA1 and BRCA2 are associated with high risks of breast and ovarian cancer. However, penetrance estimates for mutation carriers have been found to vary substantially between studies, and the observed differences in risk are consistent with the hypothesis that genetic and environmental factors modify cancer risks for women with these mutations. Direct evidence that this is the case has emerged in the past decade, through large-scale international collaborative efforts. Here, we describe the methodological challenges in the identification and characterisation of these risk-modifying factors, review the latest evidence on genetic and lifestyle/hormonal risk factors that modify breast and ovarian cancer risks for women with BRCA1 and BRCA2 mutations and outline the implications of these findings for cancer risk prediction. We also review the unresolved issues in this area of research and identify strategies of clinical implementation so that women with BRCA1 and BRCA2 mutations are no longer counselled on the basis of 'average' risk estimates. © 2016 Society for Endocrinology.

  5. Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women.

    PubMed

    Nassif, Houssam; Wu, Yirong; Page, David; Burnside, Elizabeth

    2012-01-01

    Overdiagnosis is a phenomenon in which screening identities cancer which may not go on to cause symptoms or death. Women over 65 who develop breast cancer bear the heaviest burden of overdiagnosis. This work introduces novel machine learning algorithms to improve diagnostic accuracy of breast cancer in aging populations. At the same time, we aim at minimizing unnecessary invasive procedures (thus decreasing false positives) and concomitantly addressing overdiagnosis. We develop a novel algorithm. Logical Differential Prediction Bayes Net (LDP-BN), that calculates the risk of breast disease based on mammography findings. LDP-BN uses Inductive Logic Programming (ILP) to learn relational rules, selects older-specific differentially predictive rules, and incorporates them into a Bayes Net, significantly improving its performance. In addition, LDP-BN offers valuable insight into the classification process, revealing novel older-specific rules that link mass presence to invasive, and calcification presence and lack of detectable mass to DCIS.

  6. The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer.

    PubMed

    Thakur, Satbir Singh; Li, Haocheng; Chan, Angela M Y; Tudor, Roxana; Bigras, Gilbert; Morris, Don; Enwere, Emeka K; Yang, Hua

    2018-01-01

    Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson's r = 0.909) and between users (Pearson's r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of

  7. The EPOS-CC Score: An Integration of Independent, Tumor- and Patient-Associated Risk Factors to Predict 5-years Overall Survival Following Colorectal Cancer Surgery.

    PubMed

    Haga, Yoshio; Ikejiri, Koji; Wada, Yasuo; Ikenaga, Masakazu; Koike, Shoichiro; Nakamura, Seiji; Koseki, Masato

    2015-06-01

    Surgical audit is an essential task for the estimation of postoperative outcome and comparison of quality of care. Previous studies on surgical audits focused on short-term outcomes, such as postoperative mortality. We propose a surgical audit evaluating long-term outcome following colorectal cancer surgery. The predictive model for this audit is designated as 'Estimation of Postoperative Overall Survival for Colorectal Cancer (EPOS-CC)'. Thirty-one tumor-related and physiological variables were prospectively collected in 889 patients undergoing elective resection for colorectal cancer between April 2005 and April 2007 in 16 Japanese hospitals. Postoperative overall survival was assessed over a 5-years period. The EPOS-CC score was established by selecting significant variables in a uni- and multivariate analysis and allocating a risk-adjusted multiplication factor to each variable using Cox regression analysis. For validation, the EPOS-CC score was compared to the predictive power of UICC stage. Inter-hospital variability of the observed-to-estimated 5-years survival was assessed to estimate quality of care. Among the 889 patients, 804 (90%) completed the 5-years follow-up. Univariate analysis displayed a significant correlation with 5-years survival for 14 physiological and nine tumor-related variables (p < 0.005). Highly significant p-values below 0.0001 were found for age, ASA score, severe pulmonary disease, respiratory history, performance status, hypoalbuminemia, alteration of hemoglobin, serum sodium level, and for all histological variables except tumor location. Age, TNM stage, lymphatic invasion, performance status, and serum sodium level were independent variables in the multivariate analysis and were entered the EPOS-CC model for the prediction of survival. Risk-adjusted multiplication factors between 1.5 (distant metastasis) and 0.16 (serum sodium level) were accorded to the different variables. The predictive power of EPOS-CC was superior to the one

  8. Perceived breast cancer risk: heuristic reasoning and search for a dominance structure.

    PubMed

    Katapodi, Maria C; Facione, Noreen C; Humphreys, Janice C; Dodd, Marylin J

    2005-01-01

    Studies suggest that people construct their risk perceptions by using inferential rules called heuristics. The purpose of this study was to identify heuristics that influence perceived breast cancer risk. We examined 11 interviews from women of diverse ethnic/cultural backgrounds who were recruited from community settings. Narratives in which women elaborated about their own breast cancer risk were analyzed with Argument and Heuristic Reasoning Analysis methodology, which is based on applied logic. The availability, simulation, representativeness, affect, and perceived control heuristics, and search for a dominance structure were commonly used for making risk assessments. Risk assessments were based on experiences with an abnormal breast symptom, experiences with affected family members and friends, beliefs about living a healthy lifestyle, and trust in health providers. Assessment of the potential threat of a breast symptom was facilitated by the search for a dominance structure. Experiences with family members and friends were incorporated into risk assessments through the availability, simulation, representativeness, and affect heuristics. Mistrust in health providers led to an inappropriate dependence on the perceived control heuristic. Identified heuristics appear to create predictable biases and suggest that perceived breast cancer risk is based on common cognitive patterns.

  9. Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States

    PubMed Central

    Maas, Paige; Barrdahl, Myrto; Joshi, Amit D.; Auer, Paul L.; Gaudet, Mia M.; Milne, Roger L.; Schumacher, Fredrick R.; Anderson, William F.; Check, David; Chattopadhyay, Subham; Baglietto, Laura; Berg, Christine D.; Chanock, Stephen J.; Cox, David G.; Figueroa, Jonine D.; Gail, Mitchell H.; Graubard, Barry I.; Haiman, Christopher A.; Hankinson, Susan E.; Hoover, Robert N.; Isaacs, Claudine; Kolonel, Laurence N.; Le Marchand, Loic; Lee, I-Min; Lindström, Sara; Overvad, Kim; Romieu, Isabelle; Sanchez, Maria-Jose; Southey, Melissa C.; Stram, Daniel O.; Tumino, Rosario; VanderWeele, Tyler J.; Willett, Walter C.; Zhang, Shumin; Buring, Julie E.; Canzian, Federico; Gapstur, Susan M.; Henderson, Brian E.; Hunter, David J.; Giles, Graham G; Prentice, Ross L.; Ziegler, Regina G.; Kraft, Peter; Garcia-Closas, Montse; Chatterjee, Nilanjan

    2017-01-01

    IMPORTANCE An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention. OBJECTIVE To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors. DESIGN, SETTING, AND PARTICIPANTS Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality. EXPOSURES Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors. MAIN OUTCOMES AND MEASURES Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking). RESULTS The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who

  10. Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

    PubMed

    Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

    2014-01-01

    In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

  11. Association of prostate cancer risk variants with clinicopathologic characteristics of the disease

    PubMed Central

    Xu, Jianfeng; Isaacs, Sarah D.; Sun, Jielin; Li, Ge; Wiley, Kathleen E.; Zhu, Yi; Hsu, Fang-Chi; Wiklund, Fredrik; Turner, Aubrey R.; Adams, Tamara S.; Liu, Wennuan; Trock, Bruce J.; Partin, Alan W.; Chang, Baoli; Walsh, Patrick C.; Grönberg, Henrik; Isaacs, William; Zheng, Siqun

    2009-01-01

    Purpose Fifteen independent genetic variants have been implicated in prostate cancer risk by recent genome-wide association studies. However, their association with clinicopathologic features of prostate cancer is uncertain. Experimental Design We systematically evaluated these 15 variants in 1,563 prostate cancer patients undergoing radical prostatectomy, taking advantage of the uniform tumor stage and grade information available for each of these cases. Associations of these variants with aggressiveness, pathologic Gleason scores, pathologic stage, age at diagnosis, or serum PSA levels were tested. Results After adjusting for multiple testing, none of the SNPs was individually or cumulatively associated with aggressiveness or individual clinicopathologic variables of prostate cancer such as Gleason scores, pathologic stage, or age at diagnosis of prostate cancer. The reported risk allele (G) for SNP rs2735839 in the KLK3 gene at 19q13 was more frequent in less aggressive prostate cancer patients (0.89) than in more aggressive prostate cancer patients (0.86), nominal P = 0.03, or in controls (0.86), nominal P = 0.04. Considering that this allele was also significantly associated with higher serum PSA levels among controls (nominal P = 0.003), the observed trend of higher frequency of this risk allele between less and more aggressive prostate cancer, or between less aggressive and controls may be due to detection bias of PSA screening. Conclusions Prostate cancer risk variants recently discovered from genome-wide case-control association studies are not associated with clinicopathologic variables in this population. Case-case studies are urgently needed in order to discover genetic variants that predict tumor aggressiveness. PMID:18794092

  12. Protection motivation theory in predicting intention to receive cervical cancer screening in rural Chinese women.

    PubMed

    Bai, Yang; Liu, Qing; Chen, Xinguang; Gao, Yanduo; Gong, Huiyun; Tan, Xiaodong; Zhang, Min; Tuo, Jiyu; Zhang, Yuling; Xiang, Qunying; Deng, Fenghua; Liu, Guiling

    2018-02-01

    Despite the significance of cervical cancer screening, motivating more women to participate remains a challenge in resource-limited settings. In this study, we tested the protection motivation theory (PMT) in predicting screening intentions. Participants were women from Wufeng, a typical rural county in China. Participants (n = 3000) with no cervical cancer history were recruited from 10 randomly selected villages. As mediating variables, 6 PMT constructs (Perceived Risk, Fear Arousal, Perceived Severity, Response Efficacy, Response Cost, and Self-Efficacy) were measured using the standardized questionnaire. Structural equation modeling (SEM) method was employed to test PMT-based prediction models. Of the total sample, 57.77% believed that regular screening may reduce cervical cancer risk, and 45.26% agreed that women should be screened regularly. Our data fit the PMT model well (GFI = 0.95, AGFI = 0.93, CFI = 0.90, RMSEA = 0.06, SRMR = 0.04, Chi-square/df = 2.47). Knowledge of screening was directly and positively associated with screening intention. Age, annual income, and awareness of and prior experience with screening were significantly associated with screening intention by enhancing cervical cancer risk perception and by reducing response cost (P<0.05 for both). PMT can be used as guidance to investigate cervical cancer screening intentions among rural women in China with focus on cancer knowledge, some demographic factors, and awareness of and previous experience with screening. These findings, if verified with longitudinal data, can be used for intervention program development. Copyright © 2017 John Wiley & Sons, Ltd.

  13. Assessing risk of breast cancer in an ethnically South-East Asia population (results of a multiple ethnic groups study).

    PubMed

    Gao, Fei; Machin, David; Chow, Khuan-Yew; Sim, Yu-Fan; Duffy, Stephen W; Matchar, David B; Goh, Chien-Hui; Chia, Kee-Seng

    2012-11-19

    Gail and others developed a model (GAIL) using age-at-menarche, age-at-birth of first live child, number of previous benign breast biopsy examinations, and number of first-degree-relatives with breast cancer as well as baseline age-specific breast cancer risks for predicting the 5-year risk of invasive breast cancer for Caucasian women. However, the validity of the model for projecting risk in South-East Asian women is uncertain. We evaluated GAIL and attempted to improve its performance for Singapore women of Chinese, Malay and Indian origins. Data from the Singapore Breast Screening Programme (SBSP) are used. Motivated by lower breast cancer incidence in many Asian countries, we utilised race-specific invasive breast cancer and other cause mortality rates for Singapore women to produce GAIL-SBSP. By using risk factor information from a nested case-control study within SBSP, alternative models incorporating fewer then additional risk factors were determined. Their accuracy was assessed by comparing the expected cases (E) with the observed (O) by the ratio (E/O) and 95% confidence interval (CI) and the respective concordance statistics estimated. From 28,883 women, GAIL-SBSP predicted 241.83 cases during the 5-year follow-up while 241 were reported (E/O=1.00, CI=0.88 to 1.14). Except for women who had two or more first-degree-relatives with breast cancer, satisfactory prediction was present in almost all risk categories. This agreement was reflected in Chinese and Malay, but not in Indian women. We also found that a simplified model (S-GAIL-SBSP) including only age-at-menarche, age-at-birth of first live child and number of first-degree-relatives performed similarly with associated concordance statistics of 0.5997. Taking account of body mass index and parity did not improve the calibration of S-GAIL-SBSP. GAIL can be refined by using national race-specific invasive breast cancer rates and mortality rates for causes other than breast cancer. A revised model

  14. Estimate of the risk of radiation-induced cancers after linear-accelerator-based breast-cancer radiotherapy

    NASA Astrophysics Data System (ADS)

    Koh, Eui Kwan; Seo, Jungju; Baek, Tae Seong; Chung, Eun Ji; Yoon, Myonggeun; Lee, Hyun-ho

    2013-07-01

    The aim of this study is to assess and compare the excess absolute risks (EARs) of radiation-induced cancers following conformal (3D-CRT), fixed-field intensity-modulated (IMRT) and volumetric modulated arc (RapidArc) radiation therapy in patients with breast cancer. 3D-CRT, IMRT and RapidArc were planned for 10 breast cancer patients. The organ-specific EAR for cancer induction was estimated using the organ equivalent dose (OED) based on computed dose volume histograms (DVHs) and the secondary doses measured at various points from the field edge. The average secondary dose per Gy treatment dose from 3D-CRT, measured 10 to 50 cm from the field edge, ranged from 8.27 to 1.04 mGy. The secondary doses per Gy from IMRT and RapidArc, however, ranged between 5.86 and 0.54 mGy, indicating that IMRT and RapidArc are associated with smaller doses of secondary radiation than 3D-CRT. The organ specific EARs for out-of-field organs, such as the thyroid, liver and colon, were higher with 3D-CRT than with IMRT or RapidArc. In contrast, EARs for in-field organs were much lower with 3D-CRT than with IMRT or RapidArc. The overall estimate of EAR indicated that the radiation-induced cancer risk was 1.8-2.0 times lower with 3D-CRT than with IMRT or RapidArc. Comparisons of EARs during breast irradiation suggested that the predicted risk of secondary cancers was lower with 3D-CRT than with IMRT or RapidArc.

  15. Cancer risk and PCOS.

    PubMed

    Dumesic, Daniel A; Lobo, Rogerio A

    2013-08-01

    Women with polycystic ovary syndrome (PCOS) have a 2.7-fold increased risk for developing endometrial cancer. A major factor for this increased malignancy risk is prolonged exposure of the endometrium to unopposed estrogen that results from anovulation. Additionally, secretory endometrium of some women with PCOS undergoing ovulation induction or receiving exogenous progestin exhibits progesterone resistance accompanied by dysregulation of gene expression controlling steroid action and cell proliferation. Endometrial surveillance includes transvaginal ultrasound and/or endometrial biopsy to assess thickened endometrium, prolonged amenorrhea, unopposed estrogen exposure or abnormal vaginal bleeding. Medical management for abnormal vaginal bleeding or endometrial hyperplasia consists of estrogen-progestin oral contraceptives, cyclic or continuous progestins or a levonorgestrel-releasing (Mirena) intrauterine device. Lifestyle modification with caloric restriction and exercise is appropriate to treat obesity as a concomitant risk factor for developing endometrial disease. An increased risk of ovarian cancer may also exist in some women with PCOS. There are strong data to suggest that oral contraceptive use is protective against ovarian cancer and increases with the duration of therapy. The mechanism of this protection may be through suppression of gonadotropin secretion rather than the prevention of "incessant ovulation". There is no apparent association of PCOS with breast cancer, although the high prevalence of metabolic dysfunction from obesity is a common denominator for both conditions. Recent data suggest that the use of metformin may be protective for both endometrial and breast cancer. There are insufficient data to evaluate any association between PCOS and vaginal, vulvar and cervical cancer or uterine leiomyosarcoma. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Validation of the 12-gene colon cancer recurrence score as a predictor of recurrence risk in stage II and III rectal cancer patients.

    PubMed

    Reimers, Marlies S; Kuppen, Peter J K; Lee, Mark; Lopatin, Margarita; Tezcan, Haluk; Putter, Hein; Clark-Langone, Kim; Liefers, Gerrit Jan; Shak, Steve; van de Velde, Cornelis J H

    2014-11-01

    The 12-gene Recurrence Score assay is a validated predictor of recurrence risk in stage II and III colon cancer patients. We conducted a prospectively designed study to validate this assay for prediction of recurrence risk in stage II and III rectal cancer patients from the Dutch Total Mesorectal Excision (TME) trial. RNA was extracted from fixed paraffin-embedded primary rectal tumor tissue from stage II and III patients randomized to TME surgery alone, without (neo)adjuvant treatment. Recurrence Score was assessed by quantitative real time-polymerase chain reaction using previously validated colon cancer genes and algorithm. Data were analysed by Cox proportional hazards regression, adjusting for stage and resection margin status. All statistical tests were two-sided. Recurrence Score predicted risk of recurrence (hazard ratio [HR] = 1.57, 95% confidence interval [CI] = 1.11 to 2.21, P = .01), risk of distant recurrence (HR = 1.50, 95% CI = 1.04 to 2.17, P = .03), and rectal cancer-specific survival (HR = 1.64, 95% CI = 1.15 to 2.34, P = .007). The effect of Recurrence Score was most prominent in stage II patients and attenuated with more advanced stage (P(interaction) ≤ .007 for each endpoint). In stage II, five-year cumulative incidence of recurrence ranged from 11.1% in the predefined low Recurrence Score group (48.5% of patients) to 43.3% in the high Recurrence Score group (23.1% of patients). The 12-gene Recurrence Score is a predictor of recurrence risk and cancer-specific survival in rectal cancer patients treated with surgery alone, suggesting a similar underlying biology in colon and rectal cancers. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  17. Negative HPV screening test predicts low cervical cancer risk better than negative Pap test

    Cancer.gov

    Based on a study that included more than 1 million women, investigators at NCI have determined that a negative test for HPV infection compared to a negative Pap test provides greater safety, or assurance, against future risk of cervical cancer.

  18. A germline mutation in the BRCA1 3’UTR predicts Stage IV breast cancer

    PubMed Central

    2014-01-01

    Background A germline, variant in the BRCA1 3’UTR (rs8176318) was previously shown to predict breast and ovarian cancer risk in women from high-risk families, as well as increased risk of triple negative breast cancer. Here, we tested the hypothesis that this variant predicts tumor biology, like other 3’UTR mutations in cancer. Methods The impact of the BRCA1-3’UTR-variant on BRCA1 gene expression, and altered response to external stimuli was tested in vitro using a luciferase reporter assay. Gene expression was further tested in vivo by immunoflourescence staining on breast tumor tissue, comparing triple negative patient samples with the variant (TG or TT) or non-variant (GG) BRCA1 3’UTR. To determine the significance of the variant on clinically relevant endpoints, a comprehensive collection of West-Irish breast cancer patients were tested for the variant. Finally, an association of the variant with breast screening clinical phenotypes was evaluated using a cohort of women from the High Risk Breast Program at the University of Vermont. Results Luciferase reporters with the BRCA1-3’UTR-variant (T allele) displayed significantly lower gene expression, as well as altered response to external hormonal stimuli, compared to the non-variant 3’UTR (G allele) in breast cancer cell lines. This was confirmed clinically by the finding of reduced BRCA1 gene expression in triple negative samples from patients carrying the homozygous TT variant, compared to non-variant patients. The BRCA1-3’UTR-variant (TG or TT) also associated with a modest increased risk for developing breast cancer in the West-Irish cohort (OR = 1.4, 95% CI 1.1-1.8, p = 0.033). More importantly, patients with the BRCA1-3’UTR-variant had a 4-fold increased risk of presenting with Stage IV disease (p = 0.018, OR = 3.37, 95% CI 1.3-11.0). Supporting that this finding is due to tumor biology, and not difficulty screening, obese women with the BRCA1-3’UTR-variant had

  19. Increased cancer risk in patients with periodontitis.

    PubMed

    Dizdar, Omer; Hayran, Mutlu; Guven, Deniz Can; Yılmaz, Tolga Birtan; Taheri, Sahand; Akman, Abdullah C; Bilgin, Emre; Hüseyin, Beril; Berker, Ezel

    2017-12-01

    Previous studies have noted a possible association between periodontal diseases and the risk of various cancers. We assessed cancer risk in a cohort of patients with moderate to severe periodontitis. Patients diagnosed with moderate to severe periodontitis by a periodontist between 2001 and 2010 were identified from the hospital registry. Patients younger than 35 years of age or with a prior cancer diagnosis were excluded. The age- and gender-standardized incidence rates (SIR) were calculated by dividing the number of observed cases by the number of expected cases from Turkish National Cancer Registry 2013 data. A total of 280 patients were included (median age 49.6, 54% female). Median follow-up was 12 years. Twenty-five new cancer cases were observed. Patients with periodontitis had 77% increased risk of cancer (SIR 1.77, 95% CI 1.17-2.58, p = .004). Women with periodontitis had significantly higher risk of breast cancer (SIR 2.40, 95% CI 0.88-5.33) and men with periodontitis had significantly higher risk of prostate cancer (SIR 3.75, 95% CI 0.95-10.21) and hematological cancers (SIR 6.97, 95% CI 1.77-18.98). Although showing a causal association necessitates further investigation, our results support the idea that periodontitis might be associated with increased cancer risk, particularly with hematological, breast and prostate cancers.

  20. Decision-making tools in prostate cancer: from risk grouping to nomograms.

    PubMed

    Fontanella, Paolo; Benecchi, Luigi; Grasso, Angelica; Patel, Vipul; Albala, David; Abbou, Claude; Porpiglia, Francesco; Sandri, Marco; Rocco, Bernardo; Bianchi, Giampaolo

    2017-12-01

    Prostate cancer (PCa) is the most common solid neoplasm and the second leading cause of cancer death in men. After the Partin tables were developed, a number of predictive and prognostic tools became available for risk stratification. These tools have allowed the urologist to better characterize this disease and lead to more confident treatment decisions for patients. The purpose of this study is to critically review the decision-making tools currently available to the urologist, from the moment when PCa is first diagnosed until patients experience metastatic progression and death. A systematic and critical analysis through Medline, EMBASE, Scopus and Web of Science databases was carried out in February 2016 as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The search was conducted using the following key words: "prostate cancer," "prediction tools," "nomograms." Seventy-two studies were identified in the literature search. We summarized the results into six sections: Tools for prediction of life expectancy (before treatment), Tools for prediction of pathological stage (before treatment), Tools for prediction of survival and cancer-specific mortality (before/after treatment), Tools for prediction of biochemical recurrence (before/after treatment), Tools for prediction of metastatic progression (after treatment) and in the last section biomarkers and genomics. The management of PCa patients requires a tailored approach to deliver a truly personalized treatment. The currently available tools are of great help in helping the urologist in the decision-making process. These tests perform very well in high-grade and low-grade disease, while for intermediate-grade disease further research is needed. Newly discovered markers, genomic tests, and advances in imaging acquisition through mpMRI will help in instilling confidence that the appropriate treatments are being offered to patients with prostate cancer.

  1. Prediction model of critical weight loss in cancer patients during particle therapy.

    PubMed

    Zhang, Zhihong; Zhu, Yu; Zhang, Lijuan; Wang, Ziying; Wan, Hongwei

    2018-01-01

    The objective of this study is to investigate the predictors of critical weight loss in cancer patients receiving particle therapy, and build a prediction model based on its predictive factors. Patients receiving particle therapy were enroled between June 2015 and June 2016. Body weight was measured at the start and end of particle therapy. Association between critical weight loss (defined as >5%) during particle therapy and patients' demographic, clinical characteristic, pre-therapeutic nutrition risk screening (NRS 2002) and BMI were evaluated by logistic regression and decision tree analysis. Finally, 375 cancer patients receiving particle therapy were included. Mean weight loss was 0.55 kg, and 11.5% of patients experienced critical weight loss during particle therapy. The main predictors of critical weight loss during particle therapy were head and neck tumour location, total radiation dose ≥70 Gy on the primary tumour, and without post-surgery, as indicated by both logistic regression and decision tree analysis. Prediction model that includes tumour locations, total radiation dose and post-surgery had a good predictive ability, with the area under receiver operating characteristic curve 0.79 (95% CI: 0.71-0.88) and 0.78 (95% CI: 0.69-0.86) for decision tree and logistic regression model, respectively. Cancer patients with head and neck tumour location, total radiation dose ≥70 Gy and without post-surgery were at higher risk of critical weight loss during particle therapy, and early intensive nutrition counselling or intervention should be target at this population. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  2. A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

    PubMed

    Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

    2014-01-01

    Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

  3. Adolescent meat intake and breast cancer risk

    PubMed Central

    Farvid, Maryam S; Cho, Eunyoung; Chen, Wendy Y; Eliassen, A. Heather; Willett, Walter C

    2015-01-01

    The breast is particularly vulnerable to carcinogenic influences during adolescence due to rapid proliferation of mammary cells and lack of terminal differentiation. We investigated consumption of adolescent red meat and other protein sources in relation to breast cancer risk in the Nurses' Health Study II cohort. We followed prospectively 44,231 women aged 33-52 years who, in 1998, completed a detailed questionnaire about diet during adolescence. Relative risks (RR) and 95% confidence intervals (95%CI) were estimated using Cox proportional hazard regression. We documented 1132 breast cancer cases during 13-year follow-up. In multivariable Cox regression models with major breast cancer risk factors adjustment, greater consumption of adolescent total red meat was significantly associated with higher premenopausal breast cancer risk (highest vs lowest quintiles, RR, 1.42; 95%CI, 1.05-1.94; Ptrend=0.007), but not postmenopausal breast cancer. Adolescent poultry intake was associated with lower risk of breast cancer overall (RR, 0.75; 95%CI, 0.59-0.96; for each serving/day). Adolescent intakes of iron, heme iron, fish, eggs, legumes and nuts were not associated with breast cancer. Replacement of one serving/day of total red meat with one serving of combination of poultry, fish, legumes, and nuts was associated with a 16% lower risk of breast cancer overall (RR, 0.84; 95%CI, 0.74-0.96) and a 24% lower risk of premenopausal breast cancer (RR, 0.76; 95%CI, 0.64-0.92). Higher consumption of red meat during adolescence was associated with premenopausal breast cancer. Substituting other dietary protein sources for red meat in adolescent diet may decrease premenopausal breast cancer risk. PMID:25220168

  4. Reproduction and Breast Cancer Risk

    PubMed Central

    Hanf, Volker; Hanf, Dorothea

    2014-01-01

    Summary Reproduction is doubtlessly one of the main biological meanings of life. It is therefore not surprising that various aspects of reproduction impact on breast cancer risk. Various developmental levels may become targets of breast tumorigenesis. This review follows the chronologic sequence of events in the life of a female at risk, starting with the intrauterine development. Furthermore, the influence of both contraceptive measures and fertility treatment on breast cancer development is dealt with, as well as various pregnancy-associated factors, events, and perinatal outcomes. Finally, the contribution of breast feeding to a reduced breast cancer risk is discussed. PMID:25759622

  5. Predicting the probability of mortality of gastric cancer patients using decision tree.

    PubMed

    Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

    2015-06-01

    Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

  6. [Prognostic and predictive molecular markers for urologic cancers].

    PubMed

    Hartmann, A; Schlomm, T; Bertz, S; Heinzelmann, J; Hölters, S; Simon, R; Stoehr, R; Junker, K

    2014-04-01

    Molecular prognostic factors and genetic alterations as predictive markers for cancer-specific targeted therapies are used today in the clinic for many malignancies. In recent years, many molecular markers for urogenital cancers have also been identified. However, these markers are not clinically used yet. In prostate cancer, novel next-generation sequencing methods revealed a detailed picture of the molecular changes. There is growing evidence that a combination of classical histopathological and validated molecular markers could lead to a more precise estimation of prognosis, thus, resulting in an increasing number of patients with active surveillance as a possible treatment option. In patients with urothelial carcinoma, histopathological factors but also the proliferation of the tumor, mutations in oncogenes leading to an increasing proliferation rate and changes in genes responsible for invasion and metastasis are important. In addition, gene expression profiles which could distinguish aggressive tumors with high risk of metastasis from nonmetastasizing tumors have been recently identified. In the future, this could potentially allow better selection of patients needing systemic perioperative treatment. In renal cell carcinoma, many molecular markers that are associated with metastasis and survival have been identified. Some of these markers were also validated as independent prognostic markers. Selection of patients with primarily organ-confined tumors and increased risk of metastasis for adjuvant systemic therapy could be clinically relevant in the future.

  7. Familial Risks and Estrogen Receptor-Positive Breast Cancer in Hong Kong Chinese Women

    PubMed Central

    Chan, Wing-cheong; Kwok, Chi-hei; Leung, Siu-lan; Wu, Cherry; Yu, Ignatius Tak-sun; Yu, Wai-cho; Lao, Xiangqian; Wang, Xiaorong; Wong, Carmen Ka-man; Lee, Priscilla Ming-yi; Wang, Feng; Yang, Xiaohong Rose

    2015-01-01

    Purpose The role of family history to the risk of breast cancer was analyzed by incorporating menopausal status in Hong Kong Chinese women, with a particular respect to the estrogen receptor-positive (ER+) type. Methods Seven hundred and forty seven breast cancer incident cases and 781 hospital controls who had completed information on family cancer history in first-degree relatives (nature father, mother, and siblings) were recruited. Odds ratio for breast cancer were calculated by unconditional multiple logistic regression, stratified by menopausal status (a surrogate of endogenous female sex hormone level and age) and type of relative affected with the disease. Further subgroup analysis by tumor type according to ER status was investigated. Results Altogether 52 (6.96%) breast cancer cases and 23 (2.95%) controls was found that the patients’ one or more first-degree relatives had a history of breast cancer, showing an adjusted odds ratio (OR) of 2.41 (95%CI: 1.45–4.02). An excess risk of breast cancer was restricted to the ER+ tumor (OR = 2.43, 95% CI: 1.38–4.28), with a relatively higher risk associated with an affected mother (OR = 3.97, 95%CI: 1.46–10.79) than an affected sister (OR = 2.06, 95%CI: 1.07–3.97), while the relative risk was more prominent in the subgroup of pre-menopausal women. Compared with the breast cancer overall, the familial risks to the ER+ tumor increased progressively with the number of affected first-degree relatives. Conclusions This study provides new insights on a relationship between family breast cancer history, menopausal status, and the ER+ breast cancer. A separate risk prediction model for ER+ tumor in Asian population is desired. PMID:25756203

  8. Visualization of risk of radiogenic second cancer in the organs and tissues of the human body.

    PubMed

    Zhang, Rui; Mirkovic, Dragan; Newhauser, Wayne D

    2015-04-28

    Radiogenic second cancer is a common late effect in long term cancer survivors. Currently there are few methods or tools available to visually evaluate the spatial distribution of risks of radiogenic late effects in the human body. We developed a risk visualization method and demonstrated it for radiogenic second cancers in tissues and organs of one patient treated with photon volumetric modulated arc therapy and one patient treated with proton craniospinal irradiation. Treatment plans were generated using radiotherapy treatment planning systems (TPS) and dose information was obtained from TPS. Linear non-threshold risk coefficients for organs at risk of second cancer incidence were taken from the Biological Effects of Ionization Radiation VII report. Alternative risk models including linear exponential model and linear plateau model were also examined. The predicted absolute lifetime risk distributions were visualized together with images of the patient anatomy. The risk distributions of second cancer for the two patients were visually presented. The risk distributions varied with tissue, dose, dose-risk model used, and the risk distribution could be similar to or very different from the dose distribution. Our method provides a convenient way to directly visualize and evaluate the risks of radiogenic second cancer in organs and tissues of the human body. In the future, visual assessment of risk distribution could be an influential determinant for treatment plan scoring.

  9. Knowledge and perceptions of familial and genetic risks for breast cancer risk in adolescent girls

    PubMed Central

    Bradbury, Angela R.; Patrick-Miller, Linda; Egleston, Brian L.; Schwartz, Lisa A.; Sands, Colleen B.; Shorter, Rebecca; Moore, Cynthia W.; Tuchman, Lisa; Rauch, Paula; Malhotra, Shreya; Rowan, Brianne; van Decker, Stephanie; Schmidheiser, Helen; Bealin, Lisa; Sicilia, Patrick; Daly, Mary B.

    2012-01-01

    Background Evidence suggests early events might modify adult breast cancer risk and many adolescents learn of familial and genetic risks for breast cancer. Little is known about how adolescent girls understand and respond to breast cancer risk. Methods Semi-structured interviews with 11-19 year-old girls at high-risk and population-risk for breast cancer evaluated knowledge and perceptions of breast cancer risk and risk modification. Framework analysis and descriptive statistics were utilized to analyze open-ended responses. Risk group and age differences were evaluated by Fisher’s exact and McNemar’s tests. Results 54 girls (86% of invited), 35 high-risk (65%) and 19 population-risk (35%) completed interviews. The most frequently reported risk for breast cancer was family history/hereditary predisposition (66%). Only 17% of girls were aware of BRCA1/2 genes. The majority (76%) of high-risk girls perceive themselves to be at increased risk for breast cancer, compared to 22% of population-risk girls (p=0.001). Half of girls reported that women can get breast cancer before 20 years old. The majority believe there are things women (70%) and girls (67%) can do to prevent breast cancer. Mother was the most frequently reported source of information for breast cancer among both high-risk (97%) and population-risk (89%) girls. Conclusion In this study, many high-risk girls perceive themselves to be at increased risk for breast cancer, and many girls believe that breast cancer can occur in teens. Yet, most girls believe there are things women and girls can do to prevent breast cancer. Research evaluating the impact of awareness and perceptions of breast cancer risk on psychosocial, health and risk behaviors is needed to develop strategies to optimize responses to cancer risk. PMID:23065030

  10. Occupational sedentariness and breast cancer risk.

    PubMed

    Johnsson, Anna; Broberg, Per; Johnsson, Anders; Tornberg, Åsa B; Olsson, Håkan

    2017-01-01

    Epidemiological studies have indicated that physical activity reduces the risk of developing breast cancer. More recently, sedentary behavior has been suggested as a risk factor independent of physical activity level. The purpose of the present study was to investigate occupational sedentariness and breast cancer risk in pre- and postmenopausal women. In a population-based prospective cohort study (n = 29 524), working history was assessed by a questionnaire between 1990 and 1992. Participants were classified as having: (1) sedentary occupations only; (2) mixed occupations or (3) non-sedentary occupations only. The association between occupational sedentariness and breast cancer incidence was analyzed by Cox regression, adjusted for known risk factors and participation in competitive sports. Women with a working history of occupational sedentariness had a significantly increased risk of breast cancer (adjusted HR 1.20; 95% CI 1.05, 1.37) compared with those with mixed or non-sedentary occupations. The association was stronger among women younger than 55 years (adjusted HR 1.54; 95% CI 1.20, 1.96), whereas no association was seen in women 55 years or older. Adjustment for participation in competitive sports did not change the association. We found that occupational sedentariness was associated with increased breast cancer risk, especially in women younger than 55 years. This may be a modifiable risk factor by planning breaks during the working day. Whether this reduces the risk of breast cancer needs to be further studied.

  11. An epidemiological study on occupation and cancer risk.

    PubMed

    Kato, I; Tominaga, S; Ikari, A

    1990-06-01

    The relation between occupation and cancer risk was examined on the basis of 17,164 male and 6,835 female cancer patients aged 30 years or over who were entered in the Aichi Cancer Registry during the period, 1979-1987. Controlling for age, the risk of developing lung cancer was significantly high in sales, transport-and-communications, mental, ceramics and construction workers in men, and service workers in women. The risk of developing liver cancer was significantly high in transport-and-communications and service workers in men. The risk of developing colon cancer was significantly high in professional people of both sexes and in clerical workers in men. The risk of developing female breast cancer was significantly high in professional women, administrative and clerical workers and hairdressers. The risk of developing stomach cancer was significantly high in male and female agricultural workers, while that of developing cancer of the mouth-and-pharynx was significantly high in construction workers in men and filature-and-spinning workers in women. Analysis of smoking and alcohol drinking habits, by occupation, suggested the increased risk of developing lung cancer to be associated with a greater percentage of smokers and the increased risks of developing cancers of the liver and mouth-and-pharynx to be associated with a greater percentage of daily alcohol drinkers. When the analysis was limited to smokers, the risk of developing lung cancer was still significantly high in metal, ceramics and construction workers in men.

  12. Height and Breast Cancer Risk: Evidence From Prospective Studies and Mendelian Randomization

    PubMed Central

    Zhang, Ben; Shu, Xiao-Ou; Delahanty, Ryan J.; Zeng, Chenjie; Michailidou, Kyriaki; Bolla, Manjeet K.; Wang, Qin; Dennis, Joe; Wen, Wanqing; Long, Jirong; Li, Chun; Dunning, Alison M.; Chang-Claude, Jenny; Shah, Mitul; Perkins, Barbara J.; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Bojesen, Stig E.; Nordestgaard, Børge G.; Nielsen, Sune F.; Flyger, Henrik; Lambrechts, Diether; Neven, Patrick; Wildiers, Hans; Floris, Giuseppe; Schmidt, Marjanka K.; Rookus, Matti A.; van den Hurk, Katja; de Kort, Wim L. A. M.; Couch, Fergus J.; Olson, Janet E.; Hallberg, Emily; Vachon, Celine; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Peto, Julian; dos-Santos-Silva, Isabel; Fletcher, Olivia; Johnson, Nichola; Nevanlinna, Heli; Muranen, Taru A.; Aittomäki, Kristiina; Blomqvist, Carl; Li, Jingmei; Humphreys, Keith; Brand, Judith; Guénel, Pascal; Truong, Thérèse; Cordina-Duverger, Emilie; Menegaux, Florence; Burwinkel, Barbara; Marme, Frederik; Yang, Rongxi; Surowy, Harald; Benitez, Javier; Zamora, M. Pilar; Perez, Jose I. A.; Cox, Angela; Cross, Simon S.; Reed, Malcolm W. R.; Andrulis, Irene L.; Knight, Julia A.; Glendon, Gord; Tchatchou, Sandrine; Sawyer, Elinor J.; Tomlinson, Ian; Kerin, Michael J.; Miller, Nicola; Chenevix-Trench, Georgia; Haiman, Christopher A.; Henderson, Brian E.; Schumacher, Fredrick; Marchand, Loic Le; Lindblom, Annika; Margolin, Sara; Hooning, Maartje J.; Martens, John W. M.; Tilanus-Linthorst, Madeleine M. A.; Collée, J. Margriet; Hopper, John L.; Southey, Melissa C.; Tsimiklis, Helen; Apicella, Carmel; Slager, Susan; Toland, Amanda E.; Ambrosone, Christine B.; Yannoukakos, Drakoulis; Giles, Graham G.; Milne, Roger L.; McLean, Catriona; Fasching, Peter A.; Haeberle, Lothar; Ekici, Arif B.; Beckmann, Matthias W.; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Swerdlow, Anthony J.; Ashworth, Alan; Orr, Nick; Jones, Michael; Figueroa, Jonine; Garcia-Closas, Montserrat; Brinton, Louise; Lissowska, Jolanta; Dumont, Martine; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Peterlongo, Paolo; Manoukian, Siranoush; Bonanni, Bernardo; Radice, Paolo; Bogdanova, Natalia; Antonenkova, Natalia; Dörk, Thilo; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M.; Devilee, Peter; Seynaeve, Caroline; Van Asperen, Christi J.; Jakubowska, Anna; Lubiński, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Hamann, Ute; Torres, Diana; Schmutzler, Rita K.; Neuhausen, Susan L.; Anton-Culver, Hoda; Kristensen, Vessela N.; Grenaker Alnæs, Grethe I.; Pierce, Brandon L.; Kraft, Peter; Peters, Ulrike; Lindstrom, Sara; Seminara, Daniela; Burgess, Stephen; Ahsan, Habibul; Whittemore, Alice S.; John, Esther M.; Gammon, Marilie D.; Malone, Kathleen E.; Tessier, Daniel C.; Vincent, Daniel; Bacot, Francois; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S.; González-Neira, Anna; Pita, Guillermo; Alonso, M. Rosario; Álvarez, Nuria; Herrero, Daniel; Pharoah, Paul D. P.; Simard, Jacques; Hall, Per; Hunter, David J.; Easton, Douglas F.

    2015-01-01

    Background: Epidemiological studies have linked adult height with breast cancer risk in women. However, the magnitude of the association, particularly by subtypes of breast cancer, has not been established. Furthermore, the mechanisms of the association remain unclear. Methods: We performed a meta-analysis to investigate associations between height and breast cancer risk using data from 159 prospective cohorts totaling 5216302 women, including 113178 events. In a consortium with individual-level data from 46325 case patients and 42482 control subjects, we conducted a Mendelian randomization analysis using a genetic score that comprised 168 height-associated variants as an instrument. This association was further evaluated in a second consortium using summary statistics data from 16003 case patients and 41335 control subjects. Results: The pooled relative risk of breast cancer was 1.17 (95% confidence interval [CI] = 1.15 to 1.19) per 10cm increase in height in the meta-analysis of prospective studies. In Mendelian randomization analysis, the odds ratio of breast cancer per 10cm increase in genetically predicted height was 1.22 (95% CI = 1.13 to 1.32) in the first consortium and 1.21 (95% CI = 1.05 to 1.39) in the second consortium. The association was found in both premenopausal and postmenopausal women but restricted to hormone receptor–positive breast cancer. Analyses of height-associated variants identified eight new loci associated with breast cancer risk after adjusting for multiple comparisons, including three loci at 1q21.2, DNAJC27, and CCDC91 at genome-wide significance level P < 5×10–8. Conclusions: Our study provides strong evidence that adult height is a risk factor for breast cancer in women and certain genetic factors and biological pathways affecting adult height have an important role in the etiology of breast cancer. PMID:26296642

  13. Genomic and Histopathological Tissue Biomarkers That Predict Radiotherapy Response in Localised Prostate Cancer

    PubMed Central

    Wilkins, Anna; Dearnaley, David; Somaiah, Navita

    2015-01-01

    Localised prostate cancer, in particular, intermediate risk disease, has varied survival outcomes that cannot be predicted accurately using current clinical risk factors. External beam radiotherapy (EBRT) is one of the standard curative treatment options for localised disease and its efficacy is related to wide ranging aspects of tumour biology. Histopathological techniques including immunohistochemistry and a variety of genomic assays have been used to identify biomarkers of tumour proliferation, cell cycle checkpoints, hypoxia, DNA repair, apoptosis, and androgen synthesis, which predict response to radiotherapy. Global measures of genomic instability also show exciting capacity to predict survival outcomes following EBRT. There is also an urgent clinical need for biomarkers to predict the radiotherapy fraction sensitivity of different prostate tumours and preclinical studies point to possible candidates. Finally, the increased resolution of next generation sequencing (NGS) is likely to enable yet more precise molecular predictions of radiotherapy response and fraction sensitivity. PMID:26504789

  14. A prognostic mutation panel for predicting cancer recurrence in stages II and III colorectal cancer.

    PubMed

    Sho, Shonan; Court, Colin M; Winograd, Paul; Russell, Marcia M; Tomlinson, James S

    2017-12-01

    Approximately 20-40% of stage II/III colorectal cancer (CRC) patients develop relapse. Clinicopathological factors alone are limited in detecting these patients, resulting in potential under/over-treatment. We sought to identify a prognostic tumor mutational profile that could predict CRC recurrence. Whole-exome sequencing data were obtained for 207 patients with stage II/III CRC from The Cancer Genome Atlas. Mutational landscape in relapse-free versus relapsed cohort was compared using Fisher's exact test, followed by multivariate Cox regression to identify genes associated with cancer recurrence. Bootstrap-validation was used to examine internal/external validity. We identified five prognostic genes (APAF1, DIAPH2, NTNG1, USP7, and VAV2), which were combined to form a prognostic mutation panel. Patients with ≥1 mutation(s) within this five-gene panel had worse prognosis (3-yr relapse-free survival [RFS]: 53.0%), compared to patients with no mutation (3-yr RFS: 84.3%). In multivariate analysis, the five-gene panel remained prognostic for cancer recurrence independent of stage and high-risk features (hazard ratio 3.63, 95%CI [1.93-6.83], P < 0.0001). Furthermore, its prognostic accuracy was superior to the American Joint Commission on Cancer classification (concordance-index: 0.70 vs 0.54). Our proposed mutation panel identifies CRC patients at high-risk for recurrence, which may help guide adjuvant therapy and post-operative surveillance protocols. © 2017 Wiley Periodicals, Inc.

  15. Coffee and cancer risk: a summary overview.

    PubMed

    Alicandro, Gianfranco; Tavani, Alessandra; La Vecchia, Carlo

    2017-09-01

    We reviewed available evidence on coffee drinking and the risk of all cancers and selected cancers updated to May 2016. Coffee consumption is not associated with overall cancer risk. A meta-analysis reported a pooled relative risk (RR) for an increment of 1 cup of coffee/day of 1.00 [95% confidence interval (CI): 0.99-1.01] for all cancers. Coffee drinking is associated with a reduced risk of liver cancer. A meta-analysis of cohort studies found an RR for an increment of consumption of 1 cup/day of 0.85 (95% CI: 0.81-0.90) for liver cancer and a favorable effect on liver enzymes and cirrhosis. Another meta-analysis showed an inverse relation for endometrial cancer risk, with an RR of 0.92 (95% CI: 0.88-0.96) for an increment of 1 cup/day. A possible decreased risk was found in some studies for oral/pharyngeal cancer and for advanced prostate cancer. Although data are mixed, overall, there seems to be some favorable effect of coffee drinking on colorectal cancer in case-control studies, in the absence of a consistent relation in cohort studies. For bladder cancer, the results are not consistent; however, any possible direct association is not dose and duration related, and might depend on a residual confounding effect of smoking. A few studies suggest an increased risk of childhood leukemia after maternal coffee drinking during pregnancy, but data are limited and inconsistent. Although the results of studies are mixed, the overall evidence suggests no association of coffee intake with cancers of the stomach, pancreas, lung, breast, ovary, and prostate overall. Data are limited, with RR close to unity for other neoplasms, including those of the esophagus, small intestine, gallbladder and biliary tract, skin, kidney, brain, thyroid, as well as for soft tissue sarcoma and lymphohematopoietic cancer.

  16. SU-E-T-628: Predicted Risk of Post-Irradiation Cerebral Necrosis in Pediatric Brain Cancer Patients: A Treatment Planning Comparison of Proton Vs. Photon Therapy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Freund, D; Zhang, R; Sanders, M

    Purpose: Post-irradiation cerebral necrosis (PICN) is a severe late effect that can Result from brain cancers treatment using radiation therapy. The purpose of this study was to compare the treatment plans and predicted risk of PICN after volumetric modulated arc therapy (VMAT) to the risk after passively scattered proton therapy (PSPT) and intensity modulated proton therapy (IMPT) in a cohort of pediatric patients. Methods: Thirteen pediatric patients with varying age and sex were selected for this study. A clinical treatment volume (CTV) was constructed for 8 glioma patients and 5 ependymoma patients. Prescribed dose was 54 Gy over 30 fractionsmore » to the planning volume. Dosimetric endpoints were compared between VMAT and proton plans. The normal tissue complication probability (NTCP) following VMAT and proton therapy planning was also calculated using PICN as the biological endpoint. Sensitivity tests were performed to determine if predicted risk of PICN was sensitive to positional errors, proton range errors and selection of risk models. Results: Both PSPT and IMPT plans resulted in a significant increase in the maximum dose and reduction in the total brain volume irradiated to low doses compared with the VMAT plans. The average ratios of NTCP between PSPT and VMAT were 0.56 and 0.38 for glioma and ependymoma patients respectively and the average ratios of NTCP between IMPT and VMAT were 0.67 and 0.68 for glioma and ependymoma plans respectively. Sensitivity test revealed that predicted ratios of risk were insensitive to range and positional errors but varied with risk model selection. Conclusion: Both PSPT and IMPT plans resulted in a decrease in the predictive risk of necrosis for the pediatric plans studied in this work. Sensitivity analysis upheld the qualitative findings of the risk models used in this study, however more accurate models that take into account dose and volume are needed.« less

  17. Complementary role of the Memorial Sloan Kettering Cancer Center nomogram to the American Joint Committee on Cancer system for the prediction of relapse of major salivary gland carcinoma after surgery.

    PubMed

    Chou, Wen-Chi; Chang, Kai-Ping; Lu, Chang-Hsien; Chen, Miao-Fen; Cheng, Yu-Fan; Yeh, Kun-Yun; Wang, Cheng-Hsu; Lin, Yung-Chang; Yeh, Ta-Sen

    2017-05-01

    The purpose of this study was to test the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram in predicting recurrence risk of major salivary gland carcinoma in an Asian cohort. We retrospectively enrolled 149 patients who had undergone intended curative resections for major salivary gland carcinoma between 2007 and 2012. The performance of the MSKCC nomogram and the American Joint Committee on Cancer (AJCC) seventh staging system in predicting recurrence risk was compared. The MSKCC nomogram and the AJCC staging system both accurately predicted the 5-year recurrence probabilities, with the concordance index (c-index = 0.82; 95% confidence interval [CI], 0.75-0.89 vs c-index, 0.77; 95% CI, 0.68-0.87; p = .45) in patients with major salivary gland carcinomas after curative surgeries. Comparing to the actual observed events, the calibration plot indicated that the MSKCC nomogram accurately estimated the recurrence in low-risk groups but tended to overestimate in high-risk groups. When using the MSKCC nomogram to predict the 5-year recurrence-free probability in each AJCC stage, the prediction was very good for patients with AJCC stages I and II disease (c-index = 0.92 and 0.90, respectively) and modest for those of AJCC stages III and IVa (c-index = 0.51 and 0.62, respectively). The MSKCC nomogram and the AJCC staging system each had its value in predicting recurrence of major salivary gland cancers. When using the MSKCC nomogram to predict the 5-year recurrence-free probability in each AJCC stage, the MSKCC nomogram was more accurate in predicting recurrence risks in those patients with AJCC stage I and II diseases than those with late-stage diseases. © 2017 Wiley Periodicals, Inc. Head Neck 39: 860-867, 2017. © 2017 Wiley Periodicals, Inc.

  18. Adolescent meat intake and breast cancer risk.

    PubMed

    Farvid, Maryam S; Cho, Eunyoung; Chen, Wendy Y; Eliassen, A Heather; Willett, Walter C

    2015-04-15

    The breast is particularly vulnerable to carcinogenic influences during adolescence due to rapid proliferation of mammary cells and lack of terminal differentiation. We investigated consumption of adolescent red meat and other protein sources in relation to breast cancer risk in the Nurses' Health Study II cohort. We followed prospectively 44,231 women aged 33-52 years who, in 1998, completed a detailed questionnaire about diet during adolescence. Relative risks (RR) and 95% confidence intervals (95%CI) were estimated using Cox proportional hazard regression. We documented 1132 breast cancer cases during 13-year follow-up. In multivariable Cox regression models with major breast cancer risk factors adjustment, greater consumption of total red meat in adolescence was significantly associated with higher premenopausal breast cancer risk (highest vs. lowest quintiles, RR, 1.43; 95%CI, 1.05-1.94; Ptrend  = 0.007), but not postmenopausal breast cancer. Adolescent intake of poultry was associated with lower risk of breast cancer overall (RR, 0.76; 95%CI, 0.60-0.97; for each serving/day). Adolescent intakes of iron, heme iron, fish, eggs, legumes and nuts were not associated with breast cancer. Replacement of one serving/day of total red meat with one serving of combination of poultry, fish, legumes, and nuts was associated with a 15% lower risk of breast cancer overall (RR, 0.85; 95%CI, 0.74-0.96) and a 23% lower risk of premenopausal breast cancer (RR, 0.77; 95%CI, 0.64-0.92). In conclusion, higher consumption of red meat during adolescence was associated with premenopausal breast cancer. Substituting other dietary protein sources for red meat in adolescent diet may decrease premenopausal breast cancer risk. © 2014 UICC.

  19. Health care professionals' attitudes towards population-based genetic testing and risk-stratification for ovarian cancer: a cross-sectional survey.

    PubMed

    Hann, Katie E J; Fraser, Lindsay; Side, Lucy; Gessler, Sue; Waller, Jo; Sanderson, Saskia C; Freeman, Madeleine; Jacobs, Ian; Lanceley, Anne

    2017-12-16

    Ovarian cancer is usually diagnosed at a late stage when outcomes are poor. Personalised ovarian cancer risk prediction, based on genetic and epidemiological information and risk stratified management in adult women could improve outcomes. Examining health care professionals' (HCP) attitudes to ovarian cancer risk stratified management, willingness to support women, self-efficacy (belief in one's own ability to successfully complete a task), and knowledge about ovarian cancer will help identify training needs in anticipation of personalised ovarian cancer risk prediction being introduced. An anonymous survey was distributed online to HCPs via relevant professional organisations in the UK. Kruskal-Wallis tests and pairwise comparisons were used to compare knowledge and self-efficacy scores between different types of HCPs, and attitudes toward population-based genetic testing and risk stratified management were described. Content analysis was undertaken of free text responses concerning HCPs willingness to discuss risk management options with women. One hundred forty-six eligible HCPs completed the survey: oncologists (31%); genetics clinicians (30%); general practitioners (22%); gynaecologists (10%); nurses (4%); and 'others'. Scores for knowledge of ovarian cancer and genetics, and self-efficacy in conducting a cancer risk consultation were generally high but significantly lower for general practitioners compared to genetics clinicians, oncologists, and gynaecologists. Support for population-based genetic testing was not high (<50%). Attitudes towards ovarian cancer risk stratification were mixed, although the majority of participants indicated a willingness to discuss management options with patients. Larger samples are required to investigate attitudes to population-based genetic testing for ovarian cancer risk and to establish why some HCPs are hesitant to offer testing to all adult female patients. If ovarian cancer risk assessment using genetic testing and non

  20. European cancer mortality predictions for the year 2014.

    PubMed

    Malvezzi, M; Bertuccio, P; Levi, F; La Vecchia, C; Negri, E

    2014-08-01

    From most recent available data, we projected cancer mortality statistics for 2014, for the European Union (EU) and its six more populous countries. Specific attention was given to pancreatic cancer, the only major neoplasm showing unfavorable trends in both sexes. Population and death certification data from stomach, colorectum, pancreas, lung, breast, uterus, prostate, leukemias and total cancers were obtained from the World Health Organisation database and Eurostat. Figures were derived for the EU, France, Germany, Italy, Poland, Spain and the UK. Projected 2014 numbers of deaths by age group were obtained by linear regression on estimated numbers of deaths over the most recent time period identified by a joinpoint regression model. In the EU in 2014, 1,323,600 deaths from cancer are predicted (742,500 men and 581,100 women), corresponding to standardized death rates of 138.1/100,000 men and 84.7/100,000 women, falling by 7% and 5%, respectively, since 2009. In men, predicted rates for the three major cancers (lung, colorectum and prostate cancer) are lower than in 2009, falling by 8%, 4% and 10%, respectively. In women, breast and colorectal cancers had favorable trends (-9% and -7%), but female lung cancer rates are predicted to rise 8%. Pancreatic cancer is the only neoplasm with a negative outlook in both sexes. Only in the young (25-49 years), EU trends become more favorable in men, while women keep registering slight predicted rises. Cancer mortality predictions for 2014 confirm the overall favorable cancer mortality trend in the EU, translating to an overall 26% fall in men since its peak in 1988, and 20% in women, and the avoidance of over 250,000 deaths in 2014 compared with the peak rate. Notable exceptions are female lung cancer and pancreatic cancer in both sexes. © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  1. Obesity and Cancer Risk

    MedlinePlus

    ... cancer risk. Despite the limitations of the study designs, there is consistent evidence that higher amounts of ... the National Cancer Institute.” Please note that blog posts that are written by individuals from outside the ...

  2. On cancer risk estimation of urban air pollution.

    PubMed Central

    Törnqvist, M; Ehrenberg, L

    1994-01-01

    The usefulness of data from various sources for a cancer risk estimation of urban air pollution is discussed. Considering the irreversibility of initiations, a multiplicative model is preferred for solid tumors. As has been concluded for exposure to ionizing radiation, the multiplicative model, in comparison with the additive model, predicts a relatively larger number of cases at high ages, with enhanced underestimation of risks by short follow-up times in disease-epidemiological studies. For related reasons, the extrapolation of risk from animal tests on the basis of daily absorbed dose per kilogram body weight or per square meter surface area without considering differences in life span may lead to an underestimation, and agreements with epidemiologically determined values may be fortuitous. Considering these possibilities, the most likely lifetime risks of cancer death at the average exposure levels in Sweden were estimated for certain pollution fractions or indicator compounds in urban air. The risks amount to approximately 50 deaths per 100,000 for inhaled particulate organic material (POM), with a contribution from ingested POM about three times larger, and alkenes, and butadiene cause 20 deaths, respectively, per 100,000 individuals. Also, benzene and formaldehyde are expected to be associated with considerable risk increments. Comparative potency methods were applied for POM and alkenes. Due to incompleteness of the list of compounds considered and the uncertainties of the above estimates, the total risk calculation from urban air has not been attempted here. PMID:7821292

  3. Genetic Variation at 9p22.2 and Ovarian Cancer Risk for BRCA1 and BRCA2 Mutation Carriers

    PubMed Central

    Kartsonaki, Christiana; Gayther, Simon A.; Pharoah, Paul D. P.; Sinilnikova, Olga M.; Beesley, Jonathan; Chen, Xiaoqing; McGuffog, Lesley; Healey, Sue; Couch, Fergus J.; Wang, Xianshu; Fredericksen, Zachary; Peterlongo, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Roversi, Gaia; Barile, Monica; Viel, Alessandra; Allavena, Anna; Ottini, Laura; Papi, Laura; Gismondi, Viviana; Capra, Fabio; Radice, Paolo; Greene, Mark H.; Mai, Phuong L.; Andrulis, Irene L.; Glendon, Gord; Ozcelik, Hilmi; Thomassen, Mads; Gerdes, Anne-Marie; Kruse, Torben A.; Cruger, Dorthe; Jensen, Uffe Birk; Caligo, Maria Adelaide; Olsson, Håkan; Kristoffersson, Ulf; Lindblom, Annika; Arver, Brita; Karlsson, Per; Stenmark Askmalm, Marie; Borg, Ake; Neuhausen, Susan L.; Ding, Yuan Chun; Nathanson, Katherine L.; Domchek, Susan M.; Jakubowska, Anna; Lubiński, Jan; Huzarski, Tomasz; Byrski, Tomasz; Gronwald, Jacek; Górski, Bohdan; Cybulski, Cezary; Dębniak, Tadeusz; Osorio, Ana; Durán, Mercedes; Tejada, Maria-Isabel; Benítez, Javier; Hamann, Ute; Rookus, Matti A.; Verhoef, Senno; Tilanus-Linthorst, Madeleine A.; Vreeswijk, Maaike P.; Bodmer, Danielle; Ausems, Margreet G. E. M.; van Os, Theo A.; Asperen, Christi J.; Blok, Marinus J.; Meijers-Heijboer, Hanne E. J.; Peock, Susan; Cook, Margaret; Oliver, Clare; Frost, Debra; Dunning, Alison M.; Evans, D. Gareth; Eeles, Ros; Pichert, Gabriella; Cole, Trevor; Hodgson, Shirley; Brewer, Carole; Morrison, Patrick J.; Porteous, Mary; Kennedy, M. John; Rogers, Mark T.; Side, Lucy E.; Donaldson, Alan; Gregory, Helen; Godwin, Andrew; Stoppa-Lyonnet, Dominique; Moncoutier, Virginie; Castera, Laurent; Mazoyer, Sylvie; Barjhoux, Laure; Bonadona, Valérie; Leroux, Dominique; Faivre, Laurence; Lidereau, Rosette; Nogues, Catherine; Bignon, Yves-Jean; Prieur, Fabienne; Collonge-Rame, Marie-Agnès; Venat-Bouvet, Laurence; Fert-Ferrer, Sandra; Miron, Alex; Buys, Saundra S.; Hopper, John L.; Daly, Mary B.; John, Esther M.; Terry, Mary Beth; Goldgar, David; Hansen, Thomas v. O.; Jønson, Lars; Ejlertsen, Bent; Agnarsson, Bjarni A.; Offit, Kenneth; Kirchhoff, Tomas; Vijai, Joseph; Dutra-Clarke, Ana V. C.; Przybylo, Jennifer A.; Montagna, Marco; Casella, Cinzia; Imyanitov, Evgeny N.; Janavicius, Ramunas; Blanco, Ignacio; Lázaro, Conxi; Moysich, Kirsten B.; Karlan, Beth Y.; Gross, Jenny; Beattie, Mary S.; Schmutzler, Rita; Wappenschmidt, Barbara; Meindl, Alfons; Ruehl, Ina; Fiebig, Britta; Sutter, Christian; Arnold, Norbert; Deissler, Helmut; Varon-Mateeva, Raymonda; Kast, Karin; Niederacher, Dieter; Gadzicki, Dorothea; Caldes, Trinidad; de la Hoya, Miguel; Nevanlinna, Heli; Aittomäki, Kristiina; Simard, Jacques; Soucy, Penny; Spurdle, Amanda B.; Holland, Helene; Chenevix-Trench, Georgia; Easton, Douglas F.; Antoniou, Antonis C.

    2011-01-01

    Background Germline mutations in the BRCA1 and BRCA2 genes are associated with increased risks of breast and ovarian cancers. Although several common variants have been associated with breast cancer susceptibility in mutation carriers, none have been associated with ovarian cancer susceptibility. A genome-wide association study recently identified an association between the rare allele of the single-nucleotide polymorphism (SNP) rs3814113 (ie, the C allele) at 9p22.2 and decreased risk of ovarian cancer for women in the general population. We evaluated the association of this SNP with ovarian cancer risk among BRCA1 or BRCA2 mutation carriers by use of data from the Consortium of Investigators of Modifiers of BRCA1/2. Methods We genotyped rs3814113 in 10 029 BRCA1 mutation carriers and 5837 BRCA2 mutation carriers. Associations with ovarian and breast cancer were assessed with a retrospective likelihood approach. All statistical tests were two-sided. Results The minor allele of rs3814113 was associated with a reduced risk of ovarian cancer among BRCA1 mutation carriers (per-allele hazard ratio of ovarian cancer = 0.78, 95% confidence interval = 0.72 to 0.85; P = 4.8 × 10-9) and BRCA2 mutation carriers (hazard ratio of ovarian cancer = 0.78, 95% confidence interval = 0.67 to 0.90; P = 5.5 × 10-4). This SNP was not associated with breast cancer risk among either BRCA1 or BRCA2 mutation carriers. BRCA1 mutation carriers with the TT genotype at SNP rs3814113 were predicted to have an ovarian cancer risk to age 80 years of 48%, and those with the CC genotype were predicted to have a risk of 33%. Conclusion Common genetic variation at the 9p22.2 locus was associated with decreased risk of ovarian cancer for carriers of a BRCA1 or BRCA2 mutation. PMID:21169536

  4. Setting the Threshold for Surgical Prevention in Women at Increased Risk of Ovarian Cancer.

    PubMed

    Manchanda, Ranjit; Menon, Usha

    2018-01-01

    The number of ovarian cancer cases is predicted to rise by 14% in Europe and 55% worldwide over the next 2 decades. The current absence of a screening program, rising drug/treatment costs, and only marginal improvements in survival seen over the past 30 years suggest the need for maximizing primary surgical prevention to reduce the burden of ovarian cancer. Primary surgical prevention through risk-reducing salpingo-oophorectomy (RRSO) is well established as the most effective method for preventing ovarian cancer. In the UK, it has traditionally been offered to high-risk women (>10% lifetime risk of ovarian cancer) who have completed their family. The cost-effectiveness of RRSO in BRCA1/BRCA2 carriers older than 35 years is well established. Recently, RRSO has been shown to be cost-effective in postmenopausal women at lifetime ovarian cancer risks of 5% or greater and in premenopausal women at lifetime risks greater than 4%. The acceptability, uptake, and satisfaction with RRSO at these intermediate-risk levels remain to be established. Prospective outcome data on risk-reducing salpingectomy and delayed-oophorectomy for preventing ovarian cancer is lacking, and hence, this is best offered for primary prevention within the context and safe environment of a clinical trial. An estimated 63% of ovarian cancers occur in women with greater than 4% lifetime risk and 53% in those with 5% or greater lifetime-risk. Risk-reducing salpingo-oophorectomy can be offered for primary surgical prevention to women at intermediate risk levels (4%-5% to 10%). This includes unaffected women who have completed their family and have RAD51C, RAD51D, or BRIP1 gene mutations; first-degree relatives of women with invasive epithelial ovarian cancer; BRCA mutation-negative women from high-risk breast-and-ovarian cancer or ovarian-cancer-only families. In those with BRCA1, RAD51C/RAD51D/MMR mutations and the occasional families with a history of ovarian cancer in their 40s, surgery needs to be

  5. A Preliminary Study of the Ability of the 4Kscore test, the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for Predicting High-Grade Prostate Cancer.

    PubMed

    Borque-Fernando, Á; Esteban-Escaño, L M; Rubio-Briones, J; Lou-Mercadé, A C; García-Ruiz, R; Tejero-Sánchez, A; Muñoz-Rivero, M V; Cabañuz-Plo, T; Alfaro-Torres, J; Marquina-Ibáñez, I M; Hakim-Alonso, S; Mejía-Urbáez, E; Gil-Fabra, J; Gil-Martínez, P; Ávarez-Alegret, R; Sanz, G; Gil-Sanz, M J

    2016-04-01

    To prevent the overdiagnosis and overtreatment of prostate cancer (PC), therapeutic strategies have been established such as active surveillance and focal therapy, as well as methods for clarifying the diagnosis of high-grade prostate cancer (HGPC) (defined as a Gleason score ≥7), such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test (4KsT). By means of a pilot study, we aim to test the ability of the 4KsT to identify HGPC in prostate biopsies (Bx) and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0) and the European Research Screening Prostate Cancer Risk Calculator 4 (ERSPC-RC 4). Fifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPC was agreed upon by 4 uropathologists. We compared the predictions from the various models by using the Mann-Whitney U test, area under the ROC curve (AUC) (DeLong test), probability density function (PDF), box plots and clinical utility curves. Forty-three percent of the patients had PC, and 23.5% had HGPC. The medians of probability for the 4KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPC and those without HGPC (p≤.022) and were more differentiated in the case of 4KsT (51.5% for HGPC [25-75 percentile: 25-80.5%] vs. 16% [P 25-75: 8-26.5%] for non-HGPC; p=.002). All models presented AUCs above 0.7, with no significant differences between any of them and 4KsT (p≥.20). The PDF and box plots showed good discriminative ability, especially in the ERSPC-RC 4 and 4KsT models. The utility curves showed how a cutoff of 9% for 4KsT identified all cases of HGPC and provided a 22% savings in biopsies, which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3%. The assessed predictive models offer good discriminative ability for HGPCs in Bx. The 4KsT is a good classification

  6. Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

    PubMed Central

    Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G. M.; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M.

    2014-01-01

    Background In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. Materials and Methods We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). Results We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66). Conclusion The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making. PMID:24695692

  7. Public support for alcohol policies associated with knowledge of cancer risk.

    PubMed

    Buykx, Penny; Gilligan, Conor; Ward, Bernadette; Kippen, Rebecca; Chapman, Kathy

    2015-04-01

    Several options are advocated by policy experts to mitigate alcohol-related harms, although the most effective strategies often have the least public support. While knowledge of tobacco-related health risks predicts support for relevant public health measures, it is not known whether knowledge of alcohol health risks is similarly associated with the acceptability of policies intended to reduce alcohol consumption and related harms. This study aims to gauge public support for a range of alcohol policies and to determine whether or not support is associated with knowledge of a long-term health risk of alcohol consumption, specifically cancer. 2482 adults in New South Wales (NSW), Australia, participated in an online survey. Logistic regression analysis was used to examine the association between demographic data, alcohol consumption, smoking status, knowledge of alcohol as a risk factor for cancer and support for alcohol-related policies. Most participants were supportive of health warnings, restricting access to internet alcohol advertising to young people, and requiring information on national drinking guidelines on alcohol containers. Almost half of participants supported a ban on sport sponsorship, while less than 41% supported price increases, volumetric taxation, or reducing the number of retail outlets. Only 47% of participants identified drinking too much alcohol as a risk factor for cancer. Knowledge of alcohol as a risk factor for cancer was a significant predictor of support for all policies, while level of alcohol consumption had a significant inverse relationship with policy support. The finding that support for alcohol management policies is associated with awareness that drinking too much alcohol may contribute to cancer could assist in the planning of future public health interventions. Improving awareness of the long term health risks of alcohol consumption may be one avenue to increasing public support for effective alcohol harm-reduction policies

  8. Dietary fat intake and endometrial cancer risk

    PubMed Central

    Zhao, Jing; Lyu, Chen; Gao, Jian; Du, Li; Shan, Boer; Zhang, Hong; Wang, Hua-Ying; Gao, Ying

    2016-01-01

    Abstract Since body fatness is a convincing risk factor for endometrial cancer, dietary fat intake was speculated to be associated with endometrial cancer risk. However, epidemiological studies are inconclusive. We aimed to conduct a meta-analysis to assess the associations between dietary fat intake and endometrial cancer risk. We searched the PubMed, Embase, and Web of science databases updated to September 2015. In total, 7 cohort and 14 case–control studies were included. Pooled analysis of case–control studies suggested that endometrial cancer risk was significantly increased by 5% per 10% kilocalories from total fat intake (P=0.02) and by 17% per 10 g/1000 kcal of saturated fat intake (P < 0.001). Summary of 3 cohort studies showed significant inverse association between monounsaturated fatty acids and endometrial cancer risk (odds ratio = 0.84, 95% confidence interval = 0.73–0.98) with a total of 524583 participants and 3503 incident cases. No significant associations were found for polyunsaturated fatty acids and linoleic acid. In conclusion, positive associations with endometrial cancer risk were observed for total fat and saturated fat intake in the case–control studies. Results from the cohort studies suggested higher monounsaturated fatty acids intake was significantly associated with lower endometrial cancer risk. PMID:27399120

  9. Quantifying prognosis with risk predictions.

    PubMed

    Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

    2012-01-01

    Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

  10. Irregular menses predicts ovarian cancer: Prospective evidence from the Child Health and Development Studies.

    PubMed

    Cirillo, Piera M; Wang, Erica T; Cedars, Marcelle I; Chen, Lee-May; Cohn, Barbara A

    2016-09-01

    We tested the hypothesis that irregular menstruation predicts lower risk for ovarian cancer, possibly due to less frequent ovulation. We conducted a 50-year prospective study of 15,528 mothers in the Child Health and Development Studies cohort recruited from the Kaiser Foundation Health Plan from 1959 to 1966. Irregular menstruation was classified via medical record and self-report at age 26. We identified 116 cases and 84 deaths due to ovarian cancer through 2011 via linkage to the California Cancer Registry and Vital Statistics. Contrary to expectation, women with irregular menstrual cycles had a higher risk of ovarian cancer incidence and mortality over the 50-year follow-up. Associations increased with age (p <0.05). We observed a 2-fold increased incidence and mortality by age 70 (95% confidence interval [CI] = 1.1, 3.4) rising to a 3-fold increase by age 77 (95% CI = 1.5, 6.7 for incidence; 95% CI = 1.4, 5.9 for mortality). We also found a 3-fold higher risk of mortality for high-grade serous tumors (95% CI = 1.3, 7.6) that did not vary by age. This is the first prospective study to show an association between irregular menstruation and ovarian cancer-we unexpectedly found higher risk for women with irregular cycles. These women are easy to identify and many may have polycystic ovarian syndrome. Classifying high-risk phenotypes such as irregular menstruation creates opportunities to find novel early biomarkers, refine clinical screening protocols and potentially develop new risk reduction strategies. These efforts can lead to earlier detection and better survival for ovarian cancer. © 2016 UICC.

  11. Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence.

    PubMed

    Tseng, Chih-Jen; Lu, Chi-Jie; Chang, Chi-Chang; Chen, Gin-Den; Cheewakriangkrai, Chalong

    2017-05-01

    Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Cost sharing and hereditary cancer risk: predictors of willingness-to-pay for genetic testing.

    PubMed

    Matro, Jennifer M; Ruth, Karen J; Wong, Yu-Ning; McCully, Katen C; Rybak, Christina M; Meropol, Neal J; Hall, Michael J

    2014-12-01

    Increasing use of predictive genetic testing to gauge hereditary cancer risk has been paralleled by rising cost-sharing practices. Little is known about how demographic and psychosocial factors may influence individuals' willingness-to-pay for genetic testing. The Gastrointestinal Tumor Risk Assessment Program Registry includes individuals presenting for genetic risk assessment based on personal/family cancer history. Participants complete a baseline survey assessing cancer history and psychosocial items. Willingness-to-pay items include intention for: genetic testing only if paid by insurance; testing with self-pay; and amount willing-to-pay ($25-$2,000). Multivariable models examined predictors of willingness-to-pay out-of-pocket (versus only if paid by insurance) and willingness-to-pay a smaller versus larger sum (≤$200 vs. ≥$500). All statistical tests are two-sided (α = 0.05). Of 385 evaluable participants, a minority (42%) had a personal cancer history, while 56% had ≥1 first-degree relative with colorectal cancer. Overall, 21.3% were willing to have testing only if paid by insurance, and 78.7% were willing-to-pay. Predictors of willingness-to-pay were: 1) concern for positive result; 2) confidence to control cancer risk; 3) fewer perceived barriers to colorectal cancer screening; 4) benefit of testing to guide screening (all p < 0.05). Subjects willing-to-pay a higher amount were male, more educated, had greater cancer worry, fewer relatives with colorectal cancer, and more positive attitudes toward genetic testing (all p < 0.05). Individuals seeking risk assessment are willing-to-pay out-of-pocket for genetic testing, and anticipate benefits to reducing cancer risk. Identifying factors associated with willingness-to-pay for genetic services is increasingly important as testing is integrated into routine cancer care.

  13. Cost sharing and hereditary cancer risk: Predictors of willingness-to-pay for genetic testing

    PubMed Central

    Matro, Jennifer M.; Ruth, Karen J.; Wong, Yu-Ning; McCully, Katen C.; Rybak, Christina M.; Meropol, Neal J.; Hall, Michael J.

    2015-01-01

    Increasing use of predictive genetic testing to gauge hereditary cancer risk has been paralleled by rising cost-sharing practices. Little is known about how demographic and psychosocial factors may influence individuals’ willingness-to-pay for genetic testing. The Gastrointestinal Tumor Risk Assessment Program Registry includes individuals presenting for genetic risk assessment based on personal/family cancer history. Participants complete a baseline survey assessing cancer history and psychosocial items. Willingness-to-pay items include intention for: genetic testing only if paid by insurance; testing with self-pay; and amount willing-to-pay ($25–$2000). Multivariable models examined predictors of willingness-to-pay out-of-pocket (versus only if paid by insurance) and willingness-to-pay a smaller versus larger sum (≤200 vs. ≥$500). All statistical tests are two-sided (α=0.05). Of 385 evaluable participants, a minority (42%) had a personal cancer history, while 56% had ≥1 first-degree relative with colorectal cancer. Overall, 21.3% were willing to have testing only if paid by insurance, and 78.7% were willing-to-pay. Predictors of willingness-to-pay were: 1) concern for positive result; 2) confidence to control cancer risk; 3) fewer perceived barriers to colorectal cancer screening; 4) benefit of testing to guide screening (all p<0.05). Subjects willing-to-pay a higher amount were male, more educated, had greater cancer worry, fewer relatives with colorectal cancer, and more positive attitudes toward genetic testing (all p<0.05). Individuals seeking risk assessment are willing-to-pay out-of-pocket for genetic testing, and anticipate benefits to reducing cancer risk. Identifying factors associated with willingness-to-pay for genetic services is increasingly important as testing is integrated into routine cancer care. PMID:24794065

  14. The risk of cancer as a result of elevated levels of nitrate in drinking water and vegetables in Central India.

    PubMed

    Taneja, Pinky; Labhasetwar, Pawan; Nagarnaik, Pranav; Ensink, Jeroen H J

    2017-08-01

    The objective of the present study was to determine the effect of nitrates on the incidence of gastrointestinal (GI) cancer development. Nitrate converted to nitrite under reducing conditions of gut results in the formation of N-nitrosamines which are linked to an increased gastric cancer risk. A population of 234 individuals with 78 cases of GI cancer and 156 controls residing at urban and rural settings in Nagpur and Bhandara districts of India were studied for 2 years using a case-control study. A detailed survey of 16 predictor variables using Formhub software was carried out. Nitrate concentrations in vegetables and primary drinking water supplies were measured. The logistic regression model showed that nitrate was statistically significant in predicting increasing risk of cancer when potential confounders were kept at base level (P value of 0.001 nitrate in drinking water; 0.003 for nitrate in vegetable) at P < 0.01. Exposure to nitrate in drinking water at >45 mg/L level of nitrate was associated with a higher risk of GI cancers. Analysis suggests that nitrate concentration in drinking water was found statistically significant in predicting cancer risk with an odds ratio of 1.20.

  15. Colon Cancer Risk Assessment - Gauss Program

    Cancer.gov

    An executable file (in GAUSS) that projects absolute colon cancer risk (with confidence intervals) according to NCI’s Colorectal Cancer Risk Assessment Tool (CCRAT) algorithm. GAUSS is not needed to run the program.

  16. Ovarian Cancer Screening Pilot Trial In High Risk Women — EDRN Public Portal

    Cancer.gov

    BACKGROUND: No proven ovarian cancer (OC) screening strategy exists for women who are at increased risk for the disease. A risk of ovarian cancer algorithm (ROCA) using serial CA125 values have previously shown greater positive predictive value (PPV) and sensitivity than a single CA125 in screening women at general population risk. We hypothesized that using ROCA would yield a reasonable PPV for ovarian cancer screening in a cohort at increased risk. METHODS: Between 7/2001 and 9/2006, 25 sites (14 CGN, 3 ovarian SPOREs, 1 EDRN, 7 others) prospectively enrolled patients. Inclusion criteria included: among self, 1st degree and 2nd degree relatives in same lineage either (i) BRCA 1/2 mutation, or (ii) two of OC or early onset (age 1% to ultrasound (US) and risk > 10% additionally to a gynecologic oncologist. Objectives included PPV for study indicated surgery, sensitivity, and compliance. Sample size was chosen to observe 8 OC endpoints with a power of 80% to rule out PPV < or = 10% if the true PPV = 20%.

  17. Tumor-infiltrating Neutrophils is Prognostic and Predictive for Postoperative Adjuvant Chemotherapy Benefit in Patients With Gastric Cancer.

    PubMed

    Zhang, Heng; Liu, Hao; Shen, Zhenbin; Lin, Chao; Wang, Xuefei; Qin, Jing; Qin, Xinyu; Xu, Jiejie; Sun, Yihong

    2018-02-01

    This study was aimed to investigate the prognostic value of tumor-infiltrating neutrophils (TINs) and to generate a predictive model to refine postoperative risk stratification system for patients with gastric cancer. TIN presents in various malignant tumors, but its clinical significance in gastric cancer remains obscure. The study enrolled 3 independent sets of patients with gastric cancer from 2 institutional medical centers of China. TIN was estimated by immunohistochemical staining of CD66b, and its relationship with clinicopathological features and clinical outcomes were evaluated. Prognostic accuracies were evaluated by C-index and Akaike information criterion. TINs in gastric cancer tissues ranged from 0 to 192 cells/high magnification filed (HPF), 0 to 117 cells/HPF, and 0 to 142 cells/HPF in the training, testing, and validation sets, respectively. TINs were negatively correlated with lymph node classification (P = 0.007, P = 0.041, and P = 0.032, respectively) and tumor stage (P = 0.019, P = 0.013, and P = 0.025, respectively) in the 3 sets. Moreover, multivariate analysis identified TINs and tumor node metastasis (TNM) stage as 2 independent prognostic factors for overall survival. Incorporation of TINs into well-established TNM system generated a predictive model that shows better predictive accuracy for overall survival. More importantly, patients with higher TINs were prone to overall survival benefit from postoperative adjuvant chemotherapy. These results were validated in the independent testing and validation sets. TIN in gastric cancer was identified as an independent prognostic factor, which could be incorporated into standard TNM staging system to refine risk stratification and predict for overall survival benefit from postoperative chemotherapy in patients with gastric cancer.

  18. Risk factors for breast cancer in the breast cancer risk model study of Guam and Saipan.

    PubMed

    Leon Guerrero, Rachael T; Novotny, Rachel; Wilkens, Lynne R; Chong, Marie; White, Kami K; Shvetsov, Yurii B; Buyum, Arielle; Badowski, Grazyna; Blas-Laguaña, Michelle

    2017-10-01

    Chamorro Pacific Islanders in the Mariana Islands have breast cancer incidence rates similar to, but mortality rates higher than, those of U.S. women. As breast cancer risk factors of women of the Mariana Islands may be unique because of ethnic and cultural differences, we studied established and suspected risk factors for breast cancer in this unstudied population. From 2010-2013, we conducted retrospective case-control study of female breast cancer (104 cases and 185 controls) among women in the Mariana Islands. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for each of various lifestyle-related factors from logistic regression of breast cancer, in all women and in pre- and postmenopausal women separately. Tests for interaction of risk factors with ethnicity were based on the Wald statistics for cross-product terms. Of the medical and reproductive factors considered - age at menarche, breastfeeding, number of live births, age at first live birth, hormone use, and menopause - only age at first live birth was confirmed. Age at first live birth, among parous women, was higher among cases (mean 24.9 years) than controls (mean 23.2 years); with increased breast cancer risk (OR=2.53; 95% CI, 1.04-6.19 for age≥30y compared to <20y, P for trend=0.01). Of the lifestyle factors -body mass index, waist circumference, physical activity, alcohol and betel-nut intake, and education - only waist circumference (OR=1.65; 95% CI 0.87-3.14 for the highest tertile group compared to the lowest, P for trend=0.04) was significantly associated with breast cancer risk and only in Filipino women. The association with many other established risk factors, such as BMI, hormone use and physical activity, were in the expected direction but were not significant. Associations for family history of breast cancer and alcohol intake were not evident CONCLUSIONS: The results provide a basis for cancer prevention guidance for women in the Mariana Islands. Copyright © 2017 The

  19. Risk for oral cancer from smokeless tobacco

    PubMed Central

    Janbaz, Khalid Hussain; Basser, Hibba Tul; Bokhari, Tanveer Hussain; Ahmad, Bashir

    2014-01-01

    Tobacco products which are used in a way other than smoking are known as smokeless tobacco. The most common smokeless tobaccos are chewing tobacco, naswar, snuff, snus, gutka, and topical tobacco paste. Any product which contains tobacco is not safe for human health. There are more than twenty-five compounds in smokeless tobacco which have cancer causing activity. Use of smokeless tobacco has been linked with risk of oral cancer. Smokeless tobacco contains tobacco-specific nitrosamines (TSNAs), polonium, formaldehyde, cadmium, lead, and benzo[a]pyrene, which are carcinogenic agents. Although there is presence of some compounds, carotenoids and phenolic compounds, that have cancer inhibiting properties, they are in low concentrations. Dry snuff use is linked with higher relative risks, while the use of other smokeless tobacco is of intermediate risk. Moist snuff and chewing tobacco have a very low risk for oral cancer. Therefore, from this review article, it was concluded that smokeless tobacco has risk for oral cancer – either low, medium or high depending on the balance between cancer causing agents and cancer inhibiting agents. PMID:25520574

  20. Estimated risks and optimistic self-perception of breast cancer risk in Korean women.

    PubMed

    Chung, ChaeWeon; Lee, Suk Jeong

    2013-11-01

    To determine women's perceived personal and comparative risks of breast cancer, and to examine the relationships with risk factors. Despite the increasing incidence of breast cancer in younger women and the availability of screening, women's health behaviors have not advanced accordingly. A cross-sectional survey design utilized a convenience sample of 222 women in their 30s and 40s recruited from community settings in Seoul. Self-administered questionnaire data were analyzed by descriptive statistics, the chi-squared test, and ANOVA. Risk perception levels differed significantly by breast cancer risk factors. Half of the women were optimistic about their breast cancer risk, while perceived personal risk did not reflect women's own risk factors and comparative risk differed only by the practice of clinical breast exam. Women's knowledge and awareness of their breast cancer risk factors need to be improved for appropriate risk perception and health behaviors, and accurate risk estimation could be utilized to educate them in clinical settings. © 2013.

  1. Gene panel testing for inherited cancer risk.

    PubMed

    Hall, Michael J; Forman, Andrea D; Pilarski, Robert; Wiesner, Georgia; Giri, Veda N

    2014-09-01

    Next-generation sequencing technologies have ushered in the capability to assess multiple genes in parallel for genetic alterations that may contribute to inherited risk for cancers in families. Thus, gene panel testing is now an option in the setting of genetic counseling and testing for cancer risk. This article describes the many gene panel testing options clinically available to assess inherited cancer susceptibility, the potential advantages and challenges associated with various types of panels, clinical scenarios in which gene panels may be particularly useful in cancer risk assessment, and testing and counseling considerations. Given the potential issues for patients and their families, gene panel testing for inherited cancer risk is recommended to be offered in conjunction or consultation with an experienced cancer genetic specialist, such as a certified genetic counselor or geneticist, as an integral part of the testing process. Copyright © 2014 by the National Comprehensive Cancer Network.

  2. Modeling Individual Patient Preferences for Colorectal Cancer Screening Based on Their Tolerance for Complications Risk.

    PubMed

    Taksler, Glen B; Perzynski, Adam T; Kattan, Michael W

    2017-04-01

    Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening

  3. Predictive model for survival in patients with gastric cancer.

    PubMed

    Goshayeshi, Ladan; Hoseini, Benyamin; Yousefli, Zahra; Khooie, Alireza; Etminani, Kobra; Esmaeilzadeh, Abbas; Golabpour, Amin

    2017-12-01

    Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer. This was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them. Of the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ?SD of missing values for each patient was 4.43?.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients' family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival. The study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients' survival.

  4. Increased pancreatic cancer risk following radiotherapy for testicular cancer.

    PubMed

    Hauptmann, Michael; Børge Johannesen, Tom; Gilbert, Ethel S; Stovall, Marilyn; van Leeuwen, Flora E; Rajaraman, Preetha; Smith, Susan A; Weathers, Rita E; Aleman, Berthe M P; Andersson, Michael; Curtis, Rochelle E; Dores, Graça M; Fraumeni, Joseph F; Hall, Per; Holowaty, Eric J; Joensuu, Heikki; Kaijser, Magnus; Kleinerman, Ruth A; Langmark, Frøydis; Lynch, Charles F; Pukkala, Eero; Storm, Hans H; Vaalavirta, Leila; van den Belt-Dusebout, Alexandra W; Morton, Lindsay M; Fossa, Sophie D; Travis, Lois B

    2016-09-27

    Pancreatic cancer risk is elevated among testicular cancer (TC) survivors. However, the roles of specific treatments are unclear. Among 23 982 5-year TC survivors diagnosed during 1947-1991, doses from radiotherapy to the pancreas were estimated for 80 pancreatic cancer patients and 145 matched controls. Chemotherapy details were recorded. Logistic regression was used to estimate odds ratios (ORs). Cumulative incidence of second primary pancreatic cancer was 1.1% at 30 years after TC diagnosis. Radiotherapy (72 (90%) cases and 115 (80%) controls) was associated with a 2.9-fold (95% confidence interval (CI) 1.0-7.8) increased risk. The OR increased linearly by 0.12 per Gy to the pancreas (P-trend<0.001), with an OR of 4.6 (95% CI 1.9-11.0) for ⩾25 Gy vs <25 Gy. Radiation-related risks remained elevated ⩾20 years after TC diagnosis (P=0.020). The risk increased with the number of cycles of chemotherapy with alkylating or platinum agents (P=0.057), although only one case was exposed to platinum. A dose-response relationship exists between radiation to the pancreas and subsequent cancer risk, and persists for over 20 years. These excesses, although small, should be considered when radiotherapy with exposure to the pancreas is considered for newly diagnosed patients. Additional data are needed on the role of chemotherapy.

  5. Unification of favourable intermediate-, unfavourable intermediate-, and very high-risk stratification criteria for prostate cancer.

    PubMed

    Zumsteg, Zachary S; Zelefsky, Michael J; Woo, Kaitlin M; Spratt, Daniel E; Kollmeier, Marisa A; McBride, Sean; Pei, Xin; Sandler, Howard M; Zhang, Zhigang

    2017-11-01

    To improve on the existing risk-stratification systems for prostate cancer. This was a retrospective investigation including 2 248 patients undergoing dose-escalated external beam radiotherapy (EBRT) at a single institution. We separated National Comprehensive Cancer Network (NCCN) intermediate-risk prostate cancer into 'favourable' and 'unfavourable' groups based on primary Gleason pattern, percentage of positive biopsy cores (PPBC), and number of NCCN intermediate-risk factors. Similarly, NCCN high-risk prostate cancer was stratified into 'standard' and 'very high-risk' groups based on primary Gleason pattern, PPBC, number of NCCN high-risk factors, and stage T3b-T4 disease. Patients with unfavourable-intermediate-risk (UIR) prostate cancer had significantly inferior prostate-specific antigen relapse-free survival (PSA-RFS, P < 0.001), distant metastasis-free survival (DMFS, P < 0.001), prostate cancer-specific mortality (PCSM, P < 0.001), and overall survival (OS, P < 0.001) compared with patients with favourable-intermediate-risk (FIR) prostate cancer. Similarly, patients with very high-risk (VHR) prostate cancer had significantly worse PSA-RFS (P < 0.001), DMFS (P < 0.001), and PCSM (P = 0.001) compared with patients with standard high-risk (SHR) prostate cancer. Moreover, patients with FIR and low-risk prostate cancer had similar outcomes, as did patients with UIR and SHR prostate cancer. Consequently, we propose the following risk-stratification system: Group 1, low risk and FIR; Group 2, UIR and SHR; and Group 3, VHR. These groups have markedly different outcomes, with 8-year distant metastasis rates of 3%, 9%, and 29% (P < 0.001) for Groups 1, 2, and 3, respectively, and 8-year PCSM of 1%, 4%, and 13% (P < 0.001) after EBRT. This modified stratification system was significantly more accurate than the three-tiered NCCN system currently in clinical use for all outcomes. Modifying the NCCN risk-stratification system to group FIR with low-risk patients and UIR

  6. Predictive test for chemotherapy response in resectable gastric cancer: a multi-cohort, retrospective analysis.

    PubMed

    Cheong, Jae-Ho; Yang, Han-Kwang; Kim, Hyunki; Kim, Woo Ho; Kim, Young-Woo; Kook, Myeong-Cherl; Park, Young-Kyu; Kim, Hyung-Ho; Lee, Hye Seung; Lee, Kyung Hee; Gu, Mi Jin; Kim, Ha Yan; Lee, Jinae; Choi, Seung Ho; Hong, Soonwon; Kim, Jong Won; Choi, Yoon Young; Hyung, Woo Jin; Jang, Eunji; Kim, Hyeseon; Huh, Yong-Min; Noh, Sung Hoon

    2018-05-01

    patients as low risk, 296 (47%) as intermediate risk, and 250 (40%) as high risk, and 5-year overall survival for these groups was 83·2% (95% CI 75·2-92·0), 74·8% (69·9-80·1), and 66·0% (60·1-72·4), respectively (p=0·012). The predictive single patient classifier (based on the expression of GZMB, WARS, and CDX1) assigned 281 (45%) of 625 patients in the validation cohort to the chemotherapy-benefit group and 344 (55%) to the no-benefit group. In the predicted chemotherapy-benefit group, 5-year overall survival was significantly improved in those patients who had received adjuvant chemotherapy after surgery compared with those who received surgery only (80% [95% CI 73·5-87·1] vs 64·5% [56·8-73·3]; univariate hazard ratio 0·47 [95% CI 0·30-0·75], p=0·0015), whereas no such improvement in 5-year overall survival was observed in the no-benefit group (72·9% [66·5-79·9] in patients who received chemotherapy plus surgery vs 72·5% [65·8-79·9] in patients who only had surgery; 0·93 [0·62-1·38], p=0·71). The predictive single patient classifier groups (chemotherapy benefit vs no-benefit) could predict adjuvant chemotherapy benefit in terms of 5-year overall survival in the validation cohort (p interaction =0·036 in univariate analysis). Similar results were obtained in the internal evaluation cohort. The single patient classifiers validated in this study provide clinically important prognostic information independent of standard risk-stratification methods and predicted chemotherapy response after surgery in two independent cohorts of patients with resectable, stage II-III gastric cancer. The single patient classifiers could complement TNM staging to optimise decision making in patients with resectable gastric cancer who are eligible for adjuvant chemotherapy after surgery. Further validation of these results in prospective studies is warranted. Ministry of ICT and Future Planning; Ministry of Trade, Industry, and Energy; and Ministry of Health and

  7. Serum protein profiling using an aptamer array predicts clinical outcomes of stage IIA colon cancer: A leave-one-out crossvalidation

    PubMed Central

    Huh, Jung Wook; Kim, Sung Chun; Sohn, Insuk; Jung, Sin-Ho; Kim, Hee Cheol

    2016-01-01

    Background In this study, we established and validated a model for predicting prognosis of stage IIA colon cancer patients based on expression profiles of aptamers in serum. Methods Bloods samples were collected from 227 consecutive patients with pathologic T3N0M0 (stage IIA) colon cancer. We incubated 1,149 serum molecule-binding aptamer pools of clinical significance with serum from patients to obtain aptamers bound to serum molecules, which were then amplified and marked. Oligonucleotide arrays were constructed with the base sequences of the 1,149 aptamers, and the marked products identified above were reacted with one another to produce profiles of the aptamers bound to serum molecules. These profiles were organized into low- and high-risk groups of colon cancer patients based on clinical information for the serum samples. Cox proportional hazards model and leave-one-out cross-validation (LOOCV) were used to evaluate predictive performance. Results During a median follow-up period of 5 years, 29 of the 227 patients (11.9%) experienced recurrence. There were 212 patients (93.4%) in the low-risk group and 15 patients (6.6%) in the high-risk group in our aptamer prognosis model. Postoperative recurrence significantly correlated with age and aptamer risk stratification (p = 0.046 and p = 0.001, respectively). In multivariate analysis, aptamer risk stratification (p < 0.001) was an independent predictor of recurrence. Disease-free survival curves calculated according to aptamer risk level predicted through a LOOCV procedure and age showed significant differences (p < 0.001 from permutations). Conclusion Aptamer risk stratification can be a valuable prognostic factor in stage II colon cancer patients. PMID:26908450

  8. National Cancer Societies and their public statements on alcohol consumption and cancer risk.

    PubMed

    Amin, Gopal; Siegel, Michael; Naimi, Timothy

    2018-04-25

    Studies have shown that alcohol consumption is a risk factor for oral, pharyngeal, laryngeal, esophageal, liver, colon, rectal and breast cancer. It would therefore be expected that cancer prevention organizations would incorporate these facts into their public stance on the consumption of alcohol. The aims of this study were to: (1) assess how national cancer societies in developed English-speaking countries [i.e. English-speaking countries belonging to the Organization for Economic Co-operation and Development (OECD)] communicate alcohol-related cancer risk to the public and (2) compare whether these organization's advocacy of increased alcohol taxes is in line with their advocacy of tobacco tax increases to reduce cancer risk. We searched the websites of the following national cancer organizations for all statements related to the relationship between alcohol consumption and cancer risk: Cancer Council Australia, Canadian Cancer Society, Irish Cancer Society, Cancer Society New Zealand, Cancer Research UK and the American Cancer Society. A categorical system was developed to code the qualitative data for health statements, alcohol consumption recommendations, and tax policy recommendations. Websites were analyzed in March of 2017. All organizations, with the exception of the American Cancer Society and Canadian Cancer Society, state that alcohol is a group 1 carcinogen and that even low-level alcohol consumption increases risk for some cancers. Additionally, while the American Cancer Society supports increasing tobacco taxes through its cancer action network, it has not advocated for increased alcohol taxes in relation to support for tobacco tax increases. Analysis in 2017 of the websites for national cancer societies in Australia, Canada, Ireland, New Zealand, the United Kingdom and the United States-including Cancer Council Australia, the Canadian Cancer Society, the Irish Cancer Society, Cancer Society New Zealand, Cancer Research UK and the American Cancer

  9. Genetic polymorphisms in the microRNA binding-sites of the thymidylate synthase gene predict risk and survival in gastric cancer.

    PubMed

    Shen, Rong; Liu, Hongliang; Wen, Juyi; Liu, Zhensheng; Wang, Li-E; Wang, Qiming; Tan, Dongfeng; Ajani, Jaffer A; Wei, Qingyi

    2015-09-01

    Thymidylate synthase (TYMS) plays a crucial role in folate metabolism as well as DNA synthesis and repair. We hypothesized that functional polymorphisms in the 3' UTR of TYMS are associated with gastric cancer risk and survival. In the present study, we tested our hypothesis by genotyping three potentially functional (at miRNA binding sites) TYMS SNPs (rs16430 6bp del/ins, rs2790 A>G and rs1059394 C>T) in 379 gastric cancer patients and 431 cancer-free controls. Compared with the rs16430 6bp/6bp + 6bp/0bp genotypes, the 0bp/0bp genotype was associated with significantly increased gastric cancer risk (adjusted OR = 1.72, 95% CI = 1.15-2.58). Similarly, rs2790 GG and rs1059394 TT genotypes were also associated with significantly increased risk (adjusted OR = 2.52, 95% CI = 1.25-5.10 and adjusted OR = 1.57, 95% CI = 1.04-2.35, respectively), compared with AA + AG and CC + CT genotypes, respectively. In the haplotype analysis, the T-G-0bp haplotype was associated with significantly increased gastric cancer risk, compared with the C-A-6bp haplotype (adjusted OR = 1.34, 95% CI = 1.05-1.72). Survival analysis revealed that rs16430 0bp/0bp and rs1059394 TT genotypes were also associated with poor survival in gastric cancer patients who received chemotherapy treatment (adjusted HR = 1.61, 95% CI = 1.05-2.48 and adjusted HR = 1.59, 95% CI = 1.02-2.48, respectively). These results suggest that these three variants in the miRNA binding sites of TYMS may be associated with cancer risk and survival of gastric cancer patients. Larger population studies are warranted to verify these findings. © 2014 Wiley Periodicals, Inc.

  10. Assessing risk of breast cancer in an ethnically South-East Asia population (results of a multiple ethnic groups study)

    PubMed Central

    2012-01-01

    Background Gail and others developed a model (GAIL) using age-at-menarche, age-at-birth of first live child, number of previous benign breast biopsy examinations, and number of first-degree-relatives with breast cancer as well as baseline age-specific breast cancer risks for predicting the 5-year risk of invasive breast cancer for Caucasian women. However, the validity of the model for projecting risk in South-East Asian women is uncertain. We evaluated GAIL and attempted to improve its performance for Singapore women of Chinese, Malay and Indian origins. Methods Data from the Singapore Breast Screening Programme (SBSP) are used. Motivated by lower breast cancer incidence in many Asian countries, we utilised race-specific invasive breast cancer and other cause mortality rates for Singapore women to produce GAIL-SBSP. By using risk factor information from a nested case-control study within SBSP, alternative models incorporating fewer then additional risk factors were determined. Their accuracy was assessed by comparing the expected cases (E) with the observed (O) by the ratio (E/O) and 95% confidence interval (CI) and the respective concordance statistics estimated. Results From 28,883 women, GAIL-SBSP predicted 241.83 cases during the 5-year follow-up while 241 were reported (E/O=1.00, CI=0.88 to 1.14). Except for women who had two or more first-degree-relatives with breast cancer, satisfactory prediction was present in almost all risk categories. This agreement was reflected in Chinese and Malay, but not in Indian women. We also found that a simplified model (S-GAIL-SBSP) including only age-at-menarche, age-at-birth of first live child and number of first-degree-relatives performed similarly with associated concordance statistics of 0.5997. Taking account of body mass index and parity did not improve the calibration of S-GAIL-SBSP. Conclusions GAIL can be refined by using national race-specific invasive breast cancer rates and mortality rates for causes other than

  11. A comparison of machine learning techniques for survival prediction in breast cancer

    PubMed Central

    2011-01-01

    Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. PMID:21569330

  12. Familial risks in testicular cancer as aetiological clues.

    PubMed

    Hemminki, Kari; Chen, Bowang

    2006-02-01

    We used the nationwide Swedish Family-Cancer Database to analyse the risk for testicular cancer in offspring through parental and sibling probands. Among 0 to 70-year-old offspring, 4,586 patients had testicular cancer. Standardized incidence ratios for familial risk were 3.8-fold when a father and 7.6-fold when a brother had testicular cancer. Testicular cancer was associated with leukaemia, distal colon and kidney cancer, melanoma, connective tissue tumours and lung cancer in families. Non-seminoma was associated with maternal lung cancer but the risk was highest for the late-onset cases, providing no support to the theory of the in utero effect of maternal smoking on the son's risk of testicular cancer. However, the theory cannot be excluded but should be taken up for study when further data are available on maternal smoking. The high familial risk may be the product of shared childhood environment and heritable causes.

  13. 20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years.

    PubMed

    Pan, Hongchao; Gray, Richard; Braybrooke, Jeremy; Davies, Christina; Taylor, Carolyn; McGale, Paul; Peto, Richard; Pritchard, Kathleen I; Bergh, Jonas; Dowsett, Mitch; Hayes, Daniel F

    2017-11-09

    The administration of endocrine therapy for 5 years substantially reduces recurrence rates during and after treatment in women with early-stage, estrogen-receptor (ER)-positive breast cancer. Extending such therapy beyond 5 years offers further protection but has additional side effects. Obtaining data on the absolute risk of subsequent distant recurrence if therapy stops at 5 years could help determine whether to extend treatment. In this meta-analysis of the results of 88 trials involving 62,923 women with ER-positive breast cancer who were disease-free after 5 years of scheduled endocrine therapy, we used Kaplan-Meier and Cox regression analyses, stratified according to trial and treatment, to assess the associations of tumor diameter and nodal status (TN), tumor grade, and other factors with patients' outcomes during the period from 5 to 20 years. Breast-cancer recurrences occurred at a steady rate throughout the study period from 5 to 20 years. The risk of distant recurrence was strongly correlated with the original TN status. Among the patients with stage T1 disease, the risk of distant recurrence was 13% with no nodal involvement (T1N0), 20% with one to three nodes involved (T1N1-3), and 34% with four to nine nodes involved (T1N4-9); among those with stage T2 disease, the risks were 19% with T2N0, 26% with T2N1-3, and 41% with T2N4-9. The risk of death from breast cancer was similarly dependent on TN status, but the risk of contralateral breast cancer was not. Given the TN status, the factors of tumor grade (available in 43,590 patients) and Ki-67 status (available in 7692 patients), which are strongly correlated with each other, were of only moderate independent predictive value for distant recurrence, but the status regarding the progesterone receptor (in 54,115 patients) and human epidermal growth factor receptor type 2 (HER2) (in 15,418 patients in trials with no use of trastuzumab) was not predictive. During the study period from 5 to 20 years, the

  14. Development and validation of prediction models for endometrial cancer in postmenopausal bleeding.

    PubMed

    Wong, Alyssa Sze-Wai; Cheung, Chun Wai; Fung, Linda Wen-Ying; Lao, Terence Tzu-Hsi; Mol, Ben Willem J; Sahota, Daljit Singh

    2016-08-01

    To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Out of 4383 women with PMB, 168 (3.8%) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95% confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95% CI 0.67-0.80) and 0.72 (Sensitivity=66.5%; Specificity=68.9%; +ve LR=2.14; -ve LR=0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95% CI 0.88-0.96) and 0.71 (Sensitivity=82.7%; Specificity=88.3%; +ve LR=6.38; -ve LR=0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone

  15. Factors Predictive of Sentinel Lymph Node Involvement in Primary Breast Cancer.

    PubMed

    Malter, Wolfram; Hellmich, Martin; Badian, Mayhar; Kirn, Verena; Mallmann, Peter; Krämer, Stefan

    2018-06-01

    Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for axillary staging in patients with early-stage breast cancer. The need for therapeutic ALND is the subject of ongoing debate especially after the publication of the ACOSOG Z0011 trial. In a retrospective trial with univariate and multivariate analyses, factors predictive of sentinel lymph node involvement should be analyzed in order to define tumor characteristics of breast cancer patients, where SLNB should not be spared to receive important indicators for adjuvant treatment decisions (e.g. thoracic wall irradiation after mastectomy with or without reconstruction). Between 2006 and 2010, 1,360 patients with primary breast cancer underwent SLNB with/without ALND with evaluation of tumor localization, multicentricity and multifocality, histological subtype, tumor size, grading, lymphovascular invasion (LVI), and estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status. These characteristics were retrospectively analyzed in univariate and multivariate logistic regression models to define significant predictive factors for sentinel lymph node involvement. The multivariate analysis demonstrated that tumor size and LVI (p<0.001) were independent predictive factors for metastatic sentinel lymph node involvement in patients with early-stage breast cancer. Because of the increased risk for metastatic involvement of axillary sentinel nodes in cases with larger breast cancer or diagnosis of LVI, patients with these breast cancer characteristics should not be spared from SLNB in a clinically node-negative situation in order to avoid false-negative results with a high potential for wrong indication of primary breast reconstruction or wrong non-indication of necessary post-mastectomy radiation therapy. The prognostic impact of avoidance of axillary staging with SLNB is analyzed in the ongoing prospective INSEMA trial. Copyright© 2018, International

  16. Can we trust cancer information on the Internet?--A comparison of interactive cancer risk sites.

    PubMed

    Ekman, Alexandra; Hall, Per; Litton, Jan-Eric

    2005-08-01

    To investigate the prevalence and quality of interactive cancer risk sites on the Internet. A cancer risk site was defined as a website that gave an estimate of the individual risk of developing cancer. Six search engines and one Meta crawler were used to search the Internet for cancer risk sites (including breast, prostate, colon, and lung cancer). A set of defined quality criteria for health related websites was used to evaluate the websites during 2001 and 2002. The number of cancer risk sites, as defined above, increased by 50% between 2001 and 2002. Only two out of 22 cancer risk sites fulfilled the quality criteria adequately. No signs of a change in trend (with regard to the quality criteria met) were noted in January 2005. The overall quality of the documentation on the cancer risk sites was poor and no improvement was seen during the study period. The majority of the cancer risk sites do not give reliable risk estimates.

  17. Does Metformin Reduce Cancer Risks? Methodologic Considerations.

    PubMed

    Golozar, Asieh; Liu, Shuiqing; Lin, Joeseph A; Peairs, Kimberly; Yeh, Hsin-Chieh

    2016-01-01

    The substantial burden of cancer and diabetes and the association between the two conditions has been a motivation for researchers to look for targeted strategies that can simultaneously affect both diseases and reduce their overlapping burden. In the absence of randomized clinical trials, researchers have taken advantage of the availability and richness of administrative databases and electronic medical records to investigate the effects of drugs on cancer risk among diabetic individuals. The majority of these studies suggest that metformin could potentially reduce cancer risk. However, the validity of this purported reduction in cancer risk is limited by several methodological flaws either in the study design or in the analysis. Whether metformin use decreases cancer risk relies heavily on the availability of valid data sources with complete information on confounders, accurate assessment of drug use, appropriate study design, and robust analytical techniques. The majority of the observational studies assessing the association between metformin and cancer risk suffer from methodological shortcomings and efforts to address these issues have been incomplete. Future investigations on the association between metformin and cancer risk should clearly address the methodological issues due to confounding by indication, prevalent user bias, and time-related biases. Although the proposed strategies do not guarantee a bias-free estimate for the association between metformin and cancer, they will reduce synthesis of and reporting of erroneous results.

  18. Predicting 6- and 12-Month Risk of Mortality in Patients With Platinum-Resistant Advanced-Stage Ovarian Cancer: Prognostic Model to Guide Palliative Care Referrals.

    PubMed

    Foote, Jonathan; Lopez-Acevedo, Micael; Samsa, Gregory; Lee, Paula S; Kamal, Arif H; Alvarez Secord, Angeles; Havrilesky, Laura J

    2018-02-01

    Predictive models are increasingly being used in clinical practice. The aim of the study was to develop a predictive model to identify patients with platinum-resistant ovarian cancer with a prognosis of less than 6 to 12 months who may benefit from immediate referral to hospice care. A retrospective chart review identified patients with platinum-resistant epithelial ovarian cancer who were treated at our institution between 2000 and 2011. A predictive model for survival was constructed based on the time from development of platinum resistance to death. Multivariate logistic regression modeling was used to identify significant survival predictors and to develop a predictive model. The following variables were included: time from diagnosis to platinum resistance, initial stage, debulking status, number of relapses, comorbidity score, albumin, hemoglobin, CA-125 levels, liver/lung metastasis, and the presence of a significant clinical event (SCE). An SCE was defined as a malignant bowel obstruction, pleural effusion, or ascites occurring on or before the diagnosis of platinum resistance. One hundred sixty-four patients met inclusion criteria. In the regression analysis, only an SCE and the presence of liver or lung metastasis were associated with poorer short-term survival (P < 0.001). Nine percent of patients with an SCE or liver or lung metastasis survived 6 months or greater and 0% survived 12 months or greater, compared with 85% and 67% of patients without an SCE or liver or lung metastasis, respectively. Patients with platinum-resistant ovarian cancer who have experienced an SCE or liver or lung metastasis have a high risk of death within 6 months and should be considered for immediate referral to hospice care.

  19. Radiation risk from CT: implications for cancer screening.

    PubMed

    Albert, Jeffrey M

    2013-07-01

    The cancer risks associated with patient exposure to radiation from medical imaging have become a major topic of debate. The higher doses necessary for technologies such as CT and the increasing utilization of these technologies further increase medical radiation exposure to the population. Furthermore, the use of CT for population-based cancer screening continues to be explored for common malignancies such as lung cancer and colorectal cancer. Given the known carcinogenic effects of ionizing radiation, this warrants evaluation of the balance between the benefit of early cancer detection and the risk of screening-induced malignancy. This report provides a brief review of the process of radiation carcino-genesis and the literature evaluating the risk of malignancy from CT, with a focus on the risks and benefits of CT for cancer screening. The available data suggest a small but real risk of radiation-induced malignancy from CT that could become significant at the population level with widespread use of CT-based screening. However, a growing body of literature suggests that the benefits of CT screening for lung cancer in high-risk patients and CT colonography for colorectal cancer may significantly outweigh the radiation risk. Future studies evaluating the benefits of CT screening should continue to consider potential radiation risks.

  20. Making sense of cancer risk calculators on the web.

    PubMed

    Levy, Andrea Gurmankin; Sonnad, Seema S; Kurichi, Jibby E; Sherman, Melani; Armstrong, Katrina

    2008-03-01

    Cancer risk calculators on the internet have the potential to provide users with valuable information about their individual cancer risk. However, the lack of oversight of these sites raises concerns about low quality and inconsistent information. These concerns led us to evaluate internet cancer risk calculators. After a systematic search to find all cancer risk calculators on the internet, we reviewed the content of each site for information that users should seek to evaluate the quality of a website. We then examined the consistency of the breast cancer risk calculators by having 27 women complete 10 of the breast cancer risk calculators for themselves. We also completed the breast cancer risk calculators for a hypothetical high- and low-risk woman, and compared the output to Surveillance Epidemiology and End Results estimates for the average same-age and same-race woman. Nineteen sites were found, 13 of which calculate breast cancer risk. Most sites do not provide the information users need to evaluate the legitimacy of a website. The breast cancer calculator sites vary in the risk factors they assess to calculate breast cancer risk, how they operationalize each risk factor and in the risk estimate they provide for the same individual. Internet cancer risk calculators have the potential to provide a public health benefit by educating individuals about their risks and potentially encouraging preventive health behaviors. However, our evaluation of internet calculators revealed several problems that call into question the accuracy of the information that they provide. This may lead the users of these sites to make inappropriate medical decisions on the basis of misinformation.

  1. Chronic and episodic stress predict physical symptom bother following breast cancer diagnosis.

    PubMed

    Harris, Lauren N; Bauer, Margaret R; Wiley, Joshua F; Hammen, Constance; Krull, Jennifer L; Crespi, Catherine M; Weihs, Karen L; Stanton, Annette L

    2017-12-01

    Breast cancer patients often experience adverse physical side effects of medical treatments. According to the biobehavioral model of cancer stress and disease, life stress during diagnosis and treatment may negatively influence the trajectory of women's physical health-related adjustment to breast cancer. This longitudinal study examined chronic and episodic stress as predictors of bothersome physical symptoms during the year after breast cancer diagnosis. Women diagnosed with breast cancer in the previous 4 months (N = 460) completed a life stress interview for contextual assessment of chronic and episodic stress severity at study entry and 9 months later. Physical symptom bother (e.g., pain, fatigue) was measured at study entry, every 6 weeks through 6 months, and at nine and 12 months. In multilevel structural equation modeling (MSEM) analyses, both chronic stress and episodic stress occurring shortly after diagnosis predicted greater physical symptom bother over the study period. Episodic stress reported to have occurred prior to diagnosis did not predict symptom bother in MSEM analyses, and the interaction between chronic and episodic stress on symptom bother was not significant. Results suggest that ongoing chronic stress and episodic stress occurring shortly after breast cancer diagnosis are important predictors of bothersome symptoms during and after cancer treatment. Screening for chronic stress and recent stressful life events in the months following diagnosis may help to identify breast cancer patients at risk for persistent and bothersome physical symptoms. Interventions to prevent or ameliorate treatment-related physical symptoms may confer added benefit by addressing ongoing non-cancer-related stress in women's lives.

  2. Chronic and episodic stress predict physical symptom bother following breast cancer diagnosis

    PubMed Central

    Bauer, Margaret R.; Wiley, Joshua F.; Hammen, Constance; Krull, Jennifer L.; Crespi, Catherine M.; Weihs, Karen L.; Stanton, Annette L.

    2017-01-01

    Breast cancer patients often experience adverse physical side effects of medical treatments. According to the biobehavioral model of cancer stress and disease, life stress during diagnosis and treatment may negatively influence the trajectory of women’s physical health-related adjustment to breast cancer. This longitudinal study examined chronic and episodic stress as predictors of bothersome physical symptoms during the year after breast cancer diagnosis. Women diagnosed with breast cancer in the previous 4 months (N = 460) completed a life stress interview for contextual assessment of chronic and episodic stress severity at study entry and 9 months later. Physical symptom bother (e.g., pain, fatigue) was measured at study entry, every 6 weeks through 6 months, and at nine and 12 months. In multilevel structural equation modeling (MSEM) analyses, both chronic stress and episodic stress occurring shortly after diagnosis predicted greater physical symptom bother over the study period. Episodic stress reported to have occurred prior to diagnosis did not predict symptom bother in MSEM analyses, and the interaction between chronic and episodic stress on symptom bother was not significant. Results suggest that ongoing chronic stress and episodic stress occurring shortly after breast cancer diagnosis are important predictors of bothersome symptoms during and after cancer treatment. Screening for chronic stress and recent stressful life events in the months following diagnosis may help to identify breast cancer patients at risk for persistent and bothersome physical symptoms. Interventions to prevent or ameliorate treatment-related physical symptoms may confer added benefit by addressing ongoing non-cancer-related stress in women’s lives. PMID:28528393

  3. Oncotype DX breast cancer recurrence score can be predicted with a novel nomogram using clinicopathologic data.

    PubMed

    Orucevic, Amila; Bell, John L; McNabb, Alison P; Heidel, Robert E

    2017-05-01

    Oncotype DX (ODX) recurrence score (RS) breast cancer (BC) assay is costly, and performed in only ~1/3 of estrogen receptor (ER)-positive BC patients in the USA. We have now developed a user-friendly nomogram surrogate prediction model for ODX based on a large dataset from the National Cancer Data Base (NCDB) to assist in selecting patients for which further ODX testing may not be necessary and as a surrogate for patients for which ODX testing is not affordable or available. Six clinicopathologic variables of 27,719 ODX-tested ER+/HER2-/lymph node-negative patients with 6-50 mm tumor size captured by the NCDB from 2010 to 2012 were assessed with logistic regression to predict high-risk or low-risk ODXRS test results with TAILORx-trial and commercial cut-off values; 12,763 ODX-tested patients in 2013 were used for external validation. The predictive accuracy of the regression model was yielded using a Receiver Operator Characteristic analysis. Model fit was analyzed by plotting the predicted probabilities against the actual probabilities. A user-friendly calculator version of nomograms is available online at the University of Tennessee Medical Center website (Knoxville, TN). Grade and progesterone receptor status were the highest predictors of both low-risk and high-risk ODXRS, followed by age, tumor size, histologic tumor type and lymph-vascular invasion (C-indexes-.0.85 vs. 0.88 for TAILORx-trial vs. commercial cut-off values, respectively). This is the first study of this scale showing confidently that clinicopathologic variables can be used for prediction of low-risk or high-risk ODXRS using our nomogram models. These novel nomograms will be useful tools to help physicians and patients decide whether further ODX testing is necessary and are excellent surrogates for patients for which ODX testing is not affordable or available.

  4. Early Detection of Ovarian Cancer using the Risk of Ovarian Cancer Algorithm with Frequent CA125 Testing in Women at Increased Familial Risk - Combined Results from Two Screening Trials.

    PubMed

    Skates, Steven J; Greene, Mark H; Buys, Saundra S; Mai, Phuong L; Brown, Powel; Piedmonte, Marion; Rodriguez, Gustavo; Schorge, John O; Sherman, Mark; Daly, Mary B; Rutherford, Thomas; Brewster, Wendy R; O'Malley, David M; Partridge, Edward; Boggess, John; Drescher, Charles W; Isaacs, Claudine; Berchuck, Andrew; Domchek, Susan; Davidson, Susan A; Edwards, Robert; Elg, Steven A; Wakeley, Katie; Phillips, Kelly-Anne; Armstrong, Deborah; Horowitz, Ira; Fabian, Carol J; Walker, Joan; Sluss, Patrick M; Welch, William; Minasian, Lori; Horick, Nora K; Kasten, Carol H; Nayfield, Susan; Alberts, David; Finkelstein, Dianne M; Lu, Karen H

    2017-07-15

    Purpose: Women at familial/genetic ovarian cancer risk often undergo screening despite unproven efficacy. Research suggests each woman has her own CA125 baseline; significant increases above this level may identify cancers earlier than standard 6- to 12-monthly CA125 > 35 U/mL. Experimental Design: Data from prospective Cancer Genetics Network and Gynecologic Oncology Group trials, which screened 3,692 women (13,080 woman-screening years) with a strong breast/ovarian cancer family history or BRCA1/2 mutations, were combined to assess a novel screening strategy. Specifically, serum CA125 q3 months, evaluated using a risk of ovarian cancer algorithm (ROCA), detected significant increases above each subject's baseline, which triggered transvaginal ultrasound. Specificity and positive predictive value (PPV) were compared with levels derived from general population screening (specificity 90%, PPV 10%), and stage-at-detection was compared with historical high-risk controls. Results: Specificity for ultrasound referral was 92% versus 90% ( P = 0.0001), and PPV was 4.6% versus 10% ( P > 0.10). Eighteen of 19 malignant ovarian neoplasms [prevalent = 4, incident = 6, risk-reducing salpingo-oophorectomy (RRSO) = 9] were detected via screening or RRSO. Among incident cases (which best reflect long-term screening performance), three of six invasive cancers were early-stage (I/II; 50% vs. 10% historical BRCA1 controls; P = 0.016). Six of nine RRSO-related cases were stage I. ROCA flagged three of six (50%) incident cases before CA125 exceeded 35 U/mL. Eight of nine patients with stages 0/I/II ovarian cancer were alive at last follow-up (median 6 years). Conclusions: For screened women at familial/genetic ovarian cancer risk, ROCA q3 months had better early-stage sensitivity at high specificity, and low yet possibly acceptable PPV compared with CA125 > 35 U/mL q6/q12 months, warranting further larger cohort evaluation. Clin Cancer Res; 23(14); 3628-37. ©2017 AACR . ©2017

  5. A nonsynonymous polymorphism in IRS1 modifies risk of developing breast and ovarian cancers in BRCA1 and ovarian cancer in BRCA2 mutation carriers.

    PubMed

    Ding, Yuan C; McGuffog, Lesley; Healey, Sue; Friedman, Eitan; Laitman, Yael; Paluch-Shimon, Shani-; Kaufman, Bella; Liljegren, Annelie; Lindblom, Annika; Olsson, Håkan; Kristoffersson, Ulf; Stenmark-Askmalm, Marie; Melin, Beatrice; Domchek, Susan M; Nathanson, Katherine L; Rebbeck, Timothy R; Jakubowska, Anna; Lubinski, Jan; Jaworska, Katarzyna; Durda, Katarzyna; Gronwald, Jacek; Huzarski, Tomasz; Cybulski, Cezary; Byrski, Tomasz; Osorio, Ana; Cajal, Teresa Ramóny; Stavropoulou, Alexandra V; Benítez, Javier; Hamann, Ute; Rookus, Matti; Aalfs, Cora M; de Lange, Judith L; Meijers-Heijboer, Hanne E J; Oosterwijk, Jan C; van Asperen, Christi J; Gómez García, Encarna B; Hoogerbrugge, Nicoline; Jager, Agnes; van der Luijt, Rob B; Easton, Douglas F; Peock, Susan; Frost, Debra; Ellis, Steve D; Platte, Radka; Fineberg, Elena; Evans, D Gareth; Lalloo, Fiona; Izatt, Louise; Eeles, Ros; Adlard, Julian; Davidson, Rosemarie; Eccles, Diana; Cole, Trevor; Cook, Jackie; Brewer, Carole; Tischkowitz, Marc; Godwin, Andrew K; Pathak, Harsh; Stoppa-Lyonnet, Dominique; Sinilnikova, Olga M; Mazoyer, Sylvie; Barjhoux, Laure; Léoné, Mélanie; Gauthier-Villars, Marion; Caux-Moncoutier, Virginie; de Pauw, Antoine; Hardouin, Agnès; Berthet, Pascaline; Dreyfus, Hélène; Ferrer, Sandra Fert; Collonge-Rame, Marie-Agnès; Sokolowska, Johanna; Buys, Saundra; Daly, Mary; Miron, Alex; Terry, Mary Beth; Chung, Wendy; John, Esther M; Southey, Melissa; Goldgar, David; Singer, Christian F; Tea, Muy-Kheng Maria; Gschwantler-Kaulich, Daphne; Fink-Retter, Anneliese; Hansen, Thomas V O; Ejlertsen, Bent; Johannsson, Oskar T; Offit, Kenneth; Sarrel, Kara; Gaudet, Mia M; Vijai, Joseph; Robson, Mark; Piedmonte, Marion R; Andrews, Lesley; Cohn, David; DeMars, Leslie R; DiSilvestro, Paul; Rodriguez, Gustavo; Toland, Amanda Ewart; Montagna, Marco; Agata, Simona; Imyanitov, Evgeny; Isaacs, Claudine; Janavicius, Ramunas; Lazaro, Conxi; Blanco, Ignacio; Ramus, Susan J; Sucheston, Lara; Karlan, Beth Y; Gross, Jenny; Ganz, Patricia A; Beattie, Mary S; Schmutzler, Rita K; Wappenschmidt, Barbara; Meindl, Alfons; Arnold, Norbert; Niederacher, Dieter; Preisler-Adams, Sabine; Gadzicki, Dorotehea; Varon-Mateeva, Raymonda; Deissler, Helmut; Gehrig, Andrea; Sutter, Christian; Kast, Karin; Nevanlinna, Heli; Aittomäki, Kristiina; Simard, Jacques; Spurdle, Amanda B; Beesley, Jonathan; Chen, Xiaoqing; Tomlinson, Gail E; Weitzel, Jeffrey; Garber, Judy E; Olopade, Olufunmilayo I; Rubinstein, Wendy S; Tung, Nadine; Blum, Joanne L; Narod, Steven A; Brummel, Sean; Gillen, Daniel L; Lindor, Noralane; Fredericksen, Zachary; Pankratz, Vernon S; Couch, Fergus J; Radice, Paolo; Peterlongo, Paolo; Greene, Mark H; Loud, Jennifer T; Mai, Phuong L; Andrulis, Irene L; Glendon, Gord; Ozcelik, Hilmi; Gerdes, Anne-Marie; Thomassen, Mads; Jensen, Uffe Birk; Skytte, Anne-Bine; Caligo, Maria A; Lee, Andrew; Chenevix-Trench, Georgia; Antoniou, Antonis C; Neuhausen, Susan L

    2012-08-01

    We previously reported significant associations between genetic variants in insulin receptor substrate 1 (IRS1) and breast cancer risk in women carrying BRCA1 mutations. The objectives of this study were to investigate whether the IRS1 variants modified ovarian cancer risk and were associated with breast cancer risk in a larger cohort of BRCA1 and BRCA2 mutation carriers. IRS1 rs1801123, rs1330645, and rs1801278 were genotyped in samples from 36 centers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Data were analyzed by a retrospective cohort approach modeling the associations with breast and ovarian cancer risks simultaneously. Analyses were stratified by BRCA1 and BRCA2 status and mutation class in BRCA1 carriers. Rs1801278 (Gly972Arg) was associated with ovarian cancer risk for both BRCA1 (HR, 1.43; 95% confidence interval (CI), 1.06-1.92; P = 0.019) and BRCA2 mutation carriers (HR, 2.21; 95% CI, 1.39-3.52, P = 0.0008). For BRCA1 mutation carriers, the breast cancer risk was higher in carriers with class II mutations than class I mutations (class II HR, 1.86; 95% CI, 1.28-2.70; class I HR, 0.86; 95%CI, 0.69-1.09; P(difference), 0.0006). Rs13306465 was associated with ovarian cancer risk in BRCA1 class II mutation carriers (HR, 2.42; P = 0.03). The IRS1 Gly972Arg single-nucleotide polymorphism, which affects insulin-like growth factor and insulin signaling, modifies ovarian cancer risk in BRCA1 and BRCA2 mutation carriers and breast cancer risk in BRCA1 class II mutation carriers. These findings may prove useful for risk prediction for breast and ovarian cancers in BRCA1 and BRCA2 mutation carriers. ©2012 AACR.

  6. Regular aspirin use and lung cancer risk.

    PubMed

    Moysich, Kirsten B; Menezes, Ravi J; Ronsani, Adrienne; Swede, Helen; Reid, Mary E; Cummings, K Michael; Falkner, Karen L; Loewen, Gregory M; Bepler, Gerold

    2002-11-26

    Although a large number of epidemiological studies have examined the role of aspirin in the chemoprevention of colon cancer and other solid tumors, there is a limited body of research focusing on the association between aspirin and lung cancer risk. We conducted a hospital-based case-control study to evaluate the role of regular aspirin use in lung cancer etiology. Study participants included 868 cases with primary, incident lung cancer and 935 hospital controls with non-neoplastic conditions who completed a comprehensive epidemiological questionnaire. Participants were classified as regular aspirin users if they had taken the drug at least once a week for at least one year. Results indicated that lung cancer risk was significantly lower for aspirin users compared to non-users (adjusted OR = 0.57; 95% CI 0.41-0.78). Although there was no clear evidence of a dose-response relationship, we observed risk reductions associated with greater frequency of use. Similarly, prolonged duration of use and increasing tablet years (tablets per day x years of use) was associated with reduced lung cancer risk. Risk reductions were observed in both sexes, but significant dose response relationships were only seen among male participants. When the analyses were restricted to former and current smokers, participants with the lowest cigarette exposure tended to benefit most from the potential chemopreventive effect of aspirin. After stratification by histology, regular aspirin use was significantly associated with reduced risk of small cell lung cancer and non-small cell lung cancer. Overall, results from this hospital-based case-control study suggest that regular aspirin use may be associated with reduced risk of lung cancer.

  7. Regular aspirin use and lung cancer risk

    PubMed Central

    Moysich, Kirsten B; Menezes, Ravi J; Ronsani, Adrienne; Swede, Helen; Reid, Mary E; Cummings, K Michael; Falkner, Karen L; Loewen, Gregory M; Bepler, Gerold

    2002-01-01

    Background Although a large number of epidemiological studies have examined the role of aspirin in the chemoprevention of colon cancer and other solid tumors, there is a limited body of research focusing on the association between aspirin and lung cancer risk. Methods We conducted a hospital-based case-control study to evaluate the role of regular aspirin use in lung cancer etiology. Study participants included 868 cases with primary, incident lung cancer and 935 hospital controls with non-neoplastic conditions who completed a comprehensive epidemiological questionnaire. Participants were classified as regular aspirin users if they had taken the drug at least once a week for at least one year. Results Results indicated that lung cancer risk was significantly lower for aspirin users compared to non-users (adjusted OR = 0.57; 95% CI 0.41–0.78). Although there was no clear evidence of a dose-response relationship, we observed risk reductions associated with greater frequency of use. Similarly, prolonged duration of use and increasing tablet years (tablets per day × years of use) was associated with reduced lung cancer risk. Risk reductions were observed in both sexes, but significant dose response relationships were only seen among male participants. When the analyses were restricted to former and current smokers, participants with the lowest cigarette exposure tended to benefit most from the potential chemopreventive effect of aspirin. After stratification by histology, regular aspirin use was significantly associated with reduced risk of small cell lung cancer and non-small cell lung cancer. Conclusions Overall, results from this hospital-based case-control study suggest that regular aspirin use may be associated with reduced risk of lung cancer. PMID:12453317

  8. Risks of Primary Extracolonic Cancers Following Colorectal Cancer in Lynch Syndrome

    PubMed Central

    2012-01-01

    Background Lynch syndrome is a highly penetrant cancer predisposition syndrome caused by germline mutations in DNA mismatch repair (MMR) genes. We estimated the risks of primary cancers other than colorectal cancer following a diagnosis of colorectal cancer in mutation carriers. Methods We obtained data from the Colon Cancer Family Registry for 764 carriers of an MMR gene mutation (316 MLH1, 357 MSH2, 49 MSH6, and 42 PMS2), who had a previous diagnosis of colorectal cancer. The Kaplan–Meier method was used to estimate their cumulative risk of cancers 10 and 20 years after colorectal cancer. We estimated the age-, sex-, country- and calendar period–specific standardized incidence ratios (SIRs) of cancers following colorectal cancer, compared with the general population. Results Following colorectal cancer, carriers of MMR gene mutations had the following 10-year risk of cancers in other organs: kidney, renal pelvis, ureter, and bladder (2%, 95% confidence interval [CI] = 1% to 3%); small intestine, stomach, and hepatobiliary tract (1%, 95% CI = 0.2% to 2%); prostate (3%, 95% CI = 1% to 5%); endometrium (12%, 95% CI = 8% to 17%); breast (2%, 95% CI = 1% to 4%); and ovary (1%, 95% CI = 0% to 2%). They were at elevated risk compared with the general population: cancers of the kidney, renal pelvis, and ureter (SIR = 12.54, 95% CI = 7.97 to 17.94), urinary bladder (SIR = 7.22, 95% CI = 4.08 to 10.99), small intestine (SIR = 72.68, 95% CI = 39.95 to 111.29), stomach (SIR = 5.65, 95% CI = 2.32 to 9.69), and hepatobiliary tract (SIR = 5.94, 95% CI = 1.81 to 10.94) for both sexes; cancer of the prostate (SIR = 2.05, 95% CI = 1.23 to 3.01), endometrium (SIR = 40.23, 95% CI = 27.91 to 56.06), breast (SIR = 1.76, 95% CI = 1.07 to 2.59), and ovary (SIR = 4.19, 95% CI = 1.28 to 7.97). Conclusion Carriers of MMR gene mutations who have already had a colorectal cancer are at increased risk of a greater range of cancers than the recognized spectrum of Lynch syndrome cancers

  9. Parity and risk of lung cancer in women.

    PubMed

    Paulus, Jessica K; Asomaning, Kofi; Kraft, Peter; Johnson, Bruce E; Lin, Xihong; Christiani, David C

    2010-03-01

    Patterns of lung cancer incidence suggest that gender-associated factors may influence lung cancer risk. Given the association of parity with risk of some women's cancers, the authors hypothesized that childbearing history may also be associated with lung cancer. Women enrolled in the Lung Cancer Susceptibility Study at Massachusetts General Hospital (Boston, Massachusetts) between 1992 and 2004 (1,004 cases, 848 controls) were available for analysis of the association between parity and lung cancer risk. Multivariate logistic regression was used to estimate adjusted odds ratios and 95% confidence intervals. After results were controlled for age and smoking history, women with at least 1 child had 0.71 times the odds of lung cancer as women without children (odds ratio = 0.71, 95% confidence interval: 0.52, 0.97). A significant linear trend was found: Lung cancer risk decreased with increasing numbers of children (P < 0.001). This inverse association was stronger in never smokers (P = 0.12) and was limited to women over age 50 years at diagnosis (P = 0.17). Age at first birth was not associated with risk. The authors observed a protective association between childbearing and lung cancer, adding to existing evidence that reproductive factors may moderate lung cancer risk in women.

  10. Somatic mutations in cancer: Stochastic versus predictable.

    PubMed

    Gold, Barry

    2017-02-01

    The origins of human cancers remain unclear except for a limited number of potent environmental mutagens, such as tobacco and UV light, and in rare cases, familial germ line mutations that affect tumor suppressor genes or oncogenes. A significant component of cancer etiology has been deemed stochastic and correlated with the number of stem cells in a tissue, the number of times the stem cells divide and a low incidence of random DNA polymerase errors that occur during each cell division. While somatic mutations occur during each round of DNA replication, mutations in cancer driver genes are not stochastic. Out of a total of 2843 codons, 1031 can be changed to stop codons by a single base substitution in the tumor suppressor APC gene, which is mutated in 76% of colorectal cancers (CRC). However, the nonsense mutations, which comprise 65% of all the APC driver mutations in CRC, are not random: 43% occur at Arg CGA codons, although they represent <3% of the codons. In TP53, CGA codons comprise <3% of the total 393 codons but they account for 72% and 39% of the mutations in CRC and ovarian cancer OVC, respectively. This mutation pattern is consistent with the kinetically slow, but not stochastic, hydrolytic deamination of 5-methylcytosine residues at specific methylated CpG sites to afford T·G mismatches that lead to C→T transitions and stop codons at CGA. Analysis of nonsense mutations in CRC, OVC and a number of other cancers indicates the need to expand the predictable risk factors for cancer to include, in addition to random polymerase errors, the methylation status of gene body CGA codons in tumor suppressor genes. Copyright © 2017. Published by Elsevier B.V.

  11. Persistence of type-specific human papillomavirus infection and increased long-term risk of cervical cancer.

    PubMed

    Chen, Hui-Chi; Schiffman, Mark; Lin, Ching-Yu; Pan, Mei-Hung; You, San-Lin; Chuang, Li-Chung; Hsieh, Chang-Yao; Liaw, Kai-Li; Hsing, Ann W; Chen, Chien-Jen

    2011-09-21

    Human papillomavirus (HPV) persistence is the pivotal event in cervical carcinogenesis. We followed a large-scale community-based cohort for 16 years to investigate the role of genotype-specific HPV persistence in predicting cervical cancer including invasive and in situ carcinoma. At the baseline examination in 1991-1992, 11,923 participants (aged 30-65 years) consented to HPV testing and cytology; 6923 participants were reexamined in 1993-1995. For HPV testing, we used a polymerase chain reaction-based assay that detected 39 HPV types. Women who developed cervical cancer were identified from cancer and death registries. Cumulative risks for developing cervical cancer among infected and persistently infected women were calculated by the Kaplan-Meier method. Of 10,123 women who were initially cytologically normal, 68 developed cervical cancer. The 16-year cumulative risks of subsequent cervical cancer for women with HPV16, HPV58 (without HPV16), or other carcinogenic HPV types (without HPV16 or HPV58) were 13.5%, 10.3%, and 4.0%, respectively, compared with 0.26% for HPV-negative women. Women with type-specific persistence of any carcinogenic HPV had greatly increased risk compared with women who were HPV-negative at both visits (hazard ratio = 75.4, 95% confidence interval = 31.8 to 178.9). The cumulative cervical cancer risks following persistent carcinogenic HPV infections increased with age: The risks were 5.5%, 14.4%, and 18.1% for women aged 30-44 years, 45-54 years, and 55 years and older, respectively. However, newly acquired infections were associated with a low risk of cervical cancer regardless of age. HPV negativity was associated with a very low long-term risk of cervical cancer. Persistent detection of HPV among cytologically normal women greatly increased risk. Thus, it is useful to perform repeated HPV testing following an initial positive test.

  12. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.

    PubMed

    Li, Hui; Zhu, Yitan; Burnside, Elizabeth S; Drukker, Karen; Hoadley, Katherine A; Fan, Cheng; Conzen, Suzanne D; Whitman, Gary J; Sutton, Elizabeth J; Net, Jose M; Ganott, Marie; Huang, Erich; Morris, Elizabeth A; Perou, Charles M; Ji, Yuan; Giger, Maryellen L

    2016-11-01

    Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R 2 = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical

  13. Network information improves cancer outcome prediction.

    PubMed

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. Clinical Predictive Models for Chemotherapy-Induced Febrile Neutropenia in Breast Cancer Patients: A Validation Study

    PubMed Central

    Zhu, Liling; Su, Fengxi; Jia, Weijuan; Deng, Xiaogeng

    2014-01-01

    Background Predictive models for febrile neutropenia (FN) would be informative for physicians in clinical decision making. This study aims to validate a predictive model (Jenkin’s model) that comprises pretreatment hematological parameters in early-stage breast cancer patients. Patients and Methods A total of 428 breast cancer patients who received neoadjuvant/adjuvant chemotherapy without any prophylactic use of colony-stimulating factor were included. Pretreatment absolute neutrophil counts (ANC) and absolute lymphocyte counts (ALC) were used by the Jenkin’s model to assess the risk of FN. In addition, we modified the threshold of Jenkin’s model and generated Model-A and B. We also developed Model-C by incorporating the absolute monocyte count (AMC) as a predictor into Model-A. The rates of FN in the 1st chemotherapy cycle were calculated. A valid model should be able to significantly identify high-risk subgroup of patients with FN rate >20%. Results Jenkin’s model (Predicted as high-risk when ANC≦3.1*10∧9/L;ALC≦1.5*10∧9/L) did not identify any subgroups with significantly high risk (>20%) of FN in our population, even if we used different thresholds in Model-A(ANC≦4.4*10∧9/L;ALC≦2.1*10∧9/L) or B(ANC≦3.8*10∧9/L;ALC≦1.8*10∧9/L). However, with AMC added as an additional predictor, Model-C(ANC≦4.4*10∧9/L;ALC≦2.1*10∧9/L; AMC≦0.28*10∧9/L) identified a subgroup of patients with a significantly high risk of FN (23.1%). Conclusions In our population, Jenkin’s model, cannot accurately identify patients with a significant risk of FN. The threshold should be changed and the AMC should be incorporated as a predictor, to have excellent predictive ability. PMID:24945817

  15. A Predictive Score for Thrombosis Associated with Breast, Colorectal, Lung, or Ovarian Cancer: The Prospective COMPASS-Cancer-Associated Thrombosis Study.

    PubMed

    Gerotziafas, Grigoris T; Taher, Ali; Abdel-Razeq, Hikmat; AboElnazar, Essam; Spyropoulos, Alex C; El Shemmari, Salem; Larsen, Annette K; Elalamy, Ismail

    2017-10-01

    The stratification of outpatients on chemotherapy for breast, colorectal, lung, and ovarian cancers at risk of venous thromboembolism (VTE) remains an unmet clinical need. The derivation of a risk assessment model (RAM) for VTE in these patients was the aim of the study "Prospective Comparison of Methods for thromboembolic risk assessment with clinical Perceptions and AwareneSS in real life patients-Cancer Associated Thrombosis" (COMPASS-CAT). The derivation cohort consisted of 1,023 outpatients. Patients on low molecular weight heparin (LMWH) thromboprophylaxis were excluded. Documented symptomatic VTE was the endpoint of the study. Patients had breast (61%), colorectal (17%), lung (13%), or ovarian cancer (8.6%) at localized (30%) or advanced stage (70%). In 64% of patients, cancer was diagnosed within the last 6 months prior to inclusion. Most of them were on chemotherapy when assessed. Symptomatic VTE occurred in 8.5% of patients. The COMPASS-CAT RAM includes the following variables: (a) anthracycline or anti - hormonal therapy, (b) time since cancer diagnosis, (c) central venous catheter, (d) stage of cancer, (e) presence of cardiovascular risk factors, (f) recent hospitalization for acute medical illness, (g) personal history of VTE, and (h) platelet count. At 6 months, patients stratified at low/intermediate and high-risk groups had VTE rates of 1.7% and 13.3%, respectively. The area under the curve of receiver operating characteristics analysis was 0.85. The sensitivity and specificity of the RAM were 88% and 52%, respectively. The negative and positive predictive values of the RAM were 98% and 13%, respectively. The COMPASS-CAT RAM includes reliable and easily collected VTE risk predictors and, in contrast to the Khorana score, it is applicable after the initiation of anticancer treatment in patients with common solid tumors. Its robustness for stratification of patients at high and low/intermediate VTE risk needs to be externally validated. The

  16. Fertility drugs, reproductive strategies and ovarian cancer risk.

    PubMed

    Tomao, Federica; Lo Russo, Giuseppe; Spinelli, Gian Paolo; Stati, Valeria; Prete, Alessandra Anna; Prinzi, Natalie; Sinjari, Marsela; Vici, Patrizia; Papa, Anselmo; Chiotti, Maria Stefania; Benedetti Panici, Pierluigi; Tomao, Silverio

    2014-01-01

    Several adverse effects have been related to infertility treatments, such as cancer development. In particular, the relationship between infertility, reproductive strategies, and risk of gynecological cancers has aroused much interest in recent years. The evaluation of cancer risk among women treated for infertility is very complex, mainly because of many factors that can contribute to occurrence of cancer in these patients (including parity status). This article addresses the possible association between the use of fertility treatments and the risk of ovarian cancer, through a scrupulous search of the literature published thus far in this field. Our principal objective was to give more conclusive answers on the question whether the use of fertility drug significantly increases ovarian cancer risk. Our analysis focused on the different types of drugs and different treatment schedules used. This study provides additional insights regarding the long-term relationships between fertility drugs and risk of ovarian cancer.

  17. Fruit and vegetables and cancer risk.

    PubMed

    Key, T J

    2011-01-04

    The possibility that fruit and vegetables may help to reduce the risk of cancer has been studied for over 30 years, but no protective effects have been firmly established. For cancers of the upper gastrointestinal tract, epidemiological studies have generally observed that people with a relatively high intake of fruit and vegetables have a moderately reduced risk, but these observations must be interpreted cautiously because of potential confounding by smoking and alcohol. For lung cancer, recent large prospective analyses with detailed adjustment for smoking have not shown a convincing association between fruit and vegetable intake and reduced risk. For other common cancers, including colorectal, breast and prostate cancer, epidemiological studies suggest little or no association between total fruit and vegetable consumption and risk. It is still possible that there are benefits to be identified: there could be benefits in populations with low average intakes of fruit and vegetables, such that those eating moderate amounts have a lower cancer risk than those eating very low amounts, and there could also be effects of particular nutrients in certain fruits and vegetables, as fruit and vegetables have very varied composition. Nutritional principles indicate that healthy diets should include at least moderate amounts of fruit and vegetables, but the available data suggest that general increases in fruit and vegetable intake would not have much effect on cancer rates, at least in well-nourished populations. Current advice in relation to diet and cancer should include the recommendation to consume adequate amounts of fruit and vegetables, but should put most emphasis on the well-established adverse effects of obesity and high alcohol intakes.

  18. Fruit and vegetables and cancer risk

    PubMed Central

    Key, T J

    2011-01-01

    The possibility that fruit and vegetables may help to reduce the risk of cancer has been studied for over 30 years, but no protective effects have been firmly established. For cancers of the upper gastrointestinal tract, epidemiological studies have generally observed that people with a relatively high intake of fruit and vegetables have a moderately reduced risk, but these observations must be interpreted cautiously because of potential confounding by smoking and alcohol. For lung cancer, recent large prospective analyses with detailed adjustment for smoking have not shown a convincing association between fruit and vegetable intake and reduced risk. For other common cancers, including colorectal, breast and prostate cancer, epidemiological studies suggest little or no association between total fruit and vegetable consumption and risk. It is still possible that there are benefits to be identified: there could be benefits in populations with low average intakes of fruit and vegetables, such that those eating moderate amounts have a lower cancer risk than those eating very low amounts, and there could also be effects of particular nutrients in certain fruits and vegetables, as fruit and vegetables have very varied composition. Nutritional principles indicate that healthy diets should include at least moderate amounts of fruit and vegetables, but the available data suggest that general increases in fruit and vegetable intake would not have much effect on cancer rates, at least in well-nourished populations. Current advice in relation to diet and cancer should include the recommendation to consume adequate amounts of fruit and vegetables, but should put most emphasis on the well-established adverse effects of obesity and high alcohol intakes. PMID:21119663

  19. Can Predictive Modeling Identify Head and Neck Oncology Patients at Risk for Readmission?

    PubMed

    Manning, Amy M; Casper, Keith A; Peter, Kay St; Wilson, Keith M; Mark, Jonathan R; Collar, Ryan M

    2018-05-01

    Objective Unplanned readmission within 30 days is a contributor to health care costs in the United States. The use of predictive modeling during hospitalization to identify patients at risk for readmission offers a novel approach to quality improvement and cost reduction. Study Design Two-phase study including retrospective analysis of prospectively collected data followed by prospective longitudinal study. Setting Tertiary academic medical center. Subjects and Methods Prospectively collected data for patients undergoing surgical treatment for head and neck cancer from January 2013 to January 2015 were used to build predictive models for readmission within 30 days of discharge using logistic regression, classification and regression tree (CART) analysis, and random forests. One model (logistic regression) was then placed prospectively into the discharge workflow from March 2016 to May 2016 to determine the model's ability to predict which patients would be readmitted within 30 days. Results In total, 174 admissions had descriptive data. Thirty-two were excluded due to incomplete data. Logistic regression, CART, and random forest predictive models were constructed using the remaining 142 admissions. When applied to 106 consecutive prospective head and neck oncology patients at the time of discharge, the logistic regression model predicted readmissions with a specificity of 94%, a sensitivity of 47%, a negative predictive value of 90%, and a positive predictive value of 62% (odds ratio, 14.9; 95% confidence interval, 4.02-55.45). Conclusion Prospectively collected head and neck cancer databases can be used to develop predictive models that can accurately predict which patients will be readmitted. This offers valuable support for quality improvement initiatives and readmission-related cost reduction in head and neck cancer care.

  20. Height and Breast Cancer Risk: Evidence From Prospective Studies and Mendelian Randomization.

    PubMed

    Zhang, Ben; Shu, Xiao-Ou; Delahanty, Ryan J; Zeng, Chenjie; Michailidou, Kyriaki; Bolla, Manjeet K; Wang, Qin; Dennis, Joe; Wen, Wanqing; Long, Jirong; Li, Chun; Dunning, Alison M; Chang-Claude, Jenny; Shah, Mitul; Perkins, Barbara J; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Bojesen, Stig E; Nordestgaard, Børge G; Nielsen, Sune F; Flyger, Henrik; Lambrechts, Diether; Neven, Patrick; Wildiers, Hans; Floris, Giuseppe; Schmidt, Marjanka K; Rookus, Matti A; van den Hurk, Katja; de Kort, Wim L A M; Couch, Fergus J; Olson, Janet E; Hallberg, Emily; Vachon, Celine; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Peto, Julian; Dos-Santos-Silva, Isabel; Fletcher, Olivia; Johnson, Nichola; Nevanlinna, Heli; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Li, Jingmei; Humphreys, Keith; Brand, Judith; Guénel, Pascal; Truong, Thérèse; Cordina-Duverger, Emilie; Menegaux, Florence; Burwinkel, Barbara; Marme, Frederik; Yang, Rongxi; Surowy, Harald; Benitez, Javier; Zamora, M Pilar; Perez, Jose I A; Cox, Angela; Cross, Simon S; Reed, Malcolm W R; Andrulis, Irene L; Knight, Julia A; Glendon, Gord; Tchatchou, Sandrine; Sawyer, Elinor J; Tomlinson, Ian; Kerin, Michael J; Miller, Nicola; Chenevix-Trench, Georgia; Haiman, Christopher A; Henderson, Brian E; Schumacher, Fredrick; Marchand, Loic Le; Lindblom, Annika; Margolin, Sara; Hooning, Maartje J; Martens, John W M; Tilanus-Linthorst, Madeleine M A; Collée, J Margriet; Hopper, John L; Southey, Melissa C; Tsimiklis, Helen; Apicella, Carmel; Slager, Susan; Toland, Amanda E; Ambrosone, Christine B; Yannoukakos, Drakoulis; Giles, Graham G; Milne, Roger L; McLean, Catriona; Fasching, Peter A; Haeberle, Lothar; Ekici, Arif B; Beckmann, Matthias W; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Swerdlow, Anthony J; Ashworth, Alan; Orr, Nick; Jones, Michael; Figueroa, Jonine; Garcia-Closas, Montserrat; Brinton, Louise; Lissowska, Jolanta; Dumont, Martine; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Peterlongo, Paolo; Manoukian, Siranoush; Bonanni, Bernardo; Radice, Paolo; Bogdanova, Natalia; Antonenkova, Natalia; Dörk, Thilo; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Devilee, Peter; Seynaeve, Caroline; Van Asperen, Christi J; Jakubowska, Anna; Lubiński, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Hamann, Ute; Torres, Diana; Schmutzler, Rita K; Neuhausen, Susan L; Anton-Culver, Hoda; Kristensen, Vessela N; Grenaker Alnæs, Grethe I; Pierce, Brandon L; Kraft, Peter; Peters, Ulrike; Lindstrom, Sara; Seminara, Daniela; Burgess, Stephen; Ahsan, Habibul; Whittemore, Alice S; John, Esther M; Gammon, Marilie D; Malone, Kathleen E; Tessier, Daniel C; Vincent, Daniel; Bacot, Francois; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S; González-Neira, Anna; Pita, Guillermo; Alonso, M Rosario; Álvarez, Nuria; Herrero, Daniel; Pharoah, Paul D P; Simard, Jacques; Hall, Per; Hunter, David J; Easton, Douglas F; Zheng, Wei

    2015-11-01

    Epidemiological studies have linked adult height with breast cancer risk in women. However, the magnitude of the association, particularly by subtypes of breast cancer, has not been established. Furthermore, the mechanisms of the association remain unclear. We performed a meta-analysis to investigate associations between height and breast cancer risk using data from 159 prospective cohorts totaling 5216302 women, including 113178 events. In a consortium with individual-level data from 46325 case patients and 42482 control patients, we conducted a Mendelian randomization analysis using a genetic score that comprised 168 height-associated variants as an instrument. This association was further evaluated in a second consortium using summary statistics data from 16003 case patients and 41335 control patients. The pooled relative risk of breast cancer was 1.17 (95% confidence interval [CI] = 1.15 to 1.19) per 10cm increase in height in the meta-analysis of prospective studies. In Mendelian randomization analysis, the odds ratio of breast cancer per 10cm increase in genetically predicted height was 1.22 (95% CI = 1.13 to 1.32) in the first consortium and 1.21 (95% CI = 1.05 to 1.39) in the second consortium. The association was found in both premenopausal and postmenopausal women but restricted to hormone receptor-positive breast cancer. Analyses of height-associated variants identified eight new loci associated with breast cancer risk after adjusting for multiple comparisons, including three loci at 1q21.2, DNAJC27, and CCDC91 at genome-wide significance level P < 5×10(-8). Our study provides strong evidence that adult height is a risk factor for breast cancer in women and certain genetic factors and biological pathways affecting adult height have an important role in the etiology of breast cancer. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.