Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density.

PubMed

Byström, Sanna; Eklund, Martin; Hong, Mun-Gwan; Fredolini, Claudia; Eriksson, Mikael; Czene, Kamila; Hall, Per; Schwenk, Jochen M; Gabrielson, Marike

2018-02-14

Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density. Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI). Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD. Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.

Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population

PubMed Central

Richard-Davis, Gloria; Whittemore, Brianna; Disher, Anthony; Rice, Valerie Montgomery; Lenin, Rathinasamy B; Dollins, Camille; Siegel, Eric R; Eswaran, Hari

2018-01-01

Objective: Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic’s Food and Drug Administration–approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories. Methods: Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR’s BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density–based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities. Results: The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%. Conclusions: Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase. PMID:29511356

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

NASA Astrophysics Data System (ADS)

Helge Østerås, Bjørn; Skaane, Per; Gullien, Randi; Catrine Trægde Martinsen, Anne

2018-02-01

The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra™). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra™. AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality.

PubMed

Østerås, Bjørn Helge; Skaane, Per; Gullien, Randi; Martinsen, Anne Catrine Trægde

2018-01-25

The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra ™ ). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra ™ . AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PubMed

Chowdhury, Marzana; Euhus, David; O'Donnell, Maureen; Onega, Tracy; Choudhary, Pankaj K; Biswas, Swati

2018-07-01

Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

Breast Density Estimation with Fully Automated Volumetric Method: Comparison to Radiologists' Assessment by BI-RADS Categories.

PubMed

Singh, Tulika; Sharma, Madhurima; Singla, Veenu; Khandelwal, Niranjan

2016-01-01

The objective of our study was to calculate mammographic breast density with a fully automated volumetric breast density measurement method and to compare it to breast imaging reporting and data system (BI-RADS) breast density categories assigned by two radiologists. A total of 476 full-field digital mammography examinations with standard mediolateral oblique and craniocaudal views were evaluated by two blinded radiologists and BI-RADS density categories were assigned. Using a fully automated software, mean fibroglandular tissue volume, mean breast volume, and mean volumetric breast density were calculated. Based on percentage volumetric breast density, a volumetric density grade was assigned from 1 to 4. The weighted overall kappa was 0.895 (almost perfect agreement) for the two radiologists' BI-RADS density estimates. A statistically significant difference was seen in mean volumetric breast density among the BI-RADS density categories. With increased BI-RADS density category, increase in mean volumetric breast density was also seen (P < 0.001). A significant positive correlation was found between BI-RADS categories and volumetric density grading by fully automated software (ρ = 0.728, P < 0.001 for first radiologist and ρ = 0.725, P < 0.001 for second radiologist). Pairwise estimates of the weighted kappa between Volpara density grade and BI-RADS density category by two observers showed fair agreement (κ = 0.398 and 0.388, respectively). In our study, a good correlation was seen between density grading using fully automated volumetric method and density grading using BI-RADS density categories assigned by the two radiologists. Thus, the fully automated volumetric method may be used to quantify breast density on routine mammography. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Breast Cancer Screening in the Era of Density Notification Legislation: Summary of 2014 Massachusetts Experience and Suggestion of An Evidence-Based Management Algorithm by Multi-disciplinary Expert Panel

PubMed Central

Freer, Phoebe E.; Slanetz, Priscilla J.; Haas, Jennifer S.; Tung, Nadine M.; Hughes, Kevin S.; Armstrong, Katrina; Semine, A. Alan; Troyan, Susan L.; Birdwell, Robyn L.

2015-01-01

Purpose Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. Methods We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review (ICER), the Cochrane review, National Comprehensive Cancer Network (NCCN) guidelines, American Cancer Society (ACS) recommendations, and American College of Radiology (ACR) appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. Results The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (<15% lifetime risk), do not routinely require supplemental screening per the expert consensus. Women of high risk (>20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. Conclusion We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman. PMID:26290416

Breast Density and Risk of Breast Cancer in Asian Women: A Meta-analysis of Observational Studies.

PubMed

Bae, Jong-Myon; Kim, Eun Hee

2016-11-01

The established theory that breast density is an independent predictor of breast cancer risk is based on studies targeting white women in the West. More Asian women than Western women have dense breasts, but the incidence of breast cancer is lower among Asian women. This meta-analysis investigated the association between breast density in mammography and breast cancer risk in Asian women. PubMed and Scopus were searched, and the final date of publication was set as December 31, 2015. The effect size in each article was calculated using the interval-collapse method. Summary effect sizes (sESs) and 95% confidence intervals (CIs) were calculated by conducting a meta-analysis applying a random effect model. To investigate the dose-response relationship, random effect dose-response meta-regression (RE-DRMR) was conducted. Six analytical epidemiology studies in total were selected, including one cohort study and five case-control studies. A total of 17 datasets were constructed by type of breast density index and menopausal status. In analyzing the subgroups of premenopausal vs. postmenopausal women, the percent density (PD) index was confirmed to be associated with a significantly elevated risk for breast cancer (sES, 2.21; 95% CI, 1.52 to 3.21; I 2 =50.0%). The RE-DRMR results showed that the risk of breast cancer increased 1.73 times for each 25% increase in PD in postmenopausal women (95% CI, 1.20 to 2.47). In Asian women, breast cancer risk increased with breast density measured using the PD index, regardless of menopausal status. We propose the further development of a breast cancer risk prediction model based on the application of PD in Asian women.

Development of a sampling strategy and sample size calculation to estimate the distribution of mammographic breast density in Korean women.

PubMed

Jun, Jae Kwan; Kim, Mi Jin; Choi, Kui Son; Suh, Mina; Jung, Kyu-Won

2012-01-01

Mammographic breast density is a known risk factor for breast cancer. To conduct a survey to estimate the distribution of mammographic breast density in Korean women, appropriate sampling strategies for representative and efficient sampling design were evaluated through simulation. Using the target population from the National Cancer Screening Programme (NCSP) for breast cancer in 2009, we verified the distribution estimate by repeating the simulation 1,000 times using stratified random sampling to investigate the distribution of breast density of 1,340,362 women. According to the simulation results, using a sampling design stratifying the nation into three groups (metropolitan, urban, and rural), with a total sample size of 4,000, we estimated the distribution of breast density in Korean women at a level of 0.01% tolerance. Based on the results of our study, a nationwide survey for estimating the distribution of mammographic breast density among Korean women can be conducted efficiently.

Computerized image analysis: estimation of breast density on mammograms

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Petrick, Nicholas; Sahiner, Berkman; Helvie, Mark A.; Roubidoux, Marilyn A.; Hadjiiski, Lubomir M.; Goodsitt, Mitchell M.

2000-06-01

An automated image analysis tool is being developed for estimation of mammographic breast density, which may be useful for risk estimation or for monitoring breast density change in a prevention or intervention program. A mammogram is digitized using a laser scanner and the resolution is reduced to a pixel size of 0.8 mm X 0.8 mm. Breast density analysis is performed in three stages. First, the breast region is segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique is applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification is used to classify the breast images into several classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold is automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area is then estimated. In this preliminary study, we analyzed the interobserver variation of breast density estimation by two experienced radiologists using BI-RADS lexicon. The radiologists' visually estimated percent breast densities were compared with the computer's calculation. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility in comparison with the subjective visual assessment by radiologists.

Effect of Taxane-Based Neoadjuvant Chemotherapy on Fibroglandular Tissue Volume and Percent Breast Density in the Contralateral Normal Breast: Evaluated at 3T MR

PubMed Central

Chen, Jeon-Hor; Pan, Wei-Fan; Kao, Julian; Lu, Jocelyn; Chen, Li-Kuang; Kuo, Chih-Chen; Chang, Chih-Kai; Chen, Wen-Pin; McLaren, Christine E.; Bahri, Shadfar; Mehta, Rita S.; Su, Min-Ying

2013-01-01

The aim of this study was to evaluate the change of breast density in the normal breast of patients receiving neoadjuvant chemotherapy (NAC). Forty-four breast cancer patients were studied. MRI acquisition was performed before treatment (baseline), and 4 and 12 weeks after treatment. A computer algorithm-based program was used to segment breast tissue and calculate breast volume (BV), fibroglandular tissue volume (FV) and percent density (PD) (the ratio of FV over BV x100%). The reduction of FV and PD after treatment was compared to baseline using paired t-tests with a Bonferroni-Holm correction. The association of density reduction with age was analyzed. FV and PD after NAC showed significant decreases compared to the baseline. FV was 110.0ml (67.2, 189.8) (geometric mean (interquartile range)) at baseline, 104.3ml (66.6, 164.4) after 4 weeks (p< 0.0001), and 94.7ml (60.2, 144.4) after 12 weeks (comparison to baseline, p<0.0001; comparison to 4 weeks, p=0.016). PD was 11.2% (6.4, 22.4) at baseline, 10.6% (6.6, 20.3) after 4 weeks (p< 0.0001), and 9.7% (6.2, 17.9) after 12 weeks (comparison to baseline, p=0.0001; comparison to 4 weeks, p =0.018). Younger patients tended to show a higher density reduction, but overall correlation with age was only moderate (r=0.28 for FV, p=0.07 and r=0.52 for PD, p=0.0003). Our study showed that breast density measured from MR images acquired at 3T MR can be accurately quantified using a robust computer-aided algorithm based on nonparametric nonuniformity normalization (N3) and an adaptive fuzzy C-means algorithm. Similar to doxorubicin and cyclophosphamide regimens, the taxane-based NAC regimen also caused density atrophy in the normal breast and showed reduction in FV and PD. The effect of breast density reduction was age-related and duration-related. PMID:23940080

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.

Volumetric versus area-based density assessment: comparisons using automated quantitative measurements from a large screening cohort

NASA Astrophysics Data System (ADS)

Gastounioti, Aimilia; Hsieh, Meng-Kang; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

2018-03-01

Mammographic density is an established risk factor for breast cancer. However, area-based density (ABD) measured in 2D mammograms consider the projection, rather than the actual volume of dense tissue which may be an important limitation. With the increasing utilization of digital breast tomosynthesis (DBT) in screening, there's an opportunity to routinely estimate volumetric breast density (VBD). In this study, we investigate associations between DBT-VBD and ABD extracted from standard-dose mammography (DM) and synthetic 2D digital mammography (sDM) increasingly replacing DM. We retrospectively analyzed bilateral imaging data from a random sample of 1000 women, acquired over a transitional period at our institution when all women had DBT, sDM and DM acquired as part of their routine breast screening. For each exam, ABD was measured in DM and sDM images with the publicly available "LIBRA" software, while DBT-VBD was measured using a previously validated, fully-automated computer algorithm. Spearman correlation (r) was used to compare VBD to ABD measurements. For each density measure, we also estimated the within woman intraclass correlation (ICC) and finally, to compare to clinical assessments, we performed analysis of variance (ANOVA) to evaluate the variation to the assigned clinical BI-RADS breast density category for each woman. DBT-VBD was moderately correlated to ABD from DM (r=0.70) and sDM (r=0.66). All density measures had strong bilateral symmetry (ICC = [0.85, 0.95]), but were significantly different across BI-RADS density categories (ANOVA, p<0.001). Our results contribute to further elaborating the clinical implications of breast density measures estimated with DBT which may better capture the volumetric amount of dense tissue within the breast than area-based measures and visual assessment.

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

Breast density evaluation using spectral mammography, radiologist reader assessment and segmentation techniques: a retrospective study based on left and right breast comparison

PubMed Central

Molloi, Sabee; Ding, Huanjun; Feig, Stephen

2015-01-01

Purpose The purpose of this study was to compare the precision of mammographic breast density measurement using radiologist reader assessment, histogram threshold segmentation, fuzzy C-mean segmentation and spectral material decomposition. Materials and Methods Spectral mammography images from a total of 92 consecutive asymptomatic women (50–69 years old) who presented for annual screening mammography were retrospectively analyzed for this study. Breast density was estimated using 10 radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and spectral material decomposition. The breast density correlation between left and right breasts was used to assess the precision of these techniques to measure breast composition relative to dual-energy material decomposition. Results In comparison to the other techniques, the results of breast density measurements using dual-energy material decomposition showed the highest correlation. The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and dual-energy material decomposition was calculated to be 1.95, 2.87, 2.07 and 1.00, respectively. Conclusion The results indicate that the precision of dual-energy material decomposition was approximately factor of two higher than the other techniques with regard to better correlation of breast density measurements from right and left breasts. PMID:26031229

Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

PubMed

Ding, Jie; Stopeck, Alison T; Gao, Yi; Marron, Marilyn T; Wertheim, Betsy C; Altbach, Maria I; Galons, Jean-Philippe; Roe, Denise J; Wang, Fang; Maskarinec, Gertraud; Thomson, Cynthia A; Thompson, Patricia A; Huang, Chuan

2018-04-06

Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). Retrospective study. The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with <80% fat fraction-was also calculated for comparison with FraGW. Pearson correlation was performed between MD (reference standard) and FraGW (and Fra80). Test-retest reproducibility of MRD was evaluated using the difference between test-retest measures (Δ 1-2 ) and intraclass correlation coefficient (ICC). Both FraGW and Fra80 were strongly correlated with MD (Pearson ρ: 0.96 vs. 0.90, both P < 0.0001). MRD converted from FraGW showed higher test-retest reproducibility (Δ 1-2 variation: 1.1% ± 1.2%; ICC: 0.99) compared to MD itself (literature intrareader ICC ≤0.96) and Fra80. The proposed MRD is directly comparable with MD and highly reproducible, which enables the early detection of small breast density changes and treatment response. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

A deep learning method for classifying mammographic breast density categories.

PubMed

Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

2018-01-01

Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset. Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening. © 2017 American Association of Physicists in Medicine.

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

PubMed Central

Destounis, Stamatia; Arieno, Andrea; Morgan, Renee; Roberts, Christina; Chan, Ariane

2017-01-01

Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening. PMID:28561776

Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

PubMed

Dalmış, Mehmet Ufuk; Litjens, Geert; Holland, Katharina; Setio, Arnaud; Mann, Ritse; Karssemeijer, Nico; Gubern-Mérida, Albert

2017-02-01

Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep-learning approach known as "U-net." We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in-house developed workstation. We experimented with the application of U-net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U-nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3-class (3C) U-net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas-based method and a sheetness-based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed Pearson's correlation between the breast density values computed based on manual and automated segmentations. The average DSC values for breast segmentation were 0.933, 0.944, 0.863, and 0.848 obtained from 3C U-net, 2C U-nets, atlas-based method, and sheetness-based method, respectively. The average DSC values for FGT segmentation obtained from 3C U-net, 2C U-nets, and atlas-based methods were 0.850, 0.811, and 0.671, respectively. The correlation between breast density values based on 3C U-net and manual segmentations was 0.974. This value was significantly higher than 0.957 as obtained from 2C U-nets (P < 0.0001, Steiger's Z-test with Bonferoni correction) and 0.938 as obtained from atlas-based method (P = 0.0016). In conclusion, we applied a deep-learning method, U-net, for segmenting breast and FGT in MRI in a dataset that includes a variety of MRI protocols and breast densities. Our results showed that U-net-based methods significantly outperformed the existing algorithms and resulted in significantly more accurate breast density computation. © 2016 American Association of Physicists in Medicine.

Computerized breast parenchymal analysis on DCE-MRI

NASA Astrophysics Data System (ADS)

Li, Hui; Giger, Maryellen L.; Yuan, Yading; Jansen, Sanaz A.; Lan, Li; Bhooshan, Neha; Newstead, Gillian M.

2009-02-01

Breast density has been shown to be associated with the risk of developing breast cancer, and MRI has been recommended for high-risk women screening, however, it is still unknown how the breast parenchymal enhancement on DCE-MRI is associated with breast density and breast cancer risk. Ninety-two DCE-MRI exams of asymptomatic women with normal MR findings were included in this study. The 3D breast volume was automatically segmented using a volume-growing based algorithm. The extracted breast volume was classified into fibroglandular and fatty regions based on the discriminant analysis method. The parenchymal kinetic curves within the breast fibroglandular region were extracted and categorized by use of fuzzy c-means clustering, and various parenchymal kinetic characteristics were extracted from the most enhancing voxels. Correlation analysis between the computer-extracted percent dense measures and radiologist-noted BIRADS density ratings yielded a correlation coefficient of 0.76 (p<0.0001). From kinetic analyses, 70% (64/92) of most enhancing curves showed persistent curve type and reached peak parenchymal intensity at the last postcontrast time point; with 89% (82/92) of most enhancing curves reaching peak intensity at either 4th or 5th post-contrast time points. Women with dense breast (BIRADS 3 and 4) were found to have more parenchymal enhancement at their peak time point (Ep) with an average Ep of 116.5% while those women with fatty breasts (BIRADS 1 and 2) demonstrated an average Ep of 62.0%. In conclusion, breast parenchymal enhancement may be associated with breast density and may be potential useful as an additional characteristic for assessing breast cancer risk.

Comparison of volumetric breast density estimations from mammography and thorax CT

NASA Astrophysics Data System (ADS)

Geeraert, N.; Klausz, R.; Cockmartin, L.; Muller, S.; Bosmans, H.; Bloch, I.

2014-08-01

Breast density has become an important issue in current breast cancer screening, both as a recognized risk factor for breast cancer and by decreasing screening efficiency by the masking effect. Different qualitative and quantitative methods have been proposed to evaluate area-based breast density and volumetric breast density (VBD). We propose a validation method comparing the computation of VBD obtained from digital mammographic images (VBDMX) with the computation of VBD from thorax CT images (VBDCT). We computed VBDMX by applying a conversion function to the pixel values in the mammographic images, based on models determined from images of breast equivalent material. VBDCT is computed from the average Hounsfield Unit (HU) over the manually delineated breast volume in the CT images. This average HU is then compared to the HU of adipose and fibroglandular tissues from patient images. The VBDMX method was applied to 663 mammographic patient images taken on two Siemens Inspiration (hospL) and one GE Senographe Essential (hospJ). For the comparison study, we collected images from patients who had a thorax CT and a mammography screening exam within the same year. In total, thorax CT images corresponding to 40 breasts (hospL) and 47 breasts (hospJ) were retrieved. Averaged over the 663 mammographic images the median VBDMX was 14.7% . The density distribution and the inverse correlation between VBDMX and breast thickness were found as expected. The average difference between VBDMX and VBDCT is smaller for hospJ (4%) than for hospL (10%). This study shows the possibility to compare VBDMX with the VBD from thorax CT exams, without additional examinations. In spite of the limitations caused by poorly defined breast limits, the calibration of mammographic images to local VBD provides opportunities for further quantitative evaluations.

Automatic breast density classification using a convolutional neural network architecture search procedure

NASA Astrophysics Data System (ADS)

Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

2015-03-01

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation.

PubMed

Petridou, E; Kibiro, M; Gladwell, C; Malcolm, P; Toms, A; Juette, A; Borga, M; Dahlqvist Leinhard, O; Romu, T; Kasmai, B; Denton, E

2017-07-01

To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification. Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linköping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale. The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31±0.22 (mean±SD; left) and 0.29±0.21 (right). Mean breast density on post-contrast images was 0.32±0.19 (left) and 0.32±0.2 (right). There was "almost perfect" correlation between pre- and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97-0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11-0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k=0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BI-RADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial". The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation. Copyright © 2017 The Royal College of Radiologists. All rights reserved.

New Embedded Denotes Fuzzy C-Mean Application for Breast Cancer Density Segmentation in Digital Mammograms

NASA Astrophysics Data System (ADS)

Othman, Khairulnizam; Ahmad, Afandi

2016-11-01

In this research we explore the application of normalize denoted new techniques in advance fast c-mean in to the problem of finding the segment of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if new denotes fuzzy c- mean algorithm could separate different densities for the different breast patterns. The new density segmentation is applied with multi-selection of seeds label to provide the hard constraint, whereas the seeds labels are selected based on user defined. New denotes fuzzy c- mean have been explored on images of various imaging modalities but not on huge format digital mammograms just yet. Therefore, this project is mainly focused on using normalize denoted new techniques employed in fuzzy c-mean to perform segmentation to increase visibility of different breast densities in mammography images. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology for the segmentation of mammograms on the basis of their region into different densities based categories has been tested on MIAS database and Trueta Database.

Impact of Breast Density Legislation on Breast Cancer Risk Assessment and Supplemental Screening: A Survey of 110 Radiology Facilities.

PubMed

Nayak, Lina; Miyake, Kanae K; Leung, Jessica W T; Price, Elissa R; Liu, Yueyi I; Joe, Bonnie N; Sickles, Edward A; Thomas, William R; Lipson, Jafi A; Daniel, Bruce L; Hargreaves, Jonathan; Brenner, R James; Bassett, Lawrence W; Ojeda-Fournier, Haydee; Lindfors, Karen K; Feig, Stephen A; Ikeda, Debra M

2016-09-01

Breast density notification laws, passed in 19 states as of October 2014, mandate that patients be informed of their breast density. The purpose of this study is to assess the impact of this legislation on radiology practices, including performance of breast cancer risk assessment and supplemental screening studies. A 20-question anonymous web-based survey was emailed to radiologists in the Society of Breast Imaging between August 2013 and March 2014. Statistical analysis was performed using Fisher's exact test. Around 121 radiologists from 110 facilities in 34 USA states and 1 Canadian site responded. About 50% (55/110) of facilities had breast density legislation, 36% of facilities (39/109) performed breast cancer risk assessment (one facility did not respond). Risk assessment was performed as a new task in response to density legislation in 40% (6/15) of facilities in states with notification laws. However, there was no significant difference in performing risk assessment between facilities in states with a law and those without (p < 0.831). In anticipation of breast density legislation, 33% (16/48), 6% (3/48), and 6% (3/48) of facilities in states with laws implemented handheld whole breast ultrasound (WBUS), automated WBUS, and tomosynthesis, respectively. The ratio of facilities offering handheld WBUS was significantly higher in states with a law than in states without (p < 0.001). In response to breast density legislation, more than 33% of facilities are offering supplemental screening with WBUS and tomosynthesis, and many are performing formal risk assessment for determining patient management. © 2016 Wiley Periodicals, Inc.

A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI.

PubMed

Lin, Muqing; Chan, Siwa; Chen, Jeon-Hor; Chang, Daniel; Nie, Ke; Chen, Shih-Ting; Lin, Cheng-Ju; Shih, Tzu-Ching; Nalcioglu, Orhan; Su, Min-Ying

2011-01-01

Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work. The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked. The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3+FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3+FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3+FCM algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts. Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining N3+FCM and CLIC both yield satisfactory results.

The relationship between breast density and bone mineral density in postmenopausal women.

PubMed

Buist, Diana S M; Anderson, Melissa L; Taplin, Stephen H; LaCroix, Andrea Z

2004-11-01

It is not well understood whether breast density is a marker of cumulative exposure to estrogen or a marker of recent exposure to estrogen. The authors examined the relationship between bone mineral density (BMD; a marker of lifetime estrogen exposure) and breast density. The authors conducted a cross-sectional analysis among 1800 postmenopausal women > or = 54 years. BMD data were taken from two population-based studies conducted in 1992-1993 (n = 1055) and in 1998-1999 (n = 753). The authors linked BMD data with breast density information collected as part of a mammography screening program. They used linear regression to evaluate the density relationship, adjusted for age, hormone therapy use, body mass index (BMI), and reproductive covariates. There was a small but significant negative association between BMD and breast density. The negative correlation between density measures was not explained by hormone therapy or age, and BMI was the only covariate that notably influenced the relationship. Stratification by BMI only revealed the negative correlation between bone and breast densities in women with normal BMI. There was no relationship in overweight or obese women. The same relationship was seen for all women who had never used hormone therapy, but it was not significant once stratified by BMI. BMD and breast density were not positively associated although both are independently associated with estrogen exposure. It is likely that unique organ responses obscure the relationship between the two as indicators of cumulative estrogen exposure.

Automated mammographic breast density estimation using a fully convolutional network.

PubMed

Lee, Juhun; Nishikawa, Robert M

2018-03-01

The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of our algorithm for CC view, MLO view, and CC-MLO-averaged were 0.81, 0.79, and 0.85, respectively, while those of LIBRA were 0.58, 0.71, and 0.69, respectively. For CC view and CC-MLO averaged cases, the difference in rho values between the proposed algorithm and LIBRA showed statistical significance (P < 0.006). In addition, our algorithm provided reliable PD estimates for the left and the right breast (Pearson's ρ > 0.87) and for the MLO and CC views (Pearson's ρ = 0.76). However, LIBRA showed a lower Pearson's rho value (0.66) for both the left and right breasts for the CC view. In addition, our algorithm showed an excellent ability to separate each sub BI-RADS breast density class (statistically significant, p-values = 0.0001 or less); only one comparison pair, density 1 and density 2 in the CC view, was not statistically significant (P = 0.54). However, LIBRA failed to separate breasts in density 1 and 2 for both the CC and MLO views (P > 0.64). We have developed a new deep learning based algorithm for breast density segmentation and estimation. We showed that the proposed algorithm correlated well with BI-RADS density assessments by radiologists and outperformed an existing state of the art algorithm. © 2018 American Association of Physicists in Medicine.

Model-based estimation of breast percent density in raw and processed full-field digital mammography images from image-acquisition physics and patient-image characteristics

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Nathan, Diane L.; Conant, Emily F.; Kontos, Despina

2012-03-01

Breast percent density (PD%), as measured mammographically, is one of the strongest known risk factors for breast cancer. While the majority of studies to date have focused on PD% assessment from digitized film mammograms, digital mammography (DM) is becoming increasingly common, and allows for direct PD% assessment at the time of imaging. This work investigates the accuracy of a generalized linear model-based (GLM) estimation of PD% from raw and postprocessed digital mammograms, utilizing image acquisition physics, patient characteristics and gray-level intensity features of the specific image. The model is trained in a leave-one-woman-out fashion on a series of 81 cases for which bilateral, mediolateral-oblique DM images were available in both raw and post-processed format. Baseline continuous and categorical density estimates were provided by a trained breast-imaging radiologist. Regression analysis is performed and Pearson's correlation, r, and Cohen's kappa, κ, are computed. The GLM PD% estimation model performed well on both processed (r=0.89, p<0.001) and raw (r=0.75, p<0.001) images. Model agreement with radiologist assigned density categories was also high for processed (κ=0.79, p<0.001) and raw (κ=0.76, p<0.001) images. Model-based prediction of breast PD% could allow for a reproducible estimation of breast density, providing a rapid risk assessment tool for clinical practice.

Population of 224 realistic human subject-based computational breast phantoms

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

Erickson, David W.; Wells, Jered R., E-mail: jered.wells@duke.edu; Sturgeon, Gregory M.

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was thenmore » applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.« less

Population of 224 realistic human subject-based computational breast phantoms

PubMed Central

Erickson, David W.; Wells, Jered R.; Sturgeon, Gregory M.; Dobbins, James T.; Segars, W. Paul; Lo, Joseph Y.

2016-01-01

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns. PMID:26745896

The association between mammographic breast density and bone mineral density in the study of women's health across the nation.

PubMed

Crandall, Carolyn J; Zheng, Yan; Karlamangla, Arun; Sternfeld, Barbara; Habel, Laurel A; Oestreicher, Nina; Johnston, Janet; Cauley, Jane A; Greendale, Gail A

2007-08-01

Bone mineral density and mammographic breast density are each associated with markers of lifetime estrogen exposure. The association between mammographic breast density and bone mineral density in early perimenopausal women is unknown. We analyzed data from a cohort (n = 501) of premenopausal (no change in menstrual regularity) and early perimenopausal (decreased menstrual regularity in past 3 months) participants of African-American, Caucasian, Chinese, and Japanese ethnicity in the Study of Women's Health Across the Nation. Using multivariable linear regression, we examined the cross-sectional association between percent mammographic density and bone mineral density (BMD). Percent mammographic density was statistically significantly inversely associated with hip BMD and lumbar spine BMD after adjustment (body mass index, ethnicity, age, study site, parity, alcohol intake, cigarette smoking, physical activity, age at first childbirth) in early perimenopausal, but not premenopausal, women. In early perimenopausal women, every 0.1g/cm(2) greater hip BMD predicted a 2% lower percent mammographic density (95% confidence interval -37.0 to -0.6%, p = 0.04). Mammographic breast density is inversely associated with BMD in the perimenopausal participants of this community-based cohort. The biological underpinnings of these findings may reflect differential responsiveness of breast and bone mineral density to the steroid milieu.

Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes.

PubMed

Trentham-Dietz, Amy; Kerlikowske, Karla; Stout, Natasha K; Miglioretti, Diana L; Schechter, Clyde B; Ergun, Mehmet Ali; van den Broek, Jeroen J; Alagoz, Oguzhan; Sprague, Brian L; van Ravesteyn, Nicolien T; Near, Aimee M; Gangnon, Ronald E; Hampton, John M; Chandler, Young; de Koning, Harry J; Mandelblatt, Jeanne S; Tosteson, Anna N A

2016-11-15

Biennial screening is generally recommended for average-risk women aged 50 to 74 years, but tailored screening may provide greater benefits. To estimate outcomes for various screening intervals after age 50 years based on breast density and risk for breast cancer. Collaborative simulation modeling using national incidence, breast density, and screening performance data. United States. Women aged 50 years or older with various combinations of breast density and relative risk (RR) of 1.0, 1.3, 2.0, or 4.0. Annual, biennial, or triennial digital mammography screening from ages 50 to 74 years (vs. no screening) and ages 65 to 74 years (vs. biennial digital mammography from ages 50 to 64 years). Lifetime breast cancer deaths, life expectancy and quality-adjusted life-years (QALYs), false-positive mammograms, benign biopsy results, overdiagnosis, cost-effectiveness, and ratio of false-positive results to breast cancer deaths averted. Screening benefits and overdiagnosis increase with breast density and RR. False-positive mammograms and benign results on biopsy decrease with increasing risk. Among women with fatty breasts or scattered fibroglandular density and an RR of 1.0 or 1.3, breast cancer deaths averted were similar for triennial versus biennial screening for both age groups (50 to 74 years, median of 3.4 to 5.1 vs. 4.1 to 6.5 deaths averted; 65 to 74 years, median of 1.5 to 2.1 vs. 1.8 to 2.6 deaths averted). Breast cancer deaths averted increased with annual versus biennial screening for women aged 50 to 74 years at all levels of breast density and an RR of 4.0, and those aged 65 to 74 years with heterogeneously or extremely dense breasts and an RR of 4.0. However, harms were almost 2-fold higher. Triennial screening for the average-risk subgroup and annual screening for the highest-risk subgroup cost less than $100 000 per QALY gained. Models did not consider women younger than 50 years, those with an RR less than 1, or other imaging methods. Average-risk women with low breast density undergoing triennial screening and higher-risk women with high breast density receiving annual screening will maintain a similar or better balance of benefits and harms than average-risk women receiving biennial screening. National Cancer Institute.

Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting.

PubMed

van der Waal, Daniëlle; den Heeten, Gerard J; Pijnappel, Ruud M; Schuur, Klaas H; Timmers, Johanna M H; Verbeek, André L M; Broeders, Mireille J M

2015-01-01

The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation. Digital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50-75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density (5th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density (ordinal scale) was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients (e.g., intraclass correlation coefficient [ICC]). Based on the BI-RADS classification, 40.8% of the women had 'heterogeneously or extremely dense' breasts. The median volumetric percent density was 12.1% (IQR: 9.6-16.5) for Quantra, which was higher than the Volpara estimate (median 6.6%, IQR: 4.4-10.9). The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04-5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra. Although there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized.

Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting

PubMed Central

van der Waal, Daniëlle; den Heeten, Gerard J.; Pijnappel, Ruud M.; Schuur, Klaas H.; Timmers, Johanna M. H.; Verbeek, André L. M.; Broeders, Mireille J. M.

2015-01-01

Introduction The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation. Materials and Methods Digital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50–75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density (5th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density (ordinal scale) was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients (e.g., intraclass correlation coefficient [ICC]). Results Based on the BI-RADS classification, 40.8% of the women had ‘heterogeneously or extremely dense’ breasts. The median volumetric percent density was 12.1% (IQR: 9.6–16.5) for Quantra, which was higher than the Volpara estimate (median 6.6%, IQR: 4.4–10.9). The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04–5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra. Conclusion Although there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized. PMID:26335569

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

PubMed Central

Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q.; Ducote, Justin L.; Su, Min-Ying; Molloi, Sabee

2013-01-01

Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left–right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left–right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left–right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. Conclusions: The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications. PMID:24320536

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study.

PubMed

Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q; Ducote, Justin L; Su, Min-Ying; Molloi, Sabee

2013-12-01

Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.

Does breast density measured through population-based screening independently increase breast cancer risk in Asian females?

PubMed Central

Park, Boyoung; Cho, Hye Mi; Lee, Eun Hye; Song, Seunghoon; Suh, Mina; Choi, Kui Son; Kang, Bong Joo; Ko, Kyungran; Yi, Ann; Jung, Hae Kyoung; Cha, Joo Hee; Jun, Jae Kwan

2018-01-01

Purpose The purpose of this study was to investigate the effects of breast density on breast cancer risk among women screened via a nationwide mammographic screening program. Patients and methods We conducted a nested case–control study for a randomly selected population of 1,561 breast cancer patients and 6,002 matched controls from the National Cancer Screening Program. Breast density was measured and recorded by two independent radiologists using the Breast Imaging Reporting and Data System (BI-RADS). Associations between BI-RADS density and breast cancer risk were evaluated according to screening results, time elapsed since receiving non-recall results, age, and menopausal status after adjusting for possible covariates. Results Breast cancer risk for women with extremely dense breasts was five times higher (adjusted odds ratio [aOR] =5.0; 95% confidence interval [CI]) =3.7–6.7) than that for women with an almost entirely fatty breast, although the risk differed between recalled women (aOR =3.3, 95% CI =2.3–3.6) and women with non-recalled results (aOR =12.1, 95% CI =6.3–23.3, P-heterogeneity =0.001). aORs for BI-RADS categories of breast density were similar when subjects who developed cancer after showing non-recall findings during initial screening were grouped according to time until cancer diagnosis thereafter (<1 and ≥1 year). The prevalence of dense breasts was higher in younger women, and the association between a denser breast and breast cancer was stronger in younger women (heterogeneously dense breast: aOR =7.0, 95% CI =2.4–20.3, women in their 40s) than older women (aOR =2.5, 95% CI =1.1–6.0, women in their 70s or more). In addition, while the positive association remained, irrespective of menopausal status, the effect of a dense breast on breast cancer risk was stronger in premenopausal women. Conclusion This study confirmed an increased risk of breast cancer with greater breast density in Korean women which was consistent regardless of BI-RADS assessment category, time interval after initially non-recall results, and menopausal status. PMID:29343988

Enhancement of breast periphery region in digital mammography

NASA Astrophysics Data System (ADS)

Menegatti Pavan, Ana Luiza; Vacavant, Antoine; Petean Trindade, Andre; Quini, Caio Cesar; Rodrigues de Pina, Diana

2018-03-01

Volumetric breast density has been shown to be one of the strongest risk factor for breast cancer diagnosis. This metric can be estimated using digital mammograms. During mammography acquisition, breast is compressed and part of it loses contact with the paddle, resulting in an uncompressed region in periphery with thickness variation. Therefore, reliable density estimation in the breast periphery region is a problem, which affects the accuracy of volumetric breast density measurement. The aim of this study was to enhance breast periphery to solve the problem of thickness variation. Herein, we present an automatic algorithm to correct breast periphery thickness without changing pixel value from internal breast region. The correction pixel values from periphery was based on mean values over iso-distance lines from the breast skin-line using only adipose tissue information. The algorithm detects automatically the periphery region where thickness should be corrected. A correction factor was applied in breast periphery image to enhance the region. We also compare our contribution with two other algorithms from state-of-the-art, and we show its accuracy by means of different quality measures. Experienced radiologists subjectively evaluated resulting images from the tree methods in relation to original mammogram. The mean pixel value, skewness and kurtosis from histogram of the three methods were used as comparison metric. As a result, the methodology presented herein showed to be a good approach to be performed before calculating volumetric breast density.

Current and Future Methods for Measuring Breast Density: A Brief Comparative Review

PubMed Central

Sak, Mark A.; Littrup, Peter J.; Duric, Neb; Mullooly, Maeve; Sherman, Mark E.; Gierach, Gretchen L.

2017-01-01

Breast density is one of the strongest predictors of breast cancer risk. Women with the densest breasts are 4 to 6 times more likely to develop cancer compared with those with the lowest densities. Breast density is generally assessed using mammographic imaging; however, this approach has limitations. Magnetic resonance imaging and ultrasound tomography are some alternative imaging modalities that can aid mammography in patient screening and the measurement of breast density. As breast density becomes more commonly discussed, knowledge of the advantages and limitations of breast density as a marker of risk will become more critical. This review article discusses the relationship between breast density and breast cancer risk, lists the benefits and drawbacks of using multiple different imaging modalities to measure density and briefly discusses how breast density will be applied to aid in breast cancer prevention and treatment. PMID:28943893

The need for supplemental breast cancer screening modalities: a perspective of population-based breast cancer screening programs in Japan.

PubMed

Uematsu, Takayoshi

2017-01-01

This article discusses possible supplemental breast cancer screening modalities for younger women with dense breasts from a perspective of population-based breast cancer screening program in Japan. Supplemental breast cancer screening modalities have been proposed to increase the sensitivity and detection rates of early stage breast cancer in women with dense breasts; however, there are no global guidelines that recommend the use of supplemental breast cancer screening modalities in such women. Also, no criterion standard exists for breast density assessment. Based on the current situation of breast imaging in Japan, the possible supplemental breast cancer screening modalities are ultrasonography, digital breast tomosynthesis, and breast magnetic resonance imaging. An appropriate population-based breast cancer screening program based on the balance between cost and benefit should be a high priority. Further research based on evidence-based medicine is encouraged. It is very important that the ethnicity, workforce, workflow, and resources for breast cancer screening in each country should be considered when considering supplemental breast cancer screening modalities for women with dense breasts.

Misclassification of Breast Imaging Reporting and Data System (BI-RADS) mammographic density and implications for breast density reporting legislation

PubMed Central

Gard, Charlotte C.; Aiello Bowles, Erin J.; Miglioretti, Diana L.; Taplin, Stephen H.; Rutter, Carolyn M.

2015-01-01

U.S. states have begun legislating mammographic breast density reporting to women, requiring that women undergoing screening mammography who have dense breast tissue (BI-RADS density c or d) receive written notification of their breast density; however, the impact that misclassification of breast density will have on this reporting remains unclear. The aim of this study was to assess reproducibility of the four-category Breast Imaging Reporting and Data System (BI-RADS) density measure and examine its relationship with a continuous measure of percent density. We enrolled 19 radiologists, experienced in breast imaging, from a single integrated healthcare system. Radiologists interpreted 341 screening mammograms at two points in time six months apart. We assessed intra- and inter-observer agreement in radiologists’ interpretations of BI-RADS density and explored whether agreement depended upon radiologist characteristics. We examined the relationship between BI-RADS density and percent density in a subset of 282 examinations. Intra-radiologist agreement was moderate to substantial, with kappa varying across radiologists from 0.50–0.81 (mean=0.69, 95% CI (0.63, 0.73)). Intra-radiologist agreement was higher for radiologists with ≥10 years experience interpreting mammograms (difference in mean kappa=0.10, 95% CI (0.01, 0.24)). Inter-radiologist agreement varied widely across radiologist pairs from slight to substantial, with kappa ranging from 0.02–0.72 (mean=0.46, 95% CI (0.36, 0.55)). Of 145 examinations interpreted as “non-dense” (BI-RADS density a or b) by the majority of radiologists, 82.8% were interpreted as “dense” (BI-RADS density c or d) by at least one radiologist. Of 187 examinations interpreted as “dense” by the majority of radiologists, 47.1% were interpreted as “non-dense” by at least one radiologist. While the examinations of almost half of the women in our study were interpreted clinically as having BI-RADS density c or d, only about 10% of examinations had percent density >50%. Our results suggest that breast density reporting based on a single BI-RADS density interpretation may be misleading due to high inter-radiologist variability and a lack of correspondence between BI-RADS density and percent density. PMID:26133090

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.

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

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

Keller, Brad M.; Nathan, Diane L.; Wang Yan

Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') andmore » vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Results: Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r= 0.82, p < 0.001) and processed (r= 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r= 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's {kappa}{>=} 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). Conclusions: The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.« less

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

PubMed Central

Keller, Brad M.; Nathan, Diane L.; Wang, Yan; Zheng, Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina

2012-01-01

Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Results: Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p < 0.001) and processed (r = 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r = 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's κ ≥ 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). Conclusions: The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies. PMID:22894417

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.

PubMed

Keller, Brad M; Nathan, Diane L; Wang, Yan; Zheng, Yuanjie; Gee, James C; Conant, Emily F; Kontos, Despina

2012-08-01

The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., "FOR PROCESSING") and vendor postprocessed (i.e., "FOR PRESENTATION"), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p < 0.001) and processed (r = 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r = 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's κ ≥ 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

Breast Density Legislation in New England: A Survey Study of Practicing Radiologists.

PubMed

Lourenco, Ana P; DiFlorio-Alexander, Roberta M; Slanetz, Priscilla J

2017-10-01

This study aimed to assess radiologists' knowledge about breast density legislation as well as perceived practice changes resulting from the enactment of breast density legislation. This is an institutional review board-exempt anonymous email survey of 523 members of the New England Roentgen Ray Society. In addition to radiologist demographics, survey questions addressed radiologist knowledge of breast density legislation, knowledge of breast density as a risk factor for breast cancer, recommendations for supplemental screening, and perceived practice changes resulting from density notification legislation. Of the 523 members, 96 responded, yielding an 18% response rate. Seventy-three percent of respondents practiced in a state with breast density legislation. Sixty-nine percent felt that breast density notification increased patient anxiety about breast cancer, but also increased patient (74%) and provider (66%) understanding of the effect of breast density on mammographic sensitivity. Radiologist knowledge of the relative risk of breast cancer when comparing breasts of different density was variable. Considerable confusion and controversy regarding breast density persists, even among practicing radiologists. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Knowledge of Breast Density and Awareness of Related Breast Cancer Risk

PubMed Central

Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Penner, Louis; Albrecht, Terrance L.

2013-01-01

Little is known about women’s knowledge of breast density or between-race differences in this knowledge. In the current study, we examined knowledge of breast density and awareness of its role as a breast cancer risk factor among women who had previously taken part in a breast imaging study. Seventy-seven women (54.5 % Black) returned a survey assessing perceptions and accuracy of breast density knowledge, knowledge of one’s own breast density, and breast cancer risk awareness. White women had greater perceived knowledge of breast density compared to Black women; however, differences in the accuracy of definitions of breast density were due to education. Black women were less likely to know how dense their own breasts were. Black and White women both lacked awareness that having dense breast increased breast cancer risk. The results highlight the need to disseminate information regarding breast density to women, while ensuring that the information is equally accessible to both Black and White women. PMID:23467999

Knowledge of breast density and awareness of related breast cancer risk.

PubMed

Manning, Mark A; Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Penner, Louis; Albrecht, Terrance L

2013-06-01

Little is known about women's knowledge of breast density or between-race differences in this knowledge. In the current study, we examined knowledge of breast density and awareness of its role as a breast cancer risk factor among women who had previously taken part in a breast imaging study. Seventy-seven women (54.5 % Black) returned a survey assessing perceptions and accuracy of breast density knowledge, knowledge of one's own breast density, and breast cancer risk awareness. White women had greater perceived knowledge of breast density compared to Black women; however, differences in the accuracy of definitions of breast density were due to education. Black women were less likely to know how dense their own breasts were. Black and White women both lacked awareness that having dense breast increased breast cancer risk. The results highlight the need to disseminate information regarding breast density to women, while ensuring that the information is equally accessible to both Black and White women.

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study.

PubMed

Ha, Richard; Mema, Eralda; Guo, Xiaotao; Mango, Victoria; Desperito, Elise; Ha, Jason; Wynn, Ralph; Zhao, Binsheng

2016-04-01

The amount of fibroglandular tissue (FGT) has been linked to breast cancer risk based on mammographic density studies. Currently, the qualitative assessment of FGT on mammogram (MG) and magnetic resonance imaging (MRI) is prone to intra and inter-observer variability. The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value. An IRB approved study was performed. Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were randomly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014. Blinded to the qualitative data, whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an in-house, proprietary segmentation algorithm which combines the region-based active contours and a level set approach. FGT (%) was calculated by: [segmented volume of FGT (mm(3))/(segmented volume of whole breast (mm(3))] ×100. Statistical correlation analysis was performed between quantified FGT (%) on MRI and qualitative assessments of mammographic breast density and MRI FGT. There was a significant positive correlation between quantitative MRI FGT assessment and qualitative MRI FGT (r=0.809, n=60, P<0.001) and mammographic density assessment (r=0.805, n=60, P<0.001). There was a significant correlation between qualitative MRI FGT assessment and mammographic density assessment (r=0.725, n=60, P<0.001). The four qualitative assessment categories of FGT correlated with the calculated mean quantitative FGT (%) of 4.61% (95% CI, 0-12.3%), 8.74% (7.3-10.2%), 18.1% (15.1-21.1%), 37.4% (29.5-45.3%). Quantitative measures of FGT (%) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessments. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated standardized measurements with limited intra or inter-observer variability.

Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Kano, Takuya; Koyasu, Hiromi; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Matsuo, Masayuki; Fujita, Hiroshi

2017-03-01

This paper describes a novel approach for the automatic assessment of breast density in non-contrast three-dimensional computed tomography (3D CT) images. The proposed approach trains and uses a deep convolutional neural network (CNN) from scratch to classify breast tissue density directly from CT images without segmenting the anatomical structures, which creates a bottleneck in conventional approaches. Our scheme determines breast density in a 3D breast region by decomposing the 3D region into several radial 2D-sections from the nipple, and measuring the distribution of breast tissue densities on each 2D section from different orientations. The whole scheme is designed as a compact network without the need for post-processing and provides high robustness and computational efficiency in clinical settings. We applied this scheme to a dataset of 463 non-contrast CT scans obtained from 30- to 45-year-old-women in Japan. The density of breast tissue in each CT scan was assigned to one of four categories (glandular tissue within the breast <25%, 25%-50%, 50%-75%, and >75%) by a radiologist as ground truth. We used 405 CT scans for training a deep CNN and the remaining 58 CT scans for testing the performance. The experimental results demonstrated that the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples. These results demonstrate the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images.

The variation in breast density and its relationship to delayed wound healing: a prospective study of 40 reduction mammoplasties.

PubMed

Baldwin, C J; Kelly, E J; Batchelor, A G

2010-04-01

The proportions of glandular and adipose tissue within the breast vary. This study records the variation in density of breast tissue excised at 40 consecutive bilateral breast reductions. Age, body mass index (BMI), breast size and wound healing problems were related to breast density. The removed breast tissue was weighed and volume determined by water displacement. Delayed wound healing was defined as any breast unhealed after 2 weeks. The density of excised tissue varied between 0.8 and 1.2g/cm(3). There was no correlation between age or BMI and breast density. Delayed wound healing occurred in 32% of patients. There was no correlation between delayed wound healing and breast density. However, there was a direct relationship between increasing BMI and delayed wound healing. In this study, breast density varied by up to 50%. The density of breast tissue cannot be predicted by age, BMI or breast size. There is no relationship between delayed wound healing and breast density. Copyright 2009. Published by Elsevier Ltd.

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

NASA Astrophysics Data System (ADS)

Ahn, Chul Kyun; Heo, Changyong; Jin, Heongmin; Kim, Jong Hyo

2017-03-01

Mammographic breast density is a well-established marker for breast cancer risk. However, accurate measurement of dense tissue is a difficult task due to faint contrast and significant variations in background fatty tissue. This study presents a novel method for automated mammographic density estimation based on Convolutional Neural Network (CNN). A total of 397 full-field digital mammograms were selected from Seoul National University Hospital. Among them, 297 mammograms were randomly selected as a training set and the rest 100 mammograms were used for a test set. We designed a CNN architecture suitable to learn the imaging characteristic from a multitudes of sub-images and classify them into dense and fatty tissues. To train the CNN, not only local statistics but also global statistics extracted from an image set were used. The image set was composed of original mammogram and eigen-image which was able to capture the X-ray characteristics in despite of the fact that CNN is well known to effectively extract features on original image. The 100 test images which was not used in training the CNN was used to validate the performance. The correlation coefficient between the breast estimates by the CNN and those by the expert's manual measurement was 0.96. Our study demonstrated the feasibility of incorporating the deep learning technology into radiology practice, especially for breast density estimation. The proposed method has a potential to be used as an automated and quantitative assessment tool for mammographic breast density in routine practice.

Breast Density Awareness and Knowledge, and Intentions for Breast Cancer Screening in a Diverse Sample of Women Age Eligible for Mammography.

PubMed

Santiago-Rivas, Marimer; Benjamin, Shayna; Andrews, Janna Z; Jandorf, Lina

2017-08-14

The objectives of this study were to assess breast density knowledge and breast density awareness, and to identify information associated with intention to complete routine and supplemental screening for breast cancer in a diverse sample of women age eligible for mammography. We quantitatively (self-report) assessed breast density awareness and knowledge (N = 264) in black (47.7%), Latina (35.2%), and white (17%) women recruited online and in the community. Most participants reported having heard about breast density (69.2%); less than one third knew their own breast density status (30.4%). Knowing their own breast density, believing that women should be notified of their breast density in their mammogram report, and feeling informed if being provided this information are associated with likelihood of completing mammogram. Intending mammogram completion and knowledge regarding the impact of breast density on mammogram accuracy are associated with likelihood of completing supplemental ultrasound tests of the breast. These findings help inform practitioners and policy makers about information and communication factors that influence breast cancer screening concerns and decisions. Knowing this information should prepare practitioners to better identify women who may have not been exposed to breast density messages.

Localized-atlas-based segmentation of breast MRI in a decision-making framework.

PubMed

Fooladivanda, Aida; Shokouhi, Shahriar B; Ahmadinejad, Nasrin

2017-03-01

Breast-region segmentation is an important step for density estimation and Computer-Aided Diagnosis (CAD) systems in Magnetic Resonance Imaging (MRI). Detection of breast-chest wall boundary is often a difficult task due to similarity between gray-level values of fibroglandular tissue and pectoral muscle. This paper proposes a robust breast-region segmentation method which is applicable for both complex cases with fibroglandular tissue connected to the pectoral muscle, and simple cases with high contrast boundaries. We present a decision-making framework based on geometric features and support vector machine (SVM) to classify breasts in two main groups, complex and simple. For complex cases, breast segmentation is done using a combination of intensity-based and atlas-based techniques; however, only intensity-based operation is employed for simple cases. A novel atlas-based method, that is called localized-atlas, accomplishes the processes of atlas construction and registration based on the region of interest (ROI). Atlas-based segmentation is performed by relying on the chest wall template. Our approach is validated using a dataset of 210 cases. Based on similarity between automatic and manual segmentation results, the proposed method achieves Dice similarity coefficient, Jaccard coefficient, total overlap, false negative, and false positive values of 96.3, 92.9, 97.4, 2.61 and 4.77%, respectively. The localization error of the breast-chest wall boundary is 1.97 mm, in terms of averaged deviation distance. The achieved results prove that the suggested framework performs the breast segmentation with negligible errors and efficient computational time for different breasts from the viewpoints of size, shape, and density pattern.

Robust estimation of mammographic breast density: a patient-based approach

NASA Astrophysics Data System (ADS)

Heese, Harald S.; Erhard, Klaus; Gooßen, Andre; Bulow, Thomas

2012-02-01

Breast density has become an established risk indicator for developing breast cancer. Current clinical practice reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density). We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique, left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate. In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided plausibility correction is finally employed for tissue separation. The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86) to radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).

Radiomic modeling of BI-RADS density categories

NASA Astrophysics Data System (ADS)

Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Hadjiiski, Lubomir

2017-03-01

Screening mammography is the most effective and low-cost method to date for early cancer detection. Mammographic breast density has been shown to be highly correlated with breast cancer risk. We are developing a radiomic model for BI-RADS density categorization on digital mammography (FFDM) with a supervised machine learning approach. With IRB approval, we retrospectively collected 478 FFDMs from 478 women. As a gold standard, breast density was assessed by an MQSA radiologist based on BI-RADS categories. The raw FFDMs were used for computerized density assessment. The raw FFDM first underwent log-transform to approximate the x-ray sensitometric response, followed by multiscale processing to enhance the fibroglandular densities and parenchymal patterns. Three ROIs were automatically identified based on the keypoint distribution, where the keypoints were obtained as the extrema in the image Gaussian scale-space. A total of 73 features, including intensity and texture features that describe the density and the parenchymal pattern, were extracted from each breast. Our BI-RADS density estimator was constructed by using a random forest classifier. We used a 10-fold cross validation resampling approach to estimate the errors. With the random forest classifier, computerized density categories for 412 of the 478 cases agree with radiologist's assessment (weighted kappa = 0.93). The machine learning method with radiomic features as predictors demonstrated a high accuracy in classifying FFDMs into BI-RADS density categories. Further work is underway to improve our system performance as well as to perform an independent testing using a large unseen FFDM set.

Associations of Breast Density With Demographic, Reproductive, and Lifestyle Factors in a Developing Southeast Asian Population.

PubMed

Dung Yun Trieu, Phuong; Mello-Thoms, Claudia; Peat, Jennifer K; Doan Do, Thuan; Brennan, Patrick C

2017-07-01

The aim of this study was to investigate how breast density interacted with demographic, reproductive, and lifestyle features among Vietnamese women. Mammographic density and established risk factors for breast cancer were collected from 1651 women (345 cancer cases and 1306 normal cases) in Vietnam. The association of breast density categories with potential risk factors was investigated using Spearman's test for continuous variables and χ 2 tests for categorical variables. Independent factors associated with high breast density and breast cancer in specific density groupings were assessed using logistic regression. Results showed that high breast density was significantly associated with young age, low body mass index, low number of children, early age at having the last child, premenopausal status, and increased vegetable consumption. Reproductive factors were key agents associated with breast cancer for women with high breast density, which was not so evident for women with low breast density.

Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study.

PubMed

Ding, Huanjun; Molloi, Sabee

2012-08-07

A simple and accurate measurement of breast density is crucial for the understanding of its impact in breast cancer risk models. The feasibility to quantify volumetric breast density with a photon-counting spectral mammography system has been investigated using both computer simulations and physical phantom studies. A computer simulation model involved polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector has been evaluated for breast density quantification. The figure-of-merit (FOM), which was defined as the signal-to-noise ratio of the dual energy image with respect to the square root of mean glandular dose, was chosen to optimize the imaging protocols, in terms of tube voltage and splitting energy. A scanning multi-slit photon-counting spectral mammography system has been employed in the experimental study to quantitatively measure breast density using dual energy decomposition with glandular and adipose equivalent phantoms of uniform thickness. Four different phantom studies were designed to evaluate the accuracy of the technique, each of which addressed one specific variable in the phantom configurations, including thickness, density, area and shape. In addition to the standard calibration fitting function used for dual energy decomposition, a modified fitting function has been proposed, which brought the tube voltages used in the imaging tasks as the third variable in dual energy decomposition. For an average sized 4.5 cm thick breast, the FOM was maximized with a tube voltage of 46 kVp and a splitting energy of 24 keV. To be consistent with the tube voltage used in current clinical screening exam (∼32 kVp), the optimal splitting energy was proposed to be 22 keV, which offered a FOM greater than 90% of the optimal value. In the experimental investigation, the root-mean-square (RMS) error in breast density quantification for all four phantom studies was estimated to be approximately 1.54% using standard calibration function. The results from the modified fitting function, which integrated the tube voltage as a variable in the calibration, indicated a RMS error of approximately 1.35% for all four studies. The results of the current study suggest that photon-counting spectral mammography systems may potentially be implemented for an accurate quantification of volumetric breast density, with an RMS error of less than 2%, using the proposed dual energy imaging technique.

Comparison of Breast Density Between Synthesized Versus Standard Digital Mammography.

PubMed

Haider, Irfanullah; Morgan, Matthew; McGow, Anna; Stein, Matthew; Rezvani, Maryam; Freer, Phoebe; Hu, Nan; Fajardo, Laurie; Winkler, Nicole

2018-06-12

To evaluate perceptual difference in breast density classification using synthesized mammography (SM) compared with standard or full-field digital mammography (FFDM) for screening. This institutional review board-approved, retrospective, multireader study evaluated breast density on 200 patients who underwent baseline screening mammogram during which both SM and FFDM were obtained contemporaneously from June 1, 2016, through November 30, 2016. Qualitative breast density was independently assigned by seven readers initially evaluating FFDM alone. Then, in a separate session, these same readers assigned breast density using synthetic views alone on the same 200 patients. The readers were again blinded to each other's assignment. Qualitative density assessment was based on BI-RADS fifth edition. Interreader agreement was evaluated with κ statistic using 95% confidence intervals. Testing for homogeneity in paired proportions was performed using McNemar's test with a level of significance of .05. For patients across the SM and standard 2-D data set, diagnostic testing with McNemar's test with P = 0.32 demonstrates that the minimal density transitions across FFDM and SM are not statistically significant density shifts. Taking clinical significance into account, only 8 of 200 (4%) patients had clinically significant transition (dense versus not dense). There was substantial interreader agreement with overall κ in FFDM of 0.71 (minimum 0.53, maximum 0.81) and overall SM κ average of 0.63 (minimum 0.56, maximum 0.87). Overall subjective breast density assignment by radiologists on SM is similar to density assignment on standard 2-D mammogram. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Implementation of several mathematical algorithms to breast tissue density classification

NASA Astrophysics Data System (ADS)

Quintana, C.; Redondo, M.; Tirao, G.

2014-02-01

The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.

Quantitative breast density analysis using tomosynthesis and comparison with MRI and digital mammography.

PubMed

Moon, Woo Kyung; Chang, Jie-Fan; Lo, Chung-Ming; Chang, Jung Min; Lee, Su Hyun; Shin, Sung Ui; Huang, Chiun-Sheng; Chang, Ruey-Feng

2018-02-01

Breast density at mammography has been used as markers of breast cancer risk. However, newly introduced tomosynthesis and computer-aided quantitative method could provide more reliable breast density evaluation. In the experiment, 98 tomosynthesis image volumes were obtained from 98 women. For each case, an automatic skin removal was used and followed by a fuzzy c-mean (FCM) classifier which separated the fibroglandular tissues from other tissues in breast area. Finally, percent of breast density and breast volume were calculated and the results were compared with MRI. In addition, the percent of breast density and breast area of digital mammography calculated using the software Cumulus (University of Toronto, Toronto, ON, Canada.) were also compared with 3-D modalities. Percent of breast density and breast volume, which were computed from tomosynthesis, MRI and digital mammography were 17.37% ± 4.39% and 607.12 cm 3  ± 323.01 cm 3 , 20.3% ± 8.6% and 537.59 cm 3  ± 287.74 cm 3 , and 12.03% ± 4.08%, respectively. There were significant correlations on breast density as well as volume between tomosynthesis and MRI (R = 0.482 and R = 0.805), tomosynthesis and breast density with breast area of digital mammography (R = 0.789 and R = 0.877), and MRI and breast density with breast area of digital mammography (R = 0.482 and R = 0.857) (all P values < .001). Breast density and breast volume evaluated from tomosynthesis, MRI and breast density and breast area of digital mammographic images have significant correlations and indicate that tomosynthesis could provide useful 3-D information on breast density through proposed method. Copyright © 2017 Elsevier B.V. All rights reserved.

The combined effect of mammographic texture and density on breast cancer risk: a cohort study.

PubMed

Wanders, Johanna O P; van Gils, Carla H; Karssemeijer, Nico; Holland, Katharina; Kallenberg, Michiel; Peeters, Petra H M; Nielsen, Mads; Lillholm, Martin

2018-05-02

Texture patterns have been shown to improve breast cancer risk segregation in addition to area-based mammographic density. The additional value of texture pattern scores on top of volumetric mammographic density measures in a large screening cohort has never been studied. Volumetric mammographic density and texture pattern scores were assessed automatically for the first available digital mammography (DM) screening examination of 51,400 women (50-75 years of age) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. The texture assessment method was developed in a previous study and validated in the current study. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. All screen-detected breast cancers diagnosed at the first available digital screening examination were excluded. During a median follow-up period of 4.2 (interquartile range (IQR) 2.0-6.2) years, 301 women were diagnosed with breast cancer. The associations between texture pattern scores, volumetric breast density measures and breast cancer risk were determined using Cox proportional hazard analyses. Discriminatory performance was assessed using c-indices. The median age of the women at the time of the first available digital mammography examination was 56 years (IQR 51-63). Texture pattern scores were positively associated with breast cancer risk (hazard ratio (HR) 3.16 (95% CI 2.16-4.62) (p value for trend <0.001), for quartile (Q) 4 compared to Q1). The c-index of texture was 0.61 (95% CI 0.57-0.64). Dense volume and percentage dense volume showed positive associations with breast cancer risk (HR 1.85 (95% CI 1.32-2.59) (p value for trend <0.001) and HR 2.17 (95% CI 1.51-3.12) (p value for trend <0.001), respectively, for Q4 compared to Q1). When adding texture measures to models with dense volume or percentage dense volume, c-indices increased from 0.56 (95% CI 0.53-0.59) to 0.62 (95% CI 0.58-0.65) (p < 0.001) and from 0.58 (95% CI 0.54-0.61) to 0.60 (95% CI 0.57-0.63) (p = 0.054), respectively. Deep-learning-based texture pattern scores, measured automatically on digital mammograms, are associated with breast cancer risk, independently of volumetric mammographic density, and augment the capacity to discriminate between future breast cancer and non-breast cancer cases.

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.

Dense Breasts: Answers to Commonly Asked Questions

Cancer.gov

Mammograms detect breast density, breast cancer, and breast changes that are not cancer (benign breast changes). Breast density describes the amount of glandular and fibrous tissue, as compared to fatty tissue. Learn what factors are associated with breast density and about other risk factors for breast cancer.

Prevalence of Mammographically Dense Breasts in the United States

PubMed Central

Gangnon, Ronald E.; Burt, Veronica; Trentham-Dietz, Amy; Hampton, John M.; Wellman, Robert D.; Kerlikowske, Karla; Miglioretti, Diana L.

2014-01-01

Background National legislation is under consideration that would require women with mammographically dense breasts to be informed of their breast density and encouraged to discuss supplemental breast cancer screening with their health care providers. The number of US women potentially affected by this legislation is unknown. Methods We determined the mammographic breast density distribution by age and body mass index (BMI) using data from 1518 599 mammograms conducted from 2007 through 2010 at mammography facilities in the Breast Cancer Surveillance Consortium (BCSC). We applied these breast density distributions to age- and BMI-specific counts of the US female population derived from the 2010 US Census and the National Health and Nutrition Examination Survey (NHANES) to estimate the number of US women with dense breasts. Results Overall, 43.3% (95% confidence interval [CI] = 43.1% to 43.4%) of women 40 to 74 years of age had heterogeneously or extremely dense breasts, and this proportion was inversely associated with age and BMI. Based on the age and BMI distribution of US women, we estimated that 27.6 million women (95% CI = 27.5 to 27.7 million) aged 40 to 74 years in the United States have heterogeneously or extremely dense breasts. Women aged 40 to 49 years (N = 12.3 million) accounted for 44.3% of this group. Conclusion The prevalence of dense breasts among US women of common breast cancer screening ages exceeds 25 million. Policymakers and healthcare providers should consider this large prevalence when debating breast density notification legislation and designing strategies to ensure that women who are notified have opportunities to evaluate breast cancer risk and discuss and pursue supplemental screening options if deemed appropriate. PMID:25217577

Occupation and mammographic density: A population-based study (DDM-Occup).

PubMed

García-Pérez, Javier; Pollán, Marina; Pérez-Gómez, Beatriz; González-Sánchez, Mario; Cortés Barragán, Rosa Ana; Maqueda Blasco, Jerónimo; González-Galarzo, María Carmen; Alba, Miguel Ángel; van der Haar, Rudolf; Casas, Silvia; Vicente, Cándida; Medina, Pilar; Ederra, María; Santamariña, Carmen; Moreno, María Pilar; Casanova, Francisco; Pedraz-Pingarrón, Carmen; Moreo, Pilar; Ascunce, Nieves; García, Montse; Salas-Trejo, Dolores; Sánchez-Contador, Carmen; Llobet, Rafael; Lope, Virginia

2017-11-01

High mammographic density is one of the main risk factors for breast cancer. Although several occupations have been associated with breast cancer, there are no previous occupational studies exploring the association with mammographic density. Our objective was to identify occupations associated with high mammographic density in Spanish female workers. We conducted a population-based cross-sectional study of occupational determinants of high mammographic density in Spain, based on 1476 women, aged 45-68 years, recruited from seven screening centers within the Spanish Breast Cancer Screening Program network. Reproductive, family, personal, and occupational history data were collected. The latest occupation of each woman was collected and coded according to the 1994 National Classification of Occupations. Mammographic density was assessed from the cranio-caudal mammogram of the left breast using a semi-automated computer-assisted tool. Association between mammographic density and occupation was evaluated by using mixed linear regression models, using log-transformed percentage of mammographic density as dependent variable. Models were adjusted for age, body mass index, menopausal status, parity, smoking, alcohol intake, educational level, type of mammography, first-degree relative with breast cancer, and hormonal replacement therapy use. Screening center and professional reader were included as random effects terms. Mammographic density was higher, although non-statistically significant, among secondary school teachers (e β = 1.41; 95%CI = 0.98-2.03) and nurses (e β = 1.23; 95%CI = 0.96-1.59), whereas workers engaged in the care of people (e β = 0.81; 95%CI = 0.66-1.00) and housewives (e β = 0.87; 95%CI = 0.79-0.95) showed an inverse association with mammographic density. A positive trend for every 5 years working as secondary school teachers was also detected (p-value = 0.035). Nurses and secondary school teachers were the occupations with the highest mammographic density in our study, showing the latter a positive trend with duration of employment. Future studies are necessary to confirm if these results are due to chance or are the result of a true association whose causal hypothesis is, for the moment, unknown. Copyright © 2017 Elsevier Inc. All rights reserved.

Volumetric breast density evaluation by ultrasound tomography and magnetic resonance imaging: a preliminary comparative study

NASA Astrophysics Data System (ADS)

Myc, Lukasz; Duric, Neb; Littrup, Peter; Li, Cuiping; Ranger, Bryan; Lupinacci, Jessica; Schmidt, Steven; Rama, Olsi; Bey-Knight, Lisa

2010-03-01

Since a 1976 study by Wolfe, high breast density has gained recognition as a factor strongly correlating with an increased incidence of breast cancer. These observations have led to mammographic density being designated a "risk factor" for breast cancer. Clinically, the exclusive reliance on mammography for breast density measurement has forestalled the inclusion of breast density into statistical risk models. This exclusion has in large part been due to the ionizing radiation associated with the method. Additionally, the use of mammography as valid tool for measuring a three dimensional characteristic (breast density) has been criticized for its prima facie incongruity. These shortfalls have prompted MRI studies of breast density as an alternative three-dimensional method of assessing breast density. Although, MRI is safe and can be used to measure volumetric density, its cost has prohibited its use in screening. Here, we report that sound speed measurements using a prototype ultrasound tomography device have potential for use as surrogates for breast density measurement. Accordingly, we report a strong positive linear correlation between volume-averaged sound speed of the breast and percent glandular tissue volume as assessed by MR.

Breast density in multiethnic women presenting for screening mammography.

PubMed

Oppong, Bridget A; Dash, Chiranjeev; O'Neill, Suzanne; Li, Yinan; Makambi, Kepher; Pien, Edward; Makariou, Erini; Coleman, Tesha; Adams-Campbell, Lucile L

2018-05-01

Data on ethnic variations in breast density are limited and often not inclusive of underrepresented minorities. As breast density is associated with elevated breast cancer risk, investigating racial and ethnic difference may elucidate the observed differences in breast cancer risk among different populations. We reviewed breast density from initial screening of women from the Capital Breast Care Center and Georgetown University Hospital from 2010 to 2014. Patient demographics including race, age at screening, education, menopausal status, and body mass index were abstracted. We recorded the BI-RADS density categories: (1) "fatty," (2) "scattered fibroglandular densities," (3) "heterogeneously dense," and (4) "extremely dense." Multivariable unconditional logistic regression was used to identify predictors of breast density. Density categorization was recorded for 2146 women over the 5-year period, comprising Blacks (n = 940), Hispanics (n = 893), and Whites (n = 314). Analysis of subject characteristics by breast density showed that high category is observed in younger, Hispanic, nulliparous, premenopausal, and nonobese women (t-test or chi-square test, P-values <.0001). Obese women are 70% less likely to have high density. Being Hispanic, premenopausal, and nonobese were predictive of high density on logistic regression. In this analysis of density distribution in a diverse sample, Hispanic women have the highest breast density, followed by Blacks and Whites. Unique in our findings is women who identify as Hispanic have the highest breast density and lower rates of obesity. Further investigation of the impact of obesity on breast density, especially in the understudied Hispanic group is needed. © 2017 Wiley Periodicals, Inc.

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer.

PubMed

Wang, Jeff; Kato, Fumi; Yamashita, Hiroko; Baba, Motoi; Cui, Yi; Li, Ruijiang; Oyama-Manabe, Noriko; Shirato, Hiroki

2017-04-01

Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

Minimal impact of adjuvant exemestane or tamoxifen treatment on mammographic breast density in postmenopausal breast cancer patients: a Dutch TEAM trial analysis.

PubMed

van Nes, Johanna G H; Beex, Louk V A M; Seynaeve, Caroline; Putter, Hein; Sramek, Alexandr; Lardenoije, Susanne; Duijm-de Carpentier, Marjolijn; Van Rongen, Inge; Nortier, Johan W R; Zonderland, Harmien M; van de Velde, Cornelis J H

2015-03-01

Mammographic breast density is one of the strongest independent risk factors for developing breast cancer. We examined the effect of exemestane and tamoxifen on breast density in Dutch postmenopausal early breast cancer patients participating in the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial. Analogue mammograms of selected TEAM participants before start, and after one and two (and if available after three) years of adjuvant endocrine therapy were collected centrally and reviewed. Study endpoints were change in breast density over time, and correlations between breast density and locoregional recurrence (LRR), distance recurrence (DR), and contralateral breast cancer (CBC). Mammograms of 378 patients (181 tamoxifen, 197 exemestane) were included in the current per protocol analyses. Baseline breast density was low (breast density score<50% in 75% of patients) and not different between patients randomised to exemestane or tamoxifen (coefficient 0.16, standard error 0.17). Breast density did not change during treatment in exemestane (p=0.25) or tamoxifen users (p=0.59). No relation was observed between breast density and the occurrence of a LRR [hazards ratio (HR) 0.87, 95% CI 0.45-1.68, p=0.67], a DR (HR 1.02, 95% CI 0.77-1.35, p=0.90), or CBC (HR 1.31, 95% CI 0.63-2.72, p=0.48). The in general low breast density score in early postmenopausal breast cancer patients did not substantially change over time, and this pattern was not different between tamoxifen and exemestane users. Breast density was not a predictive marker for efficacy of adjuvant endocrine therapy.

Impact of the California breast density law on primary care physicians.

PubMed

Khong, Kathleen A; Hargreaves, Jonathan; Aminololama-Shakeri, Shadi; Lindfors, Karen K

2015-03-01

To investigate primary physician awareness of the California Breast Density Notification Law and its impact on primary care practice. An online survey was distributed to 174 physicians within a single primary care network system 10 months after California's breast density notification law took effect. The survey assessed physicians' awareness of the law, perceived changes in patient levels of concern about breast density, and physician comfort levels in handling breast density management issues. The survey was completed by 77 physicians (45%). Roughly half of those surveyed (49%) reported no knowledge of the breast density notification legislation. Only 32% of respondents noted an increase in patient levels of concern about breast density compared to prior years. The majority were only "somewhat comfortable" (55%) or "not comfortable" (12%) with breast density questions, and almost one-third (32%) had referred patients to a breast health clinic for these discussions. A total of 75% of those surveyed would be interested in more specific education on the subject. Awareness among primary care clinicians of the California Breast Density Notification Law is low, and many do not feel comfortable answering breast density-related patient questions. Breast imagers and institutions may need to devote additional time and resources to primary physician education in order for density notification laws to have significant impact on patient care. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

A comparison of five methods of measuring mammographic density: a case-control study.

PubMed

Astley, Susan M; Harkness, Elaine F; Sergeant, Jamie C; Warwick, Jane; Stavrinos, Paula; Warren, Ruth; Wilson, Mary; Beetles, Ursula; Gadde, Soujanya; Lim, Yit; Jain, Anil; Bundred, Sara; Barr, Nicola; Reece, Valerie; Brentnall, Adam R; Cuzick, Jack; Howell, Tony; Evans, D Gareth

2018-02-05

High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. Women participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05). Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.

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

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.

Benign Breast Disease, Mammographic Breast Density, and the Risk of Breast Cancer

PubMed Central

2013-01-01

Background Benign breast disease and high breast density are prevalent, strong risk factors for breast cancer. Women with both risk factors may be at very high risk. Methods We included 42818 women participating in the Breast Cancer Surveillance Consortium who had no prior diagnosis of breast cancer and had undergone at least one benign breast biopsy and mammogram; 1359 women developed incident breast cancer in 6.1 years of follow-up (78.1% invasive, 21.9% ductal carcinoma in situ). We calculated hazard ratios (HRs) using Cox regression analysis. The referent group was women with nonproliferative changes and average density. All P values are two-sided. Results Benign breast disease and breast density were independently associated with breast cancer. The combination of atypical hyperplasia and very high density was uncommon (0.6% of biopsies) but was associated with the highest risk for breast cancer (HR = 5.34; 95% confidence interval [CI] = 3.52 to 8.09, P < .001). Proliferative disease without atypia (25.6% of biopsies) was associated with elevated risk that varied little across levels of density: average (HR = 1.37; 95% CI = 1.11 to 1.69, P = .003), high (HR = 2.02; 95% CI = 1.68 to 2.44, P < .001), or very high (HR = 2.05; 95% CI = 1.54 to 2.72, P < .001). Low breast density (4.5% of biopsies) was associated with low risk (HRs <1) for all benign pathology diagnoses. Conclusions Women with high breast density and proliferative benign breast disease are at very high risk for future breast cancer. Women with low breast density are at low risk, regardless of their benign pathologic diagnosis. PMID:23744877

Benign breast disease, mammographic breast density, and the risk of breast cancer.

PubMed

Tice, Jeffrey A; O'Meara, Ellen S; Weaver, Donald L; Vachon, Celine; Ballard-Barbash, Rachel; Kerlikowske, Karla

2013-07-17

Benign breast disease and high breast density are prevalent, strong risk factors for breast cancer. Women with both risk factors may be at very high risk. We included 42818 women participating in the Breast Cancer Surveillance Consortium who had no prior diagnosis of breast cancer and had undergone at least one benign breast biopsy and mammogram; 1359 women developed incident breast cancer in 6.1 years of follow-up (78.1% invasive, 21.9% ductal carcinoma in situ). We calculated hazard ratios (HRs) using Cox regression analysis. The referent group was women with nonproliferative changes and average density. All P values are two-sided. Benign breast disease and breast density were independently associated with breast cancer. The combination of atypical hyperplasia and very high density was uncommon (0.6% of biopsies) but was associated with the highest risk for breast cancer (HR = 5.34; 95% confidence interval [CI] = 3.52 to 8.09, P < .001). Proliferative disease without atypia (25.6% of biopsies) was associated with elevated risk that varied little across levels of density: average (HR = 1.37; 95% CI = 1.11 to 1.69, P = .003), high (HR = 2.02; 95% CI = 1.68 to 2.44, P < .001), or very high (HR = 2.05; 95% CI = 1.54 to 2.72, P < .001). Low breast density (4.5% of biopsies) was associated with low risk (HRs <1) for all benign pathology diagnoses. Women with high breast density and proliferative benign breast disease are at very high risk for future breast cancer. Women with low breast density are at low risk, regardless of their benign pathologic diagnosis.

Role of multidetector computed tomography in evaluating incidentally detected breast lesions.

PubMed

Moschetta, Marco; Scardapane, Arnaldo; Lorusso, Valentina; Rella, Leonarda; Telegrafo, Michele; Serio, Gabriella; Angelelli, Giuseppe; Ianora, Amato Antonio Stabile

2015-01-01

Computed tomography (CT) does not represent the primary method for the evaluation of breast lesions; however, it can detect breast abnormalities, even when performed for other reasons related to thoracic structures. The aim of this study is to evaluate the potential benefits of 320-row multidetector CT (MDCT) in evaluating and differentiating incidentally detected breast lesions by using vessel probe and 3D analysis software with net enhancement value. Sixty-two breast lesions in 46 patients who underwent 320-row chest CT examination were retrospectively evaluated. CT scans were assessed searching for the presence, location, number, morphological features, and density of breast nodules. Net enhancement was calculated by subtracting precontrast density from the density obtained by postcontrast values. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of CT were calculated for morphological features and net enhancement. Thirty of 62 lesions were found to be malignant at histological examination and 32 were found to be benign. When morphological features were considered, the sensitivity, specificity, accuracy, PPV, and NPV of CT were 87%, 100%, 88%, 100%, and 50%, respectively. Based on net enhancement, CT reached a sensitivity, specificity, accuracy, PPV, and NPV of 100%, 94%, 97%, 94%, and 100%, respectively. MDCT allows to recognize and characterize breast lesions based on morphological features. Net enhancement can be proposed as an additional accurate feature of CT.

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 averted if all women with heterogeneously or extremely dense breasts shifted to scattered fibroglandular breast density. Among postmenopausal women, 22.8% (95% CI, 18.3%-27.3%) of breast cancers could potentially be averted if all overweight and obese women attained a body mass index of less than 25. Most women with breast cancer have at least 1 breast cancer risk factor routinely documented at the time of mammography, and more than half of premenopausal and postmenopausal breast cancers are explained by these factors. These easily assessed risk factors should be incorporated into risk prediction models to stratify breast cancer risk and promote risk-based screening and targeted prevention efforts.

Correlation between quantified breast densities from digital mammography and 18F-FDG PET uptake.

PubMed

Lakhani, Paras; Maidment, Andrew D A; Weinstein, Susan P; Kung, Justin W; Alavi, Abass

2009-01-01

To correlate breast density quantified from digital mammograms with mean and maximum standardized uptake values (SUVs) from positron emission tomography (PET). This was a prospective study that included 56 women with a history of suspicion of breast cancer (mean age 49.2 +/- 9.3 years), who underwent 18F-fluoro-2-deoxyglucose (FDG)-PET imaging of their breasts as well as digital mammography. A computer thresholding algorithm was applied to the contralateral nonmalignant breasts to quantitatively estimate the breast density on digital mammograms. The breasts were also classified into one of four Breast Imaging Reporting and Data System categories for density. Comparisons between SUV and breast density were made using linear regression and the Student's t-test. Linear regression of mean SUV versus average breast density showed a positive relationship with a Pearson's correlation coefficient of R(2) = 0.83. The quantified breast densities and mean SUVs were significantly greater for mammographically dense than nondense breasts (p < 0.0001 for both). The average quantified densities and mean SUVs of the breasts were significantly greater for premenopausal than postmenopausal patients (p < 0.05). 8/51 (16%) of the patients had maximum SUVs that equaled 1.6 or greater. There is a positive linear correlation between quantified breast density on digital mammography and FDG uptake on PET. Menopausal status affects the metabolic activity of normal breast tissue, resulting in higher SUVs in pre- versus postmenopausal patients.

Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: A feasibility study

PubMed Central

Ding, Huanjun; Molloi, Sabee

2012-01-01

Purpose A simple and accurate measurement of breast density is crucial for the understanding of its impact in breast cancer risk models. The feasibility to quantify volumetric breast density with a photon-counting spectral mammography system has been investigated using both computer simulations and physical phantom studies. Methods A computer simulation model involved polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector has been evaluated for breast density quantification. The figure-of-merit (FOM), which was defined as the signal-to-noise ratio (SNR) of the dual energy image with respect to the square root of mean glandular dose (MGD), was chosen to optimize the imaging protocols, in terms of tube voltage and splitting energy. A scanning multi-slit photon-counting spectral mammography system has been employed in the experimental study to quantitatively measure breast density using dual energy decomposition with glandular and adipose equivalent phantoms of uniform thickness. Four different phantom studies were designed to evaluate the accuracy of the technique, each of which addressed one specific variable in the phantom configurations, including thickness, density, area and shape. In addition to the standard calibration fitting function used for dual energy decomposition, a modified fitting function has been proposed, which brought the tube voltages used in the imaging tasks as the third variable in dual energy decomposition. Results For an average sized breast of 4.5 cm thick, the FOM was maximized with a tube voltage of 46kVp and a splitting energy of 24 keV. To be consistent with the tube voltage used in current clinical screening exam (~ 32 kVp), the optimal splitting energy was proposed to be 22 keV, which offered a FOM greater than 90% of the optimal value. In the experimental investigation, the root-mean-square (RMS) error in breast density quantification for all four phantom studies was estimated to be approximately 1.54% using standard calibration function. The results from the modified fitting function, which integrated the tube voltage as a variable in the calibration, indicated a RMS error of approximately 1.35% for all four studies. Conclusions The results of the current study suggest that photon-counting spectral mammography systems may potentially be implemented for an accurate quantification of volumetric breast density, with an RMS error of less than 2%, using the proposed dual energy imaging technique. PMID:22771941

SU-E-I-59: Investigation of the Usefulness of a Standard Deviation and Mammary Gland Density as Indexes for Mammogram Classification.

PubMed

Takarabe, S; Yabuuchi, H; Morishita, J

2012-06-01

To investigate the usefulness of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high- density mammary glands region to a whole mammary glands region as features for classification of mammograms into four categories based on the ACR BI-RADS breast composition. We used 36 digital mediolateral oblique view mammograms (18 patients) approved by our IRB. These images were classified into the four categories of breast compositions by an experienced breast radiologist and the results of the classification were regarded as a gold standard. First, a whole mammary region in a breast was divided into two regions such as a high-density mammary glands region and a low/iso-density mammary glands region by using a threshold value that was obtained from the pixel values corresponding to a pectoral muscle region. Then the percentage of a high-density mammary glands region to a whole mammary glands region was calculated. In addition, as a new method, the standard deviation of pixel values in a whole mammary glands region was calculated as an index based on the intermingling of mammary glands and fats. Finally, all mammograms were classified by using the combination of the percentage of a high-density mammary glands region and the standard deviation of each image. The agreement rates of the classification between our proposed method and gold standard was 86% (31/36). This result signified that our method has the potential to classify mammograms. The combination of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high-density mammary glands region to a whole mammary glands region was available as features to classify mammograms based on the ACR BI- RADS breast composition. © 2012 American Association of Physicists in Medicine.

Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer.

PubMed

Duffy, Stephen W; Morrish, Oliver W E; Allgood, Prue C; Black, Richard; Gillan, Maureen G C; Willsher, Paula; Cooke, Julie; Duncan, Karen A; Michell, Michael J; Dobson, Hilary M; Maroni, Roberta; Lim, Yit Y; Purushothaman, Hema N; Suaris, Tamara; Astley, Susan M; Young, Kenneth C; Tucker, Lorraine; Gilbert, Fiona J

2018-01-01

Mammographic density has been shown to be a strong independent predictor of breast cancer and a causative factor in reducing the sensitivity of mammography. There remain questions as to the use of mammographic density information in the context of screening and risk management, and of the association with cancer in populations known to be at increased risk of breast cancer. To assess the association of breast density with presence of cancer by measuring mammographic density visually as a percentage, and with two automated volumetric methods, Quantra™ and VolparaDensity™. The TOMosynthesis with digital MammographY (TOMMY) study of digital breast tomosynthesis in the Breast Screening Programme of the National Health Service (NHS) of the United Kingdom (UK) included 6020 breast screening assessment cases (of whom 1158 had breast cancer) and 1040 screened women with a family history of breast cancer (of whom two had breast cancer). We assessed the association of each measure with breast cancer risk in these populations at enhanced risk, using logistic regression adjusted for age and total breast volume as a surrogate for body mass index (BMI). All density measures showed a positive association with presence of cancer and all declined with age. The strongest effect was seen with Volpara absolute density, with a significant 3% (95% CI 1-5%) increase in risk per 10 cm 3 of dense tissue. The effect of Volpara volumetric density on risk was stronger for large and grade 3 tumours. Automated absolute breast density is a predictor of breast cancer risk in populations at enhanced risk due to either positive mammographic findings or family history. In the screening context, density could be a trigger for more intensive imaging. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Postmortem validation of breast density using dual-energy mammography

PubMed Central

Molloi, Sabee; Ducote, Justin L.; Ding, Huanjun; Feig, Stephen A.

2014-01-01

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer. PMID:25086548

Postmortem validation of breast density using dual-energy mammography

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

Molloi, Sabee, E-mail: symolloi@uci.edu; Ducote, Justin L.; Ding, Huanjun

2014-08-15

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decompositionmore » was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer.« less

Screen-detected versus interval cancers: Effect of imaging modality and breast density in the Flemish Breast Cancer Screening Programme.

PubMed

Timmermans, Lore; Bleyen, Luc; Bacher, Klaus; Van Herck, Koen; Lemmens, Kim; Van Ongeval, Chantal; Van Steen, Andre; Martens, Patrick; De Brabander, Isabel; Goossens, Mathieu; Thierens, Hubert

2017-09-01

To investigate if direct radiography (DR) performs better than screen-film mammography (SF) and computed radiography (CR) in dense breasts in a decentralized organised Breast Cancer Screening Programme. To this end, screen-detected versus interval cancers were studied in different BI-RADS density classes for these imaging modalities. The study cohort consisted of 351,532 women who participated in the Flemish Breast Cancer Screening Programme in 2009 and 2010. Information on screen-detected and interval cancers, breast density scores of radiologist second readers, and imaging modality was obtained by linkage of the databases of the Centre of Cancer Detection and the Belgian Cancer Registry. Overall, 67% of occurring breast cancers are screen detected and 33% are interval cancers, with DR performing better than SF and CR. The interval cancer rate increases gradually with breast density, regardless of modality. In the high-density class, the interval cancer rate exceeds the cancer detection rate for SF and CR, but not for DR. DR is superior to SF and CR with respect to cancer detection rates for high-density breasts. To reduce the high interval cancer rate in dense breasts, use of an additional imaging technique in screening can be taken into consideration. • Interval cancer rate increases gradually with breast density, regardless of modality. • Cancer detection rate in high-density breasts is superior in DR. • IC rate exceeds CDR for SF and CR in high-density breasts. • DR performs better in high-density breasts for third readings and false-positives.

Comparison of subjective and fully automated methods for measuring mammographic density.

PubMed

Moshina, Nataliia; Roman, Marta; Sebuødegård, Sofie; Waade, Gunvor G; Ursin, Giske; Hofvind, Solveig

2018-02-01

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (k w ). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was k w  = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Comparison of breast density measurements made using ultrasound tomography and mammography

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Krycia, Mark; Sherman, Mark E.; Boyd, Norman; Gierach, Gretchen L.

2015-03-01

Women with elevated mammographic percent density, defined as the ratio of fibroglandular tissue area to total breast area on a mammogram are at an increased risk of developing breast cancer. Ultrasound tomography (UST) is an imaging modality that can create tomographic sound speed images of a patient's breast, which can then be used to measure breast density. These sound speed images are useful because physical tissue density is directly proportional to sound speed. The work presented here updates previous results that compared mammographic breast density measurements with UST breast density measurements within an ongoing study. The current analysis has been expanded to include 158 women with negative digital mammographic screens who then underwent a breast UST scan. Breast density was measured for both imaging modalities and preliminary analysis demonstrated strong and positive correlations (Spearman correlation coefficient rs = 0.703). Additional mammographic and UST related imaging characteristics were also analyzed and used to compare the behavior of both imaging modalities. Results suggest that UST can be used among women with negative mammographic screens as a quantitative marker of breast density that may avert shortcomings of mammography.

Comparison of Visual Assessment of Breast Density in BI-RADS 4th and 5th Editions With Automated Volumetric Measurement.

PubMed

Youk, Ji Hyun; Kim, So Jung; Son, Eun Ju; Gweon, Hye Mi; Kim, Jeong-Ah

2017-09-01

The purpose of this study was to compare visual assessments of mammographic breast density by radiologists using BI-RADS 4th and 5th editions in correlation with automated volumetric breast density measurements. A total of 337 consecutive full-field digital mammographic examinations with standard views were retrospectively assessed by two radiologists for mammographic breast density according to BI-RADS 4th and 5th editions. Fully automated measurement of the volume of fibroglandular tissue and total breast and percentage breast density was performed with a commercially available software program. Interobserver and intraobserver agreement was assessed with kappa statistics. The distributions of breast density categories for both editions of BI-RADS were compared and correlated with volumetric data. Interobserver agreement on breast density category was moderate to substantial (κ = 0.58-0.63) with use of BI-RADS 4th edition and substantial (κ = 0.63-0.66) with use of the 5th edition but without significant difference between the two editions. For intraobserver agreement between the two editions, the distributions of density category were significantly different (p < 0.0001), the proportions of dense breast increased, and the proportion of fatty breast decreased with use of the 5th edition compared with the 4th edition (p < 0.0001). All volumetric breast density data, including percentage breast density, were significantly different among density categories (p < 0.0001) and had significant correlation with visual assessment for both editions of BI-RADS (p < 0.01). Assessment using BI-RADS 5th edition revealed a higher proportion of dense breast than assessment using BI-RADS 4th edition. Nevertheless, automated volumetric density assessment had good correlation with visual assessment for both editions of BI-RADS.

TU-CD-207-01: Characterization of Breast Tissue Composition Using Spectral Mammography

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

Ding, H; Cho, H; Kumar, N

Purpose: To investigate the feasibility of characterizing the chemical composition of breast tissue, in terms of water and lipid, by using spectral mammography in simulation and postmortem studies. Methods: Analytical simulations were performed to obtain low- and high-energy signals of breast tissue based on previously reported water, lipid, and protein contents. Dual-energy decomposition was used to characterize the simulated breast tissue into water and lipid basis materials and the measured water density was compared to the known value. In experimental studies, postmortem breasts were imaged with a spectral mammography system based on a scanning multi-slit Si strip photon-counting detector. Low-more » and high-energy images were acquired simultaneously from a single exposure by sorting the recorded photons into the corresponding energy bins. Dual-energy material decomposition of the low- and high-energy images yielded individual pixel measurements of breast tissue composition in terms of water and lipid thicknesses. After imaging, each postmortem breast was chemically decomposed into water, lipid and protein. The water density calculated from chemical analysis was used as the reference gold standard. Correlation of the water density measurements between spectral mammography and chemical analysis was analyzed using linear regression. Results: Both simulation and postmortem studies showed good linear correlation between the decomposed water thickness using spectral mammography and chemical analysis. The slope of the linear fitting function in the simulation and postmortem studies were 1.15 and 1.21, respectively. Conclusion: The results indicate that breast tissue composition, in terms of water and lipid, can be accurately measured using spectral mammography. Quantitative breast tissue composition can potentially be used to stratify patients according to their breast cancer risk.« less

Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness.

PubMed

Schousboe, John T; Kerlikowske, Karla; Loh, Andrew; Cummings, Steven R

2011-07-05

Current guidelines recommend mammography every 1 or 2 years starting at age 40 or 50 years, regardless of individual risk for breast cancer. To estimate the cost-effectiveness of mammography by age, breast density, history of breast biopsy, family history of breast cancer, and screening interval. Markov microsimulation model. Surveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and the medical literature. U.S. women aged 40 to 49, 50 to 59, 60 to 69, and 70 to 79 years with initial mammography at age 40 years and breast density of Breast Imaging Reporting and Data System (BI-RADS) categories 1 to 4. Lifetime. National health payer. Mammography annually, biennially, or every 3 to 4 years or no mammography. Costs per quality-adjusted life-year (QALY) gained and number of women screened over 10 years to prevent 1 death from breast cancer. Biennial mammography cost less than $100,000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50,000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density. Mammography is expensive if the disutility of false-positive mammography results and the costs of detecting nonprogressive and nonlethal invasive cancer are considered. Results are not applicable to carriers of BRCA1 or BRCA2 mutations. Mammography screening should be personalized on the basis of a woman's age, breast density, history of breast biopsy, family history of breast cancer, and beliefs about the potential benefit and harms of screening. Eli Lilly, Da Costa Family Foundation for Research in Breast Cancer Prevention of the California Pacific Medical Center, and Breast Cancer Surveillance Consortium.

Measurement of breast density with digital breast tomosynthesis—a systematic review

PubMed Central

McEntee, M F

2014-01-01

Digital breast tomosynthesis (DBT) has gained acceptance as an adjunct to digital mammography in screening. Now that breast density reporting is mandated in several states in the USA, it is increasingly important that the methods of breast density measurement be robust, reliable and consistent. Breast density assessment with DBT needs some consideration since quantitative methods are modelled for two-dimensional (2D) mammography. A review of methods used for breast density assessment with DBT was performed. Existing evidence shows Cumulus has better reproducibility than that of the breast imaging reporting and data system (BI-RADS®) but still suffers from subjective variability; MedDensity is limited by image noise, whilst Volpara and Quantra are robust and consistent. The reported BI-RADs inter-reader breast density agreement (k) ranged from 0.65 to 0.91, with inter-reader correlation (r) ranging from 0.70 to 0.93. The correlation (r) between BI-RADS and Cumulus ranged from 0.54–0.94, whilst that of BI-RADs and MedDensity ranged from 0.48–0.78. The reported agreement (k) between BI-RADs and Volpara is 0.953. Breast density correlation between DBT and 2D mammography ranged from 0.73 to 0.97, with agreement (k) ranging from 0.56 to 0.96. To avoid variability and provide more reliable breast density information for clinicians, automated volumetric methods are preferred. PMID:25146640

Tumor phenotype and breast density in distinct categories of interval cancer: results of population-based mammography screening in Spain

PubMed Central

2014-01-01

Introduction Interval cancers are tumors arising after a negative screening episode and before the next screening invitation. They can be classified into true interval cancers, false-negatives, minimal-sign cancers, and occult tumors based on mammographic findings in screening and diagnostic mammograms. This study aimed to describe tumor-related characteristics and the association of breast density and tumor phenotype within four interval cancer categories. Methods We included 2,245 invasive tumors (1,297 screening-detected and 948 interval cancers) diagnosed from 2000 to 2009 among 645,764 women aged 45 to 69 who underwent biennial screening in Spain. Interval cancers were classified by a semi-informed retrospective review into true interval cancers (n = 455), false-negatives (n = 224), minimal-sign (n = 166), and occult tumors (n = 103). Breast density was evaluated using Boyd’s scale and was conflated into: <25%; 25 to 50%; 50 to 75%; >75%. Tumor-related information was obtained from cancer registries and clinical records. Tumor phenotype was defined as follows: luminal A: ER+/HER2- or PR+/HER2-; luminal B: ER+/HER2+ or PR+/HER2+; HER2: ER-/PR-/HER2+; triple-negative: ER-/PR-/HER2-. The association of tumor phenotype and breast density was assessed using a multinomial logistic regression model. Adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. All statistical tests were two-sided. Results Forty-eight percent of interval cancers were true interval cancers and 23.6% false-negatives. True interval cancers were associated with HER2 and triple-negative phenotypes (OR = 1.91 (95% CI:1.22-2.96), OR = 2.07 (95% CI:1.42-3.01), respectively) and extremely dense breasts (>75%) (OR = 1.67 (95% CI:1.08-2.56)). However, among true interval cancers a higher proportion of triple-negative tumors was observed in predominantly fatty breasts (<25%) than in denser breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively; <0.001). False-negatives and occult tumors had similar phenotypic characteristics to screening-detected cancers, extreme breast density being strongly associated with occult tumors (OR = 6.23 (95% CI:2.65-14.66)). Minimal-sign cancers were biologically close to true interval cancers but showed no association with breast density. Conclusions Our findings revealed that both the distribution of tumor phenotype and breast density play specific and independent roles in each category of interval cancer. Further research is needed to understand the biological basis of the overrepresentation of triple-negative phenotype among predominantly fatty breasts in true interval cancers. PMID:24410848

Using ultrasound tomography to identify the distributions of density throughout the breast

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Sherman, Mark E.; Gierach, Gretchen L.

2016-04-01

Women with high breast density are at increased risk of developing breast cancer. Breast density has usually been defined using mammography as the ratio of fibroglandular tissue to total breast area. Ultrasound tomography (UST) is an emerging modality that can also be used to measure breast density. UST creates tomographic sound speed images of the patient's breast which is useful as sound speed is directly proportional to tissue density. Furthermore, the volumetric and quantitative information contained in the sound speed images can be used to describe the distribution of breast density. The work presented here measures the UST sound speed density distributions of 165 women with negative screening mammography. Frequency distributions of the sound speed voxel information were examined for each patient. In a preliminary analysis, the UST sound speed distributions were averaged across patients and grouped by various patient and density-related factors (e.g., age, body mass index, menopausal status, average mammographic breast density). It was found that differences in the distribution of density could be easily visualized for different patient groupings. Furthermore, findings suggest that the shape of the distributions may be used to identify participants with varying amounts of dense and non-dense tissue.

Comparison of digital breast tomosynthesis and 2D digital mammography using a hybrid performance test

NASA Astrophysics Data System (ADS)

Cockmartin, Lesley; Marshall, Nicholas W.; Van Ongeval, Chantal; Aerts, Gwen; Stalmans, Davina; Zanca, Federica; Shaheen, Eman; De Keyzer, Frederik; Dance, David R.; Young, Kenneth C.; Bosmans, Hilde

2015-05-01

This paper introduces a hybrid method for performing detection studies in projection image based modalities, based on image acquisitions of target objects and patients. The method was used to compare 2D mammography and digital breast tomosynthesis (DBT) in terms of the detection performance of spherical densities and microcalcifications. The method starts with the acquisition of spheres of different glandular equivalent densities and microcalcifications of different sizes immersed in a homogeneous breast tissue simulating medium. These target objects are then segmented and the subsequent templates are fused in projection images of patients and processed or reconstructed. This results in hybrid images with true mammographic anatomy and clinically relevant target objects, ready for use in observer studies. The detection study of spherical densities used 108 normal and 178 hybrid 2D and DBT images; 156 normal and 321 hybrid images were used for the microcalcifications. Seven observers scored the presence/absence of the spheres/microcalcifications in a square region via a 5-point confidence rating scale. Detection performance in 2D and DBT was compared via ROC analysis with sub-analyses for the density of the spheres, microcalcification size, breast thickness and z-position. The study was performed on a Siemens Inspiration tomosynthesis system using patient acquisitions with an average age of 58 years and an average breast thickness of 53 mm providing mean glandular doses of 1.06 mGy (2D) and 2.39 mGy (DBT). Study results showed that breast tomosynthesis (AUC = 0.973) outperformed 2D (AUC = 0.831) for the detection of spheres (p  <  0.0001) and this applied for all spherical densities and breast thicknesses. By way of contrast, DBT was worse than 2D for microcalcification detection (AUC2D = 0.974, AUCDBT = 0.838, p  <  0.0001), with significant differences found for all sizes (150-354 µm), for breast thicknesses above 40 mm and for heights above the detector of 20 mm and above. In conclusion, the hybrid method was successfully used to produce images for a detection study; results showed breast tomosynthesis outperformed 2D for spherical densities while further optimization of DBT for microcalcifications is suggested.

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 on background risk of breast cancer.

A novel ultrasonic method for measuring breast density and breast cancer risk

NASA Astrophysics Data System (ADS)

Glide-Hurst, Carri K.; Duric, Neb; Littrup, Peter J.

2008-03-01

Women with high mammographic breast density are at 4- to 6-fold increased risk of developing breast cancer compared to women with fatty breasts. However, current breast density estimations rely on mammography, which cannot provide accurate volumetric breast representation. Therefore, we explored two techniques of breast density evaluation via ultrasound tomography. A sample of 93 patients was imaged with our clinical prototype; each dataset contained 45-75 tomograms ranging from near the chest wall through the nipple. Whole breast acoustic velocity was determined by creating image stacks and evaluating the sound speed frequency distribution. Ultrasound percent density (USPD) was determined by segmenting high sound speed areas from each tomogram using k-means clustering, integrating over the entire breast, and dividing by total breast area. Both techniques were independently evaluated using two mammographic density measures: (1) qualitative, determined by a radiologist's visual assessment using BI-RADS Categories, and (2) quantitative, via semi-automatic segmentation to calculate mammographic percent density (MPD) for craniocaudal and medio-lateral oblique mammograms. ~140 m/s difference in acoustic velocity was observed between fatty and dense BI-RADS Categories. Increased sound speed was found with increased BI-RADS Category and quantitative MPD. Furthermore, strong positive associations between USPD, BI-RADS Category, and calculated MPD were observed. These results confirm that utilizing sound speed, both for whole-breast evaluation and segmenting locally, can be implemented to evaluate breast density.

A Review on Automatic Mammographic Density and Parenchymal Segmentation

PubMed Central

He, Wenda; Juette, Arne; Denton, Erika R. E.; Oliver, Arnau

2015-01-01

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models. PMID:26171249

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

PubMed

Keller, Brad M; Chen, Jinbo; Daye, Dania; Conant, Emily F; Kontos, Despina

2015-08-25

Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided. All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80). Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.

Equol-producing status, isoflavone intake, and breast density in a sample of U.S. Chinese women.

PubMed

Tseng, Marilyn; Byrne, Celia; Kurzer, Mindy S; Fang, Carolyn Y

2013-11-01

Differences in ability to metabolize daidzein to equol might help explain inconsistent findings about isoflavones and breast cancer. We examined equol-producing status in relation to breast density, a marker of breast cancer risk, and evaluated whether an association of isoflavone intake with breast density differs by equol-producing status in a sample of Chinese immigrant women. Participants were 224 women, ages 36 to 58 years, enrolled in a study on diet and breast density. All women completed dietary recall interviews, underwent a soy challenge to assess equol-producing status, and received a mammogram assessed for breast density using a computer-assisted method. In our sample, 30% were classified as equol producers. In adjusted linear regression models, equol producers had significantly lower mean dense tissue area (32.8 vs. 37.7 cm(2), P = 0.03) and lower mean percent breast density (32% vs. 35%, P = 0.03) than nonproducers. Significant inverse associations of isoflavone intake with dense area and percent density were apparent, but only in equol producers (interaction P = 0.05 for both). These results support the possibility that equol-producing status affects breast density and that effects of isoflavones on breast density depend on ability to metabolize daidzein to equol. Although these findings warrant confirmation in a larger sample, they offer a possible explanation for the inconsistent findings about soy intake and breast density and possibly breast cancer risk as well. The findings further suggest the importance of identifying factors that influence equol-producing status and exploring appropriate targeting of interventions. ©2013 AACR.

Mammographic breast density as a risk factor for breast cancer: awareness in a recently screened clinical sample.

PubMed

O'Neill, Suzanne C; Leventhal, Kara Grace; Scarles, Marie; Evans, Chalanda N; Makariou, Erini; Pien, Edward; Willey, Shawna

2014-01-01

Breast density is an established, independent risk factor for breast cancer. Despite this, density has not been included in standard risk models or routinely disclosed to patients. However, this is changing in the face of legal mandates and advocacy efforts. Little information exists regarding women's awareness of density as a risk factor, their personal risk, and risk management options. We assessed awareness of density as a risk factor and whether sociodemographic variables, breast cancer risk factors. and perceived breast cancer risk were associated with awareness in 344 women with a recent screening mammogram at a tertiary care center. Overall, 62% of women had heard about density as a risk factor and 33% had spoken to a provider about breast density. Of the sample, 18% reported that their provider indicated that they had high breast density. Awareness of density as a risk factor was greater among White women and those with other breast cancer risk factors. Our results suggest that although a growing number of women are aware of breast density as a risk factor, this awareness varies. Growing mandates for disclosure suggest the need for patient education interventions for women at increased risk for the disease and to ensure all women are equally aware of their risks. Copyright © 2014 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Breast density characterization using texton distributions.

PubMed

Petroudi, Styliani; Brady, Michael

2011-01-01

Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. The new method represents the mammogram using textons, which can be thought of as the building blocks of texture under the operational definition of Leung and Malik as clustered filter responses. The new proposed method characterizes the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) in the breast region's corresponding texton map. The TSDM is a texture model that captures both statistical and structural texture characteristics. The normalized TSDM matrices are evaluated for mammograms from the different density classes and corresponding texture models are established. Classification is achieved using a chi-square distance measure. The fully automated TSDM breast density classification method is quantitatively evaluated on mammograms from all density classes from the Oxford Mammogram Database. The incorporation of texton spatial dependencies allows for classification accuracy reaching over 82%. The breast density classification accuracy is better using texton TSDM compared to simple texton histograms.

Breast density measurements using ultrasound tomography for patients undergoing tamoxifen treatment

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Li, Cuiping; Bey-Knight, Lisa; Sherman, Mark; Boyd, Norman; Gierach, Gretchen

2013-03-01

Women with high breast density have an increased risk of developing breast cancer. Women treated with the selective estrogen receptor modulator tamoxifen for estrogen receptor positive breast cancer experience a 50% reduction in risk of contralateral breast cancer and overall reduction of similar magnitude has been identified among high-risk women receiving the drug for prevention. Tamoxifen has been shown to reduce mammographic density, and in the IBIS-1 chemoprevention trial, risk reduction and decline in density were significantly associated. Ultrasound tomography (UST) is an imaging modality that can create tomographic sound speed images of the breast. These sound speed images are useful because breast density is proportional to sound speed. The aim of this work is to examine the relationship between USTmeasured breast density and the use of tamoxifen. So far, preliminary results for a small number of patients have been observed and are promising. Correlations between the UST-measured density and mammographic density are strong and positive, while relationships between UST density with some patient specific risk factors behave as expected. Initial results of UST examinations of tamoxifen treated patients show that approximately 45% of the patients have a decrease in density in the contralateral breast after only several months of treatment. The true effect of tamoxifen on UST-measured density cannot yet be fully determined until more data are collected. However, these promising results suggest that UST can be used to reliably assess quantitative changes in breast density over short intervals and therefore suggest that UST may enable rapid assessment of density changes associated with therapeutic and preventative interventions.

Development of a phantom to test fully automated breast density software - A work in progress.

PubMed

Waade, G G; Hofvind, S; Thompson, J D; Highnam, R; Hogg, P

2017-02-01

Mammographic density (MD) is an independent risk factor for breast cancer and may have a future role for stratified screening. Automated software can estimate MD but the relationship between breast thickness reduction and MD is not fully understood. Our aim is to develop a deformable breast phantom to assess automated density software and the impact of breast thickness reduction on MD. Several different configurations of poly vinyl alcohol (PVAL) phantoms were created. Three methods were used to estimate their density. Raw image data of mammographic images were processed using Volpara to estimate volumetric breast density (VBD%); Hounsfield units (HU) were measured on CT images; and physical density (g/cm 3 ) was calculated using a formula involving mass and volume. Phantom volume versus contact area and phantom volume versus phantom thickness was compared to values of real breasts. Volpara recognized all deformable phantoms as female breasts. However, reducing the phantom thickness caused a change in phantom density and the phantoms were not able to tolerate same level of compression and thickness reduction experienced by female breasts during mammography. Our results are promising as all phantoms resulted in valid data for automated breast density measurement. Further work should be conducted on PVAL and other materials to produce deformable phantoms that mimic female breast structure and density with the ability of being compressed to the same level as female breasts. We are the first group to have produced deformable phantoms that are recognized as breasts by Volpara software. Copyright © 2016 The College of Radiographers. All rights reserved.

Soy isoflavone supplementation and breast density in postmenopausal women

USDA-ARS?s Scientific Manuscript database

Soy isoflavones may protect against breast cancer. Breast density, a marker for breast cancer risk, increases as a result of hormone replacement therapy. We examined the relation between isoflavone supplementation and breast density using the mammograms from 358 women who participated in the multi-s...

Digital volumetric measurement of mammographic density and the risk of overlooking cancer in Japanese women.

PubMed

Sawada, Terumasa; Akashi, Sadako; Nakamura, Seigo; Kuwayama, Takashi; Enokido, Katsutoshi; Yoshida, Miwa; Hashimoto, Rikako; Ide, Toshimi; Masuda, Hiroko; Taruno, Kanae; Oyama, Hiroto; Takamaru, Tomoko; Kanada, Yoko; Ikeda, Murasaki; Kosugi, Natsuko; Sato, Hiroki; Nakayama, Sayuka; Ata, Arisa; Tonouchi, Yumi; Sakai, Haruna; Matsunaga, Yuki; Matsutani, Akiko

2017-09-01

Breast density often affects cancer detection via mammography (MMG). Because of this, additional tests are recommended for women with dense breasts. This study aimed to reveal trends in breast density among Japanese women and determine whether differences in breast density differentially affected the detection of abnormalities via MMG. We retrospectively analyzed 397 control women who underwent MMG screening as well as 269 patients who underwent surgery for breast cancer for whom preoperative MMG data were available. VolparaDensity™ (Volpara), a three-dimensional image analysis software with high reproducibility, was used to calculate breast density. Breasts were categorized according to the volumetric density grade (VDG), a measure of the percentage of dense tissue. The associations between age, VDG, and MMG density categories were analyzed. In the control group, 78% of women had dense breasts, while in the breast cancer group, 87% of patients had dense breasts. One of 36 patients with non-dense breasts (2.7%) was classified as category 1 or 2 (C-1 or C-2), indicating that abnormal findings could not be detected by MMG. The proportion of patients with breast cancer who had dense breasts and were classified as C-1 or C-2 was as high as 22.3%. The proportions of Japanese women with dense breasts were high. In addition, the false-negative rate for women with dense breasts was also high. Owing to this, Japanese women with dense breasts may need to commonly undergo additional tests to ensure detection of breast cancer in the screening MMG.

Automated Breast Density Computation in Digital Mammography and Digital Breast Tomosynthesis: Influence on Mean Glandular Dose and BIRADS Density Categorization.

PubMed

Castillo-García, Maria; Chevalier, Margarita; Garayoa, Julia; Rodriguez-Ruiz, Alejandro; García-Pinto, Diego; Valverde, Julio

2017-07-01

The study aimed to compare the breast density estimates from two algorithms on full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and to analyze the clinical implications. We selected 561 FFDM and DBT examinations from patients without breast pathologies. Two versions of a commercial software (Quantra 2D and Quantra 3D) calculated the volumetric breast density automatically in FFDM and DBT, respectively. Other parameters such as area breast density and total breast volume were evaluated. We compared the results from both algorithms using the Mann-Whitney U non-parametric test and the Spearman's rank coefficient for data correlation analysis. Mean glandular dose (MGD) was calculated following the methodology proposed by Dance et al. Measurements with both algorithms are well correlated (r ≥ 0.77). However, there are statistically significant differences between the medians (P < 0.05) of most parameters. The volumetric and area breast density median values from FFDM are, respectively, 8% and 77% higher than DBT estimations. Both algorithms classify 35% and 55% of breasts into BIRADS (Breast Imaging-Reporting and Data System) b and c categories, respectively. There are no significant differences between the MGD calculated using the breast density from each algorithm. DBT delivers higher MGD than FFDM, with a lower difference (5%) for breasts in the BIRADS d category. MGD is, on average, 6% higher than values obtained with the breast glandularity proposed by Dance et al. Breast density measurements from both algorithms lead to equivalent BIRADS classification and MGD values, hence showing no difference in clinical outcomes. The median MGD values of FFDM and DBT examinations are similar for dense breasts (BIRADS d category). Published by Elsevier Inc.

Relationship Between Mammographic Density and Breast Cancer Death in the Breast Cancer Surveillance Consortium

PubMed Central

2012-01-01

Background Women with elevated mammographic density have an increased risk of developing breast cancer. However, among women diagnosed with breast cancer, it is unclear whether higher density portends reduced survival, independent of other factors. Methods We evaluated relationships between mammographic density and risk of death from breast cancer and all causes within the US Breast Cancer Surveillance Consortium. We studied 9232 women diagnosed with primary invasive breast carcinoma during 1996–2005, with a mean follow-up of 6.6 years. Mammographic density was assessed using the Breast Imaging Reporting and Data System (BI-RADS) density classification. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards regression; women with scattered fibroglandular densities (BI-RADS 2) were the referent group. All statistical tests were two-sided. Results A total of 1795 women died, of whom 889 died of breast cancer. In multivariable analyses (adjusted for site, age at and year of diagnosis, American Joint Committee on Cancer stage, body mass index, mode of detection, treatment, and income), high density (BI-RADS 4) was not related to risk of death from breast cancer (HR = 0.92, 95% CI = 0.71 to 1.19) or death from all causes (HR = 0.83, 95% CI = 0.68 to 1.02). Analyses stratified by stage and other prognostic factors yielded similar results, except for an increased risk of breast cancer death among women with low density (BI-RADS 1) who were either obese (HR = 2.02, 95% CI = 1.37 to 2.97) or had tumors of at least 2.0cm (HR = 1.55, 95% CI = 1.14 to 2.09). Conclusions High mammographic breast density was not associated with risk of death from breast cancer or death from any cause after accounting for other patient and tumor characteristics. Thus, risk factors for the development of breast cancer may not necessarily be the same as factors influencing the risk of death after breast cancer has developed. PMID:22911616

Breast density in screening mammography in Indian population - Is it different from western population?

PubMed

Singh, Tulika; Khandelwal, Niranjan; Singla, Veenu; Kumar, Dileep; Gupta, Madhu; Singh, Gurpreet; Bal, Amanjit

2018-05-01

Mammography is the only method presently considered appropriate for mass screening of breast cancer. However, higher breast density was strongly associated with lower mammographic sensitivity. Breast density is also identified as independent and strongest risk factors for breast cancer. Studies have shown women with high breast density have four to six times increased risk of breast cancer as compare to women with fatty breast. It varies between different age group it generally decreases with increasing age in postmenopausal women and it can be different in different ethnic groups and people from different geographical areas. This study evaluates the breast density in Indian population and its relationship with the age. We reviewed of all screening mammography examinations performed from May 2012 to January 2015 at our institute PGIMER, Chandigarh, INDIA. Descriptive analyses were used to examine the association between age and breast density. A total of 6132 screening mammograms were performed. Each subgroup categorized by decade of age. There was a significant inverse relationship between age and breast density (P < .001). Twenty-two percent of patients between 40 and 49 years old had dense breasts. This percentage decreased to 9% of women in their 50s. Only 7% of women in their 60s and 8% of women in their 70s had dense breasts. This data has been compared with the Western study done in New York University (NYU) shows there is significant difference (P value <.05) in the breast density in Indian and Western population with more Indians having ACR Grade 1 and 2 and Western population having 2 and 3. We found an inverse relationship between patient age and mammographic breast density. However, there were a large proportion of young women who had lower grades of mammographic density which could potentially benefit from the use of routine screening mammography in this subgroup of patients. Moreover, the breast density of Indian population is less when compared to the Western population. This might suggest that mammography is a good modality of choice for screening Indian population. © 2017 Wiley Periodicals, Inc.

Breast cancer screening effect across breast density strata: A case-control study.

PubMed

van der Waal, Daniëlle; Ripping, Theodora M; Verbeek, André L M; Broeders, Mireille J M

2017-01-01

Breast cancer screening is known to reduce breast cancer mortality. A high breast density may affect this reduction. We assessed the effect of screening on breast cancer mortality in women with dense and fatty breasts separately. Analyses were performed within the Nijmegen (Dutch) screening programme (1975-2008), which invites women (aged 50-74 years) biennially. Performance measures were determined. Furthermore, a case-control study was performed for women having dense and women having fatty breasts. Breast density was assessed visually with a dichotomized Wolfe scale. Breast density data were available for cases. The prevalence of dense breasts among controls was estimated with age-specific rates from the general population. Sensitivity analyses were performed on these estimates. Screening performance was better in the fatty than in the dense group (sensitivity 75.7% vs 57.8%). The mortality reduction appeared to be smaller for women with dense breasts, with an odds ratio (OR) of 0.87 (95% CI 0.52-1.45) in the dense and 0.59 (95% CI 0.44-0.79) in the fatty group. We can conclude that high density results in lower screening performance and appears to be associated with a smaller mortality reduction. Breast density is thus a likely candidate for risk-stratified screening. More research is needed on the association between density and screening harms. © 2016 UICC.

Volumetric breast density affects performance of digital screening mammography.

PubMed

Wanders, Johanna O P; Holland, Katharina; Veldhuis, Wouter B; Mann, Ritse M; Pijnappel, Ruud M; Peeters, Petra H M; van Gils, Carla H; Karssemeijer, Nico

2017-02-01

To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM). We collected a consecutive series of 111,898 DM examinations (2003-2011) from one screening unit of the Dutch biennial screening program (age 50-75 years). Volumetric mammographic density was automatically assessed using Volpara. We determined screening performance measures for four density categories comparable to the American College of Radiology (ACR) breast density categories. Of all the examinations, 21.6% were categorized as density category 1 ('almost entirely fatty') and 41.5, 28.9, and 8.0% as category 2-4 ('extremely dense'), respectively. We identified 667 screen-detected and 234 interval cancers. Interval cancer rates were 0.7, 1.9, 2.9, and 4.4‰ and false positive rates were 11.2, 15.1, 18.2, and 23.8‰ for categories 1-4, respectively (both p-trend < 0.001). The screening sensitivity, calculated as the proportion of screen-detected among the total of screen-detected and interval tumors, was lower in higher density categories: 85.7, 77.6, 69.5, and 61.0% for categories 1-4, respectively (p-trend < 0.001). Volumetric mammographic density, automatically measured on digital mammograms, impacts screening performance measures along the same patterns as established with ACR breast density categories. Since measuring breast density fully automatically has much higher reproducibility than visual assessment, this automatic method could help with implementing density-based supplemental screening.

Mammographic breast density in recent and longer-standing ethiopian immigrants to israel.

PubMed

Sklair-Levy, Miri; Segev, Anat; Sella, Tamar; Calderon-Margalit, Ronit; Zippel, Douglas

2018-04-23

High breast density is associated with an increased risk of breast cancer development. Little is known concerning ethnic variations in breast density and its relevant contributing factors. We aimed to study breast density among Ethiopian immigrants to Israel in comparison with Israeli-born women and to determine any effect on breast density of the length of residency in the immigrant population. Mammographic breast density using the BI-RADS system was estimated and compared between 77 women of Ethiopian origin who live in Israel and 177 Israeli-born controls. Logistic regression analysis was performed to estimate the odds ratios (OR) for high density (BI-RADS score ≥ 3) vs low density (BI-RADS score < 3) cases, comparing the 2 origin groups. Ethiopian-born women had a crude OR of 0.15 (95% CI: 0.08-0.26) for high breast density compared with Israeli-born women. Adjustments for various cofounders did not affect the results. Time since immigration to Israel seemed to modify the relationship, with a stronger association for women who immigrated within 2 years prior to mammography (OR:0.07, 95% CI: 0.03-0.17) as opposed to women with a longer residency stay in Israel (OR:0.23, 95% CI:0.10-0.50). Adjustments of various confounders did not alter these findings. Breast density in Ethiopian immigrants to Israel is significantly lower than that of Israeli-born controls. Our study suggests a positive association between time since immigration and breast density. Future studies are required to define the possible effects of dietary change on mammographic density following immigration. © 2018 Wiley Periodicals, Inc.

Relationship between breast sound speed and mammographic percent density

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Nebojsa; Boyd, Norman; Littrup, Peter; Myc, Lukasz; Faiz, Muhammad; Li, Cuiping; Bey-Knight, Lisa

2011-03-01

Despite some shortcomings, mammography is currently the standard of care for breast cancer screening and diagnosis. However, breast ultrasound tomography is a rapidly developing imaging modality that has the potential to overcome the drawbacks of mammography. It is known that women with high breast densities have a greater risk of developing breast cancer. Measuring breast density is accomplished through the use of mammographic percent density, defined as the ratio of fibroglandular to total breast area. Using an ultrasound tomography (UST) prototype, we created sound speed images of the patient's breast, motivated by the fact that sound speed in a tissue is proportional to the density of the tissue. The purpose of this work is to compare the acoustic performance of the UST system with the measurement of mammographic percent density. A cohort of 251 patients was studied using both imaging modalities and the results suggest that the volume averaged breast sound speed is significantly related to mammographic percent density. The Spearman correlation coefficient was found to be 0.73 for the 175 film mammograms and 0.69 for the 76 digital mammograms obtained. Since sound speed measurements do not require ionizing radiation or physical compression, they have the potential to form the basis of a safe, more accurate surrogate marker of breast density.

Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

PubMed Central

Vachon, Celine M.; Scott, Christopher G.; Fasching, Peter A.; Hall, Per; Tamimi, Rulla M.; Li, Jingmei; Stone, Jennifer; Apicella, Carmel; Odefrey, Fabrice; Gierach, Gretchen L.; Jud, Sebastian M.; Heusinger, Katharina; Beckmann, Matthias W.; Pollan, Marina; Fernández-Navarro, Pablo; González-Neira, Anna; Benítez, Javier; van Gils, Carla H.; Lokate, Mariëtte; Onland-Moret, N. Charlotte; Peeters, Petra H.M.; Brown, Judith; Leyland, Jean; Varghese, Jajini S.; Easton, Douglas F.; Thompson, Deborah J.; Luben, Robert N.; Warren, Ruth ML; Wareham, Nicholas J.; Loos, Ruth JF; Khaw, Kay-Tee; Ursin, Giske; Lee, Eunjung; Gayther, Simon A.; Ramus, Susan J.; Eeles, Rosalind A.; Leach, Martin O.; Kwan-Lim, Gek; Couch, Fergus J.; Giles, Graham G.; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C.; Le Marchand, Loic; Kolonel, Laurence N.; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher A; Walker, Kate; Johnson, Nichola; McCormack, Valerie A.; Biong, Margarethe; Alnæs, Grethe I.G.; Gram, Inger Torhild; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lindström, Sara; Hankinson, Susan E.; Hunter, David J.; Andrulis, Irene L.; Knight, Julia A.; Boyd, Norman F.; Figueroa, Jonine D.; Lissowska, Jolanta; Wesolowska, Ewa; Peplonska, Beata; Bukowska, Agnieszka; Reszka, Edyta; Liu, JianJun; Eriksson, Louise; Czene, Kamila; Audley, Tina; Wu, Anna H.; Pankratz, V. Shane; Hopper, John L.; dos-Santos-Silva, Isabel

2013-01-01

Background Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biological mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to inter-individual differences in mammographic density measures. Methods We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and non-dense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, body mass index (BMI) and menopausal status. Results Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (p=0.00005) and adjusted percent density (p=0.001) whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (p=0.003), but not with adjusted dense area (p=0.07). Conclusion We identified two common breast cancer susceptibility variants associated with mammographic measures of radio-dense tissue in the breast gland. Impact We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association. PMID:22454379

Comparison of breast percent density estimation from raw versus processed digital mammograms

NASA Astrophysics Data System (ADS)

Li, Diane; Gavenonis, Sara; Conant, Emily; Kontos, Despina

2011-03-01

We compared breast percent density (PD%) measures obtained from raw and post-processed digital mammographic (DM) images. Bilateral raw and post-processed medio-lateral oblique (MLO) images from 81 screening studies were retrospectively analyzed. Image acquisition was performed with a GE Healthcare DS full-field DM system. Image post-processing was performed using the PremiumViewTM algorithm (GE Healthcare). Area-based breast PD% was estimated by a radiologist using a semi-automated image thresholding technique (Cumulus, Univ. Toronto). Comparison of breast PD% between raw and post-processed DM images was performed using the Pearson correlation (r), linear regression, and Student's t-test. Intra-reader variability was assessed with a repeat read on the same data-set. Our results show that breast PD% measurements from raw and post-processed DM images have a high correlation (r=0.98, R2=0.95, p<0.001). Paired t-test comparison of breast PD% between the raw and the post-processed images showed a statistically significant difference equal to 1.2% (p = 0.006). Our results suggest that the relatively small magnitude of the absolute difference in PD% between raw and post-processed DM images is unlikely to be clinically significant in breast cancer risk stratification. Therefore, it may be feasible to use post-processed DM images for breast PD% estimation in clinical settings. Since most breast imaging clinics routinely use and store only the post-processed DM images, breast PD% estimation from post-processed data may accelerate the integration of breast density in breast cancer risk assessment models used in clinical practice.

Decreased contralateral breast volume after mastectomy, adjuvant chemotherapy, and anti-estrogen therapy, in particular in breasts with high density.

PubMed

Ishii, Naohiro; Ando, Jiro; Harao, Michiko; Takemae, Masaru; Kishi, Kazuo

2017-10-01

Adjuvant chemotherapy and anti-estrogenic therapy can result in decreased volume of the contralateral breast, following mastectomy for the treatment of breast cancer. However, no data on the effect of adjuvant therapy on contralateral breast volume have previously been reported. We aimed to evaluate the extent to which adjuvant therapy and differences in breast density contribute to decreased breast volume. We conducted a prospective cohort study, selecting 40 nonconsecutive patients who underwent immediate breast reconstruction with mastectomy and expander insertion followed by expander replacement. We measured the contralateral breast volume before each procedure. The extent of the change was analyzed with respect to adjuvant therapy and breast density measured by preoperative mammography. The greatest decrease in breast volume was 135.1 cm 3 . The decrease in breast volume was significantly larger in the adjuvant therapy (+) group, particularly in patients with high breast density, than in the adjuvant therapy (-) group. Significant differences between the chemotherapy (+), tamoxifen (+) group and the chemotherapy (-), tamoxifen (+) group were not found. Breast density scores had a range of 2.0-3.3 (mean: 2.8). In breast reconstruction, particularly when performed in one stage, preoperative mammography findings are valuable to plastic surgeons, and possible decreases in the contralateral breast volume due to adjuvant therapy, particularly in patients with high breast density, should be considered carefully. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

No relationship between circulating levels of sex steroids and mammographic breast density: the Prospect-EPIC cohort

PubMed Central

Verheus, Martijn; Peeters, Petra HM; van Noord, Paulus AH; van der Schouw, Yvonne T; Grobbee, Diederick E; van Gils, Carla H

2007-01-01

Background High breast density is associated with increased breast cancer risk. Epidemiologic studies have shown an increase in breast cancer risk in postmenopausal women with high levels of sex steroids. Hence, sex steroids may increase postmenopausal breast cancer risk via an increase of breast density. The objective of the present study was to study the relation between circulating oestrogens and androgens as well as sex hormone binding globulin (SHBG) in relation to breast density. Methods We conducted a cross-sectional study among 775 postmenopausal women, using baseline data of a random sample of the Prospect-EPIC study. Prospect-EPIC is one of two Dutch cohorts participating in the European Prospective Investigation into Cancer and Nutrition, and women were recruited via a breast cancer screening programme. At enrolment a nonfasting blood sample was taken and a mammogram was made. Oestrone, oestradiol, dehydroepiandrosterone sulfate, androstenedione, testosterone and SHBG levels were measured, using double-antibody radioimmunoassays. Concentrations of free oestradiol and free testosterone were calculated from the measured oestradiol, testosterone and SHBG levels Mammographic dense and nondense areas were measured using a semiquantitative computerized method and the percentage breast density was calculated. Mean breast measures for quintiles of hormone or SHBG levels were estimated using linear regression analyses. Results Both oestrogens and testosterone were inversely related with percent breast density, but these relationships disappeared after adjustment for BMI. None of the sex steroids or SHBG was associated with the absolute measure of breast density, the dense area. Conclusion The results of our study do not support the hypothesis that sex steroids increase postmenopausal breast cancer risk via an increase in breast density. PMID:17692133

Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities

NASA Astrophysics Data System (ADS)

Chen, Lin; Abbey, Craig K.; Boone, John M.

2013-03-01

Previous research has demonstrated that a parameter extracted from a power function fit to the anatomical noise power spectrum, β, may be predictive of breast mass lesion detectability in x-ray based medical images of the breast. In this investigation, the value of β was compared with a number of other more widely used parameters, in order to determine the relationship between β and these other parameters. This study made use of breast CT data sets, acquired on two breast CT systems developed in our laboratory. A total of 185 breast data sets in 183 women were used, and only the unaffected breast was used (where no lesion was suspected). The anatomical noise power spectrum computed from two-dimensional region of interests (ROIs), was fit to a power function (NPS(f) = α f-β), and the exponent parameter (β) was determined using log/log linear regression. Breast density for each of the volume data sets was characterized in previous work. The breast CT data sets analyzed in this study were part of a previous study which evaluated the receiver operating characteristic (ROC) curve performance using simulated spherical lesions and a pre-whitened matched filter computer observer. This ROC information was used to compute the detectability index as well as the sensitivity at 95% specificity. The fractal dimension was computed from the same ROIs which were used for the assessment of β. The value of β was compared to breast density, detectability index, sensitivity, and fractal dimension, and the slope of these relationships was investigated to assess statistical significance from zero slope. A statistically significant non-zero slope was considered to be a positive association in this investigation. All comparisons between β and breast density, detectability index, sensitivity at 95% specificity, and fractal dimension demonstrated statistically significant association with p < 0.001 in all cases. The value of β was also found to be associated with patient age and breast diameter, parameters both related to breast density. In all associations between other parameters, lower values of β were associated with increased breast cancer detection performance. Specifically, lower values of β were associated with lower breast density, higher detectability index, higher sensitivity, and lower fractal dimension values. While causality was not and probably cannot be demonstrated, the strong, statistically significant association between the β metric and the other more widely used parameters suggest that β may be considered as a surrogate measure for breast cancer detection performance. These findings are specific to breast parenchymal patterns and mass lesions only.

Breast-density assessment with hand-held ultrasound: A novel biomarker to assess breast cancer risk and to tailor screening?

PubMed

Sanabria, Sergio J; Goksel, Orcun; Martini, Katharina; Forte, Serafino; Frauenfelder, Thomas; Kubik-Huch, Rahel A; Rominger, Marga B

2018-03-19

To assess feasibility and diagnostic accuracy of a novel hand-held ultrasound (US) method for breast density assessment that measures the speed of sound (SoS), in comparison to the ACR mammographic (MG) categories. ACR-MG density (a=fatty to d=extremely dense) and SoS-US were assessed in the retromamillary, inner and outer segments of 106 women by two radiographers. A conventional US system was used for SoS-US. A reflector served as timing reference for US signals transmitted through the breasts. Four blinded readers assessed average SoS (m/s), ΔSoS (segment-variation SoS; m/s) and the ACR-MG density. The highest SoS and ΔSoS values of the three segments were used for MG-ACR whole breast comparison. SoS-US breasts were examined in <2 min. Mean SoS values of densities a-d were 1,421 m/s (SD 14), 1,432 m/s (SD 17), 1,448 m/s (SD 20) and 1,500 m/s (SD 31), with significant differences between all groups (p<0.001). The SoS-US comfort scores and inter-reader agreement were significantly better than those for MG (1.05 vs. 2.05 and 0.982 vs. 0.774; respectively). A strong segment correlation between SoS and ACR-MG breast density was evident (r s =0.622, p=<0.001) and increased for full breast classification (r s =0.746, p=<0.001). SoS-US allowed diagnosis of dense breasts (ACR c and d) with sensitivity 86.2 %, specificity 85.2 % and AUC 0.887. Using hand-held SoS-US, radiographers measured breast density without discomfort, readers evaluated measurements with high inter-reader agreement, and SoS-US correlated significantly with ACR-MG breast-density categories. • The novel speed-of-sound ultrasound correlated significantly with mammographic ACR breast density categories. • Radiographers measured breast density without women discomfort or radiation. • SoS-US can be implemented on a standard US machine. • SoS-US shows potential for a quantifiable, cost-effective assessment of breast density.

Influence of lifestyle factors on mammographic density in postmenopausal women.

PubMed

Brand, Judith S; Czene, Kamila; Eriksson, Louise; Trinh, Thang; Bhoo-Pathy, Nirmala; Hall, Per; Celebioglu, Fuat

2013-01-01

Mammographic density is a strong risk factor for breast cancer. Apart from hormone replacement therapy (HRT), little is known about lifestyle factors that influence breast density. We examined the effect of smoking, alcohol and physical activity on mammographic density in a population-based sample of postmenopausal women without breast cancer. Lifestyle factors were assessed by a questionnaire and percentage and area measures of mammographic density were measured using computer-assisted software. General linear models were used to assess the association between lifestyle factors and mammographic density and effect modification by body mass index (BMI) and HRT was studied. Overall, alcohol intake was positively associated with percent mammographic density (P trend  = 0.07). This association was modified by HRT use (P interaction  = 0.06): increasing alcohol intake was associated with increasing percent density in current HRT users (P trend  = 0.01) but not in non-current users (P trend  = 0.82). A similar interaction between alcohol and HRT was found for the absolute dense area, with a positive association being present in current HRT users only (P interaction  = 0.04). No differences in mammographic density were observed across categories of smoking and physical activity, neither overall nor in stratified analyses by BMI and HRT use. Increasing alcohol intake is associated with an increase in mammography density, whereas smoking and physical activity do not seem to influence density. The observed interaction between alcohol and HRT may pose an opportunity for HRT users to lower their mammographic density and breast cancer risk.

Influence of Lifestyle Factors on Mammographic Density in Postmenopausal Women

PubMed Central

Brand, Judith S.; Czene, Kamila; Eriksson, Louise; Trinh, Thang; Bhoo-Pathy, Nirmala; Hall, Per; Celebioglu, Fuat

2013-01-01

Background Mammographic density is a strong risk factor for breast cancer. Apart from hormone replacement therapy (HRT), little is known about lifestyle factors that influence breast density. Methods We examined the effect of smoking, alcohol and physical activity on mammographic density in a population-based sample of postmenopausal women without breast cancer. Lifestyle factors were assessed by a questionnaire and percentage and area measures of mammographic density were measured using computer-assisted software. General linear models were used to assess the association between lifestyle factors and mammographic density and effect modification by body mass index (BMI) and HRT was studied. Results Overall, alcohol intake was positively associated with percent mammographic density (P trend  = 0.07). This association was modified by HRT use (P interaction  = 0.06): increasing alcohol intake was associated with increasing percent density in current HRT users (P trend  = 0.01) but not in non-current users (P trend  = 0.82). A similar interaction between alcohol and HRT was found for the absolute dense area, with a positive association being present in current HRT users only (P interaction  = 0.04). No differences in mammographic density were observed across categories of smoking and physical activity, neither overall nor in stratified analyses by BMI and HRT use. Conclusions Increasing alcohol intake is associated with an increase in mammography density, whereas smoking and physical activity do not seem to influence density. The observed interaction between alcohol and HRT may pose an opportunity for HRT users to lower their mammographic density and breast cancer risk. PMID:24349146

Preoperative Breast Magnetic Resonance Imaging Use by Breast Density and Family History of Breast Cancer.

PubMed

Henderson, Louise M; Hubbard, Rebecca A; Zhu, Weiwei; Weiss, Julie; Wernli, Karen J; Goodrich, Martha E; Kerlikowske, Karla; DeMartini, Wendy; Ozanne, Elissa M; Onega, Tracy

2018-01-15

Use of preoperative breast magnetic resonance imaging (MRI) among women with a new breast cancer has increased over the past decade. MRI use is more frequent in younger women and those with lobular carcinoma, but associations with breast density and family history of breast cancer are unknown. Data for 3075 women ages >65 years with stage 0-III breast cancer who underwent breast conserving surgery or mastectomy from 2005 to 2010 in the Breast Cancer Surveillance Consortium were linked to administrative claims data to assess associations of preoperative MRI use with mammographic breast density and first-degree family history of breast cancer. Multivariable logistic regression estimated adjusted odds ratios (OR) and 95% confidence intervals (95% CI) for the association of MRI use with breast density and family history, adjusting for woman and tumor characteristics. Overall, preoperative MRI use was 16.4%. The proportion of women receiving breast MRI was similar by breast density (17.6% dense, 16.9% nondense) and family history (17.1% with family history, 16.5% without family history). After adjusting for potential confounders, we found no difference in preoperative MRI use by breast density (OR = 0.95 for dense vs. nondense, 95% CI: 0.73-1.22) or family history (OR = 0.99 for family history vs. none, 95% CI: 0.73-1.32). Among women aged >65 years with breast cancer, having dense breasts or a first-degree relative with breast cancer was not associated with greater preoperative MRI use. This utilization is in keeping with lack of evidence that MRI has higher yield of malignancy in these subgroups.

Virtual setting for training in interpreting mammography images

NASA Astrophysics Data System (ADS)

Pezzuol, J. L.; Abreu, F. D. L.; Silva, S. M.; Tendolini, A.; Bissaco, M. A. Se; Rodrigues, S. C. M.

2017-03-01

This work presents a web system for the training of students or residents (users) interested in the detection of breast density in mammography images. The system consists of a breast imaging database with breast density types classified and demarcated by the specialist (tutor) or online database. The planning was based on ISO / IEC 12207. Through the browser (desktop or notebook), the user will visualize the breast images and in them will realize the markings of the density region and even classify them per the BI-RADS protocol. After marking, this will be compared to the gold standard already existing in the image base, and then the system will inform if the area demarcation has been set or not. The shape of this marking is similar to the paint brush. The evaluation was based on ISO / IEC 1926 or 25010: 2011 by 3 software development specialists and 3 in mammary radiology, evaluating usability, configuration, performance and System interface through the Likert scale-based questionnaire. Where they have totally agreed on usability, configuration, performance and partially on the interface. And as a good thing: the system is able to be accessed anywhere and at any time, the hit or error response is in real time, it can be used in the educational area, the limit of the amount of images will depend on the size of the computer memory, At the end the system sends the results achieved by e-mail to the user, reproduction of the system on any type of screen, complementation of the system with other types of breast structures. Negative points are the need for internet.

Determinants of the reliability of ultrasound tomography sound speed estimates as a surrogate for volumetric breast density

PubMed Central

Khodr, Zeina G.; Sak, Mark A.; Pfeiffer, Ruth M.; Duric, Nebojsa; Littrup, Peter; Bey-Knight, Lisa; Ali, Haythem; Vallieres, Patricia; Sherman, Mark E.; Gierach, Gretchen L.

2015-01-01

Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (two scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors’ experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1–13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater’s repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density. PMID:26429241

Determinants of the reliability of ultrasound tomography sound speed estimates as a surrogate for volumetric breast density

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

Khodr, Zeina G.; Pfeiffer, Ruth M.; Gierach, Gretchen L., E-mail: GierachG@mail.nih.gov

Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (twomore » scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors’ experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1–13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater’s repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density.« less

Longitudinal Association of Anthropometry with Mammographic Breast Density in the Study of Women's Health Across the Nation (Swan)

PubMed Central

Reeves, Katherine W.; Stone, Roslyn A.; Modugno, Francesmary; Ness, Roberta B.; Vogel, Victor G.; Weissfeld, Joel L.; Habel, Laurel A.; Sternfeld, Barbara; Cauley, Jane A.

2009-01-01

High percent mammographic breast density is strongly associated with increased breast cancer risk. Though body mass index (BMI) is positively associated with risk of postmenopausal breast cancer, BMI is negatively associated with percent breast density in cross-sectional studies. Few longitudinal studies have evaluated associations between BMI and weight and mammographic breast density. We studied the longitudinal relationships between anthropometry and breast density in a prospective cohort of 834 pre- and perimenopausal women enrolled in an ancillary study to the Study of Women's Health Across the Nation (SWAN). Routine screening mammograms were collected and read for breast density. Random intercept regression models were used to evaluate whether annual BMI change was associated with changes over time in dense breast area and percent density. The study population was 7.4% African American, 48.8% Caucasian, 21.8% Chinese, and 21.9% Japanese. Mean follow-up was 4.8 years. Mean annual weight change was +0.32 kg/year, mean change in dense area was -0.77 cm2/year, and mean change in percent density was -1.14%/year. In fully adjusted models, annual change in BMI was not significantly associated with changes in dense breast area (-0.17 cm2, 95% CI -0.64, 0.29). Borderline significant negative associations were observed between annual BMI change and annual percent density change, with percent density decreasing 0.36% (95% CI -0.74, 0.02) for a one unit increase in BMI over a year. This longitudinal study provides modest evidence that changes in BMI are not associated with changes in dense area, yet may be negatively associated with percent density. PMID:19065651

Birth weight, childhood body mass index, and height in relation to mammographic density and breast cancer: a register-based cohort study.

PubMed

Andersen, Zorana J; Baker, Jennifer L; Bihrmann, Kristine; Vejborg, Ilse; Sørensen, Thorkild I A; Lynge, Elsebeth

2014-01-20

High breast density, a strong predictor of breast cancer may be determined early in life. Childhood anthropometric factors have been related to breast cancer and breast density, but rarely simultaneously. We examined whether mammographic density (MD) mediates an association of birth weight, childhood body mass index (BMI), and height with the risk of breast cancer. 13,572 women (50 to 69 years) in the Copenhagen mammography screening program (1991 through 2001) with childhood anthropometric measurements in the Copenhagen School Health Records Register were followed for breast cancer until 2010. With logistic and Cox regression models, we investigated associations among birth weight, height, and BMI at ages 7 to 13 years with MD (mixed/dense or fatty) and breast cancer, respectively. 8,194 (60.4%) women had mixed/dense breasts, and 716 (5.3%) developed breast cancer. Childhood BMI was significantly inversely related to having mixed/dense breasts at all ages, with odds ratios (95% confidence intervals) ranging from 0.69 (0.66 to 0.72) at age 7 to 0.56 (0.53 to 0.58) at age 13, per one-unit increase in z-score. No statistically significant associations were detected between birth weight and MD, height and MD, or birth weight and breast cancer risk. BMI was inversely associated with breast cancer, with hazard ratios of 0.91 (0.83 to 0.99) at age 7 and 0.92 (0.84 to 1.00) at age 13, whereas height was positively associated with breast cancer risk (age 7, 1.06 (0.98 to 1.14) and age 13, 1.08 (1.00 to 1.16)). After additional adjustment for MD, associations of BMI with breast cancer diminished (age 7, 0.97 (0.88 to 1.06) and age 13, 1.01 (0.93 to 1.11)), but remained with height (age 7, 1.06 (0.99 to 1.15) and age 13, 1.09 (1.01 to 1.17)). Among women 50 years and older, childhood body fatness was inversely associated with the breast cancer risk, possibly via a mechanism mediated by MD, at least partially. Childhood tallness was positively associated with breast cancer risk, seemingly via a pathway independent of MD. Birth weight was not associated with MD or breast cancer in this age group.

3D MRI for Quantitative Analysis of Quadrant Percent Breast Density: Correlation with Quadrant Location of Breast Cancer.

PubMed

Chen, Jeon-Hor; Liao, Fuyi; Zhang, Yang; Li, Yifan; Chang, Chia-Ju; Chou, Chen-Pin; Yang, Tsung-Lung; Su, Min-Ying

2017-07-01

Breast cancer occurs more frequently in the upper outer (UO) quadrant, but whether this higher cancer incidence is related to the greater amount of dense tissue is not known. Magnetic resonance imaging acquires three-dimensional volumetric images and is the most suitable among all breast imaging modalities for regional quantification of density. This study applied a magnetic resonance imaging-based method to measure quadrant percent density (QPD), and evaluated its association with the quadrant location of the developed breast cancer. A total of 126 cases with pathologically confirmed breast cancer were reviewed. Only women who had unilateral breast cancer located in a clear quadrant were selected for analysis. A total of 84 women, including 47 Asian women and 37 western women, were included. An established computer-aided method was used to segment the diseased breast and the contralateral normal breast, and to separate the dense and fatty tissues. Then, a breast was further separated into four quadrants using the nipple and the centroid as anatomic landmarks. The tumor was segmented using a computer-aided method to determine its quadrant location. The distribution of cancer quadrant location, the quadrant with the highest QPD, and the proportion of cancers occurring in the highest QPD were analyzed. The highest incidence of cancer occurred in the UO quadrant (36 out of 84, 42.9%). The highest QPD was also noted most frequently in the UO quadrant (31 out of 84, 36.9%). When correlating the highest QPD with the quadrant location of breast cancer, only 17 women out of 84 (20.2%) had breast cancer occurring in the quadrant with the highest QPD. The results showed that the development of breast cancer in a specific quadrant could not be explained by the density in that quadrant, and further studies are needed to find the biological reasons accounting for the higher breast cancer incidence in the UO quadrant. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Template-based automatic breast segmentation on MRI by excluding the chest region

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

Lin, Muqing; Chen, Jeon-Hor; Wang, Xiaoyong

2013-12-15

Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary ofmore » the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. Conclusions: The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density.« less

Natural History of Breast Density and Breast Cancer Risk

DTIC Science & Technology

2001-07-01

mammography database. We have estimated breast density on the oldest mammogram from both cases and controls, using our semi-automated software and...using the oldest mammogram) with breast cancer risk. Next winter, we will continue these analyses investigating the change in density over time and

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.

InforMD: a new initiative to raise public awareness about breast density

PubMed Central

Hugo, Honor J; Zysk, Aneta; Dasari, Pallave; Britt, Kara; Hopper, John L; Stone, Jennifer; Thompson, Erik W; Ingman, Wendy V

2018-01-01

On a mammogram, breast density (also known as mammographic density) is shown as white and bright regions and is associated with reduced sensitivity in cancer detection and increased breast cancer risk. However, many Australian women are unaware of the significance of breast density as it is not routinely reported or discussed. In order to address this lack of knowledge, Australian breast cancer researchers with expertise in mammographic density formed the InforMD alliance (INformation FORum on Mammographic Density) in 2016. The alliance is working to raise awareness of breast density with the goal of improving breast cancer diagnosis and health outcomes for women. The InforMD website (www.InforMD.org.au) was launched in October 2016, coinciding with a major nationwide public awareness campaign by the alliance during breast cancer awareness month. The website contains unbiased, accurate, updated information on breast density. The website also provides summaries of major research articles in layperson language, recent news items related to breast density, links to relevant information for health professionals, events, and feature articles. Members of the public and health professionals can also subscribe for news updates. The interactive online Forum section facilitates discussion between health professionals, scientists and members of the public. To increase online traffic to the website, Facebook (www.facebook.com/BeInforMD) and Twitter (https://twitter.com/BeInforMD_) pages were launched in December 2016. Since its launch, InforMD has generated considerable interest. The public awareness campaign reached over 7 million Australians through a combination of newspaper, TV, radio, and online news. The website has attracted 13,058 unique visitors and 30,353 page views (data as of 19/12/2017). Breast cancer researchers have a significant role to play in disseminating information to the public on breast density. A combination of mainstream and social media, together with a well-informed and updated website, has laid the groundwork for the InforMD alliance to reach a wide audience. PMID:29492101

InforMD: a new initiative to raise public awareness about breast density.

PubMed

Hugo, Honor J; Zysk, Aneta; Dasari, Pallave; Britt, Kara; Hopper, John L; Stone, Jennifer; Thompson, Erik W; Ingman, Wendy V

2018-01-01

On a mammogram, breast density (also known as mammographic density) is shown as white and bright regions and is associated with reduced sensitivity in cancer detection and increased breast cancer risk. However, many Australian women are unaware of the significance of breast density as it is not routinely reported or discussed. In order to address this lack of knowledge, Australian breast cancer researchers with expertise in mammographic density formed the InforMD alliance (INformation FORum on Mammographic Density) in 2016. The alliance is working to raise awareness of breast density with the goal of improving breast cancer diagnosis and health outcomes for women. The InforMD website (www.InforMD.org.au) was launched in October 2016, coinciding with a major nationwide public awareness campaign by the alliance during breast cancer awareness month. The website contains unbiased, accurate, updated information on breast density. The website also provides summaries of major research articles in layperson language, recent news items related to breast density, links to relevant information for health professionals, events, and feature articles. Members of the public and health professionals can also subscribe for news updates. The interactive online Forum section facilitates discussion between health professionals, scientists and members of the public. To increase online traffic to the website, Facebook (www.facebook.com/BeInforMD) and Twitter (https://twitter.com/BeInforMD_) pages were launched in December 2016. Since its launch, InforMD has generated considerable interest. The public awareness campaign reached over 7 million Australians through a combination of newspaper, TV, radio, and online news. The website has attracted 13,058 unique visitors and 30,353 page views (data as of 19/12/2017). Breast cancer researchers have a significant role to play in disseminating information to the public on breast density. A combination of mainstream and social media, together with a well-informed and updated website, has laid the groundwork for the InforMD alliance to reach a wide audience.

Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

2014-03-01

Accurate assessment of a woman's risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER- cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong's test were used to compare the models' discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (p<0.001), which was statistically significantly higher than both the density-alone (p<0.001) and texture-alone regression models (p=0.04). The combination of breast density and texture measures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.

Breast density and impacts on health

PubMed Central

Cruwys, Cheryl; Pushkin, JoAnn

2017-01-01

The World Health Organization states ‘Early detection in order to improve breast cancer outcome and survival remains the cornerstone of breast cancer control’ [WHO (World Health Organization) (2017) Breast cancer: prevention and control Available from: http://www.who.int/cancer/detection/breastcancer/en/]. Breast Density Matters UK is a non-profit breast cancer organization. The organization’s mission is to educate about breast density and its screening and risk implications with the goal of achieving the earliest stage diagnosis possible for women with dense breasts. This educational mission is endorsed by breast imaging experts worldwide [Berg WA (2015–2017) Dense Breast-info Inc Available from: http://densebreast-info.org/about.aspx]. PMID:28900477

Mammographic evidence of microenvironment changes in tumorous breasts.

PubMed

Marin, Zach; Batchelder, Kendra A; Toner, Brian C; Guimond, Lyne; Gerasimova-Chechkina, Evgeniya; Harrow, Amy R; Arneodo, Alain; Khalil, Andre

2017-04-01

The microenvironment of breast tumors plays a critical role in tumorigenesis. As long as the structural integrity of the microenvironment is upheld, the tumor is suppressed. If tissue structure is lost through disruptions in the normal cell cycle, the microenvironment may act as a tumor promoter. Therefore, the properties that distinguish between healthy and tumorous tissues may not be solely in the tumor characteristics but rather in surrounding non-tumor tissue. The goal of this paper was to show preliminary evidence that tissue disruption and loss of homeostasis in breast tissue microenvironment and breast bilateral asymmetry can be quantitatively and objectively assessed from mammography via a localized, wavelet-based analysis of the whole breast. A wavelet-based multifractal formalism called the 2D Wavelet Transform Modulus Maxima (WTMM) method was used to quantitate density fluctuations from mammographic breast tissue via the Hurst exponent (H). Each entire mammogram was cut in hundreds of 360 × 360 pixel subregions in a gridding scheme of overlapping sliding windows, with each window boundary separated by 32 pixels. The 2D WTMM method was applied to each subregion individually. A data mining approach was set up to determine which metrics best discriminated between normal vs. cancer cases. These same metrics were then used, without modification, to discriminate between normal vs. benign and benign vs. cancer cases. The density fluctuations in healthy mammographic breast tissue are either monofractal anti-correlated (H < 1/2) for fatty tissue or monofractal long-range correlated (H>1/2) for dense tissue. However, tissue regions with H~1/2, as well as left vs. right breast asymetries, were found preferably in tumorous (benign or cancer) breasts vs. normal breasts, as quantified via a combination metric yielding a P-value ~ 0.0006. No metric considered showed significant differences between cancer vs. benign breasts. Since mammographic tissue regions associated with uncorrelated (H~1/2) density fluctuations were predominantly in tumorous breasts, and since the underlying physical processes associated with a H~1/2 signature are those of randomness, lack of spatial correlation, and free diffusion, it is hypothesized that this signature is also associated with tissue disruption and loss of tissue homeostasis. © 2017 American Association of Physicists in Medicine.

Breast percent density estimation from 3D reconstructed digital breast tomosynthesis images

NASA Astrophysics Data System (ADS)

Bakic, Predrag R.; Kontos, Despina; Carton, Ann-Katherine; Maidment, Andrew D. A.

2008-03-01

Breast density is an independent factor of breast cancer risk. In mammograms breast density is quantitatively measured as percent density (PD), the percentage of dense (non-fatty) tissue. To date, clinical estimates of PD have varied significantly, in part due to the projective nature of mammography. Digital breast tomosynthesis (DBT) is a 3D imaging modality in which cross-sectional images are reconstructed from a small number of projections acquired at different x-ray tube angles. Preliminary studies suggest that DBT is superior to mammography in tissue visualization, since superimposed anatomical structures present in mammograms are filtered out. We hypothesize that DBT could also provide a more accurate breast density estimation. In this paper, we propose to estimate PD from reconstructed DBT images using a semi-automated thresholding technique. Preprocessing is performed to exclude the image background and the area of the pectoral muscle. Threshold values are selected manually from a small number of reconstructed slices; a combination of these thresholds is applied to each slice throughout the entire reconstructed DBT volume. The proposed method was validated using images of women with recently detected abnormalities or with biopsy-proven cancers; only contralateral breasts were analyzed. The Pearson correlation and kappa coefficients between the breast density estimates from DBT and the corresponding digital mammogram indicate moderate agreement between the two modalities, comparable with our previous results from 2D DBT projections. Percent density appears to be a robust measure for breast density assessment in both 2D and 3D x-ray breast imaging modalities using thresholding.

A case-control study to assess the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk.

PubMed

Assi, Valentina; Massat, Nathalie J; Thomas, Susan; MacKay, James; Warwick, Jane; Kataoka, Masako; Warsi, Iqbal; Brentnall, Adam; Warren, Ruth; Duffy, Stephen W

2015-05-15

Mammographic density is a strong risk factor for breast cancer, but its potential application in risk management is not clear, partly due to uncertainties about its interaction with other breast cancer risk factors. We aimed to quantify the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk of breast cancer (average lifetime risk of 23%), in particular in premenopausal women, and to investigate its relationship with other breast cancer risk factors in this population. We present the results from a case-control study nested with the FH01 cohort study of 6,710 women mostly aged 40-49 at intermediate familial risk of breast cancer. One hundred and three cases of breast cancer were age-matched to one or two controls. Density was measured by semiautomated interactive thresholding. Absolute density, but not percent density, was a significant risk factor for breast cancer in this population after adjusting for area of nondense tissue (OR per 10 cm(2) = 1.07, 95% CI 1.00-1.15, p = 0.04). The effect was stronger in premenopausal women, who made up the majority of the study population. Absolute density remained a significant predictor of breast cancer risk after adjusting for age at menarche, age at first live birth, parity, past or present hormone replacement therapy, and the Tyrer-Cuzick 10-year relative risk estimate of breast cancer. Absolute density can improve breast cancer risk stratification and delineation of high-risk groups alongside the Tyrer-Cuzick 10-year relative risk estimate. © 2014 UICC.

Effects of multiparity and prolonged breast-feeding on maternal bone mineral density: a community-based cross-sectional study.

PubMed

Lenora, Janaka; Lekamwasam, Sarath; Karlsson, Magnus K

2009-07-01

Studies conducted in Western countries have shown that bone loss associated with pregnancy and breast-feeding is recovered after weaning. However, it is not clear whether recovery takes place after repeated pregnancies followed by prolonged periods of breast-feeding; especially in developing countries where nutritional intake is comparatively low.This study was designed to examine the effects of multiparity and prolonged breast-feeding on maternal bone mineral density (BMD) in a community-based sample of 210 Sri Lankan women, aged between 46 and 98 years. BMD of the lumbar spine (L2-L4) and femoral neck were measured by dual-energy X-ray absorptiometry. Reproductive history was recorded by using a questionnaire. Women were, first, divided into groups according to parity (nulliparous, 1-2, 3-4, and 5 or more children), and BMDs in different groups were compared, initially unadjusted and then adjusted for age. Same subjects were subdivided, again, according to the total duration of breast-feeding (0, 1-48, 49-96, and 97 months or more) and similar analysis was carried out. Women who had 5 or more children and women who had breast-fed for 97 months or more were older than the other women (p < 0.01) but no differences in height, weight or BMI were observed among the groups. Age adjusted BMD at lumbar spine and femoral neck BMDs of women grouped according to parity were not significantly different. Neither was there any difference between lumbar spine or femoral neck BMD in groups based on duration of breast-feeding. From this population-based study conducted in a developing country, we infer that history of multiparity or prolonged breast-feeding has no detrimental effects on maternal BMD in post-menopausal age.

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.

Ultrasound tomography imaging with waveform sound speed: parenchymal changes in women undergoing tamoxifen therapy

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Sherman, Mark; Gierach, Gretchen

2017-03-01

Ultrasound tomography (UST) is an emerging modality that can offer quantitative measurements of breast density. Recent breakthroughs in UST image reconstruction involve the use of a waveform reconstruction as opposed to a raybased reconstruction. The sound speed (SS) images that are created using the waveform reconstruction have a much higher image quality. These waveform images offer improved resolution and contrasts between regions of dense and fatty tissues. As part of a study that was designed to assess breast density changes using UST sound speed imaging among women undergoing tamoxifen therapy, UST waveform sound speed images were then reconstructed for a subset of participants. These initial results show that changes to the parenchymal tissue can more clearly be visualized when using the waveform sound speed images. Additional quantitative testing of the waveform images was also started to test the hypothesis that waveform sound speed images are a more robust measure of breast density than ray-based reconstructions. Further analysis is still needed to better understand how tamoxifen affects breast tissue.

Bioimpedence to Assess Breast Density as a Risk Factor for Breast Cancer in Adult Women and Adolescent Girls.

PubMed

Maskarinec, Gertraud; Morimoto, Yukiko; Laguana, Michelle B; Novotny, Rachel; Leon Guerrero, Rachael T

2016-01-01

Although high mammographic density is one of the strongest predictors of breast cancer risk, X-ray based mammography cannot be performed before the recommended screening age, especially not in adolescents and young women. Therefore, new techniques for breast density measurement are of interest. In this pilot study in Guam and Hawaii, we evaluated a radiation-free, bioimpedance device called Electrical Breast DensitometerTM (EBD; senoSENSE Medical Systems, Inc., Ontario, Canada) for measuring breast density in 95 women aged 31-82 years and 41 girls aged 8-18 years. Percent density (PD) was estimated in the women's most recent mammogram using a computer-assisted method. Correlation coefficients and linear regression were applied for statistical analysis. In adult women, mean EBD and PD values of the left and right breasts were 230±52 and 226±50 Ω and 23.7±15.1 and 24.2±15.2%, respectively. The EBD measurements were inversely correlated with PD (rSpearman=-0.52, p<0.0001); the correlation was stronger in Caucasians (rSpearman=-0.70, p<0.0001) than Asians (rSpearman=-0.54, p<0.01) and Native Hawaiian/Chamorro/Pacific Islanders (rSpearman=-0.34, p=0.06). Using 4 categories of PD (<10, 10-25, 26-50, 51-75%), the respective mean EBD values were 256±32, 249±41, 202±46, and 178±43 Ω (p<0.0001). In girls, the mean EBD values in the left and right breast were 148±40 and 155±54 Ω; EBD values decreased from Tanner stages 1 to 4 (204±14, 154±79, 136±43, and 119±16 Ω for stages 1-4, respectively) but were higher at Tanner stage 5 (165±30 Ω). With further development, this bioimpedance method may allow for investigations of breast development among adolescent, as well as assessment of breast cancer risk early in life and in populations without access to mammography.

Pilot study of quantitative analysis of background enhancement on breast MR images: association with menstrual cycle and mammographic breast density.

PubMed

Scaranelo, Anabel M; Carrillo, Maria Claudia; Fleming, Rachel; Jacks, Lindsay M; Kulkarni, Supriya R; Crystal, Pavel

2013-06-01

To perform semiautomated quantitative analysis of the background enhancement (BE) in a cohort of patients with newly diagnosed breast cancer and to correlate it with mammographic breast density and menstrual cycle. Informed consent was waived after the research ethics board approved this study. Results of 177 consecutive preoperative breast magnetic resonance (MR) examinations performed from February to December 2009 were reviewed; 147 female patients (median age, 48 years; range, 26-86 years) were included. Ordinal values of BE and breast density were described by two independent readers by using the Breast Imaging Reporting and Data System lexicon. The BE coefficient (BEC) was calculated thus: (SI2 · 100/SI1) - 100, where SI is signal intensity, SI2 is the SI enhancement measured in the largest anteroposterior dimension in the axial plane 1 minute after the contrast agent injection, and SI1is the SI before contrast agent injection. BEC was used for the quantitative analysis of BE. Menstrual cycle status was based on the last menstrual period. The Wilcoxon rank-sum or Kruskal-Wallis test was used to compare quantitative assessment groups. Cohen weighted κ was used to evaluate agreement. Of 147 patients, 68 (46%) were premenopausal and 79 (54%) were postmenopausal. The quantitative BEC was associated with the menstrual status (BEC in premenopausal women, 31.48 ± 20.68 [standard deviation]; BEC in postmenopausal women, 25.65 ± 16.74; P = .02). The percentage of overall BE was higher when the MR imaging was performed in women in the inadequate phase of the cycle (<35 days, not 7-14 days; mean BEC, 35.7) compared with women in the postmenopausal group (P = .001). Premenopausal women had significantly higher BEC when compared with postmenopausal women (P = .03). There was no significant difference in the percentage of BE between breast density groups. Premenopausal women with breast cancer, and specifically women in the inadequate phase of the cycle, presented with higher quantitative BE than postmenopausal women. No association was found between BE and breast density.

Increased vitamin D and calcium intake associated with reduced mammographic breast density among premenopausal women

PubMed Central

Fair, Alecia Malin; Lewis, Toni J.; Sanderson, Maureen; Dupont, William D.; Fletcher, Sarah; Egan, Kathleen M.; Disher, Anthony C.

2015-01-01

Vitamin D has been identified as a weak protective factor for postmenopausal breast cancer (relative risk [RR]~0.9), while high breast density has been identified as a strong risk factor (RR~4–6). To test the hypothesis that there is an association between vitamin D intake, but not circulating vitamin D levels, and mammographic breast density among women in our study we conducted a cross-sectional study of 165 screening mammography patients at Nashville General Hospital’s Breast Health Center (NGH-BHC), a public facility serving medically indigent and underserved women. Dietary and total (dietary plus supplements) vitamin D, calcium intakes were estimated by the AAFQ and blood samples were analyzed for 25-Hydroxyvitamin D [25(OH)D3]. Average percent breast density for the left and right breasts combined was estimated from digitized films using an interactive-thresholding method available through Cumulus software. After statistical adjustment for age, race and body mass index, the results revealed there were significant trends of decreasing breast density with increasing vitamin D and calcium intake among premenopausal, but not among postmenopausal women. There was no association between serum vitamin D and breast density in pre- or postmenopausal women. Confirmation of our findings in larger studies may assist in clarifying the role of vitamin D in breast density. PMID:26321093

Impact of positional difference on the measurement of breast density using MRI.

PubMed

Chen, Jeon-Hor; Chan, Siwa; Tang, Yi-Ting; Hon, Jia Shen; Tseng, Po-Chuan; Cheriyan, Angela T; Shah, Nikita Rakesh; Yeh, Dah-Cherng; Lee, San-Kan; Chen, Wen-Pin; McLaren, Christine E; Su, Min-Ying

2015-05-01

This study investigated the impact of arms/hands and body position on the measurement of breast density using MRI. Noncontrast-enhanced T1-weighted images were acquired from 32 healthy women. Each subject received four MR scans using different experimental settings, including a high resolution hands-up, a low resolution hands-up, a high resolution hands-down, and finally, another high resolution hands-up after repositioning. The breast segmentation was performed using a fully automatic chest template-based method. The breast volume (BV), fibroglandular tissue volume (FV), and percent density (PD) measured from the four MR scan settings were analyzed. A high correlation of BV, FV, and PD between any pair of the four MR scans was noted (r > 0.98 for all). Using the generalized estimating equation method, a statistically significant difference in mean BV among four settings was noted (left breast, score test p = 0.0056; right breast, score test p = 0.0016), adjusted for age and body mass index. Despite differences in BV, there were no statistically significant differences in the mean PDs among the four settings (p > 0.10 for left and right breasts). Using Bland-Altman plots, the smallest mean difference/bias and standard deviations for BV, FV, and PD were noted when comparing hands-up high vs low resolution when the breast positions were exactly the same. The authors' study showed that BV, FV, and PD measurements from MRI of different positions were highly correlated. BV may vary with positions but the measured PD did not differ significantly between positions. The study suggested that the percent density analyzed from MRI studies acquired using different arms/hands and body positions from multiple centers can be combined for analysis.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

2009-01-01

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

Radiological assessment of breast density by visual classification (BI-RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice.

PubMed

Regini, Elisa; Mariscotti, Giovanna; Durando, Manuela; Ghione, Gianluca; Luparia, Andrea; Campanino, Pier Paolo; Bianchi, Caterina Chiara; Bergamasco, Laura; Fonio, Paolo; Gandini, Giovanni

2014-10-01

This study was done to assess breast density on digital mammography and digital breast tomosynthesis according to the visual Breast Imaging Reporting and Data System (BI-RADS) classification, to compare visual assessment with Quantra software for automated density measurement, and to establish the role of the software in clinical practice. We analysed 200 digital mammograms performed in 2D and 3D modality, 100 of which positive for breast cancer and 100 negative. Radiological density was assessed with the BI-RADS classification; a Quantra density cut-off value was sought on the 2D images only to discriminate between BI-RADS categories 1-2 and BI-RADS 3-4. Breast density was correlated with age, use of hormone therapy, and increased risk of disease. The agreement between the 2D and 3D assessments of BI-RADS density was high (K 0.96). A cut-off value of 21% is that which allows us to best discriminate between BI-RADS categories 1-2 and 3-4. Breast density was negatively correlated to age (r = -0.44) and positively to use of hormone therapy (p = 0.0004). Quantra density was higher in breasts with cancer than in healthy breasts. There is no clear difference between the visual assessments of density on 2D and 3D images. Use of the automated system requires the adoption of a cut-off value (set at 21%) to effectively discriminate BI-RADS 1-2 and 3-4, and could be useful in clinical practice.

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.

Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography.

PubMed

Keller, Brad M; McCarthy, Anne Marie; Chen, Jinbo; Armstrong, Katrina; Conant, Emily F; Domchek, Susan M; Kontos, Despina

2015-03-18

Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women. In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk. The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively. The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman's risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment.

Mammographic density measured as changes in tissue structure caused by HRT

NASA Astrophysics Data System (ADS)

Raundahl, Jakob; Loog, Marco; Nielsen, Mads

2006-03-01

Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density. No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. They may be sufficient in certain cases and for single measurements, but for serial, temporal analysis it is necessary to be able to detect more subtle changes and, in addition, to be more reproducible. In this work an automated method for measuring the effect of HRT w.r.t. changes in biological density in the breast is presented. This measure is a novel measure, which provides structural information orthogonal to intensity-based methods. Hessian eigenvalues at different scales are used as features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined. In the experiments, two sets of mammograms of 50 patients from a double blind, placebo controlled HRT experiment were used. The change in density for the HRT group, measured with the new method, was significantly higher (p = 0.0002) than the change in the control group.

Correlations between female breast density and biochemical markers.

PubMed

Kim, Ji-Hye; Lee, Hae-Kag; Cho, Jae-Hwan; Park, Hyong-Keun; Yang, Han-Jun

2015-07-01

[Purpose] The aim of this study was to identify biochemical markers related to breast density. The study was performed with 200 patients who received mammography and biochemical marker testing between March 1, 2014 to October 1, 2014. [Subjects and Methods] Following the American College of Radiology, Breast Imaging Reporting and Data System (ACR BI-RADS), breast parenchymal pattern density from mammography was categorized into four grades: grade 1, almost entirely fat; grade 2, fibroglandular densities; grade 3, heterogeneously dense; and grade 4, extremely dense. Regarding biochemical markers, subjects underwent blood and urine tests after a 12-h fast. We analyzed correlations among breast density, general characteristics, and biochemical markers. [Results] Breast density-related factors were age, height, weight, body mass index (BMI), hematocrit, MCH, RDW, AST, ALT, ALP, uric acid, γGT, triglycerides, total cholesterol, HDL-cholesterol, and LDL-cholesterol. [Conclusion] The results can be used as basic and comparative data for the prevention and early control of breast cancer.

The Problem of Mammographic Breast Density - The Position of the DEGUM Working Group on Breast Ultrasound.

PubMed

Mueller-Schimpfle, M P; Brandenbusch, V C; Degenhardt, F; Duda, V; Madjar, H; Mundinger, A; Rathmann, R; Hahn, M

2016-04-01

Mammographic breast density correlates with breast cancer risk and also with the number of false-negative calls. In the USA these facts lead to the "Breast Density and Mammography Reporting Act" of 2011. In the case of mammographically dense breasts, the Working Group on Breast Ultrasound in Germany recommends explaining the advantages of adjunct imaging to women, depending on the individual breast cancer risk. Due to the particular structure of German healthcare, quality-assured breast ultrasound would be the first choice. Possible overdiagnosis, costs, potentially increased emotional stress should be addressed. In high familial breast cancer risk, genetic counselling and an intensified early detection program should be performed. © Georg Thieme Verlag KG Stuttgart · New York.

Automatic breast tissue density estimation scheme in digital mammography images

NASA Astrophysics Data System (ADS)

Menechelli, Renan C.; Pacheco, Ana Luisa V.; Schiabel, Homero

2017-03-01

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes - taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

Feasibility of estimating volumetric breast density from mammographic x-ray spectra using a cadmium telluride photon-counting detector.

PubMed

Ghammraoui, Bahaa; Badal, Andreu; Glick, Stephen J

2018-06-03

Mammographic density of glandular breast tissue has a masking effect that can reduce lesion detection accuracy and is also a strong risk factor for breast cancer. Therefore, accurate quantitative estimation of breast density is clinically important. In this study, we investigate experimentally the feasibility of quantifying volumetric breast density with spectral mammography using a CdTe-based photon-counting detector. To demonstrate proof-of-principle, this study was carried out using the single pixel Amptek XR-100T-CdTe detector. The total number of x rays recorded by the detector from a single pencil-beam projection through 50%/50% of adipose/glandular mass fraction-equivalent phantoms was measured. Material decomposition assuming two, four, and eight energy bins was then applied to characterize the inspected phantom into adipose and glandular using log-likelihood estimation, taking into account the polychromatic source, the detector response function, and the energy-dependent attenuation. Measurement tests were carried out for different doses, kVp settings, and different breast sizes. For dose of 1 mGy and above, the percent relative root mean square (RMS) errors of the estimated breast density was measured below 7% for all three phantom studies. It was also observed that some decrease in RMS errors was achieved using eight energy bins. For 3 and 4 cm thick phantoms, performance at 40 and 45 kVp showed similar performance. However, it was observed that 45 kVp showed better performance for a phantom thickness of 6 cm at low dose levels due to increased statistical variation at lower photon count levels with 40 kVp. The results of the current study suggest that photon-counting spectral mammography systems using CdTe detectors have the potential to be used for accurate quantification of volumetric breast density on a pixel-to-pixel basis, with an RMS error of less than 7%. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Breast Density Assessment by Dual Energy X-ray Absorptiometry in Women and Girls

DTIC Science & Technology

2009-07-01

of this project among adult women and adolescent girls, who will be recruited as mothers and daughters, will be to 1. Correlate breast density...scans to their association with mammographic density; 3. Assess DXA breast density by Tanner stage of breast maturation among adolescent girls; 4...cancer risk, adolescents 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC

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 with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%. Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

SU-E-T-129: Dosimetric Evaluation of the Impact of Density Correction On Dose Calculation of Breast Cancer Treatment: A Study Based On RTOG 1005 Cases

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

Li, J; Yu, Y

Purpose: RTOG 1005 requires density correction in the dose calculation of breast cancer radiation treatment. The aim of the study was to evaluate the impact of density correction on the dose calculation. Methods: Eight cases were studied, which were planned on an XiO treatment planning system with pixel-by-pixel density correction using a superposition algorithm, following RTOG 1005 protocol requirements. Four were protocol Arm 1 (standard whole breast irradiation with sequential boost) cases and four were Arm 2 (hypofractionated whole breast irradiation with concurrent boost) cases. The plans were recalculated with the same monitor units without density correction. Dose calculations withmore » and without density correction were compared. Results: Results of Arm 1 and Arm 2 cases showed similar trends in the comparison. The average differences between the calculations with and without density correction (difference = Without - With) among all the cases were: -0.82 Gy (range: -2.65∼−0.18 Gy) in breast PTV Eval D95, −0.75 Gy (range: −1.23∼0.26 Gy) in breast PTV Eval D90, −1.00 Gy (range: −2.46∼−0.29 Gy) in lumpectomy PTV Eval D95, −0.78 Gy (range: −1.30∼0.11 Gy) in lumpectomy PTV Eval D90, −0.43% (range: −0.95∼−0.14%) in ipsilateral lung V20, −0.81% (range: −1.62∼−0.26%) in V16, −1.95% (range: −4.13∼−0.84%) in V10, −2.64% (−5.55∼−1.04%) in V8, −4.19% (range: −6.92∼−1.81%) in V5, and −4.95% (range: −7.49∼−2.01%) in V4, respectively. The differences in other normal tissues were minimal. Conclusion: The effect of density correction was observed in breast target doses (an average increase of ∼1 Gy in D95 and D90, compared to the calculation without density correction) and exposed ipsilateral lung volumes in low dose region (average increases of ∼4% and ∼5% in V5 and V4, respectively)« less

The role of cone-beam breast-CT for breast cancer detection relative to breast density.

PubMed

Wienbeck, Susanne; Uhlig, Johannes; Luftner-Nagel, Susanne; Zapf, Antonia; Surov, Alexey; von Fintel, Eva; Stahnke, Vera; Lotz, Joachim; Fischer, Uwe

2017-12-01

To evaluate the impact of breast density on the diagnostic accuracy of non-contrast cone-beam breast computed tomography (CBBCT) in comparison to mammography for the detection of breast masses. A retrospective study was conducted from August 2015 to July 2016. Fifty-nine patients (65 breasts, 112 lesions) with BI-RADS, 5th edition 4 or 5 assessment in mammography and/or ultrasound of the breast received an additional non-contrast CBBCT. Independent double blind reading by two radiologists was performed for mammography and CBBCT imaging. Sensitivity, specificity and AUC were compared between the modalities. Breast lesions were histologically examined in 85 of 112 lesions (76%). The overall sensitivity for CBBCT (reader 1: 91%, reader 2: 88%) was higher than in mammography (both: 68%, p<0.001), and also for the high-density group (p<0.05). The specificity and AUC was higher for mammography in comparison to CBBCT (p<0.05 and p<0.001). The interobserver agreement (ICC) between the readers was 90% (95% CI: 86-93%) for mammography and 87% (95% CI: 82-91%) for CBBCT. Compared with two-view mammography, non-contrast CBBCT has higher sensitivity, lower specificity, and lower AUC for breast mass detection in both high and low density breasts. • Overall sensitivity for non-contrast CBBCT ranged between 88%-91%. • Sensitivity was higher for CBBCT than mammography in both density types (p<0.001). • Specificity was higher for mammography than CBBCT in both density types (p<0.05). • AUC was larger for mammography than CBBCT in both density types (p<0.001).

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.

Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories.

PubMed

Tagliafico, A S; Tagliafico, G; Cavagnetto, F; Calabrese, M; Houssami, N

2013-11-01

To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software. Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity(©), developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification. The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively). Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk. On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.

Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors.

PubMed

Gerasimova-Chechkina, Evgeniya; Toner, Brian; Marin, Zach; Audit, Benjamin; Roux, Stephane G; Argoul, Francoise; Khalil, Andre; Gileva, Olga; Naimark, Oleg; Arneodo, Alain

2016-01-01

There is growing evidence that the microenvironment surrounding a tumor plays a special role in cancer development and cancer therapeutic resistance. Tumors arise from the dysregulation and alteration of both the malignant cells and their environment. By providing tumor-repressing signals, the microenvironment can impose and sustain normal tissue architecture. Once tissue homeostasis is lost, the altered microenvironment can create a niche favoring the tumorigenic transformation process. A major challenge in early breast cancer diagnosis is thus to show that these physiological and architectural alterations can be detected with currently used screening techniques. In a recent study, we used a 1D wavelet-based multi-scale method to analyze breast skin temperature temporal fluctuations collected with an IR thermography camera in patients with breast cancer. This study reveals that the multifractal complexity of temperature fluctuations superimposed on cardiogenic and vasomotor perfusion oscillations observed in healthy breasts is lost in malignant tumor foci in cancerous breasts. Here we use a 2D wavelet-based multifractal method to analyze the spatial fluctuations of breast density in the X-ray mammograms of the same panel of patients. As compared to the long-range correlations and anti-correlations in roughness fluctuations, respectively observed in dense and fatty breast areas, some significant change in the nature of breast density fluctuations with some clear loss of correlations is detected in the neighborhood of malignant tumors. This attests to some architectural disorganization that may deeply affect heat transfer and related thermomechanics in breast tissues, corroborating the change to homogeneous monofractal temperature fluctuations recorded in cancerous breasts with the IR camera. These results open new perspectives in computer-aided methods to assist in early breast cancer diagnosis.

Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors

PubMed Central

Gerasimova-Chechkina, Evgeniya; Toner, Brian; Marin, Zach; Audit, Benjamin; Roux, Stephane G.; Argoul, Francoise; Khalil, Andre; Gileva, Olga; Naimark, Oleg; Arneodo, Alain

2016-01-01

There is growing evidence that the microenvironment surrounding a tumor plays a special role in cancer development and cancer therapeutic resistance. Tumors arise from the dysregulation and alteration of both the malignant cells and their environment. By providing tumor-repressing signals, the microenvironment can impose and sustain normal tissue architecture. Once tissue homeostasis is lost, the altered microenvironment can create a niche favoring the tumorigenic transformation process. A major challenge in early breast cancer diagnosis is thus to show that these physiological and architectural alterations can be detected with currently used screening techniques. In a recent study, we used a 1D wavelet-based multi-scale method to analyze breast skin temperature temporal fluctuations collected with an IR thermography camera in patients with breast cancer. This study reveals that the multifractal complexity of temperature fluctuations superimposed on cardiogenic and vasomotor perfusion oscillations observed in healthy breasts is lost in malignant tumor foci in cancerous breasts. Here we use a 2D wavelet-based multifractal method to analyze the spatial fluctuations of breast density in the X-ray mammograms of the same panel of patients. As compared to the long-range correlations and anti-correlations in roughness fluctuations, respectively observed in dense and fatty breast areas, some significant change in the nature of breast density fluctuations with some clear loss of correlations is detected in the neighborhood of malignant tumors. This attests to some architectural disorganization that may deeply affect heat transfer and related thermomechanics in breast tissues, corroborating the change to homogeneous monofractal temperature fluctuations recorded in cancerous breasts with the IR camera. These results open new perspectives in computer-aided methods to assist in early breast cancer diagnosis. PMID:27555823

Diffuse optical tomography with structured-light patterns to quantify breast density

NASA Astrophysics Data System (ADS)

Kwong, Jessica; Nouizi, Farouk; Cho, Jaedu; Zheng, Jie; Li, Yifan; Chen, Jeon-hor; Su, Min-Ying; Gulsen, Gultekin

2016-02-01

Breast density is an independent risk factor for breast cancer, where women with denser breasts are more likely to develop cancer. By identifying women at higher risk, healthcare providers can suggest screening at a younger age to effectively diagnose and treat breast cancer in its earlier stages. Clinical risk assessment models currently do not incorporate breast density, despite its strong correlation with breast cancer. Current methods to measure breast density rely on mammography and MRI, both of which may be difficult to use as a routine risk assessment tool. We propose to use diffuse optical tomography with structured-light to measure the dense, fibroglandular (FGT) tissue volume, which has a different chromophore signature than the surrounding adipose tissue. To test the ability of this technique, we performed simulations by creating numerical breast phantoms from segmented breast MR images. We looked at two different cases, one with a centralized FGT distribution and one with a dispersed distribution. As expected, the water and lipid volumes segmented at half-maximum were overestimated for the dispersed case. However, it was noticed that the recovered water and lipid concentrations were lower and higher, respectively, than the centralized case. This information may provide insight into the morphological distribution of the FGT and can be a correction in estimating the breast density.

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

Positive association between mammographic breast density and bone mineral density in the Postmenopausal Estrogen/Progestin Interventions Study

PubMed Central

Crandall, Carolyn; Palla, Shana; Reboussin, Beth A; Ursin, Giske; Greendale, Gail A

2005-01-01

Introduction Mammographic breast density is a strong independent risk factor for breast cancer. We hypothesized that demonstration of an association between mammographic breast density and bone mineral density (BMD) would suggest a unifying underlying mechanism influencing both breast density and BMD. Methods In a cross-sectional analysis of baseline data from the Postmenopausal Estrogen/Progestin Interventions Study (PEPI), participants were aged 45 to 64 years and were at least 1 year postmenopausal. Mammographic breast density (percentage of the breast composed of dense tissue), the outcome, was assessed with a computer-assisted percentage-density method. BMD, the primary predictor, was measured with dual-energy X-ray absorptiometry. Women quitting menopausal hormone therapy to join PEPI were designated recent hormone users. Results The mean age of the 594 women was 56 years. The average time since menopause was 5.6 years. After adjustment for age, body mass index, and cigarette smoking, in women who were not recent hormone users before trial enrollment (n = 415), mammographic density was positively associated with total hip (P = 0.04) and lumbar (P = 0.08) BMD. Mammographic density of recent hormone users (n = 171) was not significantly related to either total hip (P = 0.51) or lumbar (P = 0.44) BMD. In participants who were not recent hormone users, mammographic density was 4% greater in the highest quartile of total hip BMD than in the lowest. In participants who were not recent hormone users, mammographic density was 5% greater in the highest quartile of lumbar spine BMD than in the lowest. Conclusion Mammographic density and BMD are positively associated in women who have not recently used postmenopausal hormones. A unifying biological mechanism may link mammographic density and BMD. Recent exogenous postmenopausal hormone use may obscure the association between mammographic density and BMD by having a persistent effect on breast tissue. PMID:16280044

Segmentation of the whole breast from low-dose chest CT images

NASA Astrophysics Data System (ADS)

Liu, Shuang; Salvatore, Mary; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

2015-03-01

The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test. The automated whole breast segmentation and potential breast density readings as well as lesion detection in LDCT will provide useful information for women who have received LDCT screening, especially the ones who have not undergone mammographic screening, by providing them additional risk indicators for breast cancer with no additional radiation exposure. The two main challenges to be addressed are significant range of variations in terms of the shape and location of the breast in LDCT and the separation of pectoral muscles from the glandular tissues. The presented algorithm achieves robust whole breast segmentation using an anatomy directed rule-based method. The evaluation is performed on 20 LDCT scans by comparing the segmentation with ground truth manually annotated by a radiologist on one axial slice and two sagittal slices for each scan. The resulting average Dice coefficient is 0.880 with a standard deviation of 0.058, demonstrating that the automated segmentation algorithm achieves results consistent with manual annotations of a radiologist.

Analysis of parenchymal patterns using conspicuous spatial frequency features in mammograms applied to the BI-RADS density rating scheme

NASA Astrophysics Data System (ADS)

Perconti, Philip; Loew, Murray

2006-03-01

Automatic classification of the density of breast parenchyma is shown using a measure that is correlated to the human observer performance, and compared against the BI-RADS density rating. Increasingly popular in the United States, the Breast Imaging Reporting and Data System (BI-RADS) is used to draw attention to the increased screening difficulty associated with greater breast density; however, the BI-RADS rating scheme is subjective and is not intended as an objective measure of breast density. So, while popular, BI-RADS does not define density classes using a standardized measure, which leads to increased variability among observers. The adaptive thresholding technique is a more quantitative approach for assessing the percentage breast density, but considerable reader interaction is required. We calculate an objective density rating that is derived using a measure of local feature salience. Previously, this measure was shown to correlate well with radiologists' localization and discrimination of true positive and true negative regions-of-interest. Using conspicuous spatial frequency features, an objective density rating is obtained and correlated with adaptive thresholding, and the subjectively ascertained BI-RADS density ratings. Using 100 cases, obtained from the University of South Florida's DDSM database, we show that an automated breast density measure can be derived that is correlated with the interactive thresholding method for continuous percentage breast density, but not with the BI-RADS density rating categories for the selected cases. Comparison between interactive thresholding and the new salience percentage density resulted in a Pearson correlation of 76.7%. Using a four-category scale equivalent to the BI-RADS density categories, a Spearman correlation coefficient of 79.8% was found.

Investigation of mammographic breast density as a risk factor for ovarian cancer.

PubMed

Wernli, Karen J; O'Meara, Ellen S; Kerlikowske, Karla; Miglioretti, Diana L; Muller, Carolyn Y; Onega, Tracy; Sprague, Brian L; Henderson, Louise M; Buist, Diana S M

2014-01-01

Endogenous hormones and growth factors that increase mammographic breast density could increase ovarian cancer risk. We examined whether high breast density is associated with ovarian cancer risk. We conducted a cohort study of 724,603 women aged 40 to 79 years with 2,506,732 mammograms participating in the Breast Cancer Surveillance Consortium from 1995 to 2009. Incident epithelial ovarian cancer was diagnosed in 1373 women. We used partly conditional Cox regression to estimate the association between breast density and 5-year risk of incident epithelial ovarian cancer overall and stratified by 10-year age group. All statistical tests were two-sided. Compared with women with scattered fibroglandular densities, women with heterogeneously dense and extremely dense breast tissue had 20% and 18% increased 5-year risk of incident epithelial ovarian cancer (hazard ratio [HR] = 1.20, 95% confidence interval [CI] = 1.06 to 1.36; HR = 1.18, 95% CI = 0.93 to 1.50, respectively; P(trend) = .01). Among women aged 50 to 59 years, we observed a trend in elevated risk associated with increased breast density (P(trend) = .02); women with heterogeneously and extremely dense breast tissue had 30% (HR = 1.30; 95% CI = 1.03 to 1.64) and 65% (HR = 1.65; 95% CI = 1.12 to 2.44) increased risk, respectively, compared with women with scattered fibroglandular densities. The pattern was similar but not statistically significant at age 40 to 49 years. There were no consistent patterns of breast density and ovarian cancer risk at age 60 to 79 years. Dense breast tissue was associated with a modest increase in 5-year ovarian cancer risk in women aged 50 to 59 years but was not associated with ovarian cancer at ages 40 to 49 or 60 to 79 years.

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 cancer risk factor that is obtained from breast imaging.

Assessment of Interradiologist Agreement Regarding Mammographic Breast Density Classification Using the Fifth Edition of the BI-RADS Atlas.

PubMed

Ekpo, Ernest U; Ujong, Ujong Peter; Mello-Thoms, Claudia; McEntee, Mark F

2016-05-01

The objective of the present study was to assess interradiologist agreement regarding mammographic breast density assessment performed using the rating scale outlined in the fifth edition of the BI-RADS atlas of the American College of Radiology. Breast density assessments of 1000 cases were conducted by five radiologists from the same institution who together had recently undergone retraining in mammographic breast density classification based on the fifth edition of BI-RADS. The readers assigned breast density grades (A-D) on the basis of the BI-RADS classification scheme. Repeat assessment of 100 cases was performed by all readers 1 month after the initial assessment. A weighted kappa was used to calculate intrareader and interreader agreement. Intrareader agreement ranged from a kappa value of 0.86 (95% CI, 0.77-0.93) to 0.89 (95% CI, 0.81-0.95) on a four-category scale (categories A-D) and from 0.89 (95% CI, 0.86-0.92) to 0.94 (95% CI, 0.89-0.97) on a two-category scale (category A-B vs category C-D). Interreader agreement ranged from substantial (κ = 0.76; 95% CI, 0.73-0.78) to almost perfect (κ = 0.87; 95% CI, 0.86-0.89) on a four-category scale, and the overall weighted kappa value was substantial (0.79; 95% CI, 0.78-0.83). Interreader agreement on a two-category scale ranged from a kappa value of 0.85 (95% CI, 0.83-0.86) to 0.91 (95% CI, 0.90-0.92), and the overall weighted kappa was 0.88 (95% CI, 0.87-0.89). Overall, with regard to mammographic breast density classification, radiologists had substantial interreader agreement when a four-category scale was used and almost perfect interreader agreement when a dichotomous scale was used.

Stereotactic core needle breast biopsy marker migration: An analysis of factors contributing to immediate marker migration.

PubMed

Jain, Ashali; Khalid, Maria; Qureshi, Muhammad M; Georgian-Smith, Dianne; Kaplan, Jonah A; Buch, Karen; Grinstaff, Mark W; Hirsch, Ariel E; Hines, Neely L; Anderson, Stephan W; Gallagher, Katherine M; Bates, David D B; Bloch, B Nicolas

2017-11-01

To evaluate breast biopsy marker migration in stereotactic core needle biopsy procedures and identify contributing factors. This retrospective study analyzed 268 stereotactic biopsy markers placed in 263 consecutive patients undergoing stereotactic biopsies using 9G vacuum-assisted devices from August 2010-July 2013. Mammograms were reviewed and factors contributing to marker migration were evaluated. Basic descriptive statistics were calculated and comparisons were performed based on radiographically-confirmed marker migration. Of the 268 placed stereotactic biopsy markers, 35 (13.1%) migrated ≥1 cm from their biopsy cavity. Range: 1-6 cm; mean (± SD): 2.35 ± 1.22 cm. Of the 35 migrated biopsy markers, 9 (25.7%) migrated ≥3.5 cm. Patient age, biopsy pathology, number of cores, and left versus right breast were not associated with migration status (P> 0.10). Global fatty breast density (P= 0.025) and biopsy in the inner region of breast (P = 0.031) were associated with marker migration. Superior biopsy approach (P= 0.025), locally heterogeneous breast density, and t-shaped biopsy markers (P= 0.035) were significant for no marker migration. Multiple factors were found to influence marker migration. An overall migration rate of 13% supports endeavors of research groups actively developing new biopsy marker designs for improved resistance to migration. • Breast biopsy marker migration is documented in 13% of 268 procedures. • Marker migration is affected by physical, biological, and pathological factors. • Breast density, marker shape, needle approach etc. affect migration. • Study demonstrates marker migration prevalence; marker design improvements are needed.

Childhood factors associated with mammographic density in adult women.

PubMed

Lope, Virginia; Pérez-Gómez, Beatriz; Moreno, María Pilar; Vidal, Carmen; Salas-Trejo, Dolores; Ascunce, Nieves; Román, Isabel González; Sánchez-Contador, Carmen; Santamariña, María Carmen; Carrete, Jose Antonio Vázquez; Collado-García, Francisca; Pedraz-Pingarrón, Carmen; Ederra, María; Ruiz-Perales, Francisco; Peris, Mercé; Abad, Soledad; Cabanes, Anna; Pollán, Marina

2011-12-01

Growth and development factors could contribute to the development of breast cancer associated with an increase in mammographic density. This study examines the influence of certain childhood-related, socio-demographic and anthropometric variables on mammographic density in adult woman. The study covered 3574 women aged 45-68 years, participating in breast cancer-screening programmes in seven Spanish cities. Based on a craniocaudal mammogram, blind, anonymous measurement of mammographic density was made by a single radiologist, using Boyd's semiquantitative scale. Data associated with the early stages of life were obtained from a direct survey. Ordinal logistic regression and generalised linear models were employed to estimate the association between mammographic density and the variables covered by the questionnaire. Screening programme was introduced as a random effects term. Age, number of children, body mass index (BMI) and other childhood-related variables were used as adjustment variables, and stratified by menopausal status. A total of 811 women (23%) presented mammographic density of over 50%, and 5% of densities exceeded 75%. Our results show a greater prevalence of high mammographic density in women with low prepubertal weight (OR: 1.18; 95% CI: 1.02-1.36); marked prepubertal height (OR: 1.25; 95% CI: 0.97-1.60) and advanced age of their mothers at their birth (>39 years: OR: 1.28; 95% CI: 1.03-1.60); and a lower prevalence of high mammographic density in women with higher prepubertal weight, low birth weight and earlier menarche. The influence of these early-life factors may be explained by greater exposure to hormones and growth factors during the development of the breast gland, when breast tissue would be particularly susceptible to proliferative and carcinogenic stimulus.

Feature extraction using convolutional neural network for classifying breast density in mammographic images

NASA Astrophysics Data System (ADS)

Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is still required for evaluating the results.

Characterization of breast density in Vietnam and its association with demographic, reproductive and lifestyle factors

NASA Astrophysics Data System (ADS)

Trieu, Phuong Dung (Yun); Mello-Thoms, Claudia; Peat, Jenny; Do, Thuan Doan; Brennan, Patrick C.

2017-03-01

This study aims to investigate patterns of breast density among women in Vietnam and their association with demographic, reproductive and lifestyle features. Mammographic densities of 1,651 women were collected from the two largest breast cancer screening and treatment centers in Ha Noi and Ho Chi Minh city. Putative factors associated with breast density were obtained from self-administered questionnaires which considered demographic, reproductive and lifestyle elements and were provided by women who attended mammography examinations. Results show that a large proportion of Vietnamese women (78.4%) had a high breast density. With multivariable logistic regression, significant associations of high breast density were evident with women with less than 55 years old (OR=3.0), having BMI less than 23 (OR=2.2), experiencing pre-menopausal status (OR=2.9), having less than three children (OR=1.7), and being less than 32 years old when having their last child (OR=1.8). Participants who consumed more than two vegetable servings per day also had an increased risk of higher density (OR=2.6). The findings suggest some unique features regarding mammographic density amongst Vietnamese compared with westernized women.

Epidemiologic Studies of Isoflavones & Mammographic Density

PubMed Central

Maskarinec, Gertraud; Verheus, Martijn; Tice, Jeffrey A.

2010-01-01

Isoflavones, phytoestrogens in soy beans with estrogen-like properties, have been examined for their cancer protective effects. Mammographic density is a strong predictor of breast cancer. This review summarizes studies that have examined the association between isoflavones and breast density. Observational investigations in Hawaii and Singapore suggest slightly lower breast density among women of Asian descent with regular soy intake, but two larger studies from Japan and Singapore did not observe a protective effect. The findings from seven randomized trials with primarily Caucasian women indicate that soy or isoflavones do not modify mammographic density. Soy foods and isoflavone supplements within a nutritional range do not appear to modify breast cancer risk as assessed by mammographic density. PMID:22253990

Quantification of breast density with dual energy mammography: A simulation study

PubMed Central

Ducote, Justin L.; Molloi, Sabee

2008-01-01

Breast density, the percentage of glandular breast tissue, has been identified as an important yet underutilized risk factor in the development of breast cancer. A quantitative method to measure breast density with dual energy imaging was investigated using a computer simulation model. Two configurations to measure breast density were evaluated: the usage of monoenergetic beams and an ideal detector, and the usage of polyenergetic beams with spectra from a tungsten anode x-ray tube with a detector modeled after a digital mammography system. The simulation model calculated the mean glandular dose necessary to quantify the variability of breast density to within 1∕3%. The breast was modeled as a semicircle 10 cm in radius with equal homogenous thicknesses of adipose and glandular tissues. Breast thicknesses were considered in the range of 2–10 cm and energies in the range of 10–150 keV for the two monoenergetic beams, and 20–150 kVp for spectra with a tungsten anode x-ray tube. For a 4.2 cm breast thickness, the required mean glandular doses were 0.183 μGy for two monoenergetic beams at 19 and 71 keV, and 9.85 μGy for two polyenergetic spectra from a tungsten anode at 32 and 96 kVp with beam filtrations of 50 μm Rh and 300 μm Cu for the low and high energy beams, respectively. The results suggest that for either configuration, breast density can be precisely measured with dual energy imaging requiring only a small amount of additional dose to the breast. The possibility of using a standard screening mammogram as the low energy image is also discussed. PMID:19175100

The relationships between breast volume, breast dense volume and volumetric breast density with body mass index, body fat mass and ethnicity

NASA Astrophysics Data System (ADS)

Zakariyah, N.; Pathy, N. B.; Taib, N. A. M.; Rahmat, K.; Judy, C. W.; Fadzil, F.; Lau, S.; Ng, K. H.

2016-03-01

It has been shown that breast density and obesity are related to breast cancer risk. The aim of this study is to investigate the relationships of breast volume, breast dense volume and volumetric breast density (VBD) with body mass index (BMI) and body fat mass (BFM) for the three ethnic groups (Chinese, Malay and Indian) in Malaysia. We collected raw digital mammograms from 2450 women acquired on three digital mammography systems. The mammograms were analysed using Volpara software to obtain breast volume, breast dense volume and VBD. Body weight, BMI and BFM of the women were measured using a body composition analyser. Multivariable logistic regression was used to determine the independent predictors of increased overall breast volume, breast dense volume and VBD. Indians have highest breast volume and breast dense volume followed by Malays and Chinese. While Chinese are highest in VBD, followed by Malay and Indian. Multivariable analysis showed that increasing BMI and BFM were independent predictors of increased overall breast volume and dense volume. Moreover, BMI and BFM were independently and inversely related to VBD.

Deep convolutional neural network for mammographic density segmentation

NASA Astrophysics Data System (ADS)

Wei, Jun; Li, Songfeng; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir; Samala, Ravi K.

2018-02-01

Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists' segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p < 0.0001) by two-tailed paired t-test. This study demonstrated that the DCNN approach has the potential to replace radiologists' interactive thresholding in PD estimation on DMs.

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

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead. To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures. Case-control. San Francisco Mammography Registry and Mayo Clinic. 1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants. Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity. Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively. Neither automated nor clinical BI-RADS density was 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.

Semiautomatic estimation of breast density with DM-Scan software.

PubMed

Martínez Gómez, I; Casals El Busto, M; Antón Guirao, J; Ruiz Perales, F; Llobet Azpitarte, R

2014-01-01

To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. The study included 655 direct digital mammograms acquired using craniocaudal projections. Three experienced radiologists analyzed the density of the mammograms using DM-Scan, and the inter- and intra-observer agreement between pairs of radiologists for the Boyd and BI-RADS® scales were calculated using the intraclass correlation coefficient. The Kappa index was used to compare the inter- and intra-observer agreements with those obtained previously for visual inspection in the same set of images. For visual inspection, the mean interobserver agreement was 0,876 (95% CI: 0,873-0,879) on the Boyd scale and 0,823 (95% CI: 0,818-0,829) on the BI-RADS® scale. The mean intraobserver agreement was 0,813 (95% CI: 0,796-0,829) on the Boyd scale and 0,770 (95% CI: 0,742-0,797) on the BI-RADS® scale. For DM-Scan, the mean inter- and intra-observer agreement was 0,92, considerably higher than the agreement for visual inspection. The semiautomatic calculation of breast density using DM-Scan software is more reliable and reproducible than visual estimation and reduces the subjectivity and variability in determining breast density. Copyright © 2012 SERAM. Published by Elsevier Espana. All rights reserved.

Cost-effectiveness of annual versus biennial screening mammography for women with high mammographic breast density.

PubMed

Pataky, Reka; Ismail, Zahra; Coldman, Andrew J; Elwood, Mark; Gelmon, Karen; Hedden, Lindsay; Hislop, Greg; Kan, Lisa; McCoy, Bonnie; Olivotto, Ivo A; Peacock, Stuart

2014-12-01

The sensitivity of screening mammography is much lower among women who have dense breast tissue, compared with women who have largely fatty breasts, and they are also at much higher risk of developing the disease. Increasing mammography screening frequency from biennially to annually has been suggested as a policy option to address the elevated risk in this population. The purpose of this study was to assess the cost-effectiveness of annual versus biennial screening mammography among women aged 50-79 with dense breast tissue. A Markov model was constructed based on screening, diagnostic, and treatment pathways for the population-based screening and cancer care programme in British Columbia, Canada. Model probabilities and screening costs were calculated from screening programme data. Costs for breast cancer treatment were calculated from treatment data, and utility values were obtained from the literature. Incremental cost-effectiveness was expressed as cost per quality adjusted life year (QALY), and probabilistic sensitivity analysis was conducted. Compared with biennial screening, annual screening generated an additional 0.0014 QALYs (95% CI: -0.0480-0.0359) at a cost of $819 ($ = Canadian dollars) per patient (95% CI: 506-1185), resulting in an incremental cost effectiveness ratio of $565,912/QALY. Annual screening had a 37.5% probability of being cost-effective at a willingness-to-pay threshold of $100,000/QALY. There is considerable uncertainty about the incremental cost-effectiveness of annual mammography. Further research on the comparative effectiveness of screening strategies for women with high mammographic breast density is warranted, particularly as digital mammography and density measurement become more widespread, before cost-effectiveness can be reevaluated. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Physical Activity and Change in Mammographic Density

PubMed Central

Conroy, Shannon M.; Butler, Lesley M.; Harvey, Danielle; Gold, Ellen B.; Sternfeld, Barbara; Oestreicher, Nina; Greendale, Gail A.; Habel, Laurel A.

2010-01-01

One potential mechanism by which physical activity may protect against breast cancer is by decreasing mammographic density. Percent mammographic density, the proportion of dense breast tissue area to total breast area, declines with age and is a strong risk factor for breast cancer. The authors hypothesized that women who were more physically active would have a greater decline in percent mammographic density with age, compared with less physically active women. The authors tested this hypothesis using longitudinal data (1996–2004) from 722 participants in the Study of Women's Health Across the Nation (SWAN), a multiethnic cohort of women who were pre- and early perimenopausal at baseline, with multivariable, repeated-measures linear regression analyses. During an average of 5.6 years, the mean annual decline in percent mammographic density was 1.1% (standard deviation = 0.1). A 1-unit increase in total physical activity score was associated with a weaker annual decline in percent mammographic density by 0.09% (standard error = 0.03; P = 0.01). Physical activity was inversely associated with the change in nondense breast area (P < 0.01) and not associated with the change in dense breast area (P = 0.17). Study results do not support the hypothesis that physical activity reduces breast cancer through a mechanism that includes reduced mammographic density. PMID:20354074

Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures.

PubMed

Keller, Brad M; Nathan, Diane L; Gavenonis, Sara C; Chen, Jinbo; Conant, Emily F; Kontos, Despina

2013-05-01

Mammographic breast density, a strong risk factor for breast cancer, may be measured as either a relative percentage of dense (ie, radiopaque) breast tissue or as an absolute area from either raw (ie, "for processing") or vendor postprocessed (ie, "for presentation") digital mammograms. Given the increasing interest in the incorporation of mammographic density in breast cancer risk assessment, the purpose of this study is to determine the inherent reader variability in breast density assessment from raw and vendor-processed digital mammograms, because inconsistent estimates could to lead to misclassification of an individual woman's risk for breast cancer. Bilateral, mediolateral-oblique view, raw, and processed digital mammograms of 81 women were retrospectively collected for this study (N = 324 images). Mammographic percent density and absolute dense tissue area estimates for each image were obtained from two radiologists using a validated, interactive software tool. The variability of interreader agreement was not found to be affected by the image presentation style (ie, raw or processed, F-test: P > .5). Interreader estimates of relative and absolute breast density are strongly correlated (Pearson r > 0.84, P < .001) but systematically different (t-test, P < .001) between the two readers. Our results show that mammographic density may be assessed with equal reliability from either raw or vendor postprocessed images. Furthermore, our results suggest that the primary source of density variability comes from the subjectivity of the individual reader in assessing the absolute amount of dense tissue present in the breast, indicating the need to use standardized tools to mitigate this effect. Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.

Mammographic density and breast cancer risk by family history in women of white and Asian ancestry.

PubMed

Maskarinec, Gertraud; Nakamura, Kaylae L; Woolcott, Christy G; Conroy, Shannon M; Byrne, Celia; Nagata, Chisato; Ursin, Giske; Vachon, Celine M

2015-04-01

Mammographic density, i.e., the radiographic appearance of the breast, is a strong predictor of breast cancer risk. To determine whether the association of breast density with breast cancer is modified by a first-degree family history of breast cancer (FHBC) in women of white and Asian ancestry, we analyzed data from four case-control studies conducted in the USA and Japan. The study population included 1,699 breast cancer cases and 2,422 controls, of whom 45% reported white (N = 1,849) and 40% Asian (N = 1,633) ancestry. To standardize mammographic density assessment, a single observer re-read all mammograms using one type of interactive thresholding software. Logistic regression was applied to estimate odds ratios (OR) while adjusting for confounders. Overall, 496 (12%) of participants reported a FHBC, which was significantly associated with breast cancer risk in the adjusted model (OR 1.51; 95% CI 1.23-1.84). There was a statistically significant interaction on a multiplicative scale between FHBC and continuous percent density (per 10 % density: p = 0.03). The OR per 10% increase in percent density was higher among women with a FHBC (OR 1.30; 95% CI 1.13-1.49) than among those without a FHBC (OR 1.14; 1.09-1.20). This pattern was apparent in whites and Asians. The respective ORs were 1.45 (95% CI 1.17-1.80) versus 1.22 (95% CI 1.14-1.32) in whites, whereas the values in Asians were only 1.24 (95% CI 0.97-1.58) versus 1.09 (95% CI 1.00-1.19). These findings support the hypothesis that women with a FHBC appear to have a higher risk of breast cancer associated with percent mammographic density than women without a FHBC.

The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006.

PubMed

Chen, Lin; Ray, Shonket; Keller, Brad M; Pertuz, Said; McDonald, Elizabeth S; Conant, Emily F; Kontos, Despina

2016-09-01

Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76-0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article.

The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006

PubMed Central

Chen, Lin; Ray, Shonket; Keller, Brad M.; Pertuz, Said; McDonald, Elizabeth S.; Conant, Emily F.

2016-01-01

Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88–0.95; weighted κ = 0.83–0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76–0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. © RSNA, 2016 Online supplemental material is available for this article. PMID:27002418

Effects of multiparity and prolonged breast-feeding on maternal bone mineral density: a community-based cross-sectional study

PubMed Central

Lenora, Janaka; Lekamwasam, Sarath; Karlsson, Magnus K

2009-01-01

Background Studies conducted in Western countries have shown that bone loss associated with pregnancy and breast-feeding is recovered after weaning. However, it is not clear whether recovery takes place after repeated pregnancies followed by prolonged periods of breast-feeding; especially in developing countries where nutritional intake is comparatively low. This study was designed to examine the effects of multiparity and prolonged breast-feeding on maternal bone mineral density (BMD) in a community-based sample of 210 Sri Lankan women, aged between 46 and 98 years. Methods BMD of the lumbar spine (L2–L4) and femoral neck were measured by dual-energy X-ray absorptiometry. Reproductive history was recorded by using a questionnaire. Women were, first, divided into groups according to parity (nulliparous, 1–2, 3–4, and 5 or more children), and BMDs in different groups were compared, initially unadjusted and then adjusted for age. Same subjects were subdivided, again, according to the total duration of breast-feeding (0, 1–48, 49–96, and 97 months or more) and similar analysis was carried out. Results Women who had 5 or more children and women who had breast-fed for 97 months or more were older than the other women (p < 0.01) but no differences in height, weight or BMI were observed among the groups. Age adjusted BMD at lumbar spine and femoral neck BMDs of women grouped according to parity were not significantly different. Neither was there any difference between lumbar spine or femoral neck BMD in groups based on duration of breast-feeding. Conclusion From this population-based study conducted in a developing country, we infer that history of multiparity or prolonged breast-feeding has no detrimental effects on maternal BMD in post-menopausal age. PMID:19570205

Time domain diffuse optical spectroscopy: In vivo quantification of collagen in breast tissue

NASA Astrophysics Data System (ADS)

Taroni, Paola; Pifferi, Antonio; Quarto, Giovanna; Farina, Andrea; Ieva, Francesca; Paganoni, Anna Maria; Abbate, Francesca; Cassano, Enrico; Cubeddu, Rinaldo

2015-05-01

Time-resolved diffuse optical spectroscopy provides non-invasively the optical characterization of highly diffusive media, such as biological tissues. Light pulses are injected into the tissue and the effects of light propagation on re-emitted pulses are interpreted with the diffusion theory to assess simultaneously tissue absorption and reduced scattering coefficients. Performing spectral measurements, information on tissue composition and structure is derived applying the Beer law to the measured absorption and an empiric approximation to Mie theory to the reduced scattering. The absorption properties of collagen powder were preliminarily measured in the range of 600-1100 nm using a laboratory set-up for broadband time-resolved diffuse optical spectroscopy. Optical projection images were subsequently acquired in compressed breast geometry on 218 subjects, either healthy or bearing breast lesions, using a portable instrument for optical mammography that operates at 7 wavelengths selected in the range 635-1060 nm. For all subjects, tissue composition was estimated in terms of oxy- and deoxy-hemoglobin, water, lipids, and collagen. Information on tissue microscopic structure was also derived. Good correlation was obtained between mammographic breast density (a strong risk factor for breast cancer) and an optical index based on collagen content and scattering power (that accounts mostly for tissue collagen). Logistic regression applied to all optically derived parameters showed that subjects at high risk for developing breast cancer for their high breast density can effectively be identified based on collagen content and scattering parameters. Tissue composition assessed in breast lesions with a perturbative approach indicated that collagen and hemoglobin content are significantly higher in malignant lesions than in benign ones.

Biologic and Computational Modeling of Mammographic Density and Stromal Patterning

DTIC Science & Technology

2009-07-01

research effort. INTRODUCTION: Mammographic density serves as independent marker of short term breast cancer risk and a surrogate marker of...response to a variety of prevention agents1-3. Although a majority of breast cancers are epithelial in origin, there is evidence that stromal content of...the breast is an important predictor or mammographic density. There is increasing evidence that the stroma plays a role in breast cancer initiation4

Does the prediction of breast cancer improve using a combination of mammographic density measures compared to individual measures alone?

NASA Astrophysics Data System (ADS)

Wong Sik Hee, Joseph Ryan; Harkness, Elaine F.; Gadde, Soujanya; Lim, Yit Y.; Maxwell, Anthony J.; Evans, D. Gareth; Howell, Anthony; Astley, Susan M.

2017-03-01

High mammographic density is associated with an increased risk of breast cancer, however whether the association is stronger when there is agreement across measures is unclear. This study investigates whether a combination of density measures is a better predictor of breast cancer risk than individual methods alone. Women recruited to the Predicting Risk of Cancer At Screening (PROCAS) study and with mammographic density assessed using three different methods were included (n=33,304). Density was assessed visually using Visual Analogue Scales (VAS) and by two fully automated methods, Quantra and Volpara. Percentage breast density was divided into (high, medium and low) and combinations of measures were used to further categorise individuals (e.g. `all high'). A total of 667 breast cancers were identified and logistic regression was used to determine the relationship between breast density and breast cancer risk. In total, 44% of individuals were in the same tertile for all three measures, 8.6% were in non-adjacent (high and low) or mixed categories (high, medium and low). For individual methods the strongest association with breast cancer risk was for medium and high tertiles of VAS with odds ratios (OR) adjusted for age and BMI of 1.63 (95% CI 1.31-2.03) and 2.33 (1.87-2.90) respectively. For the combination of density methods the strongest association was for `all high' (OR 2.42, 1.77-3.31) followed by "two high" (OR 1.90, 1.35-3.31) and "two medium" (OR 1.88, 1.40-2.52). Combining density measures did not affect the magnitude of risk compared to using individual methods.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

PubMed

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A; Roubidoux, Marilyn A; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M; Samala, Ravi K

2018-01-09

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

NASA Astrophysics Data System (ADS)

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M.; Samala, Ravi K.

2018-01-01

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input ‘for processing’ DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice’s coefficient (DC) of 0.79  ±  0.13 and Pearson’s correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

Correlation between Na/K ratio and electron densities in blood samples of breast cancer patients.

PubMed

Topdağı, Ömer; Toker, Ozan; Bakırdere, Sezgin; Bursalıoğlu, Ertuğrul Osman; Öz, Ersoy; Eyecioğlu, Önder; Demir, Mustafa; İçelli, Orhan

2018-05-31

The main purpose of this study was to investigate the relationship between the electron densities and Na/K ratio which has important role in breast cancer disease. Determinations of sodium and potassium concentrations in blood samples performed with inductive coupled plasma-atomic emission spectrometry. Electron density values of blood samples were determined via ZXCOM. Statistical analyses were performed for electron densities and Na/K ratio including Kolmogorov-Smirnov normality tests, Spearman's rank correlation test and Mann-Whitney U test. It was found that the electron densities significantly differ between control and breast cancer groups. In addition, statistically significant positive correlation was found between the electron density and Na/K ratios in breast cancer group.

Nearest Neighbor Classification Using a Density Sensitive Distance Measurement

DTIC Science & Technology

2009-09-01

both the proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and...proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and the MNIST...35 1. The Wisconsin Diagnostic Breast Cancer (WDBC) Dataset..........35 2. The

Do pathological parameters differ with regard to breast density and mode of detection in breast cancer? The Malmö Diet and Cancer Study.

PubMed

Sartor, Hanna; Borgquist, Signe; Hartman, Linda; Zackrisson, Sophia

2015-02-01

Our aim was to study how breast density relates to tumor characteristics in breast cancer with emphasis on mode of detection. Among 17,035 women in the Malmö Diet and Cancer Study 826 incident cases have been diagnosed (1991-2007). Data on tumor characteristics, mode of detection, and density at diagnosis were collected. Associations between density and tumor characteristics were analyzed using logistic and ordinal logistic regression models yielding OR and 95% CI. Adjustments for age at diagnosis, BMI at baseline, and the mode of detection, were performed. In denser breasts, large tumor size was more frequent (ORadj 1.59 (1.26-2.01)) as was lymph node involvement (ORadj 1.32 (1.00-1.74)). Further, the higher the density, the lower the grade (ORadj 0.73 (0.53-1.02) for having higher grade), in screening-detected invasive breast cancer. Our findings stress the importance of considering the impact of density in mammography image interpretation and the possible associations with tumor aggressiveness. Copyright © 2014 Elsevier Ltd. All rights reserved.

Breast Density Notification Legislation and Breast Cancer Stage at Diagnosis: Early Evidence from the SEER Registry.

PubMed

Richman, Ilana; Asch, Steven M; Bendavid, Eran; Bhattacharya, Jay; Owens, Douglas K

2017-06-01

Twenty-eight states have passed breast density notification laws, which require physicians to inform women of a finding of dense breasts on mammography. To evaluate changes in breast cancer stage at diagnosis after enactment of breast density notification legislation. Using a difference-in-differences analysis, we examined changes in stage at diagnosis among women with breast cancer in Connecticut, the first state to enact legislation, compared to changes among women in control states. We used data from the Surveillance, Epidemiology, and End Results Program (SEER) registry, 2005-2013. Women ages 40-74 with breast cancer. Breast density notification legislation, enacted in Connecticut in October of 2009. Breast cancer stage at diagnosis. Our study included 466,930 women, 25,592 of whom lived in Connecticut. Legislation was associated with a 1.38-percentage-point (95 % CI 0.12 to 2.63) increase in the proportion of women in Connecticut versus control states who had localized invasive cancer at the time of diagnosis, and a 1.12-percentage-point (95 % CI -2.21 to -0.08) decline in the proportion of women with ductal carcinoma in situ at diagnosis. Breast density notification legislation was not associated with a change in the proportion of women in Connecticut versus control states with regional-stage (-0.09 percentage points, 95 % CI -1.01 to 1.02) or metastatic disease (-0.24, 95 % CI -0.75 to 0.28). County-level analyses and analyses limited to women younger than 50 found no statistically significant associations. Single intervention state, limited follow-up, potential confounding from unobserved trends. Breast density notification legislation in Connecticut was associated with a small increase in the proportion of women diagnosed with localized invasive breast cancer in individual-level but not county-level analyses. Whether this finding reflects potentially beneficial early detection or potentially harmful overdiagnosis is not known. Legislation was not associated with changes in regional or metastatic disease.

Insulin-like growth factor-I (IGF-1), IGF-binding protein-3 (IGFBP-3) and mammographic features.

PubMed

Izzo, L; Meggiorini, M L; Nofroni, I; Pala, A; De Felice, C; Meloni, P; Simari, T; Izzo, S; Pugliese, F; Impara, L; Merlini, G; Di Cello, P; Cipolla, V; Forcione, A R; Paliotta, A; Domenici, L; Bolognese, A

2012-05-01

The IGF system has recently been shown to play an important role in the regulation of breast tumor cell proliferation. However, also breast density is currently considered as the strongest breast cancer risk factor. It is not yet clear whether these factors are interrelated and if and how they are influenced by menopausal status. The purpose of this study was to examine the possible effects of IGF-1 and IGFBP-3 and IGF-1/IGFBP-3 molar ratio on mammographic density stratified by menopausal status. A group of 341 Italian women were interviewed to collect the following data: family history of breast cancer, reproductive and menstrual factors, breast biopsies, previous administration of hormonal contraceptive therapy, hormone replacement therapy (HRT) in menopause and lifestyle information. A blood sample was drawn for determination of IGF-1, IGFBP-3 levels. IGF-1/ IGFBP-3 molar ratio was then calculated. On the basis of recent mammograms the women were divided into two groups: dense breast (DB) and non-dense breast (NDB). Student's t-test was employed to assess the association between breast density and plasma level of IGF-1, IGFBP-3 and molar ratio. To assess if this relationship was similar in subgroups of pre- and postmenopausal women, the study population was stratified by menopausal status and Student's t-test was performed. Finally, multivariate analysis was employed to evaluate if there were confounding factors that might influence the relationship between growth factors and breast density. The analysis of the relationship between mammographic density and plasma level of IGF-1, IGFBP-3 and IGF-1/ IGFBP-3 molar ratio showed that IGF-1 levels and molar ratio varied in the two groups resulting in higher mean values in the DB group (IGF-1: 109.6 versus 96.6 ng/ml; p= 0.001 and molar ratio 29.4 versus 25.5 ng/ml; p= 0.001) whereas IGFBP-3 showed similar values in both groups (DB and NDB). Analysis of plasma level of IGF-1, IGFBP-3 and IGF-1/IGFBP-3 molar ratio compared to breast density after stratification of the study population by menopausal status (premenopausal and postmenopausal) showed that there was no association between the plasma of growth factors and breast density, neither in premenopausal nor in postmenopausal patients. Multivariate analysis showed that only nulliparity, premenopausal status and body mass index (BMI) are determinants of breast density. Our study provides a strong evidence of a crude association between breast density and plasma levels of IGF-1 and molar ratio. On the basis of our results, it is reasonable to assume that the role of IGF-1 and molar ratio in the pathogenesis of breast cancer might be mediated through mammographic density. IGF-1 and molar ratio might thus increase the risk of cancer by increasing mammographic density.

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer.

PubMed

Tice, Jeffrey A; Miglioretti, Diana L; Li, Chin-Shang; Vachon, Celine M; Gard, Charlotte C; Kerlikowske, Karla

2015-10-01

Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P<.001). The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model. © 2015 by American Society of Clinical Oncology.

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer

PubMed Central

Tice, Jeffrey A.; Miglioretti, Diana L.; Li, Chin-Shang; Vachon, Celine M.; Gard, Charlotte C.; Kerlikowske, Karla

2015-01-01

Purpose Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. Methods We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. Results We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P < .001). Conclusion The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model. PMID:26282663

Breast Tenderness after Initiation of Conjugated Equine Estrogens and Mammographic Density Change

PubMed Central

Crandall, Carolyn J.; Aragaki, Aaron K.; Cauley, Jane A.; McTiernan, Anne; Manson, JoAnn E.; Anderson, Garnet L.; Wactawski-Wende, Jean; Chlebowski, Rowan T.

2013-01-01

Background We examined the association between new-onset breast tenderness and change in mammographic density after initiation of conjugated equine estrogens (CEE). Methods We analyzed baseline, year 1, and year 2 data from 695 participants of the Women's Health Initiative Estrogen + Progestin (daily CEE 0.625 mg + medroxyprogesterone acetate 2.5 mg [MPA] or placebo) and Estrogen-Alone (CEE 0.625 mg or placebo) trials who participated in the Mammogram Density Ancillary Study. Using multivariable repeated measures models, we analyzed the association between new-onset breast tenderness (i.e. absence of baseline tenderness and presence of tenderness at year 1 follow-up) and change from baseline in percent mammographic density. Results Active therapy increased the odds of new-onset breast tenderness (CEE + MPA vs. placebo risk ratio [RR] 3.01, 95% confidence interval [95% CI] 1.96-4.62; CEE vs. placebo RR 1.70, 95% CI 1.14-2.53). Among women assigned to CEE + MPA, mean increase in mammographic density was greater among participants reporting new-onset of breast tenderness than among participants without new-onset breast tenderness (11.3% vs. 3.9% at year 1, 9.4% vs. 3.2% at year 2, P < 0.001). Among women assigned to CEE alone, increase in mammographic density at year 1 follow-up was not significantly different in women with new-onset breast tenderness compared to women without new-onset breast tenderness (2.4% vs. 0.6% at year 1, 2.2% vs. 1.0% at year 2, P = 0.30). Conclusions The new-onset of breast tenderness after initiation of CEE + MPA, but not CEE alone, is associated with greater increases in mammographic density. PMID:21979747

Double-blind randomized 12-month soy intervention had no effects on breast MRI fibroglandular tissue density or mammographic density

PubMed Central

Wu, Anna H.; Spicer, Darcy; Garcia, Agustin; Tseng, Chiu-Chen; Hovanessian-Larsen, Linda; Sheth, Pulin; Martin, Sue Ellen; Hawes, Debra; Russell, Christy; McDonald, Heather; Tripathy, Debu; Su, Min-Ying; Ursin, Giske; Pike, Malcolm C.

2015-01-01

Soy supplementation by breast cancer patients remains controversial. No controlled intervention studies have investigated the effects of soy supplementation on mammographic density in breast cancer patients. We conducted a double-blind, randomized, placebo-controlled intervention study in previously treated breast cancer patients (n=66) and high-risk women (n=29). We obtained digital mammograms and breast magnetic resonance imaging (MRI) scans at baseline and after 12 months of daily soy (50 mg isoflavones per day) (n=46) or placebo (n=49) tablet supplementation. The total breast area (MA) and the area of mammographic density (MD) on the mammogram was measured using a validated computer-assisted method, and mammographic density percent (MD% = 100 × MD/MA) was determined. A well-tested computer algorithm was used to quantitatively measure the total breast volume (TBV) and fibroglandular tissue volume (FGV) on the breast MRI, and the FGV percent (FGV% = 100 × FGV/TBV) was calculated. On the basis of plasma soy isoflavone levels, compliance was excellent. Small decreases in MD% measured by the ratios of month 12 to baseline levels, were seen in the soy (0.95) and the placebo (0.87) groups; these changes did not differ between the treatments (P=0.38). Small decreases in FGV% were also found in both the soy (0.90) and the placebo (0.92) groups; these changes also did not differ between the treatments (P=0.48). Results were comparable in breast cancer patients and high-risk women. We found no evidence that soy supplementation would decrease mammographic density and that MRI might be more sensitive to changes in density than mammography. PMID:26276750

Comparison of sound speed measurements on two different ultrasound tomography devices

NASA Astrophysics Data System (ADS)

Sak, Mark; Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Sherman, Mark; Gierach, Gretchen; Malyarenko, Antonina

2014-03-01

Ultrasound tomography (UST) employs sound waves to produce three-dimensional images of breast tissue and precisely measures the attenuation of sound speed secondary to breast tissue composition. High breast density is a strong breast cancer risk factor and sound speed is directly proportional to breast density. UST provides a quantitative measure of breast density based on three-dimensional imaging without compression, thereby overcoming the shortcomings of many other imaging modalities. The quantitative nature of the UST breast density measures are tied to an external standard, so sound speed measurement in breast tissue should be independent of specific hardware. The work presented here compares breast sound speed measurement obtained with two different UST devices. The Computerized Ultrasound Risk Evaluation (CURE) system located at the Karmanos Cancer Institute in Detroit, Michigan was recently replaced with the SoftVue ultrasound tomographic device. Ongoing clinical trials have used images generated from both sets of hardware, so maintaining consistency in sound speed measurements is important. During an overlap period when both systems were in the same exam room, a total of 12 patients had one or both of their breasts imaged on both systems on the same day. There were 22 sound speed scans analyzed from each system and the average breast sound speeds were compared. Images were either reconstructed using saved raw data (for both CURE and SoftVue) or were created during the image acquisition (saved in DICOM format for SoftVue scans only). The sound speed measurements from each system were strongly and positively correlated with each other. The average difference in sound speed between the two sets of data was on the order of 1-2 m/s and this result was not statistically significant. The only sets of images that showed a statistical difference were the DICOM images created during the SoftVue scan compared to the SoftVue images reconstructed from the raw data. However, the discrepancy between the sound speed values could be easily handled by uniformly increasing the DICOM sound speed by approximately 0.5 m/s. These results suggest that there is no fundamental difference in sound speed measurement for the two systems and support combining data generated with these instruments in future studies.

Amount of stroma is associated with mammographic density and stromal expression of oestrogen receptor in normal breast tissues.

PubMed

Gabrielson, Marike; Chiesa, Flaminia; Paulsson, Janna; Strell, Carina; Behmer, Catharina; Rönnow, Katarina; Czene, Kamila; Östman, Arne; Hall, Per

2016-07-01

Following female sex and age, mammographic density is considered one of the strongest risk factors for breast cancer. Despite the association between mammographic density and breast cancer risk, little is known about the underlying histology and biological basis of breast density. To better understand the mechanisms behind mammographic density we assessed morphology, proliferation and hormone receptor status in relation to mammographic density in breast tissues from healthy women. Tissues were obtained from 2012-2013 by ultrasound-guided core needle biopsy from 160 women as part of the Karma (Karolinska mammography project for risk prediction for breast cancer) project. Mammograms were collected through routine mammography screening and mammographic density was calculated using STRATUS. The histological composition, epithelial and stromal proliferation status and hormone receptor status were assessed through immunohistochemical staining. Higher mammographic density was significantly associated with a greater proportion of stromal and epithelial tissue and a lower proportion of adipose tissue. Epithelial expression levels of Ki-67, oestrogen receptor (ER) and progesterone receptor (PR) were not associated with mammographic density. Epithelial Ki-67 was associated with a greater proportion of epithelial tissue, and epithelial PR was associated with a greater proportion of stromal and a lower proportion of adipose tissue. Epithelial ER was not associated with any tissues. In contrast, expression of ER in the stroma was significantly associated with a greater proportion of stroma, and negatively associated with the amount of adipose tissue. High mammographic density is associated with higher amount of stroma and epithelium and less amount of fat, but is not associated with a change in epithelial proliferation or receptor status. Increased expressions of both epithelial PR and stromal ER are associated with a greater proportion of stroma, suggesting hormonal involvement in regulating breast tissue composition.

Digital Breast Tomosynthesis guided Near Infrared Spectroscopy: Volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions

PubMed Central

Vedantham, Srinivasan; Shi, Linxi; Michaelsen, Kelly E.; Krishnaswamy, Venkataramanan; Pogue, Brian W.; Poplack, Steven P.; Karellas, Andrew; Paulsen, Keith D.

2016-01-01

A multimodality system combining a clinical prototype digital breast tomosynthesis with its imaging geometry modified to facilitate near-infrared spectroscopic imaging has been developed. The accuracy of parameters recovered from near-infrared spectroscopy is dependent on fibroglandular tissue content. Hence, in this study, volumetric estimates of fibroglandular tissue from tomosynthesis reconstructions were determined. A kernel-based fuzzy c-means algorithm was implemented to segment tomosynthesis reconstructed slices in order to estimate fibroglandular content and to provide anatomic priors for near-infrared spectroscopy. This algorithm was used to determine volumetric breast density (VBD), defined as the ratio of fibroglandular tissue volume to the total breast volume, expressed as percentage, from 62 tomosynthesis reconstructions of 34 study participants. For a subset of study participants who subsequently underwent mammography, VBD from mammography matched for subject, breast laterality and mammographic view was quantified using commercial software and statistically analyzed to determine if it differed from tomosynthesis. Summary statistics of the VBD from all study participants were compared with prior independent studies. The fibroglandular volume from tomosynthesis and mammography were not statistically different (p=0.211, paired t-test). After accounting for the compressed breast thickness, which were different between tomosynthesis and mammography, the VBD from tomosynthesis was correlated with (r =0.809, p<0.001), did not statistically differ from (p>0.99, paired t-test), and was linearly related to, the VBD from mammography. Summary statistics of the VBD from tomosynthesis were not statistically different from prior studies using high-resolution dedicated breast computed tomography. The observation of correlation and linear association in VBD between mammography and tomosynthesis suggests that breast density associated risk measures determined for mammography are translatable to tomosynthesis. Accounting for compressed breast thickness is important when it differs between the two modalities. The fibroglandular volume from tomosynthesis reconstructions is similar to mammography indicating suitability for use during near-infrared spectroscopy. PMID:26941961

Patient awareness of breast density and interest in supplemental screening tests: comparison of an academic facility and a county hospital.

PubMed

Trinh, Long; Ikeda, Debra M; Miyake, Kanae K; Trinh, Jennifer; Lee, Kevin K; Dave, Haatal; Hanafusa, Kei; Lipson, Jafi

2015-03-01

The aim of this study was to measure women's knowledge of breast density and their attitudes toward supplemental screening tests in the setting of the California Breast Density Notification Law at an academic facility and a county hospital, serving women with higher and lower socioeconomic status, respectively. Institutional review board exemptions were obtained. A survey was administered during screening mammography at two facilities, assessing women's awareness of and interest in knowing their breast density and interest in and willingness to pay for supplemental whole breast ultrasound and contrast-enhanced spectral mammography (CEMG). The results were compared by using Fisher exact tests between groups. A total of 105 of 130 and 132 of 153 women responded to the survey at the academic and county facilities, respectively. Among respondents at the academic and county facilities, 23% and 5% were aware of their breast density, and 94% and 79% wanted to know their density. A majority were interested in supplemental ultrasonography and CEMG at both sites; however, fewer women had a willingness to pay for the supplemental tests at the county hospital compared with those at the academic facility (22% and 70%, respectively, for ultrasound, P < .0001; 20% and 65%, respectively, for CEMG, P < .0001). Both groups of women were interested in knowing their breast density and in supplemental screening tests. However, women at the county hospital were less willing to incur out-of-pocket expenses, suggesting a potential for a disparity in health care access for women of lower socioeconomic status after the enactment of breast density notification legislation. Published by Elsevier Inc.

High-performance broad-band spectroscopy for breast cancer risk assessment

NASA Astrophysics Data System (ADS)

Pawluczyk, Olga; Blackmore, Kristina; Dick, Samantha; Lilge, Lothar

2005-09-01

Medical diagnostics and screening are becoming increasingly demanding applications for spectroscopy. Although for many years the demand was satisfied with traditional spectrometers, analysis of complex biological samples has created a need for instruments capable of detecting small differences between samples. One such application is the measurement of absorbance of broad spectrum illumination by breast tissue, in order to quantify the breast tissue density. Studies have shown that breast cancer risk is closely associated with the measurement of radiographic breast density measurement. Using signal attenuation in transillumination spectroscopy in the 550-1100nm spectral range to measure breast density, has the potential to reduce the frequency of ionizing radiation, or making the test accessible to younger women; lower the cost and make the procedure more comfortable for the patient. In order to determine breast density, small spectral variances over a total attenuation of up to 8 OD have to be detected with the spectrophotometer. For this, a high performance system has been developed. The system uses Volume Phase Holographic (VPH) transmission grating, a 2D detector array for simultaneous registration of the whole spectrum with high signal to noise ratio, dedicated optical system specifically optimized for spectroscopic applications and many other improvements. The signal to noise ratio exceeding 50,000 for a single data acquisition eliminates the need for nitrogen cooled detectors and provides sufficient information to predict breast tissue density. Current studies employing transillumination breast spectroscopy (TIBS) relating to breast cancer risk assessment and monitoring are described.

Volumetric breast density is essential for predicting cosmetic outcome at the late stage after breast-conserving surgery.

PubMed

Shiina, N; Sakakibara, M; Fujisaki, K; Iwase, T; Nagashima, T; Sangai, T; Kubota, Y; Akita, S; Takishima, H; Miyazaki, M

2016-04-01

The critical issue related to breast-conserving therapy (BCT) is that cosmetic outcomes deteriorate with long-term follow-up. There is little research for breast density as a predictor of cosmetic outcomes at the late stage after BCT. To improve the long-term quality of life after BCT of breast cancer patients, the correlation of volumetric breast density (VBD) and cosmetic outcome at the late stage after BCT was evaluated. Breast volume, fibroglandular tissue volume, adipose tissue volume, and VBD were calculated on mammography using image analysis software (Volpara(®)) in 151 patients with BCT. Furthermore, the correlation of breast density and the change of breast volume over time was analyzed on mammography in 99 patients who were followed-up long-term after BCT. On multivariate analysis, VBD was a predictor of cosmetic outcome after BCT with percent breast volume excised (PBVE). Decreased adipose tissue volume and increased fibrosis were more common in patients with VBD < 15%. Furthermore, remnant breast volume continued to decrease over time in low breast density patients during long-term follow-up. 93% of patients with VBD ≥ 15% and PBVE < 10% had a better cosmetic outcome, while 60% of patients with VBD < 15% and PBVE ≥ 10% had a worse cosmetic outcome after BCT. While PBVE was involved in cosmetic outcome at the early stage after BCT, VBD was associated with cosmetic outcome at the late stage after BCT. Thus, a combination of VBD and PBVE could predict cosmetic outcome after BCT and contribute to the selection for the appropriate BCT. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Short-Term Effect of Weight Loss Surgery on Volumetric Breast Density and Fibroglandular Volume.

PubMed

Vohra, Nasreen A; Kachare, Swapnil D; Vos, Paul; Schroeder, Bruce F; Schuth, Olga; Suttle, Dylan; Fitzgerald, Timothy L; Wong, Jan H; Verbanac, Kathryn M

2017-04-01

Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m 2 . Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10 -9 ) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume. Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

PubMed Central

Lindström, Sara; Thompson, Deborah J.; Paterson, Andrew D.; Li, Jingmei; Gierach, Gretchen L.; Scott, Christopher; Stone, Jennifer; Douglas, Julie A.; dos-Santos-Silva, Isabel; Fernandez-Navarro, Pablo; Verghase, Jajini; Smith, Paula; Brown, Judith; Luben, Robert; Wareham, Nicholas J.; Loos, Ruth J.F.; Heit, John A.; Pankratz, V. Shane; Norman, Aaron; Goode, Ellen L.; Cunningham, Julie M.; deAndrade, Mariza; Vierkant, Robert A.; Czene, Kamila; Fasching, Peter A.; Baglietto, Laura; Southey, Melissa C.; Giles, Graham G.; Shah, Kaanan P.; Chan, Heang-Ping; Helvie, Mark A.; Beck, Andrew H.; Knoblauch, Nicholas W.; Hazra, Aditi; Hunter, David J.; Kraft, Peter; Pollan, Marina; Figueroa, Jonine D.; Couch, Fergus J.; Hopper, John L.; Hall, Per; Easton, Douglas F.; Boyd, Norman F.; Vachon, Celine M.; Tamimi, Rulla M.

2015-01-01

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci. PMID:25342443

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

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.

Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation: A Preliminary Study

PubMed Central

Kontos, Despina; Bakic, Predrag R.; Carton, Ann-Katherine; Troxel, Andrea B.; Conant, Emily F.; Maidment, Andrew D.A.

2009-01-01

Rationale and Objectives Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superimposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superimposition, offering superior parenchymal texture visualization compared to mammography. Our study investigates the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. Materials and Methods DBT and digital mammography (DM) images of 39 women were analyzed. Texture features, shown in studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. We compared the relative performance of DBT and DM texture features in correlating with two measures of breast cancer risk: (i) the Gail and Claus risk estimates, and (ii) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. Results No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density (PD) than DM (p ≤0.05). When dividing our study population in groups of increasing breast PD, the DBT texture features appeared to be more discriminative, having regression lines with overall lower p-values, steeper slopes, and higher R2 estimates. Conclusion Although preliminary, our results suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation. PMID:19201357

The Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

LIBRA is a fully-automatic breast density estimation software solution based on a published algorithm that works on either raw (i.e., “FOR PROCESSING”) or vendor post-processed (i.e., “FOR PRESENTATION”) digital mammography images. LIBRA has been applied to over 30,000 screening exams and is being increasingly utilized in larger studies.

Mammographic Breast Density Assessment Using Automated Volumetric Software and Breast Imaging Reporting and Data System (BIRADS) Categorization by Expert Radiologists.

PubMed

Damases, Christine N; Brennan, Patrick C; Mello-Thoms, Claudia; McEntee, Mark F

2016-01-01

To investigate agreement on mammographic breast density (MD) assessment between automated volumetric software and Breast Imaging Reporting and Data System (BIRADS) categorization by expert radiologists. Forty cases of left craniocaudal and mediolateral oblique mammograms from 20 women were used. All images had their volumetric density classified using Volpara density grade (VDG) and average volumetric breast density percentage. The same images were then classified into BIRADS categories (I-IV) by 20 American Board of Radiology examiners. The results demonstrated a moderate agreement (κ = 0.537; 95% CI = 0.234-0.699) between VDG classification and radiologists' BIRADS density assessment. Interreader agreement using BIRADS also demonstrated moderate agreement (κ = 0.565; 95% CI = 0.519-0.610) ranging from 0.328 to 0.669. Radiologists' average BIRADS was lower than average VDG scores by 0.33, with their mean being 2.13, whereas the mean VDG was 2.48 (U = -3.742; P < 0.001). VDG and BIRADS showed a very strong positive correlation (ρ = 0.91; P < 0.001) as did BIRADS and average volumetric breast density percentage (ρ = 0.94; P < 0.001). Automated volumetric breast density assessment shows moderate agreement and very strong correlation with BIRADS; interreader variations still exist within BIRADS. Because of the increasing importance of MD measurement in clinical management of patients, widely accepted, reproducible, and accurate measures of MD are required. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Giger, Maryellen L.

2016-03-01

Breast density is routinely assessed qualitatively in screening mammography. However, it is challenging to quantitatively determine a 3D density from a 2D image such as a mammogram. Furthermore, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used more frequently in the screening of high-risk populations. The purpose of our study is to segment parenchyma and to quantitatively determine volumetric breast density on pre-contrast axial DCE-MRI images (i.e., non-contrast) using a semi-automated quantitative approach. In this study, we retroactively examined 3D DCE-MRI images taken for breast cancer screening of a high-risk population. We analyzed 66 cases with ages between 28 and 76 (mean 48.8, standard deviation 10.8). DCE-MRIs were obtained on a Philips 3.0 T scanner. Our semi-automated DCE-MRI algorithm includes: (a) segmentation of breast tissue from non-breast tissue using fuzzy cmeans clustering (b) separation of dense and fatty tissues using Otsu's method, and (c) calculation of volumetric density as the ratio of dense voxels to total breast voxels. We examined the relationship between pre-contrast DCE-MRI density and clinical BI-RADS density obtained from radiology reports, and obtained a statistically significant correlation [Spearman ρ-value of 0.66 (p < 0.0001)]. Our method within precision medicine may be useful for monitoring high-risk populations.

Beyond sarcopenia: Characterization and integration of skeletal muscle quantity and radiodensity in a curable breast cancer population.

PubMed

Weinberg, Marc S; Shachar, Shlomit S; Muss, Hyman B; Deal, Allison M; Popuri, Karteek; Yu, Hyeon; Nyrop, Kirsten A; Alston, Shani M; Williams, Grant R

2018-05-01

Skeletal muscle loss, commonly known as sarcopenia, is highly prevalent and prognostic of adverse outcomes in oncology. However, there is limited information on adults with early breast cancer and examination of other skeletal muscle indices, despite the potential prognostic importance. This study characterizes and examines age-related changes in body composition of adults with early breast cancer and describes the creation of a novel integrated muscle measure. Female patients diagnosed with stage I-III breast cancer with abdominal computerized tomography (CT) scans within 12 weeks from diagnosis were identified from local tumor registry (N = 241). Skeletal muscle index (muscle area per height [cm 2 /m 2 ]), skeletal muscle density, and subcutaneous and visceral adipose tissue areas, were determined from CT L3 lumbar segments. We calculated a novel integrated skeletal measure, skeletal muscle gauge, which combines skeletal muscle index and density (SMI × SMD). 241 patients were identified with available CT imaging. Median age 52 years and range of 23-87. Skeletal muscle index and density significantly decreased with age. Using literature based cut-points, older adults (≥65 years) had significantly higher proportions of sarcopenia (63 vs 28%) and myosteatosis (90 vs 11%) compared to younger adults (<50 years). Body mass index was positively correlated with skeletal muscle index and negatively correlated with muscle density. Skeletal muscle gauge correlated better with increasing age (ρ = 0.52) than with either skeletal muscle index (ρ = 0.20) or density (ρ = 0.46). Wide variations and age-related changes in body composition metrics were found using routinely obtained abdominal CT imaging. Skeletal muscle index and density provide independent, complementary information, and the product of the two metrics, skeletal muscle gauge, requires further research to explore its impact on outcomes in women with curable breast cancer. © 2017 Wiley Periodicals, Inc.

Hair product use, age at menarche and mammographic breast density in multiethnic urban women.

PubMed

McDonald, Jasmine A; Tehranifar, Parisa; Flom, Julie D; Terry, Mary Beth; James-Todd, Tamarra

2018-01-04

Select hair products contain endocrine disrupting chemicals (EDCs) that may affect breast cancer risk. We hypothesize that, if EDCs are related to breast cancer risk, then they may also affect two important breast cancer risk factors: age at menarche and mammographic breast density. In two urban female cohorts (N = 248): 1) the New York site of the National Collaborative Perinatal Project and 2) the New York City Multiethnic Breast Cancer Project, we measured childhood and adult use of hair oils, lotions, leave-in conditioners, root stimulators, perms/relaxers, and hair dyes using the same validated questionnaire. We used multivariable relative risk regression models to examine the association between childhood hair product use and early age at menarche (defined as <11 years of age) and multivariable linear regression models to examine the association between childhood and adult hair product use and adult mammographic breast density. Early menarche was associated with ever use of childhood hair products (RR 2.3, 95% CI 1.1, 4.8) and hair oil use (RR 2.5, 95% CI 1.2, 5.2); however, additional adjustment for race/ethnicity, attenuated associations (hair products RR 1.8, 95% CI 0.8, 4.1; hair oil use RR 2.3, 95% CI 1.0, 5.5). Breast density was not associated with adult or childhood hair product or hair oil use. If confirmed in larger prospective studies, these data suggest that exposure to EDCs through hair products in early life may affect breast cancer risk by altering timing of menarche, and may operate through a mechanism distinct from breast density.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2012-10-01

training, faculty from MMC and VUMC will conduct a case-control study of mammographic breast density to investigate its’ association with obesity and...hormones and growth factors, 4) to perform statistical analyses to determine the associations between obesity and insulin resistance and mammographic...on obesity and insulin resistance as they relate to mammographic breast density. We hypothesize that: 1) obesity and insulin resistance, defined

Identifying women with dense breasts at high risk for interval cancer: a cohort study.

PubMed

Kerlikowske, Karla; Zhu, Weiwei; Tosteson, Anna N A; Sprague, Brian L; Tice, Jeffrey A; Lehman, Constance D; Miglioretti, Diana L

2015-05-19

Twenty-one states have laws requiring that women be notified if they have dense breasts and that they be advised to discuss supplemental imaging with their provider. To better direct discussions of supplemental imaging by determining which combinations of breast cancer risk and Breast Imaging Reporting and Data System (BI-RADS) breast density categories are associated with high interval cancer rates. Prospective cohort. Breast Cancer Surveillance Consortium (BCSC) breast imaging facilities. 365,426 women aged 40 to 74 years who had 831,455 digital screening mammography examinations. BI-RADS breast density, BCSC 5-year breast cancer risk, and interval cancer rate (invasive cancer ≤12 months after a normal mammography result) per 1000 mammography examinations. High interval cancer rate was defined as more than 1 case per 1000 examinations. High interval cancer rates were observed for women with 5-year risk of 1.67% or greater and extremely dense breasts or 5-year risk of 2.50% or greater and heterogeneously dense breasts (24% of all women with dense breasts). The interval rate of advanced-stage disease was highest (>0.4 case per 1000 examinations) among women with 5-year risk of 2.50% or greater and heterogeneously or extremely dense breasts (21% of all women with dense breasts). Five-year risk was low to average (0% to 1.66%) for 51.0% of women with heterogeneously dense breasts and 52.5% with extremely dense breasts, with interval cancer rates of 0.58 to 0.63 and 0.72 to 0.89 case per 1000 examinations, respectively. The benefit of supplemental imaging was not assessed. Breast density should not be the sole criterion for deciding whether supplemental imaging is justified because not all women with dense breasts have high interval cancer rates. BCSC 5-year risk combined with BI-RADS breast density can identify women at high risk for interval cancer to inform patient-provider discussions about alternative screening strategies. National Cancer Institute.

Local breast density assessment using reacquired mammographic images.

PubMed

García, Eloy; Diaz, Oliver; Martí, Robert; Diez, Yago; Gubern-Mérida, Albert; Sentís, Melcior; Martí, Joan; Oliver, Arnau

2017-08-01

The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast. We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures. Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution. This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Electromechanical Coupling Factor of Breast Tissue as a Biomarker for Breast Cancer.

PubMed

Park, Kihan; Chen, Wenjin; Chekmareva, Marina A; Foran, David J; Desai, Jaydev P

2018-01-01

This research aims to validate a new biomarker of breast cancer by introducing electromechanical coupling factor of breast tissue samples as a possible additional indicator of breast cancer. Since collagen fibril exhibits a structural organization that gives rise to a piezoelectric effect, the difference in collagen density between normal and cancerous tissue can be captured by identifying the corresponding electromechanical coupling factor. The design of a portable diagnostic tool and a microelectromechanical systems (MEMS)-based biochip, which is integrated with a piezoresistive sensing layer for measuring the reaction force as well as a microheater for temperature control, is introduced. To verify that electromechanical coupling factor can be used as a biomarker for breast cancer, the piezoelectric model for breast tissue is described with preliminary experimental results on five sets of normal and invasive ductal carcinoma (IDC) samples in the 25-45 temperature range. While the stiffness of breast tissues can be captured as a representative mechanical signature which allows one to discriminate among tissue types especially in the higher strain region, the electromechanical coupling factor shows more distinct differences between the normal and IDC groups over the entire strain region than the mechanical signature. From the two-sample -test, the electromechanical coupling factor under compression shows statistically significant differences ( 0.0039) between the two groups. The increase in collagen density in breast tissue is an objective and reproducible characteristic of breast cancer. Although characterization of mechanical tissue property has been shown to be useful for differentiating cancerous tissue from normal tissue, using a single parameter may not be sufficient for practical usage due to inherent variation among biological samples. The portable breast cancer diagnostic tool reported in this manuscript shows the feasibility of measuring multiple parameters of breast tissue allowing for practical application.

Relationship of Terminal Duct Lobular Unit Involution of the Breast with Area and Volume Mammographic Densities

PubMed Central

Gierach, Gretchen L.; Patel, Deesha A.; Pfeiffer, Ruth M.; Figueroa, Jonine D.; Linville, Laura; Papathomas, Daphne; Johnson, Jason M.; Chicoine, Rachael E.; Herschorn, Sally D.; Shepherd, John A.; Wang, Jeff; Malkov, Serghei; Vacek, Pamela M.; Weaver, Donald L.; Fan, Bo; Mahmoudzadeh, Amir Pasha; Palakal, Maya; Xiang, Jackie; Oh, Hannah; Horne, Hisani N.; Sprague, Brian L.; Hewitt, Stephen M.; Brinton, Louise A.; Sherman, Mark E.

2016-01-01

Elevated mammographic density (MD) is an established breast cancer risk factor. Reduced involution of terminal duct lobular units (TDLUs), the histologic source of most breast cancers, has been associated with higher MD and breast cancer risk. We investigated relationships of TDLU involution with area and volumetric MD, measured throughout the breast and surrounding biopsy targets (peri-lesional). Three measures inversely related to TDLU involution (TDLU count/mm2, median TDLU span, median acini count/TDLU) assessed in benign diagnostic biopsies from 348 women, ages 40–65, were related to MD area (quantified with thresholding software) and volume (assessed with a density phantom) by analysis of covariance, stratified by menopausal status and adjusted for confounders. Among premenopausal women, TDLU count was directly associated with percent peri-lesional MD (P-trend=0.03), but not with absolute dense area/volume. Greater TDLU span was associated with elevated percent dense area/volume (P-trend<0.05) and absolute peri-lesional MD (P=0.003). Acini count was directly associated with absolute peri-lesional MD (P=0.02). Greater TDLU involution (all metrics) was associated with increased nondense area/volume (P-trend≤0.04). Among postmenopausal women, TDLU measures were not significantly associated with MD. Among premenopausal women, reduced TDLU involution was associated with higher area and volumetric MD, particularly in peri-lesional parenchyma. Data indicating that TDLU involution and MD are correlated markers of breast cancer risk suggest that associations of MD with breast cancer may partly reflect amounts of at-risk epithelium. If confirmed, these results could suggest a prevention paradigm based on enhancing TDLU involution and monitoring efficacy by assessing MD reduction. PMID:26645278

Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.

PubMed

Jeffers, Abra M; Sieh, Weiva; Lipson, Jafi A; Rothstein, Joseph H; McGuire, Valerie; Whittemore, Alice S; Rubin, Daniel L

2017-02-01

Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects. © RSNA, 2016.

Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.

PubMed

Jankowska, Marta M; Natarajan, Loki; Godbole, Suneeta; Meseck, Kristin; Sears, Dorothy D; Patterson, Ruth E; Kerr, Jacqueline

2017-07-01

Background: Environmental factors may influence breast cancer; however, most studies have measured environmental exposure in neighborhoods around home residences (static exposure). We hypothesize that tracking environmental exposures over time and space (dynamic exposure) is key to assessing total exposure. This study compares breast cancer survivors' exposure to walkable and recreation-promoting environments using dynamic Global Positioning System (GPS) and static home-based measures of exposure in relation to insulin resistance. Methods: GPS data from 249 breast cancer survivors living in San Diego County were collected for one week along with fasting blood draw. Exposure to recreation spaces and walkability was measured for each woman's home address within an 800 m buffer (static), and using a kernel density weight of GPS tracks (dynamic). Participants' exposure estimates were related to insulin resistance (using the homeostatic model assessment of insulin resistance, HOMA-IR) controlled by age and body mass index (BMI) in linear regression models. Results: The dynamic measurement method resulted in greater variability in built environment exposure values than did the static method. Regression results showed no association between HOMA-IR and home-based, static measures of walkability and recreation area exposure. GPS-based dynamic measures of both walkability and recreation area were significantly associated with lower HOMA-IR ( P < 0.05). Conclusions: Dynamic exposure measurements may provide important evidence for community- and individual-level interventions that can address cancer risk inequities arising from environments wherein breast cancer survivors live and engage. Impact: This is the first study to compare associations of dynamic versus static built environment exposure measures with insulin outcomes in breast cancer survivors. Cancer Epidemiol Biomarkers Prev; 26(7); 1078-84. ©2017 AACR . ©2017 American Association for Cancer Research.

The ANDROMEDA prospective cohort study: predictive value of combined criteria to tailor breast cancer screening and new opportunities from circulating markers: study protocol.

PubMed

Giordano, Livia; Gallo, Federica; Petracci, Elisabetta; Chiorino, Giovanna; Segnan, Nereo

2017-11-22

In recent years growing interest has been posed on alternative ways to screen women for breast cancer involving different imaging techniques or adjusting screening interval by breast cancer risk estimates. A new research area is studying circulating microRNAs as molecular biomarkers potentially useful for non invasive early detection together with the analysis of single-nucleotide polymorphisms (SNPs). The Andromeda study is a prospective cohort study on women attending breast cancer screening in a northern Italian area. The aims of the study are: 1) to define appropriate women risk-based stratifications for personalized screening considering different factors (reproductive, family and biopsy history, breast density, lifestyle habits); 2) to evaluate the diagnostic accuracy of selected circulating microRNAs in a case-control study nested within the above mentioned cohort. About 21,000 women aged 46-67 years compliant to screening mammography are expected to be enrolled. At enrolment, information on well-known breast cancer risk factors and life-styles habits are collected through self-admistered questionnaires. Information on breast density and anthropometric measurements (height, weight, body composition, and waist circumference) are recorded. In addition, women are requested to provide a blood sample for serum, plasma and buffy-coat storing for subsequent molecular analyses within the nested case-control study. This investigation will be performed on approximately 233 cases (screen-detected) and 699 matched controls to evaluate SNPs and circulating microRNAs. The whole study will last three years and the cohort will be followed up for ten years to observe the onset of new breast cancer cases. Nowadays women undergo the same screening protocol, independently of their breast density and their individual risk to develop breast cancer. New criteria to better stratify women in risk groups could enable the screening strategies to target high-risk women while reducing interventions in those at low-risk. In this frame the present study will contribute in identifying the feasibility and impact of implementing personalized breast cancer screening. NCT02618538 (retrospectively registered on 27-11-2015.).

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

Comparison of variability in breast density assessment by BI-RADS category according to the level of experience.

PubMed

Eom, Hye-Joung; Cha, Joo Hee; Kang, Ji-Won; Choi, Woo Jung; Kim, Han Jun; Go, EunChae

2018-05-01

Background Only few studies have assessed variability in the results obtained by the readers with different experience levels in comparison with automated volumetric breast density measurements. Purpose To examine the variations in breast density assessment according to BI-RADS categories among readers with different experience levels and to compare it with the results of automated quantitative measurements. Material and Methods Density assignment was done for 1000 screening mammograms by six readers with three different experience levels (breast-imaging experts, general radiologists, and students). Agreement level between the results obtained by the readers and the Volpara automated volumetric breast density measurements was assessed. The agreement analysis using two categories-non-dense and dense breast tissue-was also performed. Results Intra-reader agreement for experts, general radiologists, and students were almost perfect or substantial (k = 0.74-0.95). The agreement between visual assessments of the breast-imaging experts and volumetric assessments by Volpara was substantial (k = 0.77). The agreement was moderate between the experts and general radiologists (k = 0.67) and slight between the students and Volpara (k = 0.01). The agreement for the two category groups (nondense and dense) was almost perfect between the experts and Volpara (k = 0.83). The agreement was substantial between the experts and general radiologists (k = 0.78). Conclusion We observed similar high agreement levels between visual assessments of breast density performed by radiologists and the volumetric assessments. However, agreement levels were substantially lower for the untrained readers.

Combined effects of endogenous sex hormone levels and mammographic density on postmenopausal breast cancer risk: results from the Breakthrough Generations Study

PubMed Central

Schoemaker, M J; Folkerd, E J; Jones, M E; Rae, M; Allen, S; Ashworth, A; Dowsett, M; Swerdlow, A J

2014-01-01

Background: Mammographic density and sex hormone levels are strong risk factors for breast cancer, but it is unclear whether they represent the same aetiological entity or are independent risk factors. Methods: Within the Breakthrough Generations Study cohort, we conducted a case–control study of 265 postmenopausal breast cancer cases and 343 controls with prediagnostic mammograms and blood samples. Plasma was assayed for oestradiol, testosterone and sex hormone-binding globulin (SHBG) concentrations and mammographic density assessed by Cumulus. Results: Oestradiol and testosterone were negatively and SHBG positively associated with percentage density and absolute dense area, but after adjusting for body mass index the associations remained significant only for SHBG. Breast cancer risk was independently and significantly positively associated with percentage density (P=0.002), oestradiol (P=0.002) and testosterone (P=0.007) levels. Women in the highest tertile of both density and sex hormone level were at greatest risk, with an odds ratio of 7.81 (95% confidence interval (CI): 2.89–21.1) for oestradiol and 4.57 (95% CI: 1.75–11.9) for testosterone and high density compared with those who were in the lowest tertiles. The cumulative risk of breast cancer in the highest oestradiol and density tertiles, representing 8% of controls, was estimated as 12.8% at ages 50–69 years and 19.4% at ages 20–79 years, and in the lowest tertiles was 1.7% and 4.3%, respectively. Associations of breast cancer risk with tertiles of mammographic dense area were less strong than for percentage density. Conclusions: Endogenous sex hormone levels and mammographic density are independent risk factors for postmenopausal breast cancer, which in combination can identify women who might benefit from increased frequency of screening and chemoprophylaxis. PMID:24518596

Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening

PubMed Central

Brandt, Kathleen R.; Scott, Christopher G.; Ma, Lin; Mahmoudzadeh, Amir P.; Jensen, Matthew R.; Whaley, Dana H.; Wu, Fang Fang; Malkov, Serghei; Hruska, Carrie B.; Norman, Aaron D.; Heine, John; Shepherd, John; Pankratz, V. Shane; Kerlikowske, Karla

2016-01-01

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. © RSNA, 2015 Online supplemental material is available for this article. PMID:26694052

Volumetric mammographic density: heritability and association with breast cancer susceptibility loci.

PubMed

Brand, Judith S; Humphreys, Keith; Thompson, Deborah J; Li, Jingmei; Eriksson, Mikael; Hall, Per; Czene, Kamila

2014-12-01

Mammographic density is a strong heritable trait, but data on its genetic component are limited to area-based and qualitative measures. We studied the heritability of volumetric mammographic density ascertained by a fully-automated method and the association with breast cancer susceptibility loci. Heritability of volumetric mammographic density was estimated with a variance component model in a sib-pair sample (N pairs = 955) of a Swedish screening based cohort. Associations with 82 established breast cancer loci were assessed in an independent sample of the same cohort (N = 4025 unrelated women) using linear models, adjusting for age, body mass index, and menopausal status. All tests were two-sided, except for heritability analyses where one-sided tests were used. After multivariable adjustment, heritability estimates (standard error) for percent dense volume, absolute dense volume, and absolute nondense volume were 0.63 (0.06) and 0.43 (0.06) and 0.61 (0.06), respectively (all P < .001). Percent and absolute dense volume were associated with rs10995190 (ZNF365; P = 9.0 × 10(-6) and 8.9 × 10(-7), respectively) and rs9485372 (TAB2; P = 1.8 × 10(-5) and 1.8 × 10(-3), respectively). We also observed associations of rs9383938 (ESR1) and rs2046210 (ESR1) with the absolute dense volume (P = 2.6 × 10(-4) and 4.6 × 10(-4), respectively), and rs6001930 (MLK1) and rs17356907 (NTN4) with the absolute nondense volume (P = 6.7 × 10(-6) and 8.4 × 10(-5), respectively). Our results support the high heritability of mammographic density, though estimates are weaker for absolute than percent dense volume. We also demonstrate that the shared genetic component with breast cancer is not restricted to dense tissues only. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Comparison of Mammographic Density Assessed as Volumes and Areas among Women Undergoing Diagnostic Image-Guided Breast Biopsy

PubMed Central

Gierach, Gretchen L.; Geller, Berta M.; Shepherd, John A.; Patel, Deesha A.; Vacek, Pamela M.; Weaver, Donald L.; Chicoine, Rachael E.; Pfeiffer, Ruth M.; Fan, Bo; Mahmoudzadeh, Amir Pasha; Wang, Jeff; Johnson, Jason M.; Herschorn, Sally D.; Brinton, Louise A.; Sherman, Mark E.

2014-01-01

Background Mammographic density (MD), the area of non-fatty appearing tissue divided by total breast area, is a strong breast cancer risk factor. Most MD analyses have employed visual categorizations or computer-assisted quantification, which ignore breast thickness. We explored MD volume and area, using a volumetric approach previously validated as predictive of breast cancer risk, in relation to risk factors among women undergoing breast biopsy. Methods Among 413 primarily white women, ages 40–65, undergoing diagnostic breast biopsies between 2007–2010 at an academic facility in Vermont, MD volume (cm3) was quantified in cranio-caudal views of the breast contralateral to the biopsy target using a density phantom, while MD area (cm2) was measured on the same digital mammograms using thresholding software. Risk factor associations with continuous MD measurements were evaluated using linear regression. Results Percent MD volume and area were correlated (r=0.81) and strongly and inversely associated with age, body mass index (BMI), and menopause. Both measures were inversely associated with smoking and positively associated with breast biopsy history. Absolute MD measures were correlated (r=0.46) and inversely related to age and menopause. Whereas absolute dense area was inversely associated with BMI, absolute dense volume was positively associated. Conclusions Volume and area MD measures exhibit some overlap in risk factor associations, but divergence as well, particularly for BMI. Impact Findings suggest that volume and area density measures differ in subsets of women; notably, among obese women, absolute density was higher with volumetric methods, suggesting that breast cancer risk assessments may vary for these techniques. PMID:25139935

Sex steroid metabolism polymorphisms and mammographic density in pre- and early perimenopausal women

PubMed Central

Crandall, Carolyn J; Sehl, Mary E; Crawford, Sybil L; Gold, Ellen B; Habel, Laurel A; Butler, Lesley M; Sowers, MaryFran R; Greendale, Gail A; Sinsheimer, Janet S

2009-01-01

Introduction We examined the association between mammographic density and single-nucleotide polymorphisms (SNPs) in genes encoding CYP1A1, CYP1B1, aromatase, 17β-HSD, ESR1, and ESR2 in pre- and early perimenopausal white, African-American, Chinese, and Japanese women. Methods The Study of Women's Health Across the Nation is a longitudinal community-based cohort study. We analyzed data from 451 pre- and early perimenopausal participants of the ancillary SWAN Mammographic Density study for whom we had complete information regarding mammographic density, genotypes, and covariates. With multivariate linear regression, we examined the relation between percentage mammographic breast density (outcome) and each SNP (primary predictor), adjusting for age, race/ethnicity, parity, cigarette smoking, and body mass index (BMI). Results After multivariate adjustment, the CYP1B1 rs162555 CC genotype was associated with a 9.4% higher mammographic density than the TC/TT genotype (P = 0.04). The CYP19A1 rs936306 TT genotype was associated with 6.2% lower mammographic density than the TC/CC genotype (P = 0.02). The positive association between CYP1A1 rs2606345 and mammographic density was significantly stronger among participants with BMI greater than 30 kg/m2 than among those with BMI less than 25 kg/m2 (Pinteraction = 0.05). Among white participants, the ESR1 rs2234693 CC genotype was associated with a 7.0% higher mammographic density than the CT/TT genotype (P = 0.01). Conclusions SNPs in certain genes encoding sex steroid metabolism enzymes and ESRs were associated with mammographic density. Because the encoded enzymes and ESR1 are expressed in breast tissue, these SNPs may influence breast cancer risk by altering mammographic density. PMID:19630952

Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

PubMed

Pertuz, Said; McDonald, Elizabeth S; Weinstein, Susan P; Conant, Emily F; Kontos, Despina

2016-04-01

To assess a fully automated method for volumetric breast density (VBD) estimation in digital breast tomosynthesis (DBT) and to compare the findings with those of full-field digital mammography (FFDM) and magnetic resonance (MR) imaging. Bilateral DBT images, FFDM images, and sagittal breast MR images were retrospectively collected from 68 women who underwent breast cancer screening from October 2011 to September 2012 with institutional review board-approved, HIPAA-compliant protocols. A fully automated computer algorithm was developed for quantitative estimation of VBD from DBT images. FFDM images were processed with U.S. Food and Drug Administration-cleared software, and the MR images were processed with a previously validated automated algorithm to obtain corresponding VBD estimates. Pearson correlation and analysis of variance with Tukey-Kramer post hoc correction were used to compare the multimodality VBD estimates. Estimates of VBD from DBT were significantly correlated with FFDM-based and MR imaging-based estimates with r = 0.83 (95% confidence interval [CI]: 0.74, 0.90) and r = 0.88 (95% CI: 0.82, 0.93), respectively (P < .001). The corresponding correlation between FFDM and MR imaging was r = 0.84 (95% CI: 0.76, 0.90). However, statistically significant differences after post hoc correction (α = 0.05) were found among VBD estimates from FFDM (mean ± standard deviation, 11.1% ± 7.0) relative to MR imaging (16.6% ± 11.2) and DBT (19.8% ± 16.2). Differences between VDB estimates from DBT and MR imaging were not significant (P = .26). Fully automated VBD estimates from DBT, FFDM, and MR imaging are strongly correlated but show statistically significant differences. Therefore, absolute differences in VBD between FFDM, DBT, and MR imaging should be considered in breast cancer risk assessment.

Red clover-derived isoflavones and mammographic breast density: a double-blind, randomized, placebo-controlled trial [ISRCTN42940165

PubMed Central

Atkinson, Charlotte; Warren, Ruth ML; Sala, Evis; Dowsett, Mitch; Dunning, Alison M; Healey, Catherine S; Runswick, Shirley; Day, Nicholas E; Bingham, Sheila A

2004-01-01

Introduction Isoflavones are hypothesized to protect against breast cancer, but it is not clear whether they act as oestrogens or anti-oestrogens in breast tissue. Our aim was to determine the effects of taking a red clover-derived isoflavone supplement daily for 1 year on mammographic breast density. Effects on oestradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), lymphocyte tyrosine kinase activity and menopausal symptoms were also assessed. Methods A total of 205 women (age range 49–65 years) with Wolfe P2 or DY mammographic breast patterns were randomly assigned to receive either a red clover-derived isoflavone tablet (26 mg biochanin A, 16 mg formononetin, 1 mg genistein and 0.5 mg daidzein) or placebo. Change in mammographic breast density, serum oestradiol, FSH, LH, menopausal symptoms and lymphocyte tyrosine kinase activity from baseline to 12 months were assessed. Results A total of 177 women completed the trial. Mammographic breast density decreased in both groups but the difference between the treatment and placebo was not statistically significant. There was a significant interaction between treatment group and oestrogen receptor (ESR1) PvuII polymorphism for the change in estimated percentage breast density (mean ± standard deviation): TT isoflavone 1.4 ± 12.3% and TT placebo -9.6 ± 14.2%; CT isoflavone -5.2 ± 12.0% and CT placebo -2.8 ± 10.3%; and CC isoflavone -3.4 ± 9.7% and CC placebo -1.1 ± 9.5%. There were no statistically significant treatment effects on oestradiol, FSH, or LH (assessed only in postmenopausal women), or on lymphocyte tyrosine kinase activity. Baseline levels of menopausal symptoms were low, and there were no statistically significant treatment effects on frequency of hot flushes or other menopausal symptoms. Conclusion In contrast to studies showing that conventional hormone replacement therapies increase mammographic breast density, the isoflavone supplement did not increase mammographic breast density in this population of women. Furthermore, there were no effects on oestradiol, gonadotrophins, lymphocyte tyrosine kinase activity, or menopausal symptoms. PMID:15084240

Common variants in ZNF365 are associated with both mammographic density and breast cancer risk.

PubMed

Lindström, Sara; Vachon, Celine M; Li, Jingmei; Varghese, Jajini; Thompson, Deborah; Warren, Ruth; Brown, Judith; Leyland, Jean; Audley, Tina; Wareham, Nicholas J; Loos, Ruth J F; Paterson, Andrew D; Rommens, Johanna; Waggott, Darryl; Martin, Lisa J; Scott, Christopher G; Pankratz, V Shane; Hankinson, Susan E; Hazra, Aditi; Hunter, David J; Hopper, John L; Southey, Melissa C; Chanock, Stephen J; Silva, Isabel dos Santos; Liu, JianJun; Eriksson, Louise; Couch, Fergus J; Stone, Jennifer; Apicella, Carmel; Czene, Kamila; Kraft, Peter; Hall, Per; Easton, Douglas F; Boyd, Norman F; Tamimi, Rulla M

2011-03-01

High-percent mammographic density adjusted for age and body mass index is one of the strongest risk factors for breast cancer. We conducted a meta analysis of five genome-wide association studies of percent mammographic density and report an association with rs10995190 in ZNF365 (combined P = 9.6 × 10(-10)). Common variants in ZNF365 have also recently been associated with susceptibility to breast cancer.

[Changes in mammographic features of breast cancer--comparison with previous films].

PubMed

Matsunaga, T; Hagiwara, K; Kimura, K; Kusama, M

1992-11-25

Mammographic features of 87 breast cancer patients were studied in comparison with their previous survey films. Changes in the mammographic features included microcalicification (28 cases), tumor shadow (35 cases) and intratumorous microcalicifications (6 cases). Seven cases had several extremely faint calcifications on the previous films, and three of six cases with clustered and scattered microcalcifications that extended over an entire breast quadrant had increased in number, density and extent. Eight cases in which clustered microcalcifications had increased in number, density and extent suggested a relationship between the increase in the extent of microcalcifications and length of time between visits. In most cases with tumor shadow, a slight localized increase in mammary gland density, irregular margins and straightened trabeculae were overlooked because of breast density.

Mammography

MedlinePlus

... Prior Mammograms Helps Radiologists Detect Breast Cancer MammographySavesLives.org A general information resource on breast imaging from ... doctors: Breast Density and Breast Cancer Screening RTAnswers.org Radiation Therapy for Breast Cancer MedLinePlus Mammography top ...

Third generation anthropomorphic physical phantom for mammography and DBT: incorporating voxelized 3D printing and uniform chest wall QC region

NASA Astrophysics Data System (ADS)

Zhao, Christine; Solomon, Justin; Sturgeon, Gregory M.; Gehm, Michael E.; Catenacci, Matthew; Wiley, Benjamin J.; Samei, Ehsan; Lo, Joseph Y.

2017-03-01

Physical breast phantoms provide a standard method to test, optimize, and develop clinical mammography systems, including new digital breast tomosynthesis (DBT) systems. In previous work, we produced an anthropomorphic phantom based on 500x500x500 μm breast CT data using commercial 3D printing. We now introduce an improved phantom based on a new cohort of virtual models with 155x155x155 μm voxels and fabricated through voxelized 3D printing and dithering, which confer higher resolution and greater control over contrast. This new generation includes a uniform chest wall extension for evaluating conventional QC metrics. The uniform region contains a grayscale step wedge, chest wall coverage markers, fiducial markers, spheres, and metal ink stickers of line pairs and edges to assess contrast, resolution, artifact spread function, MTF, and other criteria. We also experimented with doping photopolymer material with calcium, iodine, and zinc to increase our current contrast. In particular, zinc was discovered to significantly increase attenuation beyond 100% breast density with a linear relationship between zinc concentration and attenuation or breast density. This linear relationship was retained when the zinc-doped material was applied in conjunction with 3D printing. As we move towards our long term goal of phantoms that are indistinguishable from patients, this new generation of anthropomorphic physical breast phantom validates our voxelized printing process, demonstrates the utility of a uniform QC region with features from 3D printing and metal ink stickers, and shows potential for improved contrast via doping.

Phase II study of metformin for reduction of obesity-associated breast cancer risk: a randomized controlled trial protocol.

PubMed

Martinez, Jessica A; Chalasani, Pavani; Thomson, Cynthia A; Roe, Denise; Altbach, Maria; Galons, Jean-Philippe; Stopeck, Alison; Thompson, Patricia A; Villa-Guillen, Diana Evelyn; Chow, H-H Sherry

2016-07-19

Two-thirds of U.S. adult women are overweight or obese. High body mass index (BMI) and adult weight gain are risk factors for a number of chronic diseases, including postmenopausal breast cancer. The higher postmenopausal breast cancer risk in women with elevated BMI is likely to be attributable to related metabolic disturbances including altered circulating sex steroid hormones and adipokines, elevated pro-inflammatory cytokines, and insulin resistance. Metformin is a widely used antidiabetic drug that has demonstrated favorable effects on metabolic disturbances and as such may lead to lower breast cancer risk in obese women. Further, the anti-proliferative effects of metformin suggest it may decrease breast density, an accepted biomarker of breast cancer risk. This is a Phase II randomized, double-blind, placebo-controlled trial of metformin in overweight/obese premenopausal women who have elements of metabolic syndrome. Eligible participants will be randomized to receive metformin 850 mg BID (n = 75) or placebo (n = 75) for 12 months. The primary endpoint is change in breast density, based on magnetic resonance imaging (MRI) acquired fat-water features. Secondary outcomes include changes in serum insulin levels, serum insulin-like growth factor (IGF)-1 to insulin-like growth factor binding protein (IGFBP)-3 ratio, serum IGF-2 levels, serum testosterone levels, serum leptin to adiponectin ratio, body weight, and waist circumference. Exploratory outcomes include changes in metabolomic profiles in plasma and nipple aspirate fluid. Changes in tissue architecture as well as cellular and molecular targets in breast tissue collected in a subgroup of participants will also be explored. The study will evaluate whether metformin can result in favorable changes in breast density, select proteins and hormones, products of body metabolism, and body weight and composition. The study should help determine the potential breast cancer preventive activity of metformin in a growing population at risk for multiple diseases. ClinicalTrials.gov Identifier: NCT02028221 . Registered on January 2, 2014. Grant #: 1R01CA172444-01A1 awarded on Sept 11, 2013.

Tumor characteristics and family history in relation to mammographic density and breast cancer: The French E3N cohort.

PubMed

Maskarinec, Gertraud; Dartois, Laureen; Delaloge, Suzette; Hopper, John; Clavel-Chapelon, Françoise; Baglietto, Laura

2017-08-01

Mammographic density is a known heritable risk factor for breast cancer, but reports how tumor characteristics and family history may modify this association are inconsistent. Dense and total breast areas were assessed using Cumulus™ from pre-diagnostic mammograms for 820 invasive breast cancer cases and 820 matched controls nested within the French E3N cohort study. To allow comparisons across models, percent mammographic density (PMD) was standardized to the distribution of the controls. Odds ratios (OR) and 95% confidence intervals (CI) of breast cancer risk for mammographic density were estimated by conditional logistic regression while adjusting for age and body mass index. Heterogeneity according to tumor characteristic and family history was assessed using stratified analyses. Overall, the OR per 1 SD for PMD was 1.50 (95% CI, 1.33-1.69). No evidence for significant heterogeneity by tumor size, lymph node status, grade, and hormone receptor status (estrogen, progesterone, and HER2) was detected. However, the association of PMD was stronger for women reporting a family history of breast cancer (OR 1SD =2.25; 95% CI, 1.67-3.04) than in women reporting none (OR 1SD =1.41; 95% CI, 1.24-1.60; p heterogeneity =0.002). Similarly, effect modification by FHBC was observed using categories of PMD (p heterogeneity =0.02) with respective ORs of 15.16 (95% CI, 4.23-54.28) vs. 3.14 (95% CI, 1.89-5.22) for ≥50% vs. <10% PMD. The stronger association between mammographic density and breast cancer risk with a family history supports the hypothesis of shared genetic factors responsible for familial aggregation of breast cancer and the heritable component of mammographic density. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gene Methylation and Cytological Atypia in Random Fine Needle Aspirates for Assessment of Breast Cancer Risk

PubMed Central

Hafeez, Sidra; Bujanda, Zoila Lopez; Chatterton, Robert T.; Jacobs, Lisa K.; Khouri, Nagi F.; Ivancic, David; Kenney, Kara; Shehata, Christina; Jeter, Stacie C.; Wolfman, Judith A.; Zalles, Carola M.; Huang, Peng

2016-01-01

Methods to determine individualized breast cancer risk lack sufficient sensitivity to select women most likely to benefit from preventive strategies. Alterations in DNA methylation occur early in breast cancer. We hypothesized that cancer-specific methylation markers could enhance breast cancer risk assessment. We evaluated 380 women without a history of breast cancer. We determined their menopausal status or menstrual cycle phase, risk of developing breast cancer (Gail model), and breast density, and obtained random fine needle aspiration (rFNA) samples for assessment of cytopathology and cumulative methylation index (CMI). Eight methylated gene markers were identified through whole genome methylation analysis and included novel and previously established breast cancer detection genes. We performed correlative and multivariate linear regression analyses to evaluate DNA methylation of a gene panel as a function of clinical factors associated with breast cancer risk. CMI and individual gene methylation were independent of age, menopausal status or menstrual phase, lifetime Gail risk score, and breast density. CMI and individual gene methylation for the eight genes increased significantly (p<0.001) with increasing cytological atypia. The findings were verified with multivariate analyses correcting for age, log (Gail), log (percent density), rFNA cell number and BMI. Our results demonstrate a significant association between cytological atypia and high CMI, which does not vary with menstrual phase or menopause and is independent of Gail risk and mammographic density. Thus CMI is an excellent candidate breast cancer risk biomarker, warranting larger prospective studies to establish its utility for cancer risk assessment. PMID:27261491

Visual assessment of breast density using Visual Analogue Scales: observer variability, reader attributes and reading time

NASA Astrophysics Data System (ADS)

Ang, Teri; Harkness, Elaine F.; Maxwell, Anthony J.; Lim, Yit Y.; Emsley, Richard; Howell, Anthony; Evans, D. Gareth; Astley, Susan; Gadde, Soujanya

2017-03-01

Breast density is a strong risk factor for breast cancer and has potential use in breast cancer risk prediction, with subjective methods of density assessment providing a strong relationship with the development of breast cancer. This study aims to assess intra- and inter-observer variability in visual density assessment recorded on Visual Analogue Scales (VAS) among trained readers, and examine whether reader age, gender and experience are associated with assessed density. Eleven readers estimated the breast density of 120 mammograms on two occasions 3 years apart using VAS. Intra- and inter-observer agreement was assessed with Intraclass Correlation Coefficient (ICC) and variation between readers visualised on Bland-Altman plots. The mean scores of all mammograms per reader were used to analyse the effect of reader attributes on assessed density. Excellent intra-observer agreement (ICC>0.80) was found in the majority of the readers. All but one reader had a mean difference of <10 percentage points from the first to the second reading. Inter-observer agreement was excellent for consistency (ICC 0.82) and substantial for absolute agreement (ICC 0.69). However, the 95% limits of agreement for pairwise differences were -6.8 to 15.7 at the narrowest and 0.8 to 62.3 at the widest. No significant association was found between assessed density and reader age, experience or gender, or with reading time. Overall, the readers were consistent in their scores, although some large variations were observed. Reader evaluation and targeted training may alleviate this problem.

Collagen content as a risk factor in breast cancer? A pilot clinical study

NASA Astrophysics Data System (ADS)

Pifferi, Antonio; Quarto, Giovanna; Abbate, Francesca; Balestreri, Nicola; Menna, Simona; Cassano, Enrico; Cubeddu, Rinaldo; Taroni, Paola

2015-07-01

A retrospective pilot clinical study on time domain multi-wavelength (635 to 1060 nm) optical mammography was exploited to assess collagen as a breast-cancer risk factor on a total of 109 subjects (53 healthy and 56 with malignant lesions). An increased cancer occurrence is observed on the 15% subset of patients with higher age-matched collagen content. Further, a similar clustering based on the percentage breast density leads to a different set of patients, possibly indicating collagen as a new independent breast cancer risk factor. If confirmed statistically and on larger numbers, these results could have huge impact on personalized diagnostics, health care systems, as well as on basic research.

Serum osteoprotegerin levels and mammographic density among high-risk women.

PubMed

Moran, Olivia; Zaman, Tasnim; Eisen, Andrea; Demsky, Rochelle; Blackmore, Kristina; Knight, Julia A; Elser, Christine; Ginsburg, Ophira; Zbuk, Kevin; Yaffe, Martin; Narod, Steven A; Salmena, Leonardo; Kotsopoulos, Joanne

2018-06-01

Mammographic density is a risk factor for breast cancer but the mechanism behind this association is unclear. The receptor activator of nuclear factor κB (RANK)/RANK ligand (RANKL) pathway has been implicated in the development of breast cancer. Given the role of RANK signaling in mammary epithelial cell proliferation, we hypothesized this pathway may also be associated with mammographic density. Osteoprotegerin (OPG), a decoy receptor for RANKL, is known to inhibit RANK signaling. Thus, it is of interest to evaluate whether OPG levels modify breast cancer risk through mammographic density. We quantified serum OPG levels in 57 premenopausal and 43 postmenopausal women using an enzyme-linked immunosorbent assay (ELISA). Cumulus was used to measure percent density, dense area, and non-dense area for each mammographic image. Subjects were classified into high versus low OPG levels based on the median serum OPG level in the entire cohort (115.1 pg/mL). Multivariate models were used to assess the relationship between serum OPG levels and the measures of mammographic density. Serum OPG levels were not associated with mammographic density among premenopausal women (P ≥ 0.42). Among postmenopausal women, those with low serum OPG levels had higher mean percent mammographic density (20.9% vs. 13.7%; P = 0.04) and mean dense area (23.4 cm 2 vs. 15.2 cm 2 ; P = 0.02) compared to those with high serum OPG levels after covariate adjustment. These findings suggest that low OPG levels may be associated with high mammographic density, particularly in postmenopausal women. Targeting RANK signaling may represent a plausible, non-surgical prevention option for high-risk women with high mammographic density, especially those with low circulating OPG levels.

Introduction of an automated user-independent quantitative volumetric magnetic resonance imaging breast density measurement system using the Dixon sequence: comparison with mammographic breast density assessment.

PubMed

Wengert, Georg Johannes; Helbich, Thomas H; Vogl, Wolf-Dieter; Baltzer, Pascal; Langs, Georg; Weber, Michael; Bogner, Wolfgang; Gruber, Stephan; Trattnig, Siegfried; Pinker, Katja

2015-02-01

The purposes of this study were to introduce and assess an automated user-independent quantitative volumetric (AUQV) breast density (BD) measurement system on the basis of magnetic resonance imaging (MRI) using the Dixon technique as well as to compare it with qualitative and quantitative mammographic (MG) BD measurements. Forty-three women with normal mammogram results (Breast Imaging Reporting and Data System 1) were included in this institutional review board-approved prospective study. All participants were subjected to BD assessment with MRI using the following sequence with the Dixon technique (echo time/echo time, 6 milliseconds/2.45 milliseconds/2.67 milliseconds; 1-mm isotropic; 3 minutes 38 seconds). To test the reproducibility, a second MRI after patient repositioning was performed. The AUQV magnetic resonance (MR) BD measurement system automatically calculated percentage (%) BD. The qualitative BD assessment was performed using the American College of Radiology Breast Imaging Reporting and Data System BD categories. Quantitative BD was estimated semiautomatically using the thresholding technique Cumulus4. Appropriate statistical tests were used to assess the agreement between the AUQV MR measurements and to compare them with qualitative and quantitative MG BD estimations. The AUQV MR BD measurements were successfully performed in all 43 women. There was a nearly perfect agreement of AUQV MR BD measurements between the 2 MR examinations for % BD (P < 0.001; intraclass correlation coefficient, 0.998) with no significant differences (P = 0.384). The AUQV MR BD measurements were significantly lower than quantitative and qualitative MG BD assessment (P < 0.001). The AUQV MR BD measurement system allows a fully automated, user-independent, robust, reproducible, as well as radiation- and compression-free volumetric quantitative BD assessment through different levels of BD. The AUQV MR BD measurements were significantly lower than the currently used qualitative and quantitative MG-based approaches, implying that the current assessment might overestimate breast density with MG.

Density-based clustering analyses to identify heterogeneous cellular sub-populations

NASA Astrophysics Data System (ADS)

Heaster, Tiffany M.; Walsh, Alex J.; Landman, Bennett A.; Skala, Melissa C.

2017-02-01

Autofluorescence microscopy of NAD(P)H and FAD provides functional metabolic measurements at the single-cell level. Here, density-based clustering algorithms were applied to metabolic autofluorescence measurements to identify cell-level heterogeneity in tumor cell cultures. The performance of the density-based clustering algorithm, DENCLUE, was tested in samples with known heterogeneity (co-cultures of breast carcinoma lines). DENCLUE was found to better represent the distribution of cell clusters compared to Gaussian mixture modeling. Overall, DENCLUE is a promising approach to quantify cell-level heterogeneity, and could be used to understand single cell population dynamics in cancer progression and treatment.

Sexual minority population density and incidence of lung, colorectal and female breast cancer in California.

PubMed

Boehmer, Ulrike; Miao, Xiaopeng; Maxwell, Nancy I; Ozonoff, Al

2014-03-26

Risk factors for breast, colorectal, and lung cancer are known to be more common among lesbian, gay, and bisexual (LGB) individuals, suggesting they may be more likely to develop these cancers. Our objective was to determine differences in cancer incidence by sexual orientation, using sexual orientation data aggregated at the county level. Data on cancer incidence were obtained from the California Cancer Registry and data on sexual orientation were obtained from the California Health Interview Survey, from which a measure of age-specific LGB population density by county was calculated. Using multivariable Poisson regression models, the association between the age-race-stratified incident rate of breast, lung and colorectal cancer in each county and LGB population density was examined, with race, age group and poverty as covariates. Among men, bisexual population density was associated with lower incidence of lung cancer and with higher incidence of colorectal cancer. Among women, lesbian population density was associated with lower incidence of lung and colorectal cancer and with higher incidence of breast cancer; bisexual population density was associated with higher incidence of lung and colorectal cancer and with lower incidence of breast cancer. These study findings clearly document links between county-level LGB population density and cancer incidence, illuminating an important public health disparity.

Selective magnetic resonance imaging (MRI) in invasive lobular breast cancer based on mammographic density: does it lead to an appropriate change in surgical treatment?

PubMed

Bansal, Gaurav J; Santosh, Divya; Davies, Eleri L

2016-01-01

The purpose of this study was to evaluate whether high mammographic density can be used as one of the selection criteria for MRI in invasive lobular breast cancer (ILC). In our institute, high breast density has been used as one of the indications for performing MRI scan in patients with ILC. We divided the patients in two groups, one with MRI performed pre-operatively and other without MRI. We compared their surgical procedures and analyzed whether surgical plan was altered after MRI. In case of alteration of plan, we analyzed whether the change was adequate by comparing post-operative histological findings. Between 2011 and 2015, there were a total of 1601 breast cancers with 97 lobular cancers, out of which 36 had pre-operative MRI and 61 had no MRI scan. 12 (33.3%) had mastectomy following MRI, out of which 9 (25%) had change in surgical plan from conservation to mastectomy following MRI. There were no unnecessary mastectomies in the MRI group. However, utilization of MRI in this cohort of patients did not reduce reoperation rate (19.3%). Lobular carcinoma in situ (LCIS) was identified in 60% of reoperations on post-surgical histology. Patients in the "No MRI" group had higher mastectomy rate 26 (42.6%), which was again appropriate. High mammographic density is a useful risk stratification criterion for selective MRI in ILC within a multidisciplinary team meeting setting. Provided additional lesions identified on MRI are confirmed with biopsy, pre-operative MRI does not cause unnecessary mastectomies. Used in this selective manner, reoperation rates were not eliminated, albeit reduced when compared to literature. High mammographic breast density can be used as one of the selection criteria for pre-operative MRI in ILC without an increase in inappropriate mastectomies with potential time and cost savings. In this cohort, re-excisions were not reduced markedly with pre-operative MRI.

Quantification of breast lesion compositions using low-dose spectral mammography: A feasibility study

PubMed Central

Ding, Huanjun; Sennung, David; Cho, Hyo-Min; Molloi, Sabee

2016-01-01

Purpose: The positive predictive power for malignancy can potentially be improved, if the chemical compositions of suspicious breast lesions can be reliably measured in screening mammography. The purpose of this study is to investigate the feasibility of quantifying breast lesion composition, in terms of water and lipid contents, with spectral mammography. Methods: Phantom and tissue samples were imaged with a spectral mammography system based on silicon-strip photon-counting detectors. Dual-energy calibration was performed for material decomposition, using plastic water and adipose-equivalent phantoms as the basis materials. The step wedge calibration phantom consisted of 20 calibration configurations, which ranged from 2 to 8 cm in thickness and from 0% to 100% in plastic water density. A nonlinear rational fitting function was used in dual-energy calibration of the imaging system. Breast lesion phantoms, made from various combinations of plastic water and adipose-equivalent disks, were embedded in a breast mammography phantom with a heterogeneous background pattern. Lesion phantoms with water densities ranging from 0% to 100% were placed at different locations of the heterogeneous background phantom. The water density in the lesion phantoms was measured using dual-energy material decomposition. The thickness and density of the background phantom were varied to test the accuracy of the decomposition technique in different configurations. In addition, an in vitro study was also performed using mixtures of lean and fat bovine tissue of 25%, 50%, and 80% lean weight percentages as the background. Lesions were simulated by using breast lesion phantoms, as well as small bovine tissue samples, composed of carefully weighed lean and fat bovine tissues. The water densities in tissue samples were measured using spectral mammography and compared to measurement using chemical decomposition of the tissue. Results: The thickness of measured and known water contents was compared for various lesion configurations. There was a good linear correlation between the measured and the known values. The root-mean-square errors in water thickness measurements were 0.3 and 0.2 mm for the plastic phantom and bovine tissue backgrounds, respectively. Conclusions: The results indicate that spectral mammography can be used to accurately characterize breast lesion composition in terms of their equivalent water and lipid contents. PMID:27782705

Beyond Mammography: New Frontiers in Breast Cancer Screening

PubMed Central

Drukteinis, Jennifer S.; Mooney, Blaise P.; Flowers, Chris I.; Gatenby, Robert A

2014-01-01

Breast cancer screening remains a subject of intense and, at times, passionate debate. Mammography has long been the mainstay of breast cancer detection and is the only screening test proven to reduce mortality. Although it remains the gold standard of breast cancer screening, there is increasing awareness of subpopulations of women for whom mammography has reduced sensitivity. Mammography has also undergone increased scrutiny for false positives and excessive biopsies, which increase radiation dose, cost and patient anxiety. In response to these challenges, new technologies for breast cancer screening have been developed, including; low dose mammography; contrast enhanced mammography, tomosynthesis, automated whole breast ultrasound, molecular imaging and MRI. Here we examine some of the current controversies and promising new technologies that may improve detection of breast cancer both in the general population and in high-risk groups, such as women with dense breasts. We propose that optimal breast cancer screening will ultimately require a personalized approach based on metrics of cancer risk with selective application of specific screening technologies best suited to the individual’s age, risk, and breast density. PMID:23561631

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.

PubMed

Diz, Joana; Marreiros, Goreti; Freitas, Alberto

2016-09-01

In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

The Relationship Between Geographic Access to Plastic Surgeons and Breast Reconstruction Rates Among Women Undergoing Mastectomy for Cancer.

PubMed

Bauder, Andrew R; Gross, Cary P; Killelea, Brigid K; Butler, Paris D; Kovach, Stephen J; Fox, Justin P

2017-03-01

Despite a national health care policy requiring payers to cover breast reconstruction, rates of postmastectomy reconstruction are low, particularly among minority populations. We conducted this study to determine if geographic access to a plastic surgeon impacts breast reconstruction rates. Using 2010 inpatient and ambulatory surgery data from 10 states, we identified adult women who underwent mastectomy for breast cancer. Data were aggregated to the health service area (HSA) level and hierarchical generalized linear models were used to risk-standardize breast reconstruction rates (RSRR) across HSAs. The relationship between an HSA's RSRR and plastic surgeon density (surgeons/100,000 population) was quantified using correlation coefficients. The final cohort included 22,997 patients across 134 HSAs. There was substantial variation in plastic surgeon density (median, 1.4 surgeons/100,000; interquartile range, [0.0-2.6]/100,000) and the use of breast reconstruction (median RSRR, 43.0%; interquartile range, [29.9%-62.8%]) across HSAs. Higher plastic surgeon density was positively correlated with breast reconstruction rates (correlation coefficient = 0.66, P < 0.001) and inversely related to time between mastectomy and reconstruction (correlation coefficient = -0.19, P < 0.001). Non-white and publicly insured women were least likely to undergo breast reconstruction overall. Among privately insured patients, racial disparities were noted in high surgeon density areas (white = 79.0% vs. non-white = 63.3%; P < 0.001) but not in low surgeon density areas (34.4% vs 36.5%; P = 0.70). The lack of geographic access to a plastic surgeon serves as a barrier to breast reconstruction and may compound disparities in care associated with race and insurance status. Future efforts to improve equitable access should consider strategies to ensure access to appropriate clinical expertise.

Second-harmonic generation and fluorescence lifetime imaging microscopy through a rodent mammary imaging window

NASA Astrophysics Data System (ADS)

Young, Pamela A.; Nazir, Muhammad; Szulczewski, Michael J.; Keely, Patricia J.; Eliceiri, Kevin W.

2012-03-01

Tumor-Associated Collagen Signatures (TACS) have been identified that manifest in specific ways during breast tumor progression and that correspond to patient outcome. There are also compelling metabolic changes associated with carcinoma invasion and progression. We have characterized the difference in the autofluorescent properties of metabolic co-factors, NADH and FAD, between normal and carcinoma breast cell lines. Also, we have shown in vitro that increased collagen density alters metabolic genes which are associated with glycolysis and leads to a more invasive phenotype. Establishing the relationship between collagen density, cellular metabolism, and metastasis in physiologically relevant cancer models is crucial for developing cancer therapies. To study cellular metabolism with respect to collagen density in vivo, we use multiphoton fluorescence excitation microscopy (MPM) in conjunction with a rodent mammary imaging window implanted in defined mouse cancer models. These models are ideal for the study of collagen changes in vivo, allowing determination of corresponding metabolic changes in breast cancer invasion and progression. To measure cellular metabolism, we collect fluorescence lifetime (FLIM) signatures of NADH and FAD, which are known to change based on the microenvironment of the cells. Additionally, MPM systems are capable of collecting second harmonic generation (SHG) signals which are a nonlinear optical property of collagen. Therefore, MPM, SHG, and FLIM are powerful tools with great potential for characterizing key features of breast carcinoma in vivo. Below we present the current efforts of our collaborative group to develop intravital approaches based on these imaging techniques to look at defined mouse mammary models.

Characterizing mammographic images by using generic texture features

PubMed Central

2012-01-01

Introduction Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. Methods A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Results Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Conclusions Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy. PMID:22490545

Automated breast tissue density assessment using high order regional texture descriptors in mammography

NASA Astrophysics Data System (ADS)

Law, Yan Nei; Lieng, Monica Keiko; Li, Jingmei; Khoo, David Aik-Aun

2014-03-01

Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic. A reliable method for automatic density assessment would be beneficial and could assist radiologists in the evaluation of mammograms. To address this problem, we propose a density classification method which uses statistical features from different parts of the breast. Our method is composed of three parts: breast region identification, feature extraction and building ensemble classifiers for density assessment. It explores the potential of the features extracted from second and higher order statistical information for mammographic density classification. We further investigate the registration of bilateral pairs and time-series of mammograms. The experimental results on 322 mammograms demonstrate that (1) a classifier using features from dense regions has higher discriminative power than a classifier using only features from the whole breast region; (2) these high-order features can be effectively combined to boost the classification accuracy; (3) a classifier using these statistical features from dense regions achieves 75% accuracy, which is a significant improvement from 70% accuracy obtained by the existing approaches.

The relationship between bone mineral density and mammographic density in Korean women: the Healthy Twin study.

PubMed

Sung, Joohon; Song, Yun-Mi; Stone, Jennifer; Lee, Kayoung

2011-09-01

Mammographic density is one of the strong risk factors for breast cancer. A potential mechanism for this association is that cumulative exposure to mammographic density may reflect cumulative exposure to hormones that stimulate cell division in breast stroma and epithelium, which may have corresponding effects on breast cancer development. Bone mineral density (BMD), a marker of lifetime estrogen exposure, has been found to be associated with breast cancer. We examined the association between BMD and mammographic density in a Korean population. Study subjects were 730 Korean women selected from the Healthy Twin study. BMD (g/cm(2)) was measured with dual-energy X-ray absorptiometry. Mammographic density was measured from digital mammograms using a computer-assisted thresholding method. Linear mixed model considering familial correlations and a wide range of covariates was used for analyses. Quantitative genetic analysis was completed using SOLAR. In premenopausal women, positive associations existed between absolute dense area and BMD at ribs, pelvis, and legs, and between percent dense area and BMD at pelvis and legs. However, in postmenopausal women, there was no association between BMD at any site and mammographic density measures. An evaluation of additive genetic cross-trait correlation showed that absolute dense area had a weak-positive additive genetic cross-trait correlation with BMD at ribs and spines after full adjustment of covariates. This finding suggests that the association between mammographic density and breast cancer could, at least in part, be attributable to an estrogen-related hormonal mechanism.

Sunlight Exposure and Breast Density: A Population-Based Study

PubMed Central

Wu, Sheng-Hui; So, Edwin; Lam, Tsz-ping; Woo, Jean; Yuen, PY; Qin, Ling; Ku, Susanna

2013-01-01

Purpose This study aims to assess the association of sunlight exposure with breast cancer risk, measured by the breast density assessed from Tabár's mammographic pattern in Chinese women. Methods A total of 676 premenopausal women were recruited to participate in this study, in which 650 completed a validated sunlight exposure questionnaire via telephone. The mammograms were classified according to Tabár's classification for parenchyma, and patterns IV & V and I, II & III indicated respectively high and low risk mammographic patterns for breast cancer. The odds ratios (OR) and 95% confidence intervals (CIs) for sun exposure-related variables were estimated using unconditional logistic regression with adjustment for potential confounders. Results Among 646 participants, women with high breast cancer risk (Tabár's patterns IV &V) had less hours spent in the sun than those with low risk (I, II & III) at any age stage. A higher level of sunlight exposure was associated with a significantly lower risk having high risk Tabár's pattern. Women aged 40 to 44 years who were in the highest tertile of lifetime total hours spent in the sun had a multi-adjusted OR of 0.41 (95% CI, 0.18-0.92; p for trend=0.03) compared with those in the lowest tertile (>2.19 hr/day vs. <1.32 hr/day). For hours spent in the sun across the ages of 6 to 12 years, the comparable OR was 0.37 (95% CI, 0.15-0.91; p for trend=0.03). Conclusion These findings suggest that higher sunlight exposure is related to a lower risk of having high risk breast density pattern in premenopausal women. Our results also suggest the most relevant period of exposure is during earlier life. PMID:23843849

Total Xenoestrogen Body Burden in Relation to Mammographic Density, a Marker of Breast Cancer Risk

DTIC Science & Technology

2008-10-01

average, how often do you eat a serving of meat, including beef , chicken, lamb, or pork? Never or less than once per month 1-3 servings per...has been obtained from the Susan Komen Foundation for an ancillary study of sex hormones and breast density. The Komen Foundation is providing funds...to analyze sex hormone levels in the blood samples obtained in this study. The relation between sex hormone levels and mammographic breast density

IGF-I and mammographic density in four geographic locations: a pooled analysis.

PubMed

Maskarinec, Gertraud; Takata, Yumie; Chen, Zhao; Gram, Inger Torhild; Nagata, Chisato; Pagano, Ian; Hayashi, Kentaro; Arendell, Leslie; Skeie, Guri; Rinaldi, Sabina; Kaaks, Rudolph

2007-10-15

Insulin-like growth factor (IGF-I) and prolactin have been found to be associated with breast cancer risk and with mammographic density. In a pooled analysis from 4 geographic locations, we investigated the association of percent mammographic density with serum levels of IGF-I, IGFBP-3 and prolactin. The pooled data set included 1,327 pre- and postmenopausal women: Caucasians from Norway, Arizona and Hawaii, Japanese from Hawaii and Japan, Latina from Arizona, and Native Hawaiians from Hawaii. Serum samples were assayed for IGF-I, IGFBP-3 and prolactin levels using ELISA assays. Mammographic density was quantified using a computer-assisted density method. After stratification by menopausal status, multiple regression models estimated the relation between serum analytes and breast density. All serum analytes except prolactin among postmenopausal women differed significantly by location/ethnicity group. Among premenopausal subjects, IGF-I levels and the molar ratio were highest in Hawaii, intermediate in Japan and lowest in Arizona. For IGFBP-3, the order was reversed. Among postmenopausal subjects, Norwegian women had the highest IGF-I levels and women in Arizona had the lowest while women in Japan and Hawaii had intermediate levels. We observed no significant relation between percent density and IGF-I or prolactin levels among pre-and postmenopausal women. The significant differences in IGF-I levels by location but not ethnicity suggest that environmental factors influence IGF-I levels, whereas percent breast density varies more according to ethnic background than by location. Based on this analysis, the influence of circulating levels of IGF-I, IGFBP-3, and prolactin on percent density appears to be very small. (c) 2007 Wiley-Liss, Inc.

Mammographic breast density and breast cancer: evidence of a shared genetic basis.

PubMed

Varghese, Jajini S; Thompson, Deborah J; Michailidou, Kyriaki; Lindström, Sara; Turnbull, Clare; Brown, Judith; Leyland, Jean; Warren, Ruth M L; Luben, Robert N; Loos, Ruth J; Wareham, Nicholas J; Rommens, Johanna; Paterson, Andrew D; Martin, Lisa J; Vachon, Celine M; Scott, Christopher G; Atkinson, Elizabeth J; Couch, Fergus J; Apicella, Carmel; Southey, Melissa C; Stone, Jennifer; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Boyd, Norman F; Hall, Per; Hopper, John L; Tamimi, Rulla M; Rahman, Nazneen; Easton, Douglas F

2012-03-15

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.

Mammographic breast density and breast cancer: evidence of a shared genetic basis

PubMed Central

Varghese, Jajini S; Thompson, Deborah J; Michailidou, Kyriaki; Lindström, Sara; Turnbull, Clare; Brown, Judith; Leyland, Jean; Warren, Ruth ML; Luben, Robert N; Loos, Ruth J; Wareham, Nicholas J; Rommens, Johanna; Paterson, Andrew D; Martin, Lisa J; Vachon, Celine M; Scott, Christopher G; Atkinson, Elizabeth J; Couch, Fergus J; Apicella, Carmel; Southey, Melissa C; Stone, Jennifer; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Boyd, Norman F; Hall, Per; Hopper, John L; Tamimi, Rulla M; Rahman, Nazneen; Easton, Douglas F

2012-01-01

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3628 breast cancer cases and 5190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of SNPs were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using ten different cut-offs for the most significant density SNPs (1-10% representing 5,222-50,899 SNPs). Permutation analysis was also performed across all 10 cut-offs. The association between risk score and breast cancer was significant for all cut-offs from 3-10% of top density SNPs, being most significant for the 6% (2-sided P=0.002) to 10% (P=0.001) cut-offs (overall permutation P=0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR= 1.31 (95%CI 1.08-1.59)] compared to women in the bottom 10%. Together, our results demonstrate that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants. PMID:22266113

Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk.

PubMed

Stearns, Vered; Fackler, Mary Jo; Hafeez, Sidra; Bujanda, Zoila Lopez; Chatterton, Robert T; Jacobs, Lisa K; Khouri, Nagi F; Ivancic, David; Kenney, Kara; Shehata, Christina; Jeter, Stacie C; Wolfman, Judith A; Zalles, Carola M; Huang, Peng; Khan, Seema A; Sukumar, Saraswati

2016-08-01

Methods to determine individualized breast cancer risk lack sufficient sensitivity to select women most likely to benefit from preventive strategies. Alterations in DNA methylation occur early in breast cancer. We hypothesized that cancer-specific methylation markers could enhance breast cancer risk assessment. We evaluated 380 women without a history of breast cancer. We determined their menopausal status or menstrual cycle phase, risk of developing breast cancer (Gail model), and breast density and obtained random fine-needle aspiration (rFNA) samples for assessment of cytopathology and cumulative methylation index (CMI). Eight methylated gene markers were identified through whole-genome methylation analysis and included novel and previously established breast cancer detection genes. We performed correlative and multivariate linear regression analyses to evaluate DNA methylation of a gene panel as a function of clinical factors associated with breast cancer risk. CMI and individual gene methylation were independent of age, menopausal status or menstrual phase, lifetime Gail risk score, and breast density. CMI and individual gene methylation for the eight genes increased significantly (P < 0.001) with increasing cytological atypia. The findings were verified with multivariate analyses correcting for age, log (Gail), log (percent density), rFNA cell number, and body mass index. Our results demonstrate a significant association between cytological atypia and high CMI, which does not vary with menstrual phase or menopause and is independent of Gail risk and mammographic density. Thus, CMI is an excellent candidate breast cancer risk biomarker, warranting larger prospective studies to establish its utility for cancer risk assessment. Cancer Prev Res; 9(8); 673-82. ©2016 AACR. ©2016 American Association for Cancer Research.

Endogenous sex hormones and breast density in young women.

PubMed

Jung, Seungyoun; Stanczyk, Frank Z; Egleston, Brian L; Snetselaar, Linda G; Stevens, Victor J; Shepherd, John A; Van Horn, Linda; LeBlanc, Erin S; Paris, Kenneth; Klifa, Catherine; Dorgan, Joanne F

2015-02-01

Breast density is a strong risk factor for breast cancer and reflects epithelial and stromal content. Breast tissue is particularly sensitive to hormonal stimuli before it fully differentiates following the first full-term pregnancy. Few studies have examined associations between sex hormones and breast density among young women. We conducted a cross-sectional study among 180 women ages 25 to 29 years old who participated in the Dietary Intervention Study in Children 2006 Follow-up Study. Eighty-five percent of participants attended a clinic visit during their luteal phase of menstrual cycle. Magnetic resonance imaging measured the percentage of dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute nondense breast volume (ANDBV). Multiple-linear mixed-effect regression models were used to evaluate the association of sex hormones and sex hormone-binding globulin (SHBG) with %DBV, ADBV, and ANDBV. Testosterone was significantly positively associated with %DBV and ADBV. The multivariable geometric mean of %DBV and ADBV across testosterone quartiles increased from 16.5% to 20.3% and from 68.6 to 82.3 cm(3), respectively (Ptrend ≤ 0.03). There was no association of %DBV or ADBV with estrogens, progesterone, non-SHBG-bound testosterone, or SHBG (Ptrend ≥ 0.27). Neither sex hormones nor SHBG was associated with ANDBV except progesterone; however, the progesterone result was nonsignificant in analysis restricted to women in the luteal phase. These findings suggest a modest positive association between testosterone and breast density in young women. Hormonal influences at critical periods may contribute to morphologic differences in the breast associated with breast cancer risk later in life. ©2014 American Association for Cancer Research.

Biomechanical modelling for breast image registration

NASA Astrophysics Data System (ADS)

Lee, Angela; Rajagopal, Vijay; Chung, Jae-Hoon; Bier, Peter; Nielsen, Poul M. F.; Nash, Martyn P.

2008-03-01

Breast cancer is a leading cause of death in women. Tumours are usually detected by palpation or X-ray mammography followed by further imaging, such as magnetic resonance imaging (MRI) or ultrasound. The aim of this research is to develop a biophysically-based computational tool that will allow accurate collocation of features (such as suspicious lesions) across multiple imaging views and modalities in order to improve clinicians' diagnosis of breast cancer. We have developed a computational framework for generating individual-specific, 3D finite element models of the breast. MR images were obtained of the breast under gravity loading and neutrally buoyant conditions. Neutrally buoyant breast images, obtained whilst immersing the breast in water, were used to estimate the unloaded geometry of the breast (for present purposes, we have assumed that the densities of water and breast tissue are equal). These images were segmented to isolate the breast tissues, and a tricubic Hermite finite element mesh was fitted to the digitised data points in order to produce a customized breast model. The model was deformed, in accordance with finite deformation elasticity theory, to predict the gravity loaded state of the breast in the prone position. The unloaded breast images were embedded into the reference model and warped based on the predicted deformation. In order to analyse the accuracy of the model predictions, the cross-correlation image comparison metric was used to compare the warped, resampled images with the clinical images of the prone gravity loaded state. We believe that a biomechanical image registration tool of this kind will aid radiologists to provide more reliable diagnosis and localisation of breast cancer.

Menstrual and reproductive factors in relation to mammographic density: the Study of Women’s Health Across the Nation (SWAN)

PubMed Central

Butler, Lesley M.; Gold, Ellen B.; Greendale, Gail A.; Crandall, Carolyn J.; Modugno, Francesmary; Oestreicher, Nina; Quesenberry, Charles P.; Habel, Laurel A.

2009-01-01

Menstrual and reproductive factors may increase breast cancer risk through a pathway that includes increased mammographic density. We assessed whether known or suspected menstrual and reproductive breast cancer risk factors were cross-sectionally associated with mammographic density, by measuring area of radiographic density and total breast area on mammograms from 801 participants in the Study of Women’s Health Across the Nation (SWAN), a multi-ethnic cohort of pre- and early perimenopausal women. From multivariable linear regression, the following menstrual or reproductive factors were independently associated with percent mammographic density (area of dense breast/breast area): older age at menarche (β = 10.3, P < 0.01, for >13 vs. <12 years), premenstrual cravings and bloating (β = −3.36, P = 0.02), younger age at first full-term birth (β = −8.12, P < 0.01 for ≤23 years versus no births), greater number of births (β = −6.80, P < 0.01 for ≥3 births versus no births), and premenopausal status (β = 3.78, P < 0.01 versus early perimenopausal). Only number of births remained associated with percent density after adjustment for age, race/ethnicity, study site, body mass index (BMI), and smoking. In addition, stratified analyses revealed that the association with number of births was confined to women within the lowest BMI tertile (β = −12.2, P < 0.01 for ≥3 births versus no births). Our data support a mechanism for parity and breast cancer that involves mammographic density among pre- and early perimenopausal women that may be modified by body size. PMID:18066689

Computation of mass-density images from x-ray refraction-angle images.

PubMed

Wernick, Miles N; Yang, Yongyi; Mondal, Indrasis; Chapman, Dean; Hasnah, Moumen; Parham, Christopher; Pisano, Etta; Zhong, Zhong

2006-04-07

In this paper, we investigate the possibility of computing quantitatively accurate images of mass density variations in soft tissue. This is a challenging task, because density variations in soft tissue, such as the breast, can be very subtle. Beginning from an image of refraction angle created by either diffraction-enhanced imaging (DEI) or multiple-image radiography (MIR), we estimate the mass-density image using a constrained least squares (CLS) method. The CLS algorithm yields accurate density estimates while effectively suppressing noise. Our method improves on an analytical method proposed by Hasnah et al (2005 Med. Phys. 32 549-52), which can produce significant artefacts when even a modest level of noise is present. We present a quantitative evaluation study to determine the accuracy with which mass density can be determined in the presence of noise. Based on computer simulations, we find that the mass-density estimation error can be as low as a few per cent for typical density variations found in the breast. Example images computed from less-noisy real data are also shown to illustrate the feasibility of the technique. We anticipate that density imaging may have application in assessment of water content of cartilage resulting from osteoarthritis, in evaluation of bone density, and in mammographic interpretation.

Analyzing the management and disturbance in European forest based on self-thinning theory

NASA Astrophysics Data System (ADS)

Yan, Y.; Gielen, B.; Schelhaas, M.; Mohren, F.; Luyssaert, S.; Janssens, I. A.

2012-04-01

There is increasing awareness that natural and anthropogenic disturbance in forests affects exchange of CO2, H2O and energy between the ecosystem and the atmosphere. Consequently quantification of land use and disturbance intensity is one of the next steps needed to improve our understanding of the carbon cycle, its interactions with the atmosphere and its main drivers at local as well as at global level. The conventional NPP-based approaches to quantify the intensity of land management are limited because they lack a sound ecological basis. Here we apply a new way of characterising the degree of management and disturbance in forests using the self- thinning theory and observations of diameter at breast height and stand density. We used plot level information on dominant tree species, diameter at breast height, stand density and soil type from the French national forest inventory from 2005 to 2010. Stand density and diameter at breast height were used to parameterize the intercept of the self-thinning relationship and combined with theoretical slope to obtain an upper boundary for stand productivity given its density. Subsequently, we tested the sensitivity of the self-thinning relationship for tree species, soil type, climate and other environmental characteristics. We could find statistical differences in the self-thinning relationship between species and soil types, mainly due to the large uncertainty of the parameter estimates. Deviation from the theoretical self-thinning line defined as DBH=αN-3/4, was used as a proxy for disturbances, allowing to make spatially explicit maps of forest disturbance over France. The same framework was used to quantify the density-DBH trajectory of even-aged stand management of beech and oak over France. These trajectories will be used as a driver of forest management in the land surface model ORCHIDEE.

Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the Guernsey prospective studies.

PubMed

Torres-Mejía, Gabriela; De Stavola, Bianca; Allen, Diane S; Pérez-Gavilán, Juan J; Ferreira, Jorge M; Fentiman, Ian S; Dos Santos Silva, Isabel

2005-05-01

Mammographic features are known to be associated with breast cancer but the magnitude of the effect differs markedly from study to study. Methods to assess mammographic features range from subjective qualitative classifications to computer-automated quantitative measures. We used data from the UK Guernsey prospective studies to examine the relative value of these methods in predicting breast cancer risk. In all, 3,211 women ages > or =35 years who had a mammogram taken in 1986 to 1989 were followed-up to the end of October 2003, with 111 developing breast cancer during this period. Mammograms were classified using the subjective qualitative Wolfe classification and several quantitative mammographic features measured using computer-based techniques. Breast cancer risk was positively associated with high-grade Wolfe classification, percent breast density and area of dense tissue, and negatively associated with area of lucent tissue, fractal dimension, and lacunarity. Inclusion of the quantitative measures in the same model identified area of dense tissue and lacunarity as the best predictors of breast cancer, with risk increasing by 59% [95% confidence interval (95% CI), 29-94%] per SD increase in total area of dense tissue but declining by 39% (95% CI, 53-22%) per SD increase in lacunarity, after adjusting for each other and for other confounders. Comparison of models that included both the qualitative Wolfe classification and these two quantitative measures to models that included either the qualitative or the two quantitative variables showed that they all made significant contributions to prediction of breast cancer risk. These findings indicate that breast cancer risk is affected not only by the amount of mammographic density but also by the degree of heterogeneity of the parenchymal pattern and, presumably, by other features captured by the Wolfe classification.

Computational Simulation of Breast Compression Based on Segmented Breast and Fibroglandular Tissues on Magnetic Resonance Images

PubMed Central

Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

2010-01-01

This study presents a finite element based computational model to simulate the three-dimensional deformation of the breast and the fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and the craniocaudal and mediolateral oblique compression as used in mammography was applied. The geometry of whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo® 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the non-linear elastic tissue deformation under compression, using the MSC.Marc® software package. The model was tested in 4 cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these 4 cases at 60% compression ratio was in the range of 5-7 cm, which is the typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at 60% compression ratio was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on MRI, which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density measurements needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities – such as MRI, mammography, whole breast ultrasound, and molecular imaging – that are performed using different body positions and different compression conditions. PMID:20601773

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images.

PubMed

Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

2010-07-21

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression conditions.

Correlation of white female breast cancer incidence trends with nitrogen dioxide emission levels and motor vehicle density patterns.

PubMed

Chen, Fan; Bina, William F

2012-02-01

The long-term trend of female breast cancer incidence rates in the United States and some European countries demonstrates a similar pattern: an increasing trend in the last century followed by a declining trend in this century. The well-known risk factors cannot explain this trend. We compared the breast cancer incidence trends obtained from SEER data with the trend of nitrogen dioxides (NOx) emission and monitoring data as well as motor vehicle density data. The upward followed by downward trend of NOx is similar to the breast cancer incidence trend but with an offset of 20 years earlier. Motor vehicles are the major source of NOx emissions. The geographic distribution of motor vehicles density in 1970 in the observed US counties is positively correlated with breast cancer incidence rates (R(2) 0.8418, the correlation coefficient = 0.9175) in 1980-1995. Because both the time trend and geographic pattern are associated with breast cancer incidence rates, further studies on the relationship between breast cancer and air pollution are needed.

Genetic variation in peroxisome proliferator-activated receptor gamma, soy, and mammographic density in Singapore Chinese women.

PubMed

Lee, Eunjung; Hsu, Chris; Van den Berg, David; Ursin, Giske; Koh, Woon-Puay; Yuan, Jian-Min; Stram, Daniel O; Yu, Mimi C; Wu, Anna H

2012-04-01

PPARγ is a transcription factor important for adipogenesis and adipocyte differentiation. Data from animal studies suggest that PPARγ may be involved in breast tumorigenesis, but results from epidemiologic studies on the association between PPARγ variation and breast cancer risk have been mixed. Recent data suggest that soy isoflavones can activate PPARγ. We investigated the interrelations of soy, PPARγ, and mammographic density, a biomarker of breast cancer risk in a cross-sectional study of 2,038 women who were members of the population-based Singapore Chinese Health Study Cohort. We assessed mammographic density using a computer-assisted method. We used linear regression to examine the association between 26 tagging single-nucleotide polymorphisms (SNP) of PPARγ and their interaction with soy intake and mammographic density. To correct for multiple testing, we calculated P values adjusted for multiple correlated tests (P(ACT)). Out of the 26 tested SNPs in the PPARγ, seven SNPs were individually shown to be statistically significantly associated with mammographic density (P(ACT) = 0.008-0.049). A stepwise regression procedure identified that only rs880663 was independently associated with mammographic density which decreased by 1.89% per-minor allele (P(ACT) = 0.008). This association was significantly stronger in high-soy consumers as mammographic density decreased by 3.97% per-minor allele of rs880663 in high-soy consumers (P(ACT) = 0.006; P for interaction with lower soy intake = 0.017). Our data support that PPARγ genetic variation may be important in determining mammographic density, particularly in high-soy consumers. Our findings may help to identify molecular targets and lifestyle intervention for future prevention research. ©2012 AACR.

Quantitative immunohistochemistry of factor VIII-related antigen in breast carcinoma: a comparison of computer-assisted image analysis with established counting methods.

PubMed

Kohlberger, P D; Obermair, A; Sliutz, G; Heinzl, H; Koelbl, H; Breitenecker, G; Gitsch, G; Kainz, C

1996-06-01

Microvessel density in the area of the most intense neovascularization in invasive breast carcinoma is reported to be an independent prognostic factor. The established method of enumeration of microvessel density is to count the vessels using an ocular raster (counted microvessel density [CMVD]). The vessels were detected by staining endothelial cells using Factor VIII-related antigen. The aim of the study was to compare the CMVD results with the percentage of factor VIII-related antigen-stained area using computer-assisted image analysis. A true color red-green-blue (RGB) image analyzer based on a morphologically reduced instruction set computer processor was used to evaluate the area of stained endothelial cells. Sixty invasive breast carcinomas were included in the analysis. There was no significant correlation between the CMVD and the percentage of factor VIII-related antigen-stained area (Spearman correlation coefficient = 0.24, confidence interval = 0.02-0.46). Although high CMVD was significantly correlated with poorer recurrence free survival (P = .024), percentage of factor VIII-related antigen-stained area showed no prognostic value. Counted microvessel density and percentage of factor VIII-related antigen-stained area showed a highly significant correlation with vessel invasion (P = .0001 and P = .02, respectively). There was no correlation between CMVD and percentage of factor VIII-related antigen-stained area with other prognostic factors. In contrast to the CMVD within malignant tissue, the percentage of factor VIII-related antigen-stained area is not suitable as an indicator of prognosis in breast cancer patients.

Characterization of invisible breast cancers in digital mammography and tomosynthesis: radio-pathological correlation.

PubMed

Aguilar Angulo, P M; Romero Castellano, C; Ruiz Martín, J; Sánchez-Camacho González-Carrato, M P; Cruz Hernández, L M

To review the radio-pathologic features of symptomatic breast cancers not detected at digital mammography (DM) and digital breast tomosynthesis (DBT). Retrospective analysis of 169 lesions from symptomatic patients with breast cancer that were studied with DM, DBT, ultrasound (US) and magnetic resonance (MR). We identified occult lesions (true false negatives) in DM and DBT. Clinical data, density, US and MR findings were analyzed as well as histopathological results. We identified seven occult lesions in DM and DBT. 57% (4/7) of the lesions were identified in high-density breasts (type c and d), and the rest of them in breasts of density type b. Six carcinomas were identified at US and MR (BI-RADS 4 masses); the remaining lesion was only identified at MR. The tumor size was larger than 3cm at MRI in 57% of the lesions. All tumors were ductal infiltrating carcinomas, six of them with high stromal proportion. According to molecular classification, we found only one triple-negative breast cancer, the other lesions were luminal-type. We analyzed the tumor margins of two resected carcinomas that were not treated with neoadjuvant chemotherapy, both lesions presented margins that displaced the adjacent parenchyma without infiltrating it. Occult breast carcinomas in DM and DBT accounted for 4% of lesions detected in patients with symptoms. They were mostly masses, all of them presented the diagnosis of infiltrating ductal carcinoma (with predominance of the luminal immunophenotype) and were detected in breasts of density type b, c and d. Copyright © 2017 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Common variants in ZNF365 are associated with both mammographic density and breast cancer risk

PubMed Central

Lindström, Sara; Vachon, Celine M.; Li, Jingmei; Varghese, Jajini; Thompson, Deborah; Warren, Ruth; Brown, Judith; Leyland, Jean; Audley, Tina; Wareham, Nicholas J.; Loos, Ruth J.F.; Paterson, Andrew D.; Waggott, Darryl; Martin, Lisa J.; Scott, Christopher G.; Pankratz, V. Shane; Hankinson, Susan E.; Hazra, Aditi; Hunter, David J.; Hopper, John L.; Southey, Melissa C.; Chanock, Stephen J.; Silva, Isabel dos Santos; Liu, JianJun; Eriksson, Louise; Couch, Fergus J.; Stone, Jennifer; Apicella, Carmel; Czene, Kamila; Kraft, Peter; Hall, Per; Easton, Douglas F.; Boyd, Norman F.; Tamimi, Rulla M.

2011-01-01

High percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer. We conducted a meta-analysis of five genome-wide association studies of percent mammographic density and report an association with rs10995190 in ZNF365 (combined P=9×6·10−10). This finding might partly explain the underlying biology of the recently discovered association between common variants in ZNF365 and breast cancer risk. PMID:21278746

Radiographic evaluation of vessel count and density with quantitative magnetic resonance imaging during external breast expansion in Asian women: A prospective clinical trial.

PubMed

Myung, Yujin; Kwon, Heeyeon; Pak, Changsik; Lee, Hobin; Jeong, Jae Hoon; Heo, Chan Yeong

2016-12-01

Breast augmentation with fat transfer does not bear the risks associated with silicone implantation. The method can potentially be especially useful in Asian women, who often reject augmentation mammoplasty with implants. This prospective clinical trial evaluated the effects of external breast expansion on breast density and vessel count using magnetic resonance imaging. Thirty-four enrolled patients were instructed to apply one of two devices, the conventional BRAVA device (used in the AESTES trial) or a novel external expansion device (EVERA) designed for Asian women, continuously for 8 h per day for 12 weeks. For external expansion, the pressure was set to 25 mmHg. Follow-up examinations were performed for 4 weeks after completion of the expansion. The ratio between the fibroglandular and adipose tissues of the breast was measured using T1-weighted MRI, and the number of vessels in the breast tissue was determined before and after the treatment by contrast MRI. Additionally, the volume of the breast was measured by laser scanning before, during, and after the device application. The obtained measurements were compared within and between the groups at different time points. Six patients dropped out, while 28 completed the trial without major side effects or adverse events. External expansion significantly increased breast vessel count in both the EVERA and AESTES groups (p = 0.019, p = 0.022). However, it did not significantly change breast density in either group (p = 0.186, p = 0.638). No significant intergroup differences were noted in vessel count (p = 0.874) or density (p = 0.482). Breast volume increases after 12 weeks of application were statistically significant in both groups, with mean changes of 81 ± 22 cc (AESTES) and 98 ± 30 cc (EVERA) (p < 0.001 in both cases). External expansion resulted in a marked increase in breast vessel count but did not affect breast density. The observed increase in breast volume can be considered substantial for Asian women. Level II, therapeutic study. Copyright © 2016 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Computer Simulation of Breast Cancer Screening

DTIC Science & Technology

1999-07-01

techniques for evaluating the screening efficacy of mammography. Breast cancer growth rates, incidence rates, multiracial population demographics, death ... rates , breast cancer prognosis factors, breast density considerations, detection versus diameter probabilities, and other pertinent data have been

Differences in mammographic density between Asian and Caucasian populations: a comparative analysis.

PubMed

Rajaram, Nadia; Mariapun, Shivaani; Eriksson, Mikael; Tapia, Jose; Kwan, Pui Yoke; Ho, Weang Kee; Harun, Faizah; Rahmat, Kartini; Czene, Kamila; Taib, Nur Aishah Mohd; Hall, Per; Teo, Soo Hwang

2017-01-01

Mammographic density is a measurable and modifiable biomarker that is strongly and independently associated with breast cancer risk. Paradoxically, although Asian women have lower risk of breast cancer, studies of minority Asian women in predominantly Caucasian populations have found that Asian women have higher percent density. In this cross-sectional study, we compared the distribution of mammographic density for a matched cohort of Asian women from Malaysia and Caucasian women from Sweden, and determined if variations in mammographic density could be attributed to population differences in breast cancer risk factors. Volumetric mammographic density was compared for 1501 Malaysian and 4501 Swedish healthy women, matched on age and body mass index. We used multivariable log-linear regression to determine the risk factors associated with mammographic density and mediation analysis to identify factors that account for differences in mammographic density between the two cohorts. Compared to Caucasian women, percent density was 2.0% higher among Asian women (p < 0.001), and dense volume was 5.7 cm 3 higher among pre-menopausal Asian women (p < 0.001). Dense volume was 3.0 cm 3 lower among post-menopausal Asian women (p = 0.009) compared to post-menopausal Caucasian women, and this difference was attributed to population differences in height, weight, and parity (p < 0.001). Our analysis suggests that among post-menopausal women, population differences in mammographic density and risk to breast cancer may be accounted for by height, weight, and parity. Given that pre-menopausal Asian and Caucasian women have similar population risk to breast cancer but different dense volume, development of more appropriate biomarkers of risk in pre-menopausal women is required.

Analysis of percent density estimates from digital breast tomosynthesis projection images

NASA Astrophysics Data System (ADS)

Bakic, Predrag R.; Kontos, Despina; Zhang, Cuiping; Yaffe, Martin J.; Maidment, Andrew D. A.

2007-03-01

Women with dense breasts have an increased risk of breast cancer. Breast density is typically measured as the percent density (PD), the percentage of non-fatty (i.e., dense) tissue in breast images. Mammographic PD estimates vary, in part, due to the projective nature of mammograms. Digital breast tomosynthesis (DBT) is a novel radiographic method in which 3D images of the breast are reconstructed from a small number of projection (source) images, acquired at different positions of the x-ray focus. DBT provides superior visualization of breast tissue and has improved sensitivity and specificity as compared to mammography. Our long-term goal is to test the hypothesis that PD obtained from DBT is superior in estimating cancer risk compared with other modalities. As a first step, we have analyzed the PD estimates from DBT source projections since the results would be independent of the reconstruction method. We estimated PD from MLO mammograms (PD M) and from individual DBT projections (PD T). We observed good agreement between PD M and PD T from the central projection images of 40 women. This suggests that variations in breast positioning, dose, and scatter between mammography and DBT do not negatively affect PD estimation. The PD T estimated from individual DBT projections of nine women varied with the angle between the projections. This variation is caused by the 3D arrangement of the breast dense tissue and the acquisition geometry.

Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound.

PubMed

Häberle, Lothar; Hack, Carolin C; Heusinger, Katharina; Wagner, Florian; Jud, Sebastian M; Uder, Michael; Beckmann, Matthias W; Schulz-Wendtland, Rüdiger; Wittenberg, Thomas; Fasching, Peter A

2017-08-30

Tumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient's risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure). This observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments ("observed PMD") were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation. About 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model. Automatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

PubMed

Kallenberg, Michiel; Petersen, Kersten; Nielsen, Mads; Ng, Andrew Y; Pengfei Diao; Igel, Christian; Vachon, Celine M; Holland, Katharina; Winkel, Rikke Rass; Karssemeijer, Nico; Lillholm, Martin

2016-05-01

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Cytochrome P450 1A2 (CYP1A2) activity, mammographic density, and oxidative stress: a cross-sectional study

PubMed Central

Hong, Chi-Chen; Tang, Bing-Kou; Rao, Venketeshwer; Agarwal, Sanjiv; Martin, Lisa; Tritchler, David; Yaffe, Martin; Boyd, Norman F

2004-01-01

Introduction Mammographically dense breast tissue is a strong predictor of breast cancer risk, and is influenced by both mitogens and mutagens. One enzyme that is able to affect both the mitogenic and mutagenic characteristics of estrogens is cytochrome P450 1A2 (CYP1A2), which is principally responsible for the metabolism of 17β-estradiol. Methods In a cross-sectional study of 146 premenopausal and 149 postmenopausal women, we examined the relationships between CYP1A2 activity, malondialdehyde (MDA) levels, and mammographic density. In vivo CYP1A2 activity was assessed by measuring caffeine metabolites in urine. Levels of serum and urinary MDA, and MDA–deoxyguanosine adducts in DNA were measured. Mammograms were digitized and measured using a computer-assisted method. Results CYP1A2 activity in postmenopausal women, but not in premenopausal women, was positively associated with mammographic density, suggesting that increased CYP1A2 activity after the menopause is a risk factor for breast cancer. In premenopausal women, but not in postmenopausal women, CYP1A2 activity was positively associated with serum and urinary MDA levels; there was also some evidence that CYP1A2 activity was more positively associated with percentage breast density when MDA levels were high, and more negatively associated with percentage breast density when MDA levels were low. Conclusion These findings provide further evidence that variation in the activity level of enzymes involved in estrogen metabolism is related to levels of mammographic density and potentially to breast cancer risk. PMID:15217501

Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging

PubMed Central

Pertuz, Said; McDonald, Elizabeth S.; Weinstein, Susan P.; Conant, Emily F.

2016-01-01

Purpose To assess a fully automated method for volumetric breast density (VBD) estimation in digital breast tomosynthesis (DBT) and to compare the findings with those of full-field digital mammography (FFDM) and magnetic resonance (MR) imaging. Materials and Methods Bilateral DBT images, FFDM images, and sagittal breast MR images were retrospectively collected from 68 women who underwent breast cancer screening from October 2011 to September 2012 with institutional review board–approved, HIPAA-compliant protocols. A fully automated computer algorithm was developed for quantitative estimation of VBD from DBT images. FFDM images were processed with U.S. Food and Drug Administration–cleared software, and the MR images were processed with a previously validated automated algorithm to obtain corresponding VBD estimates. Pearson correlation and analysis of variance with Tukey-Kramer post hoc correction were used to compare the multimodality VBD estimates. Results Estimates of VBD from DBT were significantly correlated with FFDM-based and MR imaging–based estimates with r = 0.83 (95% confidence interval [CI]: 0.74, 0.90) and r = 0.88 (95% CI: 0.82, 0.93), respectively (P < .001). The corresponding correlation between FFDM and MR imaging was r = 0.84 (95% CI: 0.76, 0.90). However, statistically significant differences after post hoc correction (α = 0.05) were found among VBD estimates from FFDM (mean ± standard deviation, 11.1% ± 7.0) relative to MR imaging (16.6% ± 11.2) and DBT (19.8% ± 16.2). Differences between VDB estimates from DBT and MR imaging were not significant (P = .26). Conclusion Fully automated VBD estimates from DBT, FFDM, and MR imaging are strongly correlated but show statistically significant differences. Therefore, absolute differences in VBD between FFDM, DBT, and MR imaging should be considered in breast cancer risk assessment. © RSNA, 2015 Online supplemental material is available for this article. PMID:26491909

Trends and Variation in Use of Breast Reconstruction in Patients With Breast Cancer Undergoing Mastectomy in the United States

PubMed Central

Jagsi, Reshma; Jiang, Jing; Momoh, Adeyiza O.; Alderman, Amy; Giordano, Sharon H.; Buchholz, Thomas A.; Kronowitz, Steven J.; Smith, Benjamin D.

2014-01-01

Purpose Concerns exist regarding breast cancer patients' access to breast reconstruction, which provides important psychosocial benefits. Patients and Methods Using the MarketScan database, a claims-based data set of US patients with employment-based insurance, we identified 20,560 women undergoing mastectomy for breast cancer from 1998 to 2007. We evaluated time trends using the Cochran-Armitage test and correlated reconstruction use with plastic-surgery workforce density and other treatments using multivariable regression. Results Median age of our sample was 51 years. Reconstruction use increased from 46% in 1998 to 63% in 2007 (P < .001), with increased use of implants and decreased use of autologous techniques over time (P < .001). Receipt of bilateral mastectomy also increased: from 3% in 1998 to 18% in 2007 (P < .001). Patients receiving bilateral mastectomy were more likely to receive reconstruction (odds ratio [OR], 2.3; P < .001) and patients receiving radiation were less likely to receive reconstruction (OR, 0.44; P < .001). Rates of reconstruction receipt varied dramatically by geographic region, with associations with plastic surgeon density in each state and county-level income. Autologous techniques were more often used in patients who received both reconstruction and radiation (OR, 1.8; P < .001) and less frequently used in patients with capitated insurance (OR, 0.7; P < .001), patients undergoing bilateral mastectomy (OR, 0.5; P < .001), or patients in the highest income quartile (OR, 0.7; P = .006). Delayed reconstruction was performed in 21% of patients who underwent reconstruction. Conclusion Breast reconstruction has increased over time, but it has wide geographic variability. Receipt of other treatments correlates with the use of and approaches toward reconstruction. Further research and interventions are needed to ensure equitable access to this important component of multidisciplinary treatment of breast cancer. PMID:24550418

Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age

PubMed Central

Burnside, Elizabeth S.; Lee, Sandra J.; Bennette, Carrie; Near, Aimee M.; Alagoz, Oguzhan; Huang, Hui; van den Broek, Jeroen J.; Kim, Joo Yeon; Ergun, Mehmet A.; van Ravesteyn, Nicolien T.; Stout, Natasha K.; de Koning, Harry J.; Mandelblatt, Jeanne S.

2017-01-01

Background There are no publicly available tools designed specifically to assist policy makers to make informed decisions about the optimal ages of breast cancer screening initiation for different populations of US women. Objective To use three established simulation models to develop a web-based tool called Mammo OUTPuT. Methods The simulation models use the 1970 US birth cohort and common parameters for incidence, digital screening performance, and treatment effects. Outcomes include breast cancers diagnosed, breast cancer deaths averted, breast cancer mortality reduction, false-positive mammograms, benign biopsies, and overdiagnosis. The Mammo OUTPuT tool displays these outcomes for combinations of age at screening initiation (every year from 40 to 49), annual versus biennial interval, lifetime versus 10-year horizon, and breast density, compared to waiting to start biennial screening at age 50 and continuing to 74. The tool was piloted by decision makers (n = 16) who completed surveys. Results The tool demonstrates that benefits in the 40s increase linearly with earlier initiation age, without a specific threshold age. Likewise, the harms of screening increase monotonically with earlier ages of initiation in the 40s. The tool also shows users how the balance of benefits and harms varies with breast density. Surveys revealed that 100% of users (16/16) liked the appearance of the site; 94% (15/16) found the tool helpful; and 94% (15/16) would recommend the tool to a colleague. Conclusions This tool synthesizes a representative subset of the most current CISNET (Cancer Intervention and Surveillance Modeling Network) simulation model outcomes to provide policy makers with quantitative data on the benefits and harms of screening women in the 40s. Ultimate decisions will depend on program goals, the population served, and informed judgments about the weight of benefits and harms. PMID:29376135

Breast cancer patients with dense breasts do not have increased death risk

Cancer.gov

High mammographic breast density, which is a marker of increased risk of developing breast cancer, does not seem to increase the risk of death among breast cancer patients, according to a study led by Gretchen L. Gierach, Ph.D., NCI. Image shows physician

Study of nuclear morphometry on cytology specimens of benign and malignant breast lesions: A study of 122 cases

PubMed Central

Kashyap, Anamika; Jain, Manjula; Shukla, Shailaja; Andley, Manoj

2017-01-01

Background: Breast cancer has emerged as a leading site of cancer among women in India. Fine needle aspiration cytology (FNAC) has been routinely applied in assessment of breast lesions. Cytological evaluation in breast lesions is subjective with a “gray zone” of 6.9–20%. Quantitative evaluation of nuclear size, shape, texture, and density parameters by morphometry can be of diagnostic help in breast tumor. Aims: To apply nuclear morphometry on cytological breast aspirates and assess its role in differentiating between benign and malignant breast lesions with derivation of suitable cut-off values between the two groups. Settings and Designs: The present study was a descriptive cross-sectional hospital-based study of nuclear morphometric parameters of benign and malignant cases. Materials and Methods: The study included 50 benign breast disease (BBD), 8 atypical ductal hyperplasia (ADH), and 64 carcinoma cases. Image analysis was performed on Papanicolaou-stained FNAC slides by Nikon Imaging Software (NIS)–Elements Advanced Research software (Version 4.00). Nuclear morphometric parameters analyzed included 5 nuclear size, 2 shape, 4 texture, and 2 density parameters. Results: Nuclear morphometry could differentiate between benign and malignant aspirates with a gradually increasing nuclear size parameters from BBD to ADH to carcinoma. Cut-off values of 31.93 μm2, 6.325 μm, 5.865 μm, 7.855 μm, and 21.55 μm for mean nuclear area, equivalent diameter, minimum feret, maximum ferret, and perimeter, respectively, were derived between benign and malignant cases, which could correctly classify 7 out of 8 ADH cases. Conclusion: Nuclear morphometry is a highly objective tool that could be used to supplement FNAC in differentiating benign from malignant lesions, with an important role in cases with diagnostic dilemma. PMID:28182052

Study of nuclear morphometry on cytology specimens of benign and malignant breast lesions: A study of 122 cases.

PubMed

Kashyap, Anamika; Jain, Manjula; Shukla, Shailaja; Andley, Manoj

2017-01-01

Breast cancer has emerged as a leading site of cancer among women in India. Fine needle aspiration cytology (FNAC) has been routinely applied in assessment of breast lesions. Cytological evaluation in breast lesions is subjective with a "gray zone" of 6.9-20%. Quantitative evaluation of nuclear size, shape, texture, and density parameters by morphometry can be of diagnostic help in breast tumor. To apply nuclear morphometry on cytological breast aspirates and assess its role in differentiating between benign and malignant breast lesions with derivation of suitable cut-off values between the two groups. The present study was a descriptive cross-sectional hospital-based study of nuclear morphometric parameters of benign and malignant cases. The study included 50 benign breast disease (BBD), 8 atypical ductal hyperplasia (ADH), and 64 carcinoma cases. Image analysis was performed on Papanicolaou-stained FNAC slides by Nikon Imaging Software (NIS)-Elements Advanced Research software (Version 4.00). Nuclear morphometric parameters analyzed included 5 nuclear size, 2 shape, 4 texture, and 2 density parameters. Nuclear morphometry could differentiate between benign and malignant aspirates with a gradually increasing nuclear size parameters from BBD to ADH to carcinoma. Cut-off values of 31.93 μm 2 , 6.325 μm, 5.865 μm, 7.855 μm, and 21.55 μm for mean nuclear area, equivalent diameter, minimum feret, maximum ferret, and perimeter, respectively, were derived between benign and malignant cases, which could correctly classify 7 out of 8 ADH cases. Nuclear morphometry is a highly objective tool that could be used to supplement FNAC in differentiating benign from malignant lesions, with an important role in cases with diagnostic dilemma.

Detection of breast cancer with full-field digital mammography and computer-aided detection.

PubMed

The, Juliette S; Schilling, Kathy J; Hoffmeister, Jeffrey W; Friedmann, Euvondia; McGinnis, Ryan; Holcomb, Richard G

2009-02-01

The purpose of this study was to evaluate computer-aided detection (CAD) performance with full-field digital mammography (FFDM). CAD (Second Look, version 7.2) was used to evaluate 123 cases of breast cancer detected with FFDM (Senographe DS). Retrospectively, CAD sensitivity was assessed using breast density, mammographic presentation, histopathology results, and lesion size. To determine the case-based false-positive rate, patients with four standard views per case were included in the study group. Eighteen unilateral mammography examinations with nonstandard views were excluded, resulting in a sample of 105 bilateral cases. CAD detected 115 (94%) of 123 cancer cases: six of six (100%) in fatty breasts, 63 of 66 (95%) in breasts containing scattered fibroglandular densities, 43 of 46 (93%) in heterogeneously dense breasts, and three of five (60%) in extremely dense breasts. CAD detected 93% (41/44) of cancers manifesting as calcifications, 92% (57/62) as masses, and 100% (17/17) as mixed masses and calcifications. CAD detected 94% of the invasive ductal carcinomas (n = 63), 100% of the invasive lobular carcinomas (n = 7), 91% of the other invasive carcinomas (n = 11), and 93% of the ductal carcinomas in situ (n = 42). CAD sensitivity for cancers 1-10 mm (n = 55) was 89%; 11-20 mm (n = 37), 97%; 21-30 mm (n = 16), 100%; and larger than 30 mm (n = 15), 93%. The CAD false-positive rate was 2.3 marks per four-image case. CAD with FFDM showed a high sensitivity in identifying cancers manifesting as calcifications and masses. Sensitivity was maintained in cancers with lower mammographic sensitivity, including invasive lobular carcinomas and small neoplasms (1-20 mm). CAD with FFDM should be effective in assisting radiologists with earlier detection of breast cancer. Future studies are needed to assess CAD accuracy in larger populations.

Comparative prognostic relevance of breast intra-tumoral microvessel density evaluated by CD105 and CD146: A pilot study of 42 cases.

PubMed

Martinez, Leandro Marcelo; Labovsky, Vivian; Calcagno, María de Luján; Davies, Kevin Mauro; Rivello, Hernán Garcia; Wernicke, Alejandra; Calvo, Juan Carlos; Chasseing, Norma Alejandra

2016-04-01

Angiogenesis is a key process for metastatic progression. While it has been established that the evaluation of breast tumoral microvessel density by CD105 marker is a potential prognostic parameter, its evaluation by CD146 marker has been poorly studied. The purpose of this study was to compare the prognostic value of intra-tumoral microvessel density assayed by CD105 and CD146 in early breast cancer patients. 42 women with breast infiltrative ductal carcinoma (I and II-stages) were retrospectively reviewed. Intra-tumoral microvessel density was immunohistochemically examined using antibodies anti-CD105 and CD146 in paraffin-embedded tissues, and their association with classical prognostic-markers, metastatic recurrence, metastasis-free survival and overall survival was analyzed. High microvessel density assessed by CD146 was significantly associated with a higher risk of developing metastasis (p=0.0310) and a shorter metastasis-free survival (p=0.0197). In contrast, when we used the CD105-antibody, we did not find any significant association. Finally, CD146 showed to be an independent predictive indicator for metastasis-free survival (p=0.0055). Our data suggest that the intra-tumoral microvessel density evaluated by CD146 may be a more suitable predictor of metastatic development than that evaluated by CD105 in early breast cancer. Copyright © 2016 Elsevier GmbH. All rights reserved.

The contributions of breast density and common genetic variation to breast cancer risk.

PubMed

Vachon, Celine M; Pankratz, V Shane; Scott, Christopher G; Haeberle, Lothar; Ziv, Elad; Jensen, Matthew R; Brandt, Kathleen R; Whaley, Dana H; Olson, Janet E; Heusinger, Katharina; Hack, Carolin C; Jud, Sebastian M; Beckmann, Matthias W; Schulz-Wendtland, Ruediger; Tice, Jeffrey A; Norman, Aaron D; Cunningham, Julie M; Purrington, Kristen S; Easton, Douglas F; Sellers, Thomas A; Kerlikowske, Karla; Fasching, Peter A; Couch, Fergus J

2015-05-01

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The challenge of meeting nutrient needs of infants and young children during the period of complementary feeding: an evolutionary perspective.

PubMed

Dewey, Kathryn G

2013-12-01

Breast-fed infants and young children need complementary foods with a very high nutrient density (particularly for iron and zinc), especially at ages 6-12 mo. However, in low-income countries, their diet is usually dominated by cereal-based porridges with low nutrient density and poor mineral bioavailability. Complementary feeding diets typically fall short in iron and zinc and sometimes in other nutrients. These gaps in nutritional adequacy of infant diets have likely been a characteristic of human diets since the agricultural revolution ~10,000 y ago. Estimates of nutrient intakes before then, based on hypothetical diets of preagricultural humans, suggest that infants had much higher intakes of key nutrients than is true today and would have been able to meet their nutrient needs from the combination of breast milk and premasticated foods provided by their mothers. Strategies for achieving adequate nutrition for infants and young children in modern times must address the challenge of meeting nutrient needs from largely cereal-based diets.

Circulating insulin-like growth factor-I, insulin-like growth factor binding protein-3 and terminal duct lobular unit involution of the breast: a cross-sectional study of women with benign breast disease.

PubMed

Horne, Hisani N; Sherman, Mark E; Pfeiffer, Ruth M; Figueroa, Jonine D; Khodr, Zeina G; Falk, Roni T; Pollak, Michael; Patel, Deesha A; Palakal, Maya M; Linville, Laura; Papathomas, Daphne; Geller, Berta; Vacek, Pamela M; Weaver, Donald L; Chicoine, Rachael; Shepherd, John; Mahmoudzadeh, Amir Pasha; Wang, Jeff; Fan, Bo; Malkov, Serghei; Herschorn, Sally; Hewitt, Stephen M; Brinton, Louise A; Gierach, Gretchen L

2016-02-18

Terminal duct lobular units (TDLUs) are the primary structures from which breast cancers and their precursors arise. Decreased age-related TDLU involution and elevated mammographic density are both correlated and independently associated with increased breast cancer risk, suggesting that these characteristics of breast parenchyma might be linked to a common factor. Given data suggesting that increased circulating levels of insulin-like growth factors (IGFs) factors are related to reduced TDLU involution and increased mammographic density, we assessed these relationships using validated quantitative methods in a cross-sectional study of women with benign breast disease. Serum IGF-I, IGFBP-3 and IGF-I:IGFBP-3 molar ratios were measured in 228 women, ages 40-64, who underwent diagnostic breast biopsies yielding benign diagnoses at University of Vermont affiliated centers. Biopsies were assessed for three separate measures inversely related to TDLU involution: numbers of TDLUs per unit of tissue area ("TDLU count"), median TDLU diameter ("TDLU span"), and number of acini per TDLU ("acini count"). Regression models, stratified by menopausal status and adjusted for potential confounders, were used to assess the associations of TDLU count, median TDLU span and median acini count per TDLU with tertiles of circulating IGFs. Given that mammographic density is associated with both IGF levels and breast cancer risk, we also stratified these associations by mammographic density. Higher IGF-I levels among postmenopausal women and an elevated IGF-I:IGFBP-3 ratio among all women were associated with higher TDLU counts, a marker of decreased lobular involution (P-trend = 0.009 and <0.0001, respectively); these associations were strongest among women with elevated mammographic density (P-interaction <0.01). Circulating IGF levels were not significantly associated with TDLU span or acini count per TDLU. These results suggest that elevated IGF levels may define a sub-group of women with high mammographic density and limited TDLU involution, two markers that have been related to increased breast cancer risk. If confirmed in prospective studies with cancer endpoints, these data may suggest that evaluation of IGF signaling and its downstream effects may have value for risk prediction and suggest strategies for breast cancer chemoprevention through inhibition of the IGF system.

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

Increased extracellular matrix density decreases MCF10A breast cell acinus formation in 3D culture conditions.

PubMed

Lance, Amanda; Yang, Chih-Chao; Swamydas, Muthulekha; Dean, Delphine; Deitch, Sandy; Burg, Karen J L; Dréau, Didier

2016-01-01

The extracellular matrix (ECM) contributes to the generation and dynamic of normal breast tissue, in particular to the generation of polarized acinar and ductal structures. In vitro 3D culture conditions, including variations in the composition of the ECM, have been shown to directly influence the formation and organization of acinus-like and duct-like structures. Furthermore, the density of the ECM appears to also play a role in the normal mammary tissue and tumour formation. Here we show that the density of the ECM directly influences the number, organization and function of breast acini. Briefly, non-malignant human breast MCF10A cells were incubated in increasing densities of a Matrigel®-collagen I matrix. Elastic moduli near and distant to the acinus structures were measured by atomic force microscopy, and the number of acinus structures was determined. Immunochemistry was used to investigate the expression levels of E-cadherin, laminin, matrix metalloproteinase-14 and ß-casein in MCF10A cells. The modulus of the ECM was significantly increased near the acinus structures and the number of acinus structures decreased with the increase in Matrigel-collagen I density. As evaluated by the expression of laminin, the organization of the acinus structures present was altered as the density of the ECM increased. Increases in both E-cadherin and MMP14 expression by MCF10A cells as ECM density increased were also observed. In contrast, MCF10A cells expressed lower ß-casein levels as the ECM density increased. Taken together, these observations highlight the key role of ECM density in modulating the number, organization and function of breast acini. Copyright © 2013 John Wiley & Sons, Ltd.

The Effect of California's Breast Density Notification Legislation on Breast Cancer Screening.

PubMed

Chau, Stephanie Lynn; Alabaster, Amy; Luikart, Karin; Brenman, Leslie Manace; Habel, Laurel A

2017-04-01

Half of US states mandate women be notified if they have dense breasts on their mammogram, yet guidelines and data on supplemental screening modalities are limited. Breast density (BD) refers to the extent that breast tissue appears radiographically dense on mammograms. High BD reduces the sensitivity of screening mammography and increases breast cancer risk. The aim of this study was to determine the potential impact of California's 2013 BD notification legislation on breast cancer screening patterns. We conducted a cohort study of women aged 40 to 74 years who were members of a large Northern California integrated health plan (approximately 3.9 million members) in 2011-2015. We calculated pre- and post-legislation rates of screening mammography and magnetic resonance imaging (MRI). We also examined whether women with dense breasts (defined as BI-RADS density c or d) had higher MRI rates than women with nondense breasts (defined as BI-RADS density a or b). After adjustment for race/ethnicity, age, body mass index, medical facility, neighborhood median income, and cancer history, there was a relative 6.6% decrease (relative risk [RR] 0.934, confidence interval [CI] 0.92-0.95) in the rate of screening mammography, largely driven by a decrease among women <50 years. While infrequent, there was a relative 16% increase (RR 1.16, CI 1.07-1.25) in the rate of screening MRI, with the greatest increase among the youngest women. In the postlegislation period, women with extremely dense breasts (BI-RADS d) had 2.77 times (CI 1.93-3.95) the odds of a MRI within 9 months of a screening mammogram compared with women with nondense breasts (BI-RADS b). In this setting, MRI rates increased in the postlegislation period. In addition, women with higher BD were more likely to have supplementary MRI. The decrease in mammography rates seen primarily among younger women may have been due to changes in national screening guidelines.

Optical identification of subjects at high risk for developing breast cancer

NASA Astrophysics Data System (ADS)

Taroni, Paola; Quarto, Giovanna; Pifferi, Antonio; Ieva, Francesca; Paganoni, Anna Maria; Abbate, Francesca; Balestreri, Nicola; Menna, Simona; Cassano, Enrico; Cubeddu, Rinaldo

2013-06-01

A time-domain multiwavelength (635 to 1060 nm) optical mammography was performed on 147 subjects with recent x-ray mammograms available, and average breast tissue composition (water, lipid, collagen, oxy- and deoxyhemoglobin) and scattering parameters (amplitude a and slope b) were estimated. Correlation was observed between optically derived parameters and mammographic density [Breast Imaging and Reporting Data System (BI-RADS) categories], which is a strong risk factor for breast cancer. A regression logistic model was obtained to best identify high-risk (BI-RADS 4) subjects, based on collagen content and scattering parameters. The model presents a total misclassification error of 12.3%, sensitivity of 69%, specificity of 94%, and simple kappa of 0.84, which compares favorably even with intraradiologist assignments of BI-RADS categories.

Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force.

PubMed

Melnikow, Joy; Fenton, Joshua J; Whitlock, Evelyn P; Miglioretti, Diana L; Weyrich, Meghan S; Thompson, Jamie H; Shah, Kunal

2016-02-16

Screening mammography has lower sensitivity and specificity in women with dense breasts, who experience higher breast cancer risk. To perform a systematic review of reproducibility of Breast Imaging Reporting and Data System (BI-RADS) density categorization and test performance and clinical outcomes of supplemental screening with breast ultrasonography, magnetic resonance imaging (MRI), and digital breast tomosynthesis (DBT) in women with dense breasts and negative mammography results. MEDLINE, PubMed, EMBASE, and Cochrane database from January 2000 to July 2015. Studies reporting BI-RADS density reproducibility or supplemental screening results for women with dense breasts. Quality assessment and abstraction of 24 studies from 7 countries; 6 studies were good-quality. Three good-quality studies reported reproducibility of BI-RADS density; 13% to 19% of women were recategorized between "dense" and "nondense" at subsequent screening. Two good-quality studies reported that sensitivity of ultrasonography for women with negative mammography results ranged from 80% to 83%; specificity, from 86% to 94%; and positive predictive value (PPV), from 3% to 8%. The sensitivity of MRI ranged from 75% to 100%; specificity, from 78% to 94%; and PPV, from 3% to 33% (3 studies). Rates of additional cancer detection with ultrasonography were 4.4 per 1000 examinations (89% to 93% invasive); recall rates were 14%. Use of MRI detected 3.5 to 28.6 additional cancer cases per 1000 examinations (34% to 86% invasive); recall rates were 12% to 24%. Rates of cancer detection with DBT increased by 1.4 to 2.5 per 1000 examinations compared with mammography alone (3 studies). Recall rates ranged from 7% to 11%, compared with 7% to 17% with mammography alone. No studies examined breast cancer outcomes. Good-quality evidence was sparse. Studies were small and CIs were wide. Definitions of recall were absent or inconsistent. Density ratings may be recategorized on serial screening mammography. Supplemental screening of women with dense breasts finds additional breast cancer but increases false-positive results. Use of DBT may reduce recall rates. Effects of supplemental screening on breast cancer outcomes remain unclear. Agency for Healthcare Research and Quality.

Supine breast US: how to correlate breast lesions from prone MRI

PubMed Central

Telegrafo, Michele; Rella, Leonarda; Stabile Ianora, Amato A; Angelelli, Giuseppe

2016-01-01

Objective: To evaluate spatial displacement of breast lesions from prone MR to supine ultrasound positions, and to determine whether the degree of displacement may be associated with breast density and lesion histotype. Methods: 380 patients underwent breast MR and second-look ultrasound. The MR and ultrasound lesion location within the breast gland, distances from anatomical landmarks (nipple, skin and pectoral muscle), spatial displacement (distance differences from the landmarks within the same breast region) and region displacement (breast region change) were prospectively evaluated. Differences between MR and ultrasound measurements, association between the degree of spatial displacement and both breast density and lesion histotypes were calculated. Results: In 290/380 (76%) patients, 300 MR lesions were detected. 285/300 (95%) lesions were recognized on ultrasound. By comparing MR and ultrasound, spatial displacement occurred in 183/285 (64.3%) cases while region displacement in 102/285 (35.7%) cases with a circumferential movement along an arc centred on the nipple, having supine ultrasound as the reference standard. A significant association between the degree of lesion displacement and breast density was found (p < 0.00001) with a significant higher displacement in case of fatty breasts. No significant association between the degree of displacement and lesion histotype was found (p = 0.1). Conclusion: Lesion spatial displacement from MRI to ultrasound may occur especially in adipose breasts. Lesion–nipple distance and circumferential displacement from the nipple need to be considered for ultrasound lesion detection. Advances in knowledge: Second-look ultrasound breast lesion detection could be improved by calculating the lesion–nipple distance and considering that spatial displacement from MRI occurs with a circumferential movement along an arc centred on the nipple. PMID:26689093

Supine breast US: how to correlate breast lesions from prone MRI.

PubMed

Telegrafo, Michele; Rella, Leonarda; Stabile Ianora, Amato A; Angelelli, Giuseppe; Moschetta, Marco

2016-01-01

To evaluate spatial displacement of breast lesions from prone MR to supine ultrasound positions, and to determine whether the degree of displacement may be associated with breast density and lesion histotype. 380 patients underwent breast MR and second-look ultrasound. The MR and ultrasound lesion location within the breast gland, distances from anatomical landmarks (nipple, skin and pectoral muscle), spatial displacement (distance differences from the landmarks within the same breast region) and region displacement (breast region change) were prospectively evaluated. Differences between MR and ultrasound measurements, association between the degree of spatial displacement and both breast density and lesion histotypes were calculated. In 290/380 (76%) patients, 300 MR lesions were detected. 285/300 (95%) lesions were recognized on ultrasound. By comparing MR and ultrasound, spatial displacement occurred in 183/285 (64.3%) cases while region displacement in 102/285 (35.7%) cases with a circumferential movement along an arc centred on the nipple, having supine ultrasound as the reference standard. A significant association between the degree of lesion displacement and breast density was found (p < 0.00001) with a significant higher displacement in case of fatty breasts. No significant association between the degree of displacement and lesion histotype was found (p = 0.1). Lesion spatial displacement from MRI to ultrasound may occur especially in adipose breasts. Lesion-nipple distance and circumferential displacement from the nipple need to be considered for ultrasound lesion detection. Second-look ultrasound breast lesion detection could be improved by calculating the lesion-nipple distance and considering that spatial displacement from MRI occurs with a circumferential movement along an arc centred on the nipple.

Cost-effectiveness of digital mammography breast cancer screening.

PubMed

Tosteson, Anna N A; Stout, Natasha K; Fryback, Dennis G; Acharyya, Suddhasatta; Herman, Benjamin A; Hannah, Lucy G; Pisano, Etta D

2008-01-01

The DMIST (Digital Mammography Imaging Screening Trial) reported improved breast cancer detection with digital mammography compared with film mammography in selected population subgroups, but it did not assess the economic value of digital relative to film mammography screening. To evaluate the cost-effectiveness of digital mammography screening for breast cancer. Validated, discrete-event simulation model. Data from DMIST and publicly available U.S. data. U.S. women age 40 years or older. Lifetime. Societal and Medicare. All-film mammography screening; all-digital mammography screening; and targeted digital mammography screening, which is age-targeted digital mammography (for women <50 years of age) and age- and density-targeted digital mammography (for women <50 years of age or women > or =50 years of age with dense breasts). Cost per quality-adjusted life-year (QALY) gained. All-digital mammography screening cost $331,000 (95% CI, $268,000 to $403,000) per QALY gained relative to all-film mammography screening but was more costly and less effective than targeted digital mammography screening. Targeted digital mammography screening resulted in more screen-detected cases of cancer and fewer deaths from cancer than either all-film or all-digital mammography screening, with cost-effectiveness estimates ranging from $26,500 (CI, $21,000 to $33,000) per QALY gained for age-targeted digital mammography to $84,500 (CI, $75,000 to $93,000) per QALY gained for age- and density-targeted digital mammography. In the Medicare population, the cost-effectiveness of density-targeted digital mammography screening varied from a base-case estimate of $97,000 (CI, $77,000 to $131,000) to $257,000 per QALY gained (CI, $91,000 to $536,000) in the alternative-case analyses, in which the sensitivity of film mammography was increased and the sensitivity of digital mammography in women with nondense breasts was decreased. Results were sensitive to the cost of digital mammography and to the prevalence of dense breasts. Results were dependent on model assumptions and DMIST findings. Relative to film mammography, screening for breast cancer by using all-digital mammography is not cost-effective. Age-targeted screening with digital mammography seems cost-effective, whereas density-targeted screening strategies are more costly and of uncertain value, particularly among women age 65 years or older.

Association of mammographic image feature change and an increasing risk trend of developing breast cancer: an assessment

NASA Astrophysics Data System (ADS)

Tan, Maxine; Leader, Joseph K.; Liu, Hong; Zheng, Bin

2015-03-01

We recently investigated a new mammographic image feature based risk factor to predict near-term breast cancer risk after a woman has a negative mammographic screening. We hypothesized that unlike the conventional epidemiology-based long-term (or lifetime) risk factors, the mammographic image feature based risk factor value will increase as the time lag between the negative and positive mammography screening decreases. The purpose of this study is to test this hypothesis. From a large and diverse full-field digital mammography (FFDM) image database with 1278 cases, we collected all available sequential FFDM examinations for each case including the "current" and 1 to 3 most recently "prior" examinations. All "prior" examinations were interpreted negative, and "current" ones were either malignant or recalled negative/benign. We computed 92 global mammographic texture and density based features, and included three clinical risk factors (woman's age, family history and subjective breast density BIRADS ratings). On this initial feature set, we applied a fast and accurate Sequential Forward Floating Selection (SFFS) feature selection algorithm to reduce feature dimensionality. The features computed on both mammographic views were individually/ separately trained using two artificial neural network (ANN) classifiers. The classification scores of the two ANNs were then merged with a sequential ANN. The results show that the maximum adjusted odds ratios were 5.59, 7.98, and 15.77 for using the 3rd, 2nd, and 1st "prior" FFDM examinations, respectively, which demonstrates a higher association of mammographic image feature change and an increasing risk trend of developing breast cancer in the near-term after a negative screening.

Adolescent intake of animal fat and red meat in relation to premenopausal mammographic density

PubMed Central

Bertrand, Kimberly A.; Burian, Rosemarie A.; Eliassen, A. Heather; Willett, Walter C.; Tamimi, Rulla M.

2016-01-01

Purpose Adolescence is hypothesized to be a time period of particular susceptibility to breast cancer risk factors. Red meat and fat intake during high school was positively associated with risk of breast cancer among premenopausal women in the Nurses’ Health Study II (NHSII). High mammographic density is a strong predictor of breast cancer risk but there is limited research on dietary factors associated with breast density. To test the hypothesis that high intake of animal fat or red meat during adolescence is associated with mammographic density, we analyzed data from premenopausal women in the NHSII. Methods Participants recalled adolescent diet on a high school food frequency questionnaire. We assessed absolute and percent mammographic density on digitized analog film mammograms for 687 premenopausal women with no history of cancer. We used generalized linear regression to quantify associations of adolescent animal fat and red meat intake with mammographic density, adjusting for age, body mass index, and other predictors of mammographic density. Results Adolescent animal fat intake was significantly positively associated with premenopausal mammographic density, with a mean percent density of 39.2% in the lowest quartile of adolescent animal fat intake vs. 43.1% in the highest quartile (p-trend: 0.03). A non-significant positive association was also observed for adolescent red meat intake (p-trend: 0.14). Conclusions These findings suggest that higher adolescent animal fat intake is weakly associated with percent mammographic density in premenopausal women. PMID:26791521

Neighborhood influences on recreational physical activity and survival after breast cancer.

PubMed

Keegan, Theresa H M; Shariff-Marco, Salma; Sangaramoorthy, Meera; Koo, Jocelyn; Hertz, Andrew; Schupp, Clayton W; Yang, Juan; John, Esther M; Gomez, Scarlett L

2014-10-01

Higher levels of physical activity have been associated with improved survival after breast cancer diagnosis. However, no previous studies have considered the influence of the social and built environment on physical activity and survival among breast cancer patients. Our study included 4,345 women diagnosed with breast cancer (1995-2008) from two population-based studies conducted in the San Francisco Bay Area. We examined questionnaire-based moderate/strenuous recreational physical activity during the 3 years before diagnosis. Neighborhood characteristics were based on data from the 2000 US Census, business listings, parks, farmers' markets, and Department of Transportation. Survival was evaluated using multivariable Cox proportional hazards models, with follow-up through 2009. Women residing in neighborhoods with no fast-food restaurants (vs. fewer fast-food restaurants) to other restaurants, high traffic density, and a high percentage of foreign-born residents were less likely to meet physical activity recommendations set by the American Cancer Society. Women who were not recreationally physically active had a 22% higher risk of death from any cause than women that were the most active. Poorer overall survival was associated with lower neighborhood socioeconomic status (SES) (p(trend) = 0.02), whereas better breast cancer-specific survival was associated with a lack of parks, especially among women in high-SES neighborhoods. Certain aspects of the neighborhood have independent associations with recreational physical activity among breast cancer patients and their survival. Considering neighborhood factors may aide in the design of more effective, tailored physical activity programs for breast cancer survivors.

Fully automated segmentation of the pectoralis muscle boundary in breast MR images

NASA Astrophysics Data System (ADS)

Wang, Lei; Filippatos, Konstantinos; Friman, Ola; Hahn, Horst K.

2011-03-01

Dynamic Contrast Enhanced MRI (DCE-MRI) of the breast is emerging as a novel tool for early tumor detection and diagnosis. The segmentation of the structures in breast DCE-MR images, such as the nipple, the breast-air boundary and the pectoralis muscle, serves as a fundamental step for further computer assisted diagnosis (CAD) applications, e.g. breast density analysis. Moreover, the previous clinical studies show that the distance between the posterior breast lesions and the pectoralis muscle can be used to assess the extent of the disease. To enable automatic quantification of the distance from a breast tumor to the pectoralis muscle, a precise delineation of the pectoralis muscle boundary is required. We present a fully automatic segmentation method based on the second derivative information represented by the Hessian matrix. The voxels proximal to the pectoralis muscle boundary exhibit roughly the same Eigen value patterns as a sheet-like object in 3D, which can be enhanced and segmented by a Hessian-based sheetness filter. A vector-based connected component filter is then utilized such that only the pectoralis muscle is preserved by extracting the largest connected component. The proposed method was evaluated quantitatively with a test data set which includes 30 breast MR images by measuring the average distances between the segmented boundary and the annotated surfaces in two ground truth sets, and the statistics showed that the mean distance was 1.434 mm with the standard deviation of 0.4661 mm, which shows great potential for integration of the approach in the clinical routine.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2015-12-01

Health Center, a public facility serving medically indigent and underserved women. Dietary and total ( dietary plus supplements ) vitamin D and calcium...Cancer Study Questionnaire [18] were used to categorize dietary intake and supplement use of vitamin D and calcium into tertiles. The Harvard African...Byrne C, Evers KA, Daly MB. Dietary intake and breast density in high- risk women: a cross-sectional study. Breast Cancer Res 2007;9:R72. [4] Yaghjyan L

Building a Better Model: A Comprehensive Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Apply Resources

DTIC Science & Technology

2014-10-01

variability with well trained readers. Figure 7: comparison between the PD (percent density using Cumulus area) and the automatic PD. The...evaluation of outlier correction, comparison of several different software methods, precision measurement, and evaluation of variation by mammography...chart review for selected cases (month 4-6). Comparison of information from the Breast Cancer Database and medical records showed good consistency

Analysis of tumour-infiltrating lymphocytes reveals two new biologically different subgroups of breast ductal carcinoma in situ.

PubMed

Beguinot, Marie; Dauplat, Marie-Melanie; Kwiatkowski, Fabrice; Lebouedec, Guillaume; Tixier, Lucie; Pomel, Christophe; Penault-Llorca, Frederique; Radosevic-Robin, Nina

2018-02-03

Tumour-infiltrating lymphocytes (TILs) have been demonstrated to significantly influence prognosis and response to therapy of invasive breast cancer (IBC). Thus, it has been suggested that TIL density or/and immunophenotype could serve as biomarkers for selection of IBC patients for immunotherapy. However, much less is known about significance of TILs in breast ductal carcinoma in situ (DCIS). We retrospectively investigated TIL density and immunophenotype in 96 pure DCIS and 35 microinvasive carcinomas (miCa). TIL density was assessed on H&E-stained breast biopsy sections as the percentage of tumour stromal area occupied by TILs, and classified into 4 grades: 0 (0%-9%), 1 (10-29%), 2 (30-49%) and 3 (50%-100%). TIL immunophenotype was assessed by immunohistochemistry for CD8, CD4, FoxP3, CD38 or CD20. Compared to pure DCIS, miCa contained significantly more cases with TIL density grade 3 (p = 0.028). Concordantly, CD8+, CD4+ and CD38+ cells were more numerous in miCa than in pure DCIS. In the pure DCIS subgroup with TIL density grades 2 and 3, all TIL subpopulations were more numerous than in the pure DCIS with TIL density grades 0 and 1, however the ratio between T-lymphocytes (CD8+ and CD4+) and B-lymphocytes (CD20+) was significantly lower (p = 0.029). On the other side, this ratio was significantly higher in miCa, in comparison with pure DCIS having TIL density grades 2 and 3 (p = 0.017). By cluster analysis of tumour cell pathobiological features we demonstrated similarity between miCa and the pure DCIS with TIL density grades 2 and 3. The only significant difference between those two categories was in the ratio of T- to B-TILs, higher in miCa. Results indicate that TIL density level can distinguish 2 biologically different DCIS subgroups, one of which (DCIS with ≥30% TILs, the TIL-rich DCIS) is like miCa. Similarity of TIL-rich pure DCIS and miCa as well as the role of B-lymphocytes in DCIS invasiveness are worth further investigating with regards to the potential development of immunotherapy-based prevention of DCIS progression.

Automated 3D Ultrasound Image Segmentation to Aid Breast Cancer Image Interpretation

PubMed Central

Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Yuan, Jie; Wang, Xueding; Carson, Paul L.

2015-01-01

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. PMID:26547117

Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer

NASA Astrophysics Data System (ADS)

Wang, Yuxin; Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Du, Sidan; Yuan, Jie; Wang, Xueding; Carson, Paul L.

2016-04-01

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

Postmenopausal hormone therapy and changes in mammographic density.

PubMed

van Duijnhoven, Fränzel J B; Peeters, Petra H M; Warren, Ruth M L; Bingham, Sheila A; van Noord, Paulus A H; Monninkhof, Evelyn M; Grobbee, Diederick E; van Gils, Carla H

2007-04-10

Hormone therapy (HT) use has been associated with an increased breast cancer risk. We explored the underlying mechanism further by determining the effects of HT on mammographic density, a measure of dense tissue in the breast and a consistent breast cancer risk factor. A total of 620 HT users and 620 never users from the Dutch Prospect-European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 175 HT users and 161 never users from the United Kingdom EPIC-Norfolk cohort were included. For HT users, one mammogram before and one mammogram during HT use was included. For never users, mammograms with similar time intervals were included. Mammographic density was assessed using a computer-assisted method. Changes in density were analyzed using linear regression. The median time between mammograms was 3.0 years and the median duration of HT use was 1 year. The absolute mean decline in percent density was larger in never users (7.3%) than in estrogen therapy users (6.4%; P = .22) and combined HT users (3.5%; P < .01). The effect of HT appeared to be high in a small number of women, whereas most women were unaffected. Our results suggest that HT use, and especially estrogen and progestin use, slows the changes from dense patterns to more fatty patterns that are normally seen in women with increasing age. Given that it is postulated that lifetime cumulative exposure to high density may be related to breast cancer risk, a delay in density decline in HT users potentially could explain their increased breast cancer risk.

Endogenous estrogens and breast cancer risk: the case for prospective cohort studies.

PubMed Central

Toniolo, P G

1997-01-01

It is generally agreed that estrogens, and possibly androgens, are important in the etiology of breast cancer, but no consensus exists as to the precise estrogenic or androgenic environment that characterizes risk, or the exogenous factors that influence the hormonal milieu. Nearly all the epidemiological studies conducted in the 1970s and 1980s were hospital-based case-control studies in which specimen sampling was performed well after the clinical appearance of the disease. Early prospective cohort studies also had limitations in their small sample sizes or short follow-up periods. However, more recent case-control studies nested within large cohorts, such as the New York University Women's Health Study and the Ormoni e Dieta nell'Eziologia dei Tumori study in Italy, are generating new data indicating that increased levels of estrone, estradiol and bioavailable estradiol, as well as their androgenic precursors, may be associated with a 4- to 6-fold increase in the risk of postmenopausal breast cancer. Further new evidence, which complements and expands the observations from the latter studies, shows that women with the thickest bone density, which may be a surrogate for cumulated exposure to hormones, experience severalfold increased risk of subsequent breast cancer as compared to women with thin bones. These data suggests that endogenous sex hormones are a key factor in the etiology of postmenopausal breast cancer. New prospective cohort studies should be conducted to examine the role of endogenous sex hormones in blood and urine samples obtained early in the natural history of breast cancer jointly with an assessment of bone density and of other important risk factors, such as mammographic density, physical activity, body weight, and markers of individual susceptibility, which may confer increased risk through an effect on the metabolism of endogenous hormones or through specific metabolic responses to Western lifestyle and diet. PMID:9168000

Retrospective and comparative analysis of (99m)Tc-Sestamibi breast specific gamma imaging versus mammography, ultrasound, and magnetic resonance imaging for the detection of breast cancer in Chinese women.

PubMed

Yu, Xiuyan; Hu, Guoming; Zhang, Zhigang; Qiu, Fuming; Shao, Xuan; Wang, Xiaochen; Zhan, Hongwei; Chen, Yiding; Deng, Yongchuan; Huang, Jian

2016-07-11

Diagnosing breast cancer during the early stage may be helpful for decreasing cancer-related mortality. In Western developed countries, mammographies have been the gold standard for breast cancer detection. However, Chinese women usually have denser and smaller-sized breasts compared to Caucasian women, which decreases the diagnostic accuracy of mammography. However, breast specific gamma imaging, a type of molecular functional breast imaging, has been used for the accurate diagnosis of breast cancer and is not influenced by breast density. Our objective was to analyze the breast specific gamma imaging (BSGI) diagnostic value for Chinese women. During a 2-year period, 357 women were diagnosed and treated at our oncology department and received BSGI in addition to mammography (MMG), ultrasound (US) and magnetic resonance imaging (MRI) for diagnostic assessment. We investigated the sensitivity and specificity of each method of detection and compared the biological profiles of the four imaging methods. A total of 357 women received a final surgical pathology diagnosis, with 168 malignant diseases (58.5 %) and 119 benign diseases (41.5 %). Of these, 166 underwent the four imaging tests preoperatively. The sensitivity of BSGI was 80.35 and 82.14 % by US, 75.6 % by MMG, and 94.06 % by MRI. Furthermore, the breast cancer diagnosis specificity of BSGI was high (83.19 % vs. 77.31 % vs. 66.39 % vs. 67.69 %, respectively). The BSGI diagnostic sensitivity for mammographic breast density in women was superior to mammography and more sensitive for non-luminal A subtypes (luminal A vs. non-luminal A, 68.63 % vs. 88.30 %). BSGI may help improve the ability to diagnose early stage breast cancer for Chinese women, particularly for ductal carcinoma in situ (DCIS), mammographic breast density and non-luminal A breast cancer.

Are Qualitative Assessments of Background Parenchymal Enhancement, Amount of Fibroglandular Tissue on MR Images, and Mammographic Density Associated with Breast Cancer Risk?

PubMed Central

Dontchos, Brian N.; Partridge, Savannah C.; Korde, Larissa A.; Lam, Diana L.; Scheel, John R.; Peacock, Sue; Lehman, Constance D.

2015-01-01

Purpose To investigate whether qualitative magnetic resonance (MR) imaging assessments of background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT), and mammographic density are associated with risk of developing breast cancer in women who are at high risk. Materials and Methods In this institutional review board–approved HIPAA-compliant retrospective study, all screening breast MR images obtained from January 2006 to December 2011 in women aged 18 years or older and at high risk for but without a history of breast cancer were identified. Women in whom breast cancer was diagnosed after index MR imaging comprised the cancer cohort, and one-to-one matching (age and BRCA status) of each woman with breast cancer to a control subject was performed by using MR images obtained in women who did not develop breast cancer with follow-up time maximized. Amount of BPE, BPE pattern (peripheral vs central), amount of FGT at MR imaging, and mammographic density were assessed on index images. Imaging features were compared between cancer and control cohorts by using conditional logistic regression. Results Twenty-three women at high risk (mean age, 47 years ± 10 [standard deviation]; six women had BRCA mutations) with no history of breast cancer underwent screening breast MR imaging; in these women, a diagnosis of breast cancer (invasive, n = 12; in situ, n = 11) was made during the follow-up interval. Women with mild, moderate, or marked BPE were nine times more likely to receive a diagnosis of breast cancer during the follow-up interval than were those with minimal BPE (P = .007; odds ratio = 9.0; 95% confidence interval: 1.1, 71.0). BPE pattern, MR imaging amount of FGT, and mammographic density were not significantly different between the cohorts (P = .5, P = .5, and P = .4, respectively). Conclusion Greater BPE was associated with a higher probability of developing breast cancer in women at high risk for cancer and warrants further study. © RSNA, 2015 Online supplemental material is available for this article. PMID:25965809

Supplemental Screening for Breast Cancer in Women with Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force

PubMed Central

Melnikow, Joy; Fenton, Joshua J.; Whitlock, Evelyn P.; Miglioretti, Diana L.; Weyrich, Meghan S.; Thompson, Jamie H.; Shah, Kunal

2016-01-01

Background Screening mammography has lower sensitivity and specificity in women with dense breasts, who experience higher breast cancer risk. Purpose Systematic review of: reproducibility of BI-RADS density categorization; test performance and clinical outcomes of supplemental screening with breast ultrasound, magnetic resonance imaging (MRI), and digital breast tomosynthesis (DBT) in women with dense breasts and negative mammography. Data Sources MEDLINE, PubMed, Embase, and Cochrane January 2000–July 2015. Study Selection Studies reporting BI-RADS density reproducibility or supplemental screening results for women with dense breasts. Data Extraction Quality assessment and abstraction of twenty-four studies from seven countries; six were good quality. Data Synthesis Three good-quality studies reported reproducibility of BI-RADS density; 13–19% of women were re-categorized between “dense” and “non-dense” at subsequent screening. Two good-quality studies reported ultrasound sensitivity for women with negative mammography ranging from 80–83%; specificity 86–94%; and positive predictive value (PPV) 3–8%. MRI sensitivity ranged from 75–100%, specificity 78–94%, and PPV 3–33% (3 studies). Ultrasound additional cancer detection rates were 4.4 per 1,000 exams (89–93% invasive); recall rates were 14%. MRI detected 3.5–28.6 additional cancers per 1,000 exams (34–86% invasive); recall rates were 12–24 %. DBT cancer detection rates increased by 1.4–2.5 per 1000 exams compared to mammography alone (3 studies). Recall rates ranged from 7–11%, compared to 7–17% with mammography alone. No studies examined breast cancer outcomes. Limitations Good quality evidence was sparse. Studies were small and confidence intervals were wide. Definitions of recall were absent or inconsistent. Conclusions Density ratings may be re-categorized on serial screening mammograms. Supplemental screening of women with dense breasts finds additional breast cancers, but increases false-positives. DBT may reduce recall rates. Supplemental screening impacts on breast cancer outcomes remain unclear. Primary Funding Source Agency for Healthcare Research and Quality PMID:26757021

Second generation anthropomorphic physical phantom for mammography and DBT: Incorporating voxelized 3D printing and inkjet printing of iodinated lesion inserts

NASA Astrophysics Data System (ADS)

Sikaria, Dhiraj; Musinsky, Stephanie; Sturgeon, Gregory M.; Solomon, Justin; Diao, Andrew; Gehm, Michael E.; Samei, Ehsan; Glick, Stephen J.; Lo, Joseph Y.

2016-03-01

Physical phantoms are needed for the evaluation and optimization of new digital breast tomosynthesis (DBT) systems. Previously, we developed an anthropomorphic phantom based on human subject breast CT data and fabricated using commercial 3D printing. We now present three key advancements: voxelized 3D printing, photopolymer material doping, and 2D inkjet printing of lesion inserts. First, we bypassed the printer's control software in order to print in voxelized form instead of conventional STL surfaces, thus improving resolution and allowing dithering to mix the two photopolymer materials into arbitrary proportions. We demonstrated ability to print details as small as 150μm, and dithering to combine VeroWhitePlus and TangoPlus in 10% increments. Second, to address the limited attenuation difference among commercial photopolymers, we evaluated a beta sample from Stratasys with increased TiO2 doping concentration up to 2.5%, which corresponded to 98% breast density. By spanning 36% to 98% breast density, this doubles our previous contrast. Third, using inkjet printers modified to print with iopamidol, we created 2D lesion patterns on paper that can be sandwiched into the phantom. Inkjet printing has advantages of being inexpensive and easy, and more contrast can be delivered through overprinting. Printing resolution was maintained at 210 μm horizontally and 330 μm vertically even after 10 overprints. Contrast increased linearly with overprinting at 0.7% per overprint. Together, these three new features provide the basis for creating a new anthropomorphic physical breast phantom with improved resolution and contrast, as well as the ability to insert 2D lesions for task-based assessment of performance.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

PubMed

Zheng, Yuanjie; Keller, Brad M; Ray, Shonket; Wang, Yan; Conant, Emily F; Gee, James C; Kontos, Despina

2015-07-01

Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.

Digital histologic analysis reveals morphometric patterns of age-related involution in breast epithelium and stroma.

PubMed

Sandhu, Rupninder; Chollet-Hinton, Lynn; Kirk, Erin L; Midkiff, Bentley; Troester, Melissa A

2016-02-01

Complete age-related regression of mammary epithelium, often termed postmenopausal involution, is associated with decreased breast cancer risk. However, most studies have qualitatively assessed involution. We quantitatively analyzed epithelium, stroma, and adipose tissue from histologically normal breast tissue of 454 patients in the Normal Breast Study. High-resolution digital images of normal breast hematoxylin and eosin-stained slides were partitioned into epithelium, adipose tissue, and nonfatty stroma. Percentage area and nuclei per unit area (nuclear density) were calculated for each component. Quantitative data were evaluated in association with age using linear regression and cubic spline models. Stromal area decreased (P = 0.0002), and adipose tissue area increased (P < 0.0001), with an approximate 0.7% change in area for each component, until age 55 years when these area measures reached a steady state. Although epithelial area did not show linear changes with age, epithelial nuclear density decreased linearly beginning in the third decade of life. No significant age-related trends were observed for stromal or adipose nuclear density. Digital image analysis offers a high-throughput method for quantitatively measuring tissue morphometry and for objectively assessing age-related changes in adipose tissue, stroma, and epithelium. Epithelial nuclear density is a quantitative measure of age-related breast involution that begins to decline in the early premenopausal period. Copyright © 2015 Elsevier Inc. All rights reserved.

Breast Cancer Prevention (PDQ®)—Health Professional Version

Cancer.gov

Risk factors for breast cancer are female sex and advancing age, inherited risk, breast density, obesity, alcohol consumption, and exposure to ionizing radiation. Interventions to prevent breast cancer include chemoprevention (e.g. SERMs, AIs), risk-reducing surgery (e.g. mastectomy, oophorectomy). Review the evidence on risk factors and interventions to prevent breast cancer in this expert-reviewed summary.

Awareness of Breast Density and Its Impact on Breast Cancer Detection and Risk

PubMed Central

Rhodes, Deborah J.; Radecki Breitkopf, Carmen; Ziegenfuss, Jeanette Y.; Jenkins, Sarah M.; Vachon, Celine M.

2015-01-01

Purpose Legislation mandating disclosure of breast density (BD) information has passed in 21 states; however, actual awareness of BD and knowledge of its impact on breast cancer detection and risk are unknown. Methods We conducted a national cross-sectional survey administered in English and Spanish using a probability-based sample of screening-age women, with oversampling of Connecticut, the only state with BD legislation in effect for > 1 year before the survey. Results Of 2,311 women surveyed, 65% responded. Overall, 58% of women had heard of BD, 49% knew that BD affects breast cancer detection, and 53% knew that BD affects cancer risk. After multivariable adjustment, increased BD awareness was associated with white non-Hispanic race/ethnicity (Hispanic v white non-Hispanic: odds ratio [OR], 0.23; P < .001), household income (OR, 1.07 per category increase; P < .001), education (OR, 1.19 per category increase; P < .001), diagnostic evaluation after a mammogram (OR, 2.64; P < .001), and postmenopausal hormone therapy (OR, 1.69; P = .002). Knowledge of the masking effect of BD was associated with higher household income (OR, 1.10; P < .001), education (OR, 1.22; P = .01), prior breast biopsy (OR, 2.16; P < .001), and residing in Connecticut (Connecticut v other states: OR, 3.82; P = .003). Connecticut residents were also more likely to have discussed their BD with a health care provider (67% v 43% for residents of other US states; P = .001). Conclusion Disparities in BD awareness and knowledge exist by race/ethnicity, education, and income. BD legislation seems to be effective in increasing knowledge of BD impact on breast cancer detection. These findings support continued and targeted efforts to improve BD awareness and knowledge among women eligible for screening mammography. PMID:25732156

Awareness of breast density and its impact on breast cancer detection and risk.

PubMed

Rhodes, Deborah J; Radecki Breitkopf, Carmen; Ziegenfuss, Jeanette Y; Jenkins, Sarah M; Vachon, Celine M

2015-04-01

Legislation mandating disclosure of breast density (BD) information has passed in 21 states; however, actual awareness of BD and knowledge of its impact on breast cancer detection and risk are unknown. We conducted a national cross-sectional survey administered in English and Spanish using a probability-based sample of screening-age women, with oversampling of Connecticut, the only state with BD legislation in effect for > 1 year before the survey. Of 2,311 women surveyed, 65% responded. Overall, 58% of women had heard of BD, 49% knew that BD affects breast cancer detection, and 53% knew that BD affects cancer risk. After multivariable adjustment, increased BD awareness was associated with white non-Hispanic race/ethnicity (Hispanic v white non-Hispanic: odds ratio [OR], 0.23; P < .001), household income (OR, 1.07 per category increase; P < .001), education (OR, 1.19 per category increase; P < .001), diagnostic evaluation after a mammogram (OR, 2.64; P < .001), and postmenopausal hormone therapy (OR, 1.69; P = .002). Knowledge of the masking effect of BD was associated with higher household income (OR, 1.10; P < .001), education (OR, 1.22; P = .01), prior breast biopsy (OR, 2.16; P < .001), and residing in Connecticut (Connecticut v other states: OR, 3.82; P = .003). Connecticut residents were also more likely to have discussed their BD with a health care provider (67% v 43% for residents of other US states; P = .001). Disparities in BD awareness and knowledge exist by race/ethnicity, education, and income. BD legislation seems to be effective in increasing knowledge of BD impact on breast cancer detection. These findings support continued and targeted efforts to improve BD awareness and knowledge among women eligible for screening mammography. © 2015 by American Society of Clinical Oncology.

Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants.

PubMed

Chan, Siwa; Chen, Jeon-Hor; Li, Shunshan; Chang, Rita; Yeh, Darh-Cherng; Chang, Ruey-Feng; Yeh, Lee-Ren; Kwong, Jessica; Su, Min-Ying

2017-04-17

To investigate the relationship between mammographic density measured in four quadrants of a breast with the location of the occurred cancer. One hundred and ten women diagnosed with unilateral breast cancer that could be determined in one specific breast quadrant were retrospectively studied. Women with previous cancer/breast surgery were excluded. The craniocaudal (CC) and mediolateral oblique (MLO) mammography of the contralateral normal breast were used to separate a breast into 4 quadrants: Upper-Outer (UO), Upper-Inner (UI), Lower-Outer (LO), and Lower-Inner (LI). The breast area (BA), dense area (DA), and percent density (PD) in each quadrant were measured by using the fuzzy-C-means segmentation. The BA, DA, and PD were compared between patients who had cancer occurring in different quadrants. The upper-outer quadrant had the highest BA (37 ± 15 cm 2 ) and DA (7.1 ± 2.9 cm 2 ), with PD = 20.0 ± 5.8%. The order of BA and DA in the 4 separated quadrants were: UO > UI > LO > LI, and almost all pair-wise comparisons showed significant differences. For tumor location, 67 women (60.9%) had tumor in UO, 16 (14.5%) in UI, 7 (6.4%) in LO, and 20 (18.2%) in LI quadrant, respectively. The estimated odds and the 95% confidence limits of tumor development in the UO, UI, LO and LI quadrants were 1.56 (1.06, 2.29), 0.17 (0.10, 0.29), 0.07 (0.03, 0.15), and 0.22 (0.14, 0.36), respectively. In these 4 groups of women, the order of quadrant BA and DA were all the same (UO > UI > LO > LI), and there was no significant difference in BA, DA or PD among them (all p > 0.05). Breast cancer was most likely to occur in the UO quadrant, which was also the quadrant with highest BA and DA; but for women with tumors in other quadrants, the density in that quadrant was not the highest. Therefore, there was no direct association between quadrant density and tumor occurrence.

Lymphangiogenesis assessed using three methods is related to tumour grade, breast cancer subtype and expression of basal marker.

PubMed

Niemiec, Joanna; Adamczyk, Agnieszka; Ambicka, Aleksandra; Mucha-Małecka, Anna; Wysocki, Wojciech; Mituś, Jerzy; Ryś, Janusz

2012-11-01

Lymphangiogenesis is a potential indicator of cancer patients' survival. However, there is no standardisation of methodologies applied to the assessment of lymphatic vessel density. In 156 invasive ductal breast cancers (T  1/N+/M0), lymphatic and blood vessels were visualised using podoplanin and CD34, respectively. Based on these markers expression, four parameters were assessed: (i) distribution of podoplanin-stained vessels (DPV) - the percentage of fields with at least one lymphatic vessel (a simple method proposed by us), (ii) lymphatic vessel density (LVD), (iii) LVD to microvessel density ratio (LVD/MVD) and (iv) the expression of podoplanin in cancer-associated fibroblasts. Next, we estimated relations between the above-mentioned parameters and: (i) breast cancer subtype, (ii) tumour grade, and (iii) basal markers expression. We found that intensive lymphangiogenesis, assessed using all studied methods, is positively related to high tumour grade, triple negative or HER2 subtype and expression of basal markers. Whereas, the absence of podoplanin expression in fibroblasts of cancer stroma is related to luminal A subtype, low tumour grade or lack of basal markers expression. Distribution of podoplanin-stained vessels, assessed by a simple method proposed by us (indicating the percentage of fields with at least one lymphatic vessel), might be used instead of the "hot-spot" method.

A comprehensive tool for measuring mammographic density changes over time.

PubMed

Eriksson, Mikael; Li, Jingmei; Leifland, Karin; Czene, Kamila; Hall, Per

2018-06-01

Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time. Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment. The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3-1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60-0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001. The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up.

A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women.

PubMed

Zhang, Xi; Lin, Xi; Tan, Yanjuan; Zhu, Ying; Wang, Hui; Feng, Ruimei; Tang, Guoxue; Zhou, Xiang; Li, Anhua; Qiao, Youlin

2018-04-01

The automated breast ultrasound system (ABUS) is a potential method for breast cancer detection; however, its diagnostic performance remains unclear. We conducted a hospital-based multicenter diagnostic study to evaluate the clinical performance of the ABUS for breast cancer detection by comparing it to handheld ultrasound (HHUS) and mammography (MG). Eligible participants underwent HHUS and ABUS testing; women aged 40-69 years additionally underwent MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). Women in the BI-RADS categories 1-2 were considered negative. Women classified as BI-RADS 3 underwent magnetic resonance imaging to distinguish true- and false-negative results. Core aspiration or surgical biopsy was performed in women classified as BI-RADS 4-5, followed by a pathological diagnosis. Kappa values and agreement rates were calculated between ABUS, HHUS and MG. A total of 1,973 women were included in the final analysis. Of these, 1,353 (68.6%) and 620 (31.4%) were classified as BI-RADS categories 1-3 and 4-5, respectively. In the older age group, the agreement rate and Kappa value between the ABUS and HHUS were 94.0% and 0.860 (P<0.001), respectively; they were 89.2% and 0.735 (P<0.001) between the ABUS and MG, respectively. Regarding consistency between imaging and pathology results, 78.6% of women classified as BI-RADS 4-5 based on the ABUS were diagnosed with precancerous lesions or cancer; which was 7.2% higher than that of women based on HHUS. For BI-RADS 1-2, the false-negative rates of the ABUS and HHUS were almost identical and were much lower than those of MG. We observed a good diagnostic reliability for the ABUS. Considering its performance for breast cancer detection in women with high-density breasts and its lower operator dependence, the ABUS is a promising option for breast cancer detection in China.

Tailored breast cancer screening program with microdose mammography, US, and MR Imaging: short-term results of a pilot study in 40-49-year-old women.

PubMed

Venturini, Elena; Losio, Claudio; Panizza, Pietro; Rodighiero, Maria Grazia; Fedele, Isabella; Tacchini, Simona; Schiani, Elena; Ravelli, Silvia; Cristel, Giulia; Panzeri, Marta Maria; De Cobelli, Francesco; Del Maschio, Alessandro

2013-08-01

To evaluate the feasibility, performance, and cost of a breast cancer screening program aimed at 40-49-year-old women and tailored to their risk profile with supplemental ultrasonography (US) and magnetic resonance (MR) imaging. The institutional review board approved this study, and informed written consent was obtained. A total of 3017 40-49-year-old women were invited to participate. The screening program was tailored to lifetime risk (Gail test) and mammographic density (according to Breast Imaging Reporting and Data Systems [BI-RADS] criteria) with supplemental US or MR imaging and bilateral two-view microdose mammography. The indicators suggested by European guidelines, US incremental cancer detection rate (CDR), and estimated costs were evaluated. A total of 1666 women (67.5% participation rate) were recruited. The average lifetime risk of breast cancer was 11.6%, and nine women had a high risk of breast cancer; 917 women (55.0%) had a high density score (BI-RADS density category 3 or 4). The average glandular dose for screening examinations was 1.49 mGy. Screening US was performed in 835 study participants (50.1%), mostly due to high breast density (800 of 1666 women [48.0%]). Screening MR imaging was performed in nine women (0.5%) at high risk for breast cancer. Breast cancer was diagnosed in 14 women (8.4 cases per 1000 women). Twelve diagnoses were made with microdose mammography, and two were made with supplemental US in dense breasts (2.4 cases per 1000 women). All patients were submitted for surgery, and 10 underwent breast-conserving surgery. The sentinel lymph node was evaluated in 11 patients, resulting in negative findings in six. Pathologic analysis resulted in the diagnosis of four ductal carcinomas in situ and 10 invasive carcinomas (five at stage I). A tailored breast cancer screening program in 40-49-year-old women yielded a greater-than-expected number of cancers, most of which were low-stage disease.

Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach.

PubMed

Andersson, Therese M-L; Crowther, Michael J; Czene, Kamila; Hall, Per; Humphreys, Keith

2017-11-01

Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50-74 years, diagnosed with breast cancer in Sweden during 1993-1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Molecular breast imaging using a dedicated high-performance instrument

NASA Astrophysics Data System (ADS)

O'Connor, Michael K.; Wagenaar, Douglas; Hruska, Carrie B.; Phillips, Stephen; Caravaglia, Gina; Rhodes, Deborah

2006-08-01

In women with radiographically dense breasts, the sensitivity of mammography is less than 50%. With the increase in the percent of women with dense breasts, it is important to look at alternative screening techniques for this population. This article reviews the strengths and weaknesses of current imaging techniques and focuses on recent developments in semiconductor-based gamma camera systems that offer significant improvements in image quality over that achievable with single-crystal sodium iodide systems. We have developed a technique known as Molecular Breast Imaging (MBI) using small field of view Cadmium Zinc Telluride (CZT) gamma cameras that permits the breast to be imaged in a similar manner to mammography, using light pain-free compression. Computer simulations and experimental studies have shown that use of low-energy high sensitivity collimation coupled with the excellent energy resolution and intrinsic spatial resolution of CZT detectors provides optimum image quality for the detection of small breast lesions. Preliminary clinical studies with a prototype dual-detector system have demonstrated that Molecular Breast Imaging has a sensitivity of ~90% for the detection of breast tumors less than 10 mm in diameter. By comparison, conventional scintimammography only achieves a sensitivity of 50% in the detection of lesions < 10 mm. Because Molecular Breast Imaging is not affected by breast density, this technique may offer an important adjunct to mammography in the evaluation of women with dense breast parenchyma.

Fractal Analysis of Visual Search Activity for Mass Detection During Mammographic Screening

DOE PAGES

Alamudun, Folami T.; Yoon, Hong-Jun; Hudson, Kathy; ...

2017-02-21

Purpose: The objective of this study was to assess the complexity of human visual search activity during mammographic screening using fractal analysis and to investigate its relationship with case and reader characteristics. Methods: The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) and 10 readers (three board certified radiologists and seven radiology residents), formed the corpus data for this study. The fractal dimension of the readers’ visual scanning patternsmore » was computed with the Minkowski–Bouligand box-counting method and used as a measure of gaze complexity. Individual factor and group-based interaction ANOVA analysis was performed to study the association between fractal dimension, case pathology, breast density, and reader experience level. The consistency of the observed trends depending on gaze data representation was also examined. Results: Case pathology, breast density, reader experience level, and individual reader differences are all independent predictors of the visual scanning pattern complexity when screening for breast cancer. No higher order effects were found to be significant. Conclusions: Fractal characterization of visual search behavior during mammographic screening is dependent on case properties and image reader characteristics.« less

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

Alamudun, Folami T.; Yoon, Hong-Jun; Hudson, Kathy

Purpose: The objective of this study was to assess the complexity of human visual search activity during mammographic screening using fractal analysis and to investigate its relationship with case and reader characteristics. Methods: The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) and 10 readers (three board certified radiologists and seven radiology residents), formed the corpus data for this study. The fractal dimension of the readers’ visual scanning patternsmore » was computed with the Minkowski–Bouligand box-counting method and used as a measure of gaze complexity. Individual factor and group-based interaction ANOVA analysis was performed to study the association between fractal dimension, case pathology, breast density, and reader experience level. The consistency of the observed trends depending on gaze data representation was also examined. Results: Case pathology, breast density, reader experience level, and individual reader differences are all independent predictors of the visual scanning pattern complexity when screening for breast cancer. No higher order effects were found to be significant. Conclusions: Fractal characterization of visual search behavior during mammographic screening is dependent on case properties and image reader characteristics.« less

Neighborhood influences on recreational physical activity and survival after breast cancer

PubMed Central

Shariff-Marco, Salma; Sangaramoorthy, Meera; Koo, Jocelyn; Hertz, Andrew; Schupp, Clayton W.; Yang, Juan; John, Esther M.; Gomez, Scarlett L.

2014-01-01

Purpose Higher levels of physical activity have been associated with improved survival after breast cancer diagnosis. However, no previous studies have considered the influence of the social and built environment on physical activity and survival among breast cancer patients. Methods Our study included 4,345 women diagnosed with breast cancer (1995–2008) from two population-based studies conducted in the San Francisco Bay Area. We examined questionnaire-based moderate/strenuous recreational physical activity during the 3 years before diagnosis. Neighborhood characteristics were based on data from the 2000 US Census, business listings, parks, farmers’ markets, and Department of Transportation. Survival was evaluated using multivariable Cox proportional hazards models, with follow-up through 2009. Results Women residing in neighborhoods with no fast-food restaurants (vs. fewer fast-food restaurants) to other restaurants, high traffic density, and a high percentage of foreign-born residents were less likely to meet physical activity recommendations set by the American Cancer Society. Women who were not recreationally physically active had a 22 % higher risk of death from any cause than women that were the most active. Poorer overall survival was associated with lower neighborhood socioeconomic status (SES) (p trend = 0.02), whereas better breast cancer-specific survival was associated with a lack of parks, especially among women in high-SES neighborhoods. Conclusion Certain aspects of the neighborhood have independent associations with recreational physical activity among breast cancer patients and their survival. Considering neighborhood factors may aide in the design of more effective, tailored physical activity programs for breast cancer survivors. PMID:25088804

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.

Breast mass segmentation in mammograms combining fuzzy c-means and active contours

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2018-04-01

Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.

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

Bueno, G.; Ruiz, M.; Sanchez, S

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Association of Vascular Endothelial Growth Factor Expression with Tumor Angiogenesis and with Early Relapse in Primary Breast Cancer

PubMed Central

Hoshina, Seigo; Takayanagi, Toshiaki; Tominaga, Takeshi

1994-01-01

Angiogenesis is an independent prognostic indicator in breast cancer. In this report, the relationship between expression of vascular endothclial growth factor (VEGF; a selective mitogen for endothelial cells) and the microvessel density was examined in 103 primary breast cancers. The expression of VEGF was evaluated by immunocytochemical staining using anti‐VEGF antibody. The microvessel density, which was determined by immunostaining for factor VIII antigen, in VEGF‐rich tumors was clearly higher than that in VEGF‐poor tumors (P<0.01). There was a good correlation between VEGF expression and the increment of microvessel density. Furthermore, postoperative survey demonstrated that the relapse‐free survival rate of VEGF‐rich tumors was significantly worse than that of VEGF‐poor tumors. It was suggested that the expression of VEGF is closely associated with the promotion of angiogenesis and with early relapse in primary breast cancer. PMID:7525523

Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications.

PubMed

Fowler, Erin E E; Vachon, Celine M; Scott, Christopher G; Sellers, Thomas A; Heine, John J

2014-08-01

Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data. We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PDa was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PDa performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PDa operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PDa performance as a function of data representation and investigated a variation analysis of the transformation. The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PDa could not be assessed on the raw images without preprocessing. However, PDa had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PDa. Although variability in the transform influenced the respective PDa distribution, it did not affect the measurement's association with breast cancer. PDa assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes.

PubMed

Yaghjyan, Lusine; Tamimi, Rulla M; Bertrand, Kimberly A; Scott, Christopher G; Jensen, Matthew R; Pankratz, V Shane; Brandt, Kathy; Visscher, Daniel; Norman, Aaron; Couch, Fergus; Shepherd, John; Fan, Bo; Chen, Yunn-Yi; Ma, Lin; Beck, Andrew H; Cummings, Steven R; Kerlikowske, Karla; Vachon, Celine M

2017-09-01

We examined the associations of mammographic breast density with breast cancer risk by tumor aggressiveness and by menopausal status and current postmenopausal hormone therapy. This study included 2596 invasive breast cancer cases and 4059 controls selected from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses' Health Study, Nurses' Health Study II, and San Francisco Mammography Registry. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density with breast cancer risk by tumor aggressiveness (defined as presence of at least two of the following tumor characteristics: size ≥2 cm, grade 2/3, ER-negative status, or positive nodes), stratified by menopausal status and current hormone therapy. Overall, the positive association of PD and borderline inverse association of NDA with breast cancer risk was stronger in aggressive vs. non-aggressive tumors (≥51 vs. 11-25% OR 2.50, 95% CI 1.94-3.22 vs. OR 2.03, 95% CI 1.70-2.43, p-heterogeneity = 0.03; NDA 4th vs. 2nd quartile OR 0.54, 95% CI 0.41-0.70 vs. OR 0.71, 95% CI 0.59-0.85, p-heterogeneity = 0.07). However, there were no differences in the association of DA with breast cancer by aggressive status. In the stratified analysis, there was also evidence of a stronger association of PD and NDA with aggressive tumors among postmenopausal women and, in particular, current estrogen+progesterone users (≥51 vs. 11-25% OR 3.24, 95% CI 1.75-6.00 vs. OR 1.93, 95% CI 1.25-2.98, p-heterogeneity = 0.01; NDA 4th vs. 2nd quartile OR 0.43, 95% CI 0.21-0.85 vs. OR 0.56, 95% CI 0.35-0.89, p-heterogeneity = 0.01), even though the interaction was not significant. Our findings suggest that associations of mammographic density with breast cancer risk differ by tumor aggressiveness. While there was no strong evidence that these associations differed by menopausal status or hormone therapy, they did appear more prominent among current estrogen+progesterone users.

Neighbourhoods matter too: the association between neighbourhood socioeconomic position, population density and breast, prostate and lung cancer incidence in Denmark between 2004 and 2008.

PubMed

Meijer, Mathias; Bloomfield, Kim; Engholm, Gerda

2013-01-01

Previous studies have shown that cancer incidence is related to a number of individual factors, including socioeconomic status. The aim of this study was to refine the current knowledge about indicators associated with cancer incidence by evaluating the influence of neighbourhood characteristics on breast, prostate and lung cancer incidence in Denmark. All women aged 30-83 years were followed for breast cancer between 2004 and 2008, men between 50 and 83 years were followed for prostate cancer and both sexes between ages 50 and 83 were followed for lung cancer. Registry data obtained from Statistics Denmark included age, sex, availability of breast cancer screening, marital status, education, disposable income and occupational socioeconomic status on the individual level and population density and neighbourhood socioeconomic status (the proportion of unemployed) on the parish level. Frailty modelling with individuals on the first level and parishes on the second level was conducted. A significantly lower HR of breast cancer was found in areas with low population density (HR=0.93; CI 0.88 to 0.99), while neighbourhood unemployment had no effect. Inhabitants of lower unemployment areas had a higher risk of prostate cancer (HR=1.14; CI 1.08 to 1.21) compared with those in higher unemployment areas, whereas population density had no effect. Risk of lung cancer was lower in areas with lowest population density (HR=0.80; CI 0.74 to 0.85) and lowest in areas with lowest unemployment (HR=0.88; CI 0.84 to 0.92). In addition to individual-level factors, characteristics on the neighbourhood level also have an influence on breast, prostate and lung cancer incidence.

Interactions of alcohol and postmenopausal hormone use in regards to mammographic breast density.

PubMed

Yaghjyan, Lusine; Colditz, Graham; Eliassen, Heather; Rosner, Bernard; Gasparova, Aleksandra; Tamimi, Rulla M

2018-06-25

We investigated the association of alcohol intake with mammographic breast density in postmenopausal women by their hormone therapy (HT) status. This study included 2,100 cancer-free postmenopausal women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root transformed. Alcohol consumption was assessed with a food frequency questionnaire (0, < 5, and ≥ 5 g/day). Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to examine associations between alcohol and breast density measures in women with no HT history, current, and past HT users. In multivariable analyses, we found no associations of alcohol consumption with PD (p trend = 0.32) and DA (p trend = 0.53) and an inverse association with NDA (β = - 0.41, 95% CI - 0.73, - 0.09 for ≥ 5 g/day, p trend < 0.01). In the stratified analysis by HT status, alcohol was not associated with PD in any of the strata. We found a significant inverse association of alcohol with NDA among past HT users (β = - 0.79, 95% CI - 1.51, - 0.07 for ≥ 5 g/day, p trend = 0.02). There were no significant interactions between alcohol and HT in relation to PD, DA, and NDA (p interaction = 0.19, 0.42, and 0.43, respectively). Our findings suggest that associations of alcohol with breast density do not vary by HT status.

Increased COX-2 expression in epithelial and stromal cells of high mammographic density tissues and in a xenograft model of mammographic density.

PubMed

Chew, G L; Huo, C W; Huang, D; Hill, P; Cawson, J; Frazer, H; Hopper, J L; Haviv, I; Henderson, M A; Britt, K; Thompson, E W

2015-08-01

Mammographic density (MD) adjusted for age and body mass index is one of the strongest known risk factors for breast cancer. Given the high attributable risk of MD for breast cancer, chemoprevention with a safe and available agent that reduces MD and breast cancer risk would be beneficial. Cox-2 has been implicated in MD-related breast cancer risk, and was increased in stromal cells in high MD tissues in one study. Our study assessed differential Cox-2 expression in epithelial and stromal cells in paired samples of high and low MD human breast tissue, and in a validated xenograft biochamber model of MD. We also examined the effects of endocrine treatment upon Cox-2 expression in high and low MD tissues in the MD xenograft model. Paired high and low MD human breast tissue samples were immunostained for Cox-2, then assessed for differential expression and staining intensity in epithelial and stromal cells. High and low MD human breast tissues were separately maintained in biochambers in mice treated with Tamoxifen, oestrogen or placebo implants, then assessed for percentage Cox-2 staining in epithelial and stromal cells. Percentage Cox-2 staining was greater for both epithelial (p = 0.01) and stromal cells (p < 0.0001) of high compared with low MD breast tissues. In high MD biochamber tissues, percentage Cox-2 staining was greater in stromal cells of oestrogen-treated versus placebo-treated tissues (p = 0.05).

Investigation of the accuracy of breast tissue segmentation methods for the purpose of developing breast deformation models for use in adaptive radiotherapy

NASA Astrophysics Data System (ADS)

Juneja, P.; Harris, E. J.; Evans, P. M.

2014-03-01

Realistic modelling of breast deformation requires the breast tissue to be segmented into fibroglandular and fatty tissue and assigned suitable material properties. There are a number of breast tissue segmentation methods proposed and used in the literature. The purpose of this study was to validate and compare the accuracy of various segmentation methods and to investigate the effect of the tissue distribution on the segmentation accuracy. Computed tomography (CT) data for 24 patients, both in supine and prone positions were segmented into fibroglandular and fatty tissue. The segmentation methods explored were: physical density thresholding; interactive thresholding; fuzzy c-means clustering (FCM) with three classes (FCM3) and four classes (FCM4); and k-means clustering. Validation was done in two-stages: firstly, a new approach, supine-prone validation based on the assumption that the breast composition should appear the same in the supine and prone scans was used. Secondly, outlines from three experts were used for validation. This study found that FCM3 gave the most accurate segmentation of breast tissue from CT data and that the segmentation accuracy is adversely affected by the sparseness of the fibroglandular tissue distribution.

A novel and reliable computational intelligence system for breast cancer detection.

PubMed

Zadeh Shirazi, Amin; Seyyed Mahdavi Chabok, Seyyed Javad; Mohammadi, Zahra

2018-05-01

Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-organizing map (SOM) and complex-valued neural network (CVNN), for reliable detection of breast cancer. The dataset used in this paper consists of 822 patients with five features (patient's breast mass shape, margin, density, patient's age, and Breast Imaging Reporting and Data System assessment). The proposed model was used for the first time and can be categorized in two stages. In the first stage, considering the input features, SOM technique was used to cluster the patients with the most similarity. Then, in the second stage, for each cluster, the patient's features were applied to complex-valued neural network and dealt with to classify breast cancer severity (benign or malign). The obtained results corresponding to each patient were compared to the medical diagnosis results using receiver operating characteristic analyses and confusion matrix. In the testing phase, health and disease detection ratios were 94 and 95%, respectively. Accordingly, the superiority of the proposed model was proved and can be used for reliable and robust detection of breast cancer.

Quantitative evaluation of redox ratio and collagen characteristics during breast cancer chemotherapy using two-photon intrinsic imaging.

PubMed

Wu, Shulian; Huang, Yudian; Tang, Qinggong; Li, Zhifang; Horng, Hannah; Li, Jiatian; Wu, Zaihua; Chen, Yu; Li, Hui

2018-03-01

Preoperative neoadjuvant treatment in locally advanced breast cancer is recognized as an effective adjuvant therapy, as it improves treatment outcomes. However, the potential complications remain a threat, so there is an urgent clinical need to assess both the tumor response and changes in its microenvironment using non-invasive and precise identification techniques. Here, two-photon microscopy was employed to detect morphological alterations in breast cancer progression and recession throughout chemotherapy. The changes in structure were analyzed based on the autofluorescence and collagen of differing statuses. Parameters, including optical redox ratio, the ratio of second harmonic generation and auto-fluorescence signal, collagen density, and collagen shape orientation, were studied. Results indicate that these parameters are potential indicators for evaluating breast tumors and their microenvironment changes during progression and chemotherapy. Combined analyses of these parameters could provide a quantitative, novel method for monitoring tumor therapy.

The effect of weight change on changes in breast density measures over menopause in a breast cancer screening cohort.

PubMed

Wanders, Johanna Olga Pauline; Bakker, Marije Fokje; Veldhuis, Wouter Bernard; Peeters, Petra Huberdina Maria; van Gils, Carla Henrica

2015-05-30

High weight and high percentage mammographic breast density are both breast cancer risk factors but are negatively correlated. Therefore, we wanted to obtain more insight into this apparent paradox. We investigated in a longitudinal study how weight change over menopause is related to changes in mammographic breast features. Five hundred ninety-one participants of the EPIC-NL cohort were divided into three groups according to their prospectively measured weight change over menopause: (1) weight loss (more than -3.0 %), (2) stable weight (between -3.0 % and +3.0 %), and (3) weight gain (more than 3.0 %). SPSS GLM univariate analysis was used to determine both the mean breast measure changes in, and the trend over, the weight change groups. Over a median period of 5 years, the mean changes in percent density in these groups were -5.0 % (95 % confidence interval (CI) -8.0; -2.1), -6.8 % (95 % CI -9.0; -4.5), and -10.2 % (95 % CI -12.5; -7.9), respectively (P-trend = 0.001). The mean changes in dense area were -16.7 cm(2) (95 % CI -20.1; -13.4), -16.4 cm(2) (95 % CI -18.9; -13.9), and -18.1 cm(2) (95 % CI -20.6; -15.5), respectively (P-trend = 0.437). Finally, the mean changes in nondense area were -6.1 cm(2) (95 % CI -11.9; -0.4), -0.6 cm(2) (95 % CI -4.9; 3.8), and 5.3 cm(2) (95 % CI 0.9; 9.8), respectively (P-trend < 0.001). Going through menopause is associated with a decrease in both percent density and dense area. Owing to an increase in the nondense tissue, the decrease in percent density is largest in women who gain weight. The decrease in dense area is not related to weight change. So the fact that both high percent density and high weight or weight gain are associated with high postmenopausal breast cancer risk can probably not be explained by an increase (or slower decrease) of dense area in women gaining weight compared with women losing weight or maintaining a stable weight. These results suggest that weight and dense area are presumably two independent postmenopausal breast cancer risk factors.

Breast imaging with the SoftVue imaging system: first results

NASA Astrophysics Data System (ADS)

Duric, Neb; Littrup, Peter; Schmidt, Steven; Li, Cuiping; Roy, Olivier; Bey-Knight, Lisa; Janer, Roman; Kunz, Dave; Chen, Xiaoyang; Goll, Jeffrey; Wallen, Andrea; Zafar, Fouzaan; Allada, Veerendra; West, Erik; Jovanovic, Ivana; Li, Kuo; Greenway, William

2013-03-01

For women with dense breast tissue, who are at much higher risk for developing breast cancer, the performance of mammography is at its worst. Consequently, many early cancers go undetected when they are the most treatable. Improved cancer detection for women with dense breasts would decrease the proportion of breast cancers diagnosed at later stages, which would significantly lower the mortality rate. The emergence of whole breast ultrasound provides good performance for women with dense breast tissue, and may eliminate the current trade-off between the cost effectiveness of mammography and the imaging performance of more expensive systems such as magnetic resonance imaging. We report on the performance of SoftVue, a whole breast ultrasound imaging system, based on the principles of ultrasound tomography. SoftVue was developed by Delphinus Medical Technologies and builds on an early prototype developed at the Karmanos Cancer Institute. We present results from preliminary testing of the SoftVue system, performed both in the lab and in the clinic. These tests aimed to validate the expected improvements in image performance. Initial qualitative analyses showed major improvements in image quality, thereby validating the new imaging system design. Specifically, SoftVue's imaging performance was consistent across all breast density categories and had much better resolution and contrast. The implications of these results for clinical breast imaging are discussed and future work is described.

Quantification of mammographic masking risk with volumetric breast density maps: how to select women for supplemental screening

NASA Astrophysics Data System (ADS)

Holland, Katharina; van Gils, Carla H.; Wanders, Johanna OP; Mann, Ritse M.; Karssemeijer, Nico

2016-03-01

The sensitivity of mammograms is low for women with dense breasts, since cancers may be masked by dense tissue. In this study, we investigated methods to identify women with density patterns associated with a high masking risk. Risk measures are derived from volumetric breast density maps. We used the last negative screening mammograms of 93 women who subsequently presented with an interval cancer (IC), and, as controls, 930 randomly selected normal screening exams from women without cancer. Volumetric breast density maps were computed from the mammograms, which provide the dense tissue thickness at each location. These were used to compute absolute and percentage glandular tissue volume. We modeled the masking risk for each pixel location using the absolute and percentage dense tissue thickness and we investigated the effect of taking the cancer location probability distribution (CLPD) into account. For each method, we selected cases with the highest masking measure (by thresholding) and computed the fraction of ICs as a function of the fraction of controls selected. The latter can be interpreted as the negative supplemental screening rate (NSSR). Between the models, when incorporating CLPD, no significant differences were found. In general, the methods performed better when CLPD was included. At higher NSSRs some of the investigated masking measures had a significantly higher performance than volumetric breast density. These measures may therefore serve as an alternative to identify women with a high risk for a masked cancer.

Long-term results of conservative surgery and radiotherapy for ductal carcinoma in situ using lung density correction: the University of Michigan experience.

PubMed

Ben-David, Merav A; Sturtz, David E; Griffith, Kent A; Douglas, Kathye R; Hayman, James A; Lichter, Allen S; Pierce, Lori J

2007-01-01

The purpose of the study was to review the treatment outcomes of 198 patients treated with breast-conserving surgery (BCS) and whole breast radiation therapy using lung density correction for ductal carcinoma in situ (DCIS). Between April 1985 and December 2002, 198 patients with 200 lesions diagnosed as DCIS (AJCC stage 0) were treated at the University of Michigan. All underwent BCS and whole breast radiotherapy. Median total follow-up was 6.2 years (range: 0.8-18.2). The 5- and 10-year cumulative rates of in-breast only failure were 5.9% (95% CI: 2.6-9.3%) and 9.8% (95% CI: 5.2-14.4%), respectively. Factors that significantly predicted for an increased risk of local failure were family history of breast cancer, positive or close surgical margins and age

The effect of preoperative serum triglycerides and high-density lipoprotein-cholesterol levels on the prognosis of breast cancer.

PubMed

Li, Xing; Tang, Hailin; Wang, Jin; Xie, Xinhua; Liu, Peng; Kong, Yanan; Ye, Feng; Shuang, Zeyu; Xie, Zeming; Xie, Xiaoming

2017-04-01

Although dyslipidemia has been documented to be associated with several types of cancer including breast cancer, it remains uncertainty the prognostic value of serum lipid in breast cancer. The purpose of this study is to evaluate the association between the preoperative plasma lipid profile and the prognostic of breast cancer patients. The levels of preoperative serum lipid profile (including cholesterol [CHO], Triglycerides [TG], high-density lipoprotein-cholesterol [HDL-C], low-density lipoprotein-cholesterol [LDL-C], apolipoprotein A-I [ApoAI], and apolipoprotein B [ApoB]) and the clinical data were retrospectively collected and reviewed in 1044 breast cancer patients undergoing operation. Kaplan-Meier method and the Cox proportional hazards regression model were used in analyzing the overall survival [OS] and disease-free survival [DFS]. Combining the receiver-operating characteristic and Kaplan-Meier analysis, we found that preoperative lower TG and HDL-C level were risk factors of breast cancer patients. In multivariate analyses, a decreased HDL-C level showed significant association with worse OS (HR: 0.528; 95% CI: 0.302-0.923; P = 0.025), whereas a decreased TG level showed significant association with worse DFS (HR: 0.569; 95% CI: 0.370-0.873; P = 0.010). Preoperative serum levels of TG and HDL-C may be independent factor to predict outcome in breast cancer patient. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Mammographic density changes following discontinuation of tamoxifen in premenopausal women with oestrogen receptor-positive breast cancer.

PubMed

Kim, Won Hwa; Cho, Nariya; Kim, Young-Seon; Yi, Ann

2018-04-06

To evaluate the changes in mammographic density after tamoxifen discontinuation in premenopausal women with oestrogen receptor-positive breast cancers and the underlying factors METHODS: A total of 213 consecutive premenopausal women with breast cancer who received tamoxifen treatment after curative surgery and underwent three mammograms (baseline, after tamoxifen treatment, after tamoxifen discontinuation) were included. Changes in mammographic density after tamoxifen discontinuation were assessed qualitatively (decrease, no change, or increase) by two readers and measured quantitatively by semi-automated software. The association between % density change and clinicopathological factors was evaluated using univariate and multivariate regression analyses. After tamoxifen discontinuation, a mammographic density increase was observed in 31.9% (68/213, reader 1) to 22.1% (47/213, reader 2) by qualitative assessment, with a mean density increase of 1.8% by quantitative assessment compared to density before tamoxifen discontinuation. In multivariate analysis, younger age (≤ 39 years) and greater % density decline after tamoxifen treatment (≥ 17.0%) were independent factors associated with density change after tamoxifen discontinuation (p < .001 and p = .003, respectively). Tamoxifen discontinuation was associated with mammographic density change with a mean density increase of 1.8%, which was associated with younger age and greater density change after tamoxifen treatment. • Increased mammographic density after tamoxifen discontinuation can occur in premenopausal women. • Mean density increase after tamoxifen discontinuation was 1.8%. • Density increase is associated with age and density decrease after tamoxifen.

Mammographic density is the main correlate of tumors detected on ultrasound but not on mammography.

PubMed

Häberle, Lothar; Fasching, Peter A; Brehm, Barbara; Heusinger, Katharina; Jud, Sebastian M; Loehberg, Christian R; Hack, Carolin C; Preuss, Caroline; Lux, Michael P; Hartmann, Arndt; Vachon, Celine M; Meier-Meitinger, Martina; Uder, Michael; Beckmann, Matthias W; Schulz-Wendtland, Rüdiger

2016-11-01

Although mammography screening programs do not include ultrasound examinations, some diagnostic units do provide women with both mammography and ultrasonography. This article is concerned with estimating the risk of a breast cancer patient diagnosed in a hospital-based mammography unit having a tumor that is visible on ultrasound but not on mammography. A total of 1,399 women with invasive breast cancer from a hospital-based diagnostic mammography unit were included in this retrospective study. For inclusion, mammograms from the time of the primary diagnosis had to be available for computer-assisted assessment of percentage mammographic density (PMD), as well as Breast Imaging Reporting and Data System (BIRADS) assessment of mammography. In addition, ultrasound findings were available for the complete cohort as part of routine diagnostic procedures, regardless of any patient or imaging characteristics. Logistic regression analyses were conducted to identify predictors of mammography failure, defined as BIRADS assessment 1 or 2. The probability that the visibility of a tumor might be masked at diagnosis was estimated using a regression model with the identified predictors. Tumors were only visible on ultrasound in 107 cases (7.6%). PMD was the strongest predictor for mammography failure, but age, body mass index and previous breast surgery also influenced the risk, independently of the PMD. Risk probabilities ranged from 1% for a defined low-risk group up to 40% for a high-risk group. These findings might help identify women who should be offered ultrasound examinations in addition to mammography. © 2016 UICC.

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.

Use of Autoantibodies to Detect the Onset of Breast Cancer

PubMed Central

Mangé, Alain; Solassol, Jérôme

2014-01-01

The widespread use of screening mammography has resulted in increased detection of early-stage breast disease, particularly for in situ carcinoma and early-stage breast cancer. However, the majority of women with abnormalities noted on screening mammograms are not diagnosed with cancer because of several factors, including radiologist assessment, patient age, breast density, malpractice concerns, and quality control procedures. Although magnetic resonance imaging is a highly sensitive detection tool that has become standard for women at very high risk of developing breast cancer, it lacks sufficient specificity and costeffectiveness for use as a general screening tool. Therefore, there is an important need to improve screening and diagnosis of early-invasive and noninvasive tumors, that is, in situ carcinoma. The great potential for molecular tools to improve breast cancer outcomes based on early diagnosis has driven the search for diagnostic biomarkers. Identification of tumor-specific markers capable of eliciting an immune response in the early stages of tumor development seems to provide an effective approach for early diagnosis. The aim of this review is to describe several autoantibodies identified during breast cancer diagnosis. We will focus on these molecules highlighted in the past two years and discuss the potential future use of autoantibodies as biomarkers of early-stage breast cancer. PMID:25143958

Energy intake and dietary patterns in childhood and throughout adulthood and mammographic density: results from a British prospective cohort.

PubMed

Mishra, Gita D; dos Santos Silva, Isabel; McNaughton, Sarah A; Stephen, Alison; Kuh, Diana

2011-02-01

To examine the role of energy intake and dietary patterns in childhood and throughout adulthood on subsequent mammographic density. Prospective data were available from a cohort of 1161 British women followed up since their birth in 1946. Dietary intakes at age 4 years were determined by 24-hour recalls and during adulthood, average food consumed at ages 36 and 43 years by 5-day food records. Dietary patterns were determined by factor analysis. Associations between energy intake, dietary patterns, and percent breast density were investigated using regression analysis. During adulthood, energy intake was positively associated with percent breast density (adjusted regression coefficient [per SD) (95% CI): 0.12 (0.01, 0.23)]. The effect of the high fat and sugar dietary pattern remained similar when adjusted for total energy intake [0.06 (-0.01, 0.13)]. There was no evidence of an associations for the patterns low fat, high fiber pattern 0.03 (-0.04, 0.11); the alcohol and fish -0.02 (-0.13, 0.17); meat, potatoes, and vegetables -0.03 (-0.10, 0.04). No association was found for dietary pattern at age 4 and percent breast density. This study supports the hypothesis that overall energy intake during middle life is a determinant of subsequent mammographic breast density measured 15 years later.

Maternal Anthropometry and Mammographic Density in Adult Daughters.

PubMed

Michels, Karin B; Cohn, Barbara A; Goldberg, Mandy; Flom, Julie D; Dougan, Marcelle; Terry, Mary Beth

2016-11-01

We examined the relation between maternal anthropometry and mammographic density in the adult daughter using prospectively collected data. Our study included a total of 700 mother-daughter dyads participating in an adult follow-up of women born in 2 US birth cohorts: the Child Health and Development Study and the Boston, Massachusetts, and Providence, Rhode Island sites of the National Collaborative Perinatal Project. We observed an increased percent breast density at a mean age of 43.1 years in the daughters of mothers who gained 5 kg or less during pregnancy compared with mother-daughter pairs in which the mother gained 5 to 10 kg (β = 4.8, 95% confidence interval: 1.0 to 8.6). The daughters of mothers who were overweight at the time of conception (prepregnancy BMI ≥25) and who gained >5 kg during pregnancy had a lower percent density (β = -3.2, 95% confidence interval: -6.2 to -0.2) compared with mothers with a BMI <25 at conception who gained >5 kg. We did not find any strong and consistent patterns between maternal anthropometry and the daughter's breast density, a strong predictor of breast cancer risk. A modest association between low gestational weight gain and increased breast density 40 years later in the daughter was observed, even after accounting for adult body size, and if confirmed, possible mechanisms need to be further elucidated. Copyright © 2016 by the American Academy of Pediatrics.

Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide

PubMed Central

Maskarinec, Gertraud; Perez-Gomez, Beatriz; Vachon, Celine; Miao, Hui; Lajous, Martín; López-Ridaura, Ruy; Rice, Megan; Pereira, Ana; Garmendia, Maria Luisa; Tamimi, Rulla M.; Bertrand, Kimberly; Kwong, Ava; Ursin, Giske; Lee, Eunjung; Qureshi, Samera A.; Ma, Huiyan; Moss, Sue; Allen, Steve; Ndumia, Rose; Vinayak, Sudhir; Teo, Soo-Hwang; Mariapun, Shivaani; Fadzli, Farhana; Bukowska, Agnieszka; Nagata, Chisato; Stone, Jennifer; Ozmen, Vahit; Aribal, Mustafa Erkin; Schüz, Joachim; Wanders, Johanna O. P.; Sirous, Reza; Sirous, Mehri; Kim, Jisun; Lee, Jong Won; Dickens, Caroline; Hartman, Mikael; Chia, Kee-Seng; Chiarelli, Anna M.; Linton, Linda; Pollan, Marina; Flugelman, Anath Arzee; Salem, Dorria; Kamal, Rasha; Boyd, Norman; dos-Santos-Silva, Isabel; McCormack, Valerie

2017-01-01

Background Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known. Methods and findings We examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35–85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (–0.46 cm [95% CI: −0.53, −0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I2) was 16.5%. Among premenopausal women, the √PD difference per 10-year increase in age was −0.24 cm (95% CI: −0.34, −0.14; I2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in √PD (−0.38 cm [95% CI: −0.44, −0.33]; I2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature. Conclusions Declines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction. PMID:28666001

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study.

PubMed

Hruska, Carrie B; Geske, Jennifer R; Swanson, Tiffinee N; Mammel, Alyssa N; Lake, David S; Manduca, Armando; Conners, Amy Lynn; Whaley, Dana H; Scott, Christopher G; Carter, Rickey E; Rhodes, Deborah J; O'Connor, Michael K; Vachon, Celine M

2018-06-05

Background parenchymal uptake (BPU), which refers to the level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor, independent of mammographic density. Prior analyses have used subjective categories to describe BPU. We evaluate a new quantitative method for assessing BPU by testing its reproducibility, comparing quantitative results with previously established subjective BPU categories, and determining the association of quantitative BPU with breast cancer risk. Two nonradiologist operators independently performed region-of-interest analysis on MBI images viewed in conjunction with corresponding digital mammograms. Quantitative BPU was defined as a unitless ratio of the average pixel intensity (counts/pixel) within the fibroglandular tissue versus the average pixel intensity in fat. Operator agreement and the correlation of quantitative BPU measures with subjective BPU categories assessed by expert radiologists were determined. Percent density on mammograms was estimated using Cumulus. The association of quantitative BPU with breast cancer (per one unit BPU) was examined within an established case-control study of 62 incident breast cancer cases and 177 matched controls. Quantitative BPU ranged from 0.4 to 3.2 across all subjects and was on average higher in cases compared to controls (1.4 versus 1.2, p < 0.007 for both operators). Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p < 0.0001, for each paired combination of two operators and two radiologists). Interoperator and intraoperator agreement in the quantitative BPU measure, assessed by intraclass correlation, was 0.92 and 0.98, respectively. Quantitative BPU measures showed either no correlation or weak negative correlation with mammographic percent density. In a model adjusted for body mass index and percent density, higher quantitative BPU was associated with increased risk of breast cancer for both operators (OR = 4.0, 95% confidence interval (CI) 1.6-10.1, and 2.4, 95% CI 1.2-4.7). Quantitative measurement of BPU, defined as the ratio of average counts in fibroglandular tissue relative to that in fat, can be reliably performed by nonradiologist operators with a simple region-of-interest analysis tool. Similar to results obtained with subjective BPU categories, quantitative BPU is a functional imaging biomarker of breast cancer risk, independent of mammographic density and hormonal factors.

Building a Better Model: A Personalized Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Improve Application of Resources

DTIC Science & Technology

2014-10-01

density using automated methods will be optimized during this study through the evaluation of outlier correction, comparison of several different...7 VBD comparison y = 1.3477x - 1.3764 R2 = 0.8213 0 10 20 30 40 50 60 70 0 10 20 30 40 50 VBD volpara [%] VB D cu m ul us [% ] VBD cumulus...Access database and chart review. 5c. Conduct chart review for selected cases (month 4-6). Comparison of information from the Breast Cancer

Association of infertility and fertility treatment with mammographic density in a large screening-based cohort of women: a cross-sectional study.

PubMed

Lundberg, Frida E; Johansson, Anna L V; Rodriguez-Wallberg, Kenny; Brand, Judith S; Czene, Kamila; Hall, Per; Iliadou, Anastasia N

2016-04-13

Ovarian stimulation drugs, in particular hormonal agents used for controlled ovarian stimulation (COS) required to perform in vitro fertilization, increase estrogen and progesterone levels and have therefore been suspected to influence breast cancer risk. This study aims to investigate whether infertility and hormonal fertility treatment influences mammographic density, a strong hormone-responsive risk factor for breast cancer. Cross-sectional study including 43,313 women recruited to the Karolinska Mammography Project between 2010 and 2013. Among women who reported having had infertility, 1576 had gone through COS, 1429 had had hormonal stimulation without COS and 5958 had not received any hormonal fertility treatment. Percent and absolute mammographic densities were obtained using the volumetric method Volpara™. Associations with mammographic density were assessed using multivariable generalized linear models, estimating mean differences (MD) with 95 % confidence intervals (CI). After multivariable adjustment, women with a history of infertility had 1.53 cm(3) higher absolute dense volume compared to non-infertile women (95 % CI: 0.70 to 2.35). Among infertile women, only those who had gone through COS treatment had a higher absolute dense volume than those who had not received any hormone treatment (adjusted MD 3.22, 95 % CI: 1.10 to 5.33). No clear associations were observed between infertility, fertility treatment and percent volumetric density. Overall, women reporting infertility had more dense tissue in the breast. The higher absolute dense volume in women treated with COS may indicate a treatment effect, although part of the association might also be due to the underlying infertility. Continued monitoring of cancer risk in infertile women, especially those who undergo COS, is warranted.

SU-F-I-14: 3D Breast Digital Phantom for XACT Imaging

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

Tang, S; Laaroussi, R; Chen, J

Purpose: The X-ray induced acoustic computed tomography (XACT) is a new imaging modality which combines X-ray contrast and high ultrasonic resolution in a single modality. Using XACT in breast imaging, a 3D breast volume can be imaged by only one pulsed X-ray radiation, which could dramatically reduce the imaging dose for patients undergoing breast cancer screening and diagnosis. A 3D digital phantom that contains both X-ray properties and acoustic properties of different tissue types is indeed needed for developing and optimizing the XACT system. The purpose of this study is to offer a realistic breast digital phantom as a valuablemore » tool for improving breast XACT imaging techniques and potentially leading to better diagnostic outcomes. Methods: A series of breast CT images along the coronal plane from a patient who has breast calcifications are used as the source images. A HU value based segmentation algorithm is employed to identify breast tissues in five categories, namely the skin tissue, fat tissue, glandular tissue, chest bone and calcifications. For each pixel, the dose related parameters, such as material components and density, and acoustic related parameters, such as frequency-dependent acoustic attenuation coefficient and bandwidth, are assigned based on tissue types. Meanwhile, other parameters which are used in sound propagation, including the sound speed, thermal expansion coefficient, and heat capacity are also assigned to each tissue. Results: A series of 2D tissue type image is acquired first and the 3D digital breast phantom is obtained by using commercial 3D reconstruction software. When giving specific settings including dose depositions and ultrasound center frequency, the X-ray induced initial pressure rise can be calculated accordingly. Conclusion: The proposed 3D breast digital phantom represents a realistic breast anatomic structure and provides a valuable tool for developing and evaluating the system performance for XACT.« less

The effect of change in body mass index on volumetric measures of mammographic density

PubMed Central

Hart, Vicki; Reeves, Katherine W.; Sturgeon, Susan R.; Reich, Nicholas G.; Sievert, Lynnette Leidy; Kerlikowske, Karla; Ma, Lin; Shepherd, John; Tice, Jeffrey A.; Mahmoudzadeh, Amir Pasha; Malkov, Serghei; Sprague, Brian L.

2015-01-01

Background Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear. Methods We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007-2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV) and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history. Results In cross-sectional analysis, BMI was positively associated with DV (β=2.95 cm3, 95% CI 2.69, 3.21) and inversely associated with PDV (β=-2.03%, 95% CI -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β=-1.01 cm3, 95% CI -1.59, -0.42) and PDV (β=-1.17%, 95% CI -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women. Conclusion Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation. Impact Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI. PMID:26315554

Breast segmentation in MR images using three-dimensional spiral scanning and dynamic programming

NASA Astrophysics Data System (ADS)

Jiang, Luan; Lian, Yanyun; Gu, Yajia; Li, Qiang

2013-03-01

Magnetic resonance (MR) imaging has been widely used for risk assessment and diagnosis of breast cancer in clinic. To develop a computer-aided diagnosis (CAD) system, breast segmentation is the first important and challenging task. The accuracy of subsequent quantitative measurement of breast density and abnormalities depends on accurate definition of the breast area in the images. The purpose of this study is to develop and evaluate a fully automated method for accurate segmentation of breast in three-dimensional (3-D) MR images. A fast method was developed to identify bounding box, i.e., the volume of interest (VOI), for breasts. A 3-D spiral scanning method was used to transform the VOI of each breast into a single two-dimensional (2-D) generalized polar-coordinate image. Dynamic programming technique was applied to the transformed 2-D image for delineating the "optimal" contour of the breast. The contour of the breast in the transformed 2-D image was utilized to reconstruct the segmentation results in the 3-D MR images using interpolation and lookup table. The preliminary results on 17 cases show that the proposed method can obtain accurate segmentation of the breast based on subjective observation. By comparing with the manually delineated region of 16 breasts in 8 cases, an overlap index of 87.6% +/- 3.8% (mean +/- SD), and a volume agreement of 93.4% +/- 4.5% (mean +/- SD) were achieved, respectively. It took approximately 3 minutes for our method to segment the breast in an MR scan of 256 slices.

Collagen Matrix Density Drives the Metabolic Shift in Breast Cancer Cells.

PubMed

Morris, Brett A; Burkel, Brian; Ponik, Suzanne M; Fan, Jing; Condeelis, John S; Aguirre-Ghiso, Julio A; Castracane, James; Denu, John M; Keely, Patricia J

2016-11-01

Increased breast density attributed to collagen I deposition is associated with a 4-6 fold increased risk of developing breast cancer. Here, we assessed cellular metabolic reprogramming of mammary carcinoma cells in response to increased collagen matrix density using an in vitro 3D model. Our initial observations demonstrated changes in functional metabolism in both normal mammary epithelial cells and mammary carcinoma cells in response to changes in matrix density. Further, mammary carcinoma cells grown in high density collagen matrices displayed decreased oxygen consumption and glucose metabolism via the tricarboxylic acid (TCA) cycle compared to cells cultured in low density matrices. Despite decreased glucose entry into the TCA cycle, levels of glucose uptake, cell viability, and ROS were not different between high and low density matrices. Interestingly, under high density conditions the contribution of glutamine as a fuel source to drive the TCA cycle was significantly enhanced. These alterations in functional metabolism mirrored significant changes in the expression of metabolic genes involved in glycolysis, oxidative phosphorylation, and the serine synthesis pathway. This study highlights the broad importance of the collagen microenvironment to cellular expression profiles, and shows that changes in density of the collagen microenvironment can modulate metabolic shifts of cancer cells. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Habitat use of woodpeckers in the Big Woods of eastern Arkansas

USGS Publications Warehouse

Krementz, David G.; Lehnen, Sarah E.; Luscier, J.D.

2012-01-01

The Big Woods of eastern Arkansas contain some of the highest densities of woodpeckers recorded within bottomland hardwood forests of the southeastern United States. A better understanding of habitat use patterns by these woodpeckers is a priority for conservationists seeking to maintain these high densities in the Big Woods and the Lower Mississippi Alluvial Valley as a whole. Hence, we used linear mixed-effects and linear models to estimate the importance of habitat characteristics to woodpecker density in the Big Woods during the breeding seasons of 2006 and 2007 and the winter of 2007. Northern flicker Colaptes auratus density was negatively related to tree density both for moderate (. 25 cm diameter at breast height) and larger trees (>61 cm diameter at breast height). Red-headed woodpeckers Melanerpes erythrocephalus also had a negative relationship with density of large (. 61 cm diameter at breast height) trees. Bark disfiguration (an index of tree health) was negatively related to red-bellied woodpecker Melanerpes carolinus and yellow-bellied sapsucker Sphyrapicus varius densities. No measured habitat variables explained pileated woodpecker Dryocopus pileatus density. Overall, the high densities of woodpeckers observed in our study suggest that the current forest management of the Big Woods of Arkansas is meeting the nesting, roosting, and foraging requirements for these birds.

Building a Better Model: A Personalized Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Improve Application of Resources

DTIC Science & Technology

2013-10-01

MAMMOGRAM 5 OTHER BREAST IMAGING TEST (E.G., MRI , ULTRASOUND) 6 THEMOGRAPHY 7 NOTHING 8 DK/REF PROBE: ANYTHING ELSE? IF NECESSARY: JUST THE...SKIP IF HOWCHECK = 5] Have you ever had any other breast imaging procedure designed to detect breast cancer (for example, an MRI or ultrasound? 1 YES...SELECT ALL THAT APPLY) 1 Another mammogram 2 Ultrasound of the breast 3 MRI of the breast 4 OTHER [specify:] 31 5 NONE 6 DK/REF {Q: ADDSURG

Estrogens and women's health: interrelation of coronary heart disease, breast cancer and osteoporosis.

PubMed

Kuller, L H; Matthews, K A; Meilahn, E N

2000-11-30

The determinants of blood levels of estrogen, estrogen metabolites, and relation to receptors and post-transitional effects are the likely primary cause of breast cancer. Very high risk women for breast cancer can now be identified by measuring bone mineral density and hormone levels. These high risk women have rates of breast cancer similar to risk of myocardial infarction. They are candidates for SERM therapies to reduce risk of breast cancer. The completion of the Women's Health Initiative and other such trials will likely provide a definite association of risk and benefit of both estrogen alone and estrogen-progesterone therapy, coronary heart disease, osteoporotic fracture, and breast cancer. The potential intervention of hormone replacement therapy, obesity, or weight gain and increased atherogenic lipoproteinemia may be of concern and confound the results of clinical trials. Estrogens, clearly, are important in the risk of bone loss and osteoporotic fracture. Obesity is the primary determinant of postmenopausal estrogen levels and reduced risk of fracture. Weight reduction may increase rates of bone loss and fracture. Clinical trials that evaluate weight loss should monitor effects on bone. The beneficial addition of increased physical activity, higher dose of calcium or vitamin D, or use of bone reabsorption drugs in coordination with weight loss should be evaluated. Any therapy that raises blood estrogen or metabolite activity and decreases bone loss may increase risk of breast cancer. Future clinical trials must evaluate multiple endpoints such as CHD, osteoporosis, and breast cancer within the study. The use of surrogate markers such as bone mineral density, coronary calcium, carotid intimal medial thickness and plaque, endothelial function, breast density, hormone levels and metabolites could enhance the evaluation of risk factors, genetic-environmental intervention, and new therapies.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

PubMed

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-10-01

To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%. A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

PubMed Central

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-01-01

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors’ classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors’ automatic classification and manual segmentation were 91.6% ± 2.0%. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. PMID:23039675

Mammographic compression in Asian women.

PubMed

Lau, Susie; Abdul Aziz, Yang Faridah; Ng, Kwan Hoong

2017-01-01

To investigate: (1) the variability of mammographic compression parameters amongst Asian women; and (2) the effects of reducing compression force on image quality and mean glandular dose (MGD) in Asian women based on phantom study. We retrospectively collected 15818 raw digital mammograms from 3772 Asian women aged 35-80 years who underwent screening or diagnostic mammography between Jan 2012 and Dec 2014 at our center. The mammograms were processed using a volumetric breast density (VBD) measurement software (Volpara) to assess compression force, compression pressure, compressed breast thickness (CBT), breast volume, VBD and MGD against breast contact area. The effects of reducing compression force on image quality and MGD were also evaluated based on measurement obtained from 105 Asian women, as well as using the RMI156 Mammographic Accreditation Phantom and polymethyl methacrylate (PMMA) slabs. Compression force, compression pressure, CBT, breast volume, VBD and MGD correlated significantly with breast contact area (p<0.0001). Compression parameters including compression force, compression pressure, CBT and breast contact area were widely variable between [relative standard deviation (RSD)≥21.0%] and within (p<0.0001) Asian women. The median compression force should be about 8.1 daN compared to the current 12.0 daN. Decreasing compression force from 12.0 daN to 9.0 daN increased CBT by 3.3±1.4 mm, MGD by 6.2-11.0%, and caused no significant effects on image quality (p>0.05). Force-standardized protocol led to widely variable compression parameters in Asian women. Based on phantom study, it is feasible to reduce compression force up to 32.5% with minimal effects on image quality and MGD.

Design of a sensitive grating-based phase contrast mammography prototype (Conference Presentation)

NASA Astrophysics Data System (ADS)

Arboleda Clavijo, Carolina; Wang, Zhentian; Köhler, Thomas; van Stevendaal, Udo; Martens, Gerhard; Bartels, Matthias; Villanueva-Perez, Pablo; Roessl, Ewald; Stampanoni, Marco

2017-03-01

Grating-based phase contrast mammography can help facilitate breast cancer diagnosis, as several research works have demonstrated. To translate this technique to the clinics, it has to be adapted to cover a large field of view within a limited exposure time and with a clinically acceptable radiation dose. This indicates that a straightforward approach would be to install a grating interferometer (GI) into a commercial mammography device. We developed a wave propagation based optimization method to select the most convenient GI designs in terms of phase and dark-field sensitivities for the Philips Microdose Mammography (PMM) setup. The phase sensitivity was defined as the minimum detectable breast tissue electron density gradient, whereas the dark-field sensitivity was defined as its corresponding signal-to-noise Ratio (SNR). To be able to derive sample-dependent sensitivity metrics, a visibility reduction model for breast tissue was formulated, based on previous research works on the dark-field signal and utilizing available Ultra-Small-Angle X-ray Scattering (USAXS) data and the outcomes of measurements on formalin-fixed breast tissue specimens carried out in tube-based grating interferometers. The results of this optimization indicate the optimal scenarios for each metric are different and fundamentally depend on the noise behavior of the signals and the visibility reduction trend with respect to the system autocorrelation length. In addition, since the inter-grating distance is constrained by the space available between the breast support and the detector, the best way we have to improve sensitivity is to count on a small G2 pitch.

Feasibility of anomaly detection and characterization using trans-admittance mammography with 60 × 60 electrode array

NASA Astrophysics Data System (ADS)

Zhao, Mingkang; Wi, Hun; Lee, Eun Jung; Woo, Eung Je; In Oh, Tong

2014-10-01

Electrical impedance imaging has the potential to detect an early stage of breast cancer due to higher admittivity values compared with those of normal breast tissues. The tumor size and extent of axillary lymph node involvement are important parameters to evaluate the breast cancer survival rate. Additionally, the anomaly characterization is required to distinguish a malignant tumor from a benign tumor. In order to overcome the limitation of breast cancer detection using impedance measurement probes, we developed the high density trans-admittance mammography (TAM) system with 60 × 60 electrode array and produced trans-admittance maps obtained at several frequency pairs. We applied the anomaly detection algorithm to the high density TAM system for estimating the volume and position of breast tumor. We tested four different sizes of anomaly with three different conductivity contrasts at four different depths. From multifrequency trans-admittance maps, we can readily observe the transversal position and estimate its volume and depth. Specially, the depth estimated values were obtained accurately, which were independent to the size and conductivity contrast when applying the new formula using Laplacian of trans-admittance map. The volume estimation was dependent on the conductivity contrast between anomaly and background in the breast phantom. We characterized two testing anomalies using frequency difference trans-admittance data to eliminate the dependency of anomaly position and size. We confirmed the anomaly detection and characterization algorithm with the high density TAM system on bovine breast tissue. Both results showed the feasibility of detecting the size and position of anomaly and tissue characterization for screening the breast cancer.

Calcium and Vitamin D Supplementation and Loss of Bone Mineral Density in Women Undergoing Breast Cancer Therapy

PubMed Central

Datta, Mridul; Schwartz, Gary G.

2013-01-01

An unintended consequence of breast cancer therapies is an increased risk of osteoporosis due to accelerated bone loss. We conducted a systematic review of calcium and/or vitamin D (Ca±D) supplementation trials for maintaining bone mineral density (BMD) in women with breast cancer using the “before-after” data from the Ca±D supplemented comparison group of trials evaluating the effect of drugs such as bisphosphonates on BMD. Whether Ca±D supplements increase BMD in women undergoing breast cancer therapy has never been tested against an unsupplemented control group. However, results from 16 trials indicate that the Ca±D doses tested (500-1500 mg calcium; 200-1000 IU vitamin D) were inadequate to prevent BMD loss in these women. Cardiovascular disease is the main cause of mortality in women with breast cancer. Because calcium supplements may increase cardiovascular disease risk, future trials should evaluate the safety and efficacy of Ca±D supplementation in women undergoing breast cancer therapy. PMID:23932583

What we need to know about dense breasts: implications for breast cancer screening.

PubMed

Carreira Gómez, M C; Estrada Blan, M C

High breast density and its relationship to the risk of breast cancer has become a hot topic in the medical literature and in the lay press, especially in the United States, where it has brought about changes in the legal framework that require radiologists to inform clinicians and patients about breast density. Radiologists, who are mainly responsible for this information, need to know the scientific evidence and controversies regarding this subject. The discussion is centered on the real importance of the risk, the limitation that not having standardized methods of measurement represents, and the possible application of complementary screening techniques (ultrasound, magnetic resonance imaging, or tomosynthesis) for which clear recommendations have yet to be established. We need controlled studies that evaluate the application of these techniques in women with dense breasts, including the possibility that they can lead to overdiagnosis. Copyright © 2016 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Association between mammographic density and pregnancies relative to age and BMI: a breast cancer case-only analysis.

PubMed

Hack, Carolin C; Emons, Julius; Jud, Sebastian M; Heusinger, Katharina; Adler, Werner; Gass, Paul; Haeberle, Lothar; Heindl, Felix; Hein, Alexander; Schulz-Wendtland, Rüdiger; Uder, Michael; Hartmann, Arndt; Beckmann, Matthias W; Fasching, Peter A; Pöhls, Uwe G

2017-12-01

Percentage mammographic density (PMD) is a major risk factor for breast cancer (BC). It is strongly associated with body mass index (BMI) and age, which are themselves risk factors for breast cancer. This analysis investigated the association between the number of full-term pregnancies and PMD in different subgroups relative to age and BMI. Patients were identified in the breast cancer database of the University Breast Center for Franconia. A total of 2410 patients were identified, for whom information on parity, age, and BMI, and a mammogram from the time of first diagnosis were available for assessing PMD. Linear regression analyses were conducted to investigate the influence on PMD of the number of full-term pregnancies (FTPs), age, BMI, and interaction terms between them. As in previous studies, age, number of FTPs, and BMI were found to be associated with PMD in the expected direction. However, including the respective interaction terms improved the prediction of PMD even further. Specifically, the association between PMD and the number of FTPs differed in young patients under the age of 45 (mean decrease of 0.37 PMD units per pregnancy) from the association in older age groups (mean decrease between 2.29 and 2.39 PMD units). BMI did not alter the association between PMD and the number of FTPs. The effect of pregnancies on mammographic density does not appear to become apparent before the age of menopause. The mechanism that drives the effect of pregnancies on mammographic density appears to be counter-regulated by other influences on mammographic density in younger patients.

B-Spline Filtering for Automatic Detection of Calcification Lesions in Mammograms

NASA Astrophysics Data System (ADS)

Bueno, G.; Sánchez, S.; Ruiz, M.

2006-10-01

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Lymphocyte density determined by computational pathology validated as a predictor of response to neoadjuvant chemotherapy in breast cancer: secondary analysis of the ARTemis trial.

PubMed

Ali, H R; Dariush, A; Thomas, J; Provenzano, E; Dunn, J; Hiller, L; Vallier, A-L; Abraham, J; Piper, T; Bartlett, J M S; Cameron, D A; Hayward, L; Brenton, J D; Pharoah, P D P; Irwin, M J; Walton, N A; Earl, H M; Caldas, C

2017-08-01

We have previously shown lymphocyte density, measured using computational pathology, is associated with pathological complete response (pCR) in breast cancer. The clinical validity of this finding in independent studies, among patients receiving different chemotherapy, is unknown. The ARTemis trial randomly assigned 800 women with early stage breast cancer between May 2009 and January 2013 to three cycles of docetaxel, followed by three cycles of fluorouracil, epirubicin and cyclophosphamide once every 21 days with or without four cycles of bevacizumab. The primary endpoint was pCR (absence of invasive cancer in the breast and lymph nodes). We quantified lymphocyte density within haematoxylin and eosin (H&E) whole slide images using our previously described computational pathology approach: for every detected lymphocyte the average distance to the nearest 50 lymphocytes was calculated and the density derived from this statistic. We analyzed both pre-treatment biopsies and post-treatment surgical samples of the tumour bed. Of the 781 patients originally included in the primary endpoint analysis of the trial, 609 (78%) were included for baseline lymphocyte density analyses and a subset of 383 (49% of 781) for analyses of change in lymphocyte density. The main reason for loss of patients was the availability of digitized whole slide images. Pre-treatment lymphocyte density modelled as a continuous variable was associated with pCR on univariate analysis (odds ratio [OR], 2.92; 95% CI, 1.78-4.85; P < 0.001) and after adjustment for clinical covariates (OR, 2.13; 95% CI, 1.24-3.67; P = 0.006). Increased pre- to post-treatment lymphocyte density showed an independent inverse association with pCR (adjusted OR, 0.1; 95% CI, 0.033-0.31; P < 0.001). Lymphocyte density in pre-treatment biopsies was validated as an independent predictor of pCR in breast cancer. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients. NCT01093235. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Polymorphisms in the estrogen receptor alpha gene (ESR1), daily cycling estrogen and mammographic density phenotypes.

PubMed

Fjeldheim, F N; Frydenberg, H; Flote, V G; McTiernan, A; Furberg, A-S; Ellison, P T; Barrett, E S; Wilsgaard, T; Jasienska, G; Ursin, G; Wist, E A; Thune, I

2016-10-07

Single nucleotide polymorphisms (SNPs) involved in the estrogen pathway and SNPs in the estrogen receptor alpha gene (ESR1 6q25) have been linked to breast cancer development, and mammographic density is an established breast cancer risk factor. Whether there is an association between daily estradiol levels, SNPs in ESR1 and premenopausal mammographic density phenotypes is unknown. We assessed estradiol in daily saliva samples throughout an entire menstrual cycle in 202 healthy premenopausal women in the Norwegian Energy Balance and Breast Cancer Aspects I study. DNA was genotyped using the Illumina Golden Gate platform. Mammograms were taken between days 7 and 12 of the menstrual cycle, and digitized mammographic density was assessed using a computer-assisted method (Madena). Multivariable regression models were used to study the association between SNPs in ESR1, premenopausal mammographic density phenotypes and daily cycling estradiol. We observed inverse linear associations between the minor alleles of eight measured SNPs (rs3020364, rs2474148, rs12154178, rs2347867, rs6927072, rs2982712, rs3020407, rs9322335) and percent mammographic density (p-values: 0.002-0.026), these associations were strongest in lean women (BMI, ≤23.6 kg/m 2. ). The odds of above-median percent mammographic density (>28.5 %) among women with major homozygous genotypes were 3-6 times higher than those of women with minor homozygous genotypes in seven SNPs. Women with rs3020364 major homozygous genotype had an OR of 6.46 for above-median percent mammographic density (OR: 6.46; 95 % Confidence Interval 1.61, 25.94) when compared to women with the minor homozygous genotype. These associations were not observed in relation to absolute mammographic density. No associations between SNPs and daily cycling estradiol were observed. However, we suggest, based on results of borderline significance (p values: 0.025-0.079) that the level of 17β-estradiol for women with the minor genotype for rs3020364, rs24744148 and rs2982712 were lower throughout the cycle in women with low (<28.5 %) percent mammographic density and higher in women with high (>28.5 %) percent mammographic density, when compared to women with the major genotype. Our results support an association between eight selected SNPs in the ESR1 gene and percent mammographic density. The results need to be confirmed in larger studies.

High-density lipoprotein of patients with breast cancer complicated with type 2 diabetes mellitus promotes cancer cells adhesion to vascular endothelium via ICAM-1 and VCAM-1 upregulation.

PubMed

Huang, Xiaoqin; He, Dan; Ming, Jia; He, Yubin; Zhou, Champion; Ren, Hui; He, Xin; Wang, Chenguang; Jin, Jingru; Ji, Liang; Willard, Belinda; Pan, Bing; Zheng, Lemin

2016-02-01

Adhesion of disseminating tumor cells to vascular endothelium is a pivotal starting point in the metastasis cascade. We have shown previously that diabetic high-density lipoprotein (HDL) has the capability of promoting breast cancer metastasis, and this report summarizes our more recent work studying the role of abnormal HDL in facilitating the adhesion of the circulating tumor cells to the endothelium. This is an initiating step in breast cancer metastasis, and this work assesses the role of ICAM-1 and VCAM-1 in this process. MDA-MB-231, MCF 7, and human umbilical vein endothelial cells (HUVECs) were treated with normal HDL from healthy controls (N-HDL), HDL from breast cancer patients (B-HDL), or HDL from breast cancer patients complicated with type 2 diabetes mellitus (BD-HDL), and the cell adhesion abilities were determined. ICAM-1 and VCAM-1 expression as well as the protein kinase C (PKC) activity were evaluated. The effect of PKC inhibitor and PKC siRNA on adhesion was also studied. The immunohistochemical staining of ICAM-1, VCAM-1, and E-selectin from breast cancer patients and breast cancer patients complicated with type 2 diabetes mellitus (T2DM) were examined. Our results indicate that BD-HDL promoted an increase in breast cancer cell adhesion to HUVECs and stimulated higher ICAM-1 and VCAM-1 expression on the cells surface of both breast cancer and HUVEC cells, along with the activation of PKC. Increased tumor cell (TC)-HUVEC adhesion, as well as ICAM-1 and VCAM-1 expression induced by BD-HDL, could be inhibited by staurosporine and PKC siRNA. In addition, a Db/db type 2 diabetes mouse model has more TC-Vascular Endothelium adhesion compared to a normal model. However, BD patients have a lower expression of ICAM-1, VCAM-1, and E-selectin in their tumor tissues. BD-HDL facilitates the adhesion of tumor cells to vascular endothelium by upregulating the expression of ICAM-1 and VCAM-1, thereby promoting the initial progression of breast cancer metastasis. This work indicates a prospective utilization of HDL-based strategies in the treatment of breast cancer patients with type 2 diabetes.

Mammographic Breast Density Evaluation in Korean Women Using Fully Automated Volumetric Assessment

PubMed Central

2016-01-01

The purpose was to present mean breast density of Korean women according to age using fully automated volumetric assessment. This study included 5,967 screening normal or benign mammograms (mean age, 46.2 ± 9.7; range, 30–89 years), from cancer-screening program. We evaluated mean fibroglandular tissue volume, breast tissue volume, volumetric breast density (VBD), and the results were 53.7 ± 30.8 cm3, 383.8 ± 205.2 cm3, and 15.8% ± 7.3%. The frequency of dense breasts and mean VBD by age group were 94.3% and 19.1% ± 6.7% for the 30s (n = 1,484), 91.4% and 17.2% ± 6.8% for the 40s (n = 2,706), 72.2% and 12.4% ± 6.2% for the 50s (n = 1,138), 44.0% and 8.6% ± 4.3% for the 60s (n = 89), 39.1% and 8.0% ± 3.8% for the 70s (n = 138), and 39.1% and 8.0% ± 3.5% for the 80s (n = 12). The frequency of dense breasts was higher in younger women (n = 4,313, 92.3%) than older women (n = 1,654, 59.8%). Mean VBD decreased with aging or menopause, and was about 16% for 46-year-old-Korean women, much higher than in other countries. The proportion of dense breasts sharply decreases in Korean women between 40 and 69 years of age. PMID:26955249

Volume and tissue composition preserving deformation of breast CT images to simulate breast compression in mammographic imaging

NASA Astrophysics Data System (ADS)

Han, Tao; Chen, Lingyun; Lai, Chao-Jen; Liu, Xinming; Shen, Youtao; Zhong, Yuncheng; Ge, Shuaiping; Yi, Ying; Wang, Tianpeng; Shaw, Chris C.

2009-02-01

Images of mastectomy breast specimens have been acquired with a bench top experimental Cone beam CT (CBCT) system. The resulting images have been segmented to model an uncompressed breast for simulation of various CBCT techniques. To further simulate conventional or tomosynthesis mammographic imaging for comparison with the CBCT technique, a deformation technique was developed to convert the CT data for an uncompressed breast to a compressed breast without altering the breast volume or regional breast density. With this technique, 3D breast deformation is separated into two 2D deformations in coronal and axial views. To preserve the total breast volume and regional tissue composition, each 2D deformation step was achieved by altering the square pixels into rectangular ones with the pixel areas unchanged and resampling with the original square pixels using bilinear interpolation. The compression was modeled by first stretching the breast in the superior-inferior direction in the coronal view. The image data were first deformed by distorting the voxels with a uniform distortion ratio. These deformed data were then deformed again using distortion ratios varying with the breast thickness and re-sampled. The deformation procedures were applied in the axial view to stretch the breast in the chest wall to nipple direction while shrinking it in the mediolateral to lateral direction re-sampled and converted into data for uniform cubic voxels. Threshold segmentation was applied to the final deformed image data to obtain the 3D compressed breast model. Our results show that the original segmented CBCT image data were successfully converted into those for a compressed breast with the same volume and regional density preserved. Using this compressed breast model, conventional and tomosynthesis mammograms were simulated for comparison with CBCT.

Feasibility of generating quantitative composition images in dual energy mammography: a simulation study

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Choi, Seungyeon; Kim, Hee-Joung

2016-03-01

Breast cancer is one of the most common malignancies in women. For years, mammography has been used as the gold standard for localizing breast cancer, despite its limitation in determining cancer composition. Therefore, the purpose of this simulation study is to confirm the feasibility of obtaining tumor composition using dual energy digital mammography. To generate X-ray sources for dual energy mammography, 26 kVp and 39 kVp voltages were generated for low and high energy beams, respectively. Additionally, the energy subtraction and inverse mapping functions were applied to provide compositional images. The resultant images showed that the breast composition obtained by the inverse mapping function with cubic fitting achieved the highest accuracy and least noise. Furthermore, breast density analysis with cubic fitting showed less than 10% error compare to true values. In conclusion, this study demonstrated the feasibility of creating individual compositional images and capability of analyzing breast density effectively.

Heterodyne frequency-domain multispectral diffuse optical tomography of breast cancer in the parallel-plane transmission geometry

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

Ban, H. Y.; Kavuri, V. C., E-mail: venk@physics.up

Purpose: The authors introduce a state-of-the-art all-optical clinical diffuse optical tomography (DOT) imaging instrument which collects spatially dense, multispectral, frequency-domain breast data in the parallel-plate geometry. Methods: The instrument utilizes a CCD-based heterodyne detection scheme that permits massively parallel detection of diffuse photon density wave amplitude and phase for a large number of source–detector pairs (10{sup 6}). The stand-alone clinical DOT instrument thus offers high spatial resolution with reduced crosstalk between absorption and scattering. Other novel features include a fringe profilometry system for breast boundary segmentation, real-time data normalization, and a patient bed design which permits both axial and sagittalmore » breast measurements. Results: The authors validated the instrument using tissue simulating phantoms with two different chromophore-containing targets and one scattering target. The authors also demonstrated the instrument in a case study breast cancer patient; the reconstructed 3D image of endogenous chromophores and scattering gave tumor localization in agreement with MRI. Conclusions: Imaging with a novel parallel-plate DOT breast imager that employs highly parallel, high-resolution CCD detection in the frequency-domain was demonstrated.« less

Risk factors for breast cancer in a cohort of mammographic screening program: a nested case-control study within the FRiCaM study.

PubMed

Bravi, Francesca; Decarli, Adriano; Russo, Antonio Giampiero

2018-05-01

Breast cancer is the most common cancer diagnosis and the leading cause of cancer death among women in the world, and differences across populations indicate a role of hormonal, reproductive and lifestyle factors. This study is based on a cohort of 78,050 women invited to undergo a mammogram by Local Health Authority of Milan, between 2003 and 2007. We carried out a nested case-control study including all the 3303 incident breast cancer cases diagnosed up to 2015, and 9909 controls matched by age and year of enrollment. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated using logistic regression models. The ORs were 0.88 (95% CI: 0.78-0.98) for an age at menarche ≥14 years and 1.39 (95% CI: 1.07-1.81) for an age of 30 years or older at first pregnancy. Body mass index (BMI) was positively associated with breast cancer risk in women older than 50 years (OR = 1.89, 95% CI: 1.54-2.31, for BMI≥30 vs. <20), while the association tended to be inverse in younger women. A high mammographic density increased breast cancer risk (OR = 2.61, 95% CI: 2.02-3.38 for density >75% vs. adipose tissue). The ORs were 1.67 (95% CI: 1.47-1.89) and 2.04 (95% CI: 1.38-3.00) for one first-degree relative and two or more relatives affected by breast cancer, respectively. Our study confirms the role of major recognized risk factors for breast cancer in our population and provides the basis for a stratification of the participants in the mammographic screening according to different levels of risk. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Comparison of synthetic mammography, reconstructed from digital breast tomosynthesis, and digital mammography: evaluation of lesion conspicuity and BI-RADS assessment categories.

PubMed

Mariscotti, Giovanna; Durando, Manuela; Houssami, Nehmat; Fasciano, Mirella; Tagliafico, Alberto; Bosco, Davide; Casella, Cristina; Bogetti, Camilla; Bergamasco, Laura; Fonio, Paolo; Gandini, Giovanni

2017-12-01

To compare the interpretive performance of synthetic mammography (SM), reconstructed from digital breast tomosynthesis (DBT), and full-field digital mammography (FFDM) in a diagnostic setting, covering different conditions of breast density and mammographic signs. A retrospective analysis was conducted on 231 patients, who underwent FFDM and DBT (from which SM images were reconstructed) between September 2014-September 2015. The study included 250 suspicious breast lesions, all biopsy proven: 148 (59.2%) malignant and 13 (5.2%) high-risk lesions were confirmed by surgery, 89 (35.6%) benign lesions had radiological follow-up. Two breast radiologists, blinded to histology, independently reviewed all cases. Readings were performed with SM alone, then with FFDM, collecting data on: probability of malignancy for each finding, lesion conspicuity, mammographic features and dimensions of detected lesions. Agreement between readers was good for BI-RADS classification (Cohen's k-coefficient = 0.93 ± 0.02) and for lesion dimension (Wilcoxon's p = 0.76). Visibility scores assigned to SM and FFDM for each lesion were similar for non-dense and dense breasts, however, there were significant differences (p = 0.0009) in distribution of mammographic features subgroups. SM and FFDM had similar sensitivities in non-dense (respectively 94 vs. 91%) and dense breasts (88 vs. 80%) and for all mammographic signs (93 vs. 87% for asymmetric densities, 96 vs. 75% for distortion, 92 vs. 85% for microcalcifications, and both 94% for masses). Based on all data, there was a significant difference in sensitivity for SM (92%) vs. FFDM (87%), p = 0.02, whereas the two modalities yielded similar results for specificity (SM: 60%, FFDM: 62%, p = 0.21). SM alone showed similar interpretive performance to FFDM, confirming its potential role as an alternative to FFDM in women having tomosynthesis, with the added advantage of halving the patient's dose exposure.

Role of Nuclear Morphometry in Breast Cancer and its Correlation with Cytomorphological Grading of Breast Cancer: A Study of 64 Cases.

PubMed

Kashyap, Anamika; Jain, Manjula; Shukla, Shailaja; Andley, Manoj

2018-01-01

Fine needle aspiration cytology (FNAC) is a simple, rapid, inexpensive, and reliable method of diagnosis of breast mass. Cytoprognostic grading in breast cancers is important to identify high-grade tumors. Computer-assisted image morphometric analysis has been developed to quantitate as well as standardize various grading systems. To apply nuclear morphometry on cytological aspirates of breast cancer and evaluate its correlation with cytomorphological grading with derivation of suitable cutoff values between various grades. Descriptive cross-sectional hospital-based study. This study included 64 breast cancer cases (29 of grade 1, 22 of grade 2, and 13 of grade 3). Image analysis was performed on Papanicolaou stained FNAC slides by NIS -Elements Advanced Research software (Ver 4.00). Nuclear morphometric parameters analyzed included 5 nuclear size, 2 shape, 4 texture, and 2 density parameters. Nuclear size parameters showed an increase in values with increasing cytological grades of carcinoma. Nuclear shape parameters were not found to be significantly different between the three grades. Among nuclear texture parameters, sum intensity, and sum brightness were found to be different between the three grades. Nuclear morphometry can be applied to augment the cytology grading of breast cancer and thus help in classifying patients into low and high-risk groups.

A novel automatic segmentation workflow of axial breast DCE-MRI

NASA Astrophysics Data System (ADS)

Besbes, Feten; Gargouri, Norhene; Damak, Alima; Sellami, Dorra

2018-04-01

In this paper we propose a novel process of a fully automatic breast tissue segmentation which is independent from expert calibration and contrast. The proposed algorithm is composed by two major steps. The first step consists in the detection of breast boundaries. It is based on image content analysis and Moore-Neighbour tracing algorithm. As a processing step, Otsu thresholding and neighbors algorithm are applied. Then, the external area of breast is removed to get an approximated breast region. The second preprocessing step is the delineation of the chest wall which is considered as the lowest cost path linking three key points; These points are located automatically at the breast. They are respectively, the left and right boundary points and the middle upper point placed at the sternum region using statistical method. For the minimum cost path search problem, we resolve it through Dijkstra algorithm. Evaluation results reveal the robustness of our process face to different breast densities, complex forms and challenging cases. In fact, the mean overlap between manual segmentation and automatic segmentation through our method is 96.5%. A comparative study shows that our proposed process is competitive and faster than existing methods. The segmentation of 120 slices with our method is achieved at least in 20.57+/-5.2s.

Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density.

PubMed

Haakensen, Vilde D; Biong, Margarethe; Lingjærde, Ole Christian; Holmen, Marit Muri; Frantzen, Jan Ole; Chen, Ying; Navjord, Dina; Romundstad, Linda; Lüders, Torben; Bukholm, Ida K; Solvang, Hiroko K; Kristensen, Vessela N; Ursin, Giske; Børresen-Dale, Anne-Lise; Helland, Aslaug

2010-01-01

Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD. SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer.

Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density

PubMed Central

2010-01-01

Introduction Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Methods Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD. Results SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Conclusions Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer. PMID:20799965

Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

NASA Astrophysics Data System (ADS)

He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer

2015-03-01

Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

Effects of Mediation-Based Stress Reduction in Younger Women With Breast Cancer

DTIC Science & Technology

1999-10-01

that is -20% of energy as fat and high in fiber and micronutrients obtained from a variety of plant sources; 3) increase patient confidence (i.e...and ’functional constituent’ densities of the diet by substituting fruits, vegetables, whole grains, and low-fat meats and dairy products for high

Fully automated chest wall line segmentation in breast MRI by using context information

NASA Astrophysics Data System (ADS)

Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Localio, A. Russell; Schnall, Mitchell D.; Kontos, Despina

2012-03-01

Breast MRI has emerged as an effective modality for the clinical management of breast cancer. Evidence suggests that computer-aided applications can further improve the diagnostic accuracy of breast MRI. A critical and challenging first step for automated breast MRI analysis, is to separate the breast as an organ from the chest wall. Manual segmentation or user-assisted interactive tools are inefficient, tedious, and error-prone, which is prohibitively impractical for processing large amounts of data from clinical trials. To address this challenge, we developed a fully automated and robust computerized segmentation method that intensively utilizes context information of breast MR imaging and the breast tissue's morphological characteristics to accurately delineate the breast and chest wall boundary. A critical component is the joint application of anisotropic diffusion and bilateral image filtering to enhance the edge that corresponds to the chest wall line (CWL) and to reduce the effect of adjacent non-CWL tissues. A CWL voting algorithm is proposed based on CWL candidates yielded from multiple sequential MRI slices, in which a CWL representative is generated and used through a dynamic time warping (DTW) algorithm to filter out inferior candidates, leaving the optimal one. Our method is validated by a representative dataset of 20 3D unilateral breast MRI scans that span the full range of the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) fibroglandular density categorization. A promising performance (average overlay percentage of 89.33%) is observed when the automated segmentation is compared to manually segmented ground truth obtained by an experienced breast imaging radiologist. The automated method runs time-efficiently at ~3 minutes for each breast MR image set (28 slices).

Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI.

PubMed

Niukkanen, Anton; Arponen, Otso; Nykänen, Aki; Masarwah, Amro; Sutela, Anna; Liimatainen, Timo; Vanninen, Ritva; Sudah, Mazen

2017-10-18

Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52.4 ± 12.3 years) with 105 disease-free breasts undergoing diagnostic 3.0-T breast MRI and either digital mammography or tomosynthesis. Two observers analyzed MRI images for breast and FGT volumes and FGT-% from T1-weighted images (0.7-, 2.0-, and 4.0-mm-thick slices) using K-means clustering, data from histogram, and active contour algorithms. Reference values were obtained with Quantra software. Inter-rater agreement for MRI measurements made with 2-mm-thick slices was excellent: for FGT-%, r = 0.994 (95% CI 0.990-0.997); for breast volume, r = 0.985 (95% CI 0.934-0.994); and for FGT volume, r = 0.979 (95% CI 0.958-0.989). MRI-based FGT-% correlated strongly with MBD in mammography (r = 0.819-0.904, P < 0.001) and moderately to high with MBD in tomosynthesis (r = 0.630-0.738, P < 0.001). K-means clustering-based assessments of the proportion of the fibroglandular tissue in the breast at MRI are highly reproducible. In the future, quantitative assessment of FGT-% to complement visual estimation of FGT should be performed on a more regular basis as it provides a component which can be incorporated into the individual's breast cancer risk stratification.

Effects of varied energy density of complementary foods on breast-milk intakes and total energy consumption by healthy, breastfed Bangladeshi children.

PubMed

Islam, M Munirul; Peerson, Janet M; Ahmed, Tahmeed; Dewey, Kathryn G; Brown, Kenneth H

2006-04-01

Information is needed to design studies of the effects of complementary feeding regimens on children's intakes of complementary foods (CFs) and breast milk. We evaluated the effects of varied energy density of CFs on the time until stabilization of dietary intakes and on total daily energy intakes (EIs) and breast-milk intakes. CFs with low [0.4 kcal/g (LD)] and high [1.5 kcal/g (HD)] energy density were fed 3 times/d to 10 children (aged 9-18 mo) during 2 randomly assigned sequences of three 8-d diet periods (HD-LD-HD or LD-HD-LD) along with ad libitum breastfeeding. CF and breast-milk intakes were measured. Intakes of the HD diet and breast milk did not vary by day of period, but intake of the LD diet increased progressively. During days 5-7 of the last 2 diet periods in each sequence, more of the LD than of the HD diet was consumed (752 +/- 252 and 439 +/- 111 g/d, respectively; P < 0.001), but EIs from CFs were greater with the HD diet. Breast-milk consumption was slightly less (192 +/- 115 and 234 +/- 121 g/d, respectively; P = 0.03) but total daily EI was greater (774 +/- 175 and 441 +/- 85 kcal/d, respectively; P < 0.001) during the HD than during the LD diet period. New information on the effects of newly introduced diets on daily intakes of these diets and of breast milk can be used to design future studies. Total daily EIs were greater with the HD diet despite its negative effects on breast-milk intakes.

Breast dose in mammography is about 30% lower when realistic heterogeneous glandular distributions are considered

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

Hernandez, Andrew M., E-mail: amhern@ucdavis.edu; Seibert, J. Anthony; Boone, John M.

2015-11-15

Purpose: Current dosimetry methods in mammography assume that the breast is comprised of a homogeneous mixture of glandular and adipose tissues. Three-dimensional (3D) dedicated breast CT (bCT) data sets were used previously to assess the complex anatomical structure within the breast, characterizing the statistical distribution of glandular tissue in the breast. The purpose of this work was to investigate the effect of bCT-derived heterogeneous glandular distributions on dosimetry in mammography. Methods: bCT-derived breast diameters, volumes, and 3D fibroglandular distributions were used to design realistic compressed breast models comprised of heterogeneous distributions of glandular tissue. The bCT-derived glandular distributions were fitmore » to biGaussian functions and used as probability density maps to assign the density distributions within compressed breast models. The MCNPX 2.6.0 Monte Carlo code was used to estimate monoenergetic normalized mean glandular dose “DgN(E)” values in mammography geometry. The DgN(E) values were then weighted by typical mammography x-ray spectra to determine polyenergetic DgN (pDgN) coefficients for heterogeneous (pDgN{sub hetero}) and homogeneous (pDgN{sub homo}) cases. The dependence of estimated pDgN values on phantom size, volumetric glandular fraction (VGF), x-ray technique factors, and location of the heterogeneous glandular distributions was investigated. Results: The pDgN{sub hetero} coefficients were on average 35.3% (SD, 4.1) and 24.2% (SD, 3.0) lower than the pDgN{sub homo} coefficients for the Mo–Mo and W–Rh x-ray spectra, respectively, across all phantom sizes and VGFs when the glandular distributions were centered within the breast phantom in the coronal plane. At constant breast size, increasing VGF from 7.3% to 19.1% lead to a reduction in pDgN{sub hetero} relative to pDgN{sub homo} of 23.6%–27.4% for a W–Rh spectrum. Displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width in the superior and inferior directions, resulted in a 37.3% and a −26.6% change in the pDgN{sub hetero} coefficient, respectively, relative to the centered distribution for the Mo–Mo spectrum. Lateral displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width, resulted in a 1.5% change in the pDgN{sub hetero} coefficient relative to the centered distribution for the W–Rh spectrum. Conclusions: Introducing bCT-derived heterogeneous glandular distributions into mammography phantom design resulted in decreased glandular dose relative to the widely used homogeneous assumption. A homogeneous distribution overestimates the amount of glandular tissue near the entrant surface of the breast, where dose deposition is exponentially higher. While these findings are based on clinically measured distributions of glandular tissue using a large cohort of women, future work is required to improve the classification of glandular distributions based on breast size and overall glandular fraction.« less

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.

Relationship of mast cell density with lymphangiogenesis and prognostic parameters in breast carcinoma.

PubMed

Keser, Sevinc H; Kandemir, Nilufer O; Ece, Dilek; Gecmen, Gonca G; Gul, Aylin E; Barisik, Nagehan O; Sensu, Sibel; Buyukuysal, Cagatay; Barut, Figen

2017-04-01

In many cancers, mast cell density (MCD) in the tumor microenvironment is associated with tumor progression and, to a greater extent, angiogenesis. Our study was designed to investigate the correlation between MCD, tumor lymphangiogenesis, and several well-established prognostic parameters in breast cancer. One hundred and four cases of invasive breast carcinoma diagnosed in our clinic between 2007 and 2011 were included. Mast cells and lymphatic vessels were stained with toluidine blue and D2-40, respectively, and their densities were calculated in various areas of tumors and lymph nodes. The variables of MCD and lymphatic vessel density (LVD) were compared using prognostic parameters as well as with each other. As tumor size and volume increased, MCD increased comparably in metastatic lymph nodes; intratumoral and peritumoral LVD also increased. Lymphovascular invasion, lymphatic invasion, perineural invasion, and estrogen receptor positivity were positively related to intratumoral MCD. The relationship between peritumoral MCD and nontumoral breast tissue MCD was statistically significant. Stage was correlated with MCD in metastatic lymph nodes. Metastatic lymph node MCD and intratumoral MCD were also significantly related. Stage, lymphatic invasion, perineural invasion, lymphovascular invasion, and metastatic lymph node MCD were all correlated with intratumoral and/or peritumoral LVD. As nuclear grade increased, intratumoral LVD became higher. In breast carcinoma, MCD, depending on its location, was related to several prognostic parameters. Notably, mast cells may have at least some effect on lymphangiogenesis, which appears to be a predictor of tumor progression. Copyright © 2017. Published by Elsevier Taiwan.

Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk.

PubMed

Kopans, Daniel B

2008-02-01

Numerous studies have suggested a link between breast tissue patterns, as defined with mammography, and risk for breast cancer. There may be a relationship, but the author believes all of these studies have methodological flaws. It is impossible, with the parameters used in these studies, to accurately measure the percentage of tissues by volume when two-dimensional x-ray mammographic images are used. Without exposure values, half-value layer information, and knowledge of the compressed thickness of the breast, an accurate volume of tissue cannot be calculated. The great variability in positioning the breast for a mammogram is also an uncontrollable factor in measuring tissue density. Computerized segmentation algorithms can accurately assess the percentage of the x-ray image that is "dense," but this does not accurately measure the true volume of tissue. Since the percentage of dense tissue is ultimately measured in relation to the complete volume of the breast, defining the true boundaries of the breast is also a problem. Studies that purport to show small percentage differences between groups are likely inaccurate. Future investigations need to use three-dimensional information. (c) RSNA, 2008.

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.

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis.

PubMed

Gray, Ewan; Donten, Anna; Karssemeijer, Nico; van Gils, Carla; Evans, D Gareth; Astley, Sue; Payne, Katherine

2017-09-01

To identify the incremental costs and consequences of stratified national breast screening programs (stratified NBSPs) and drivers of relative cost-effectiveness. A decision-analytic model (discrete event simulation) was conceptualized to represent four stratified NBSPs (risk 1, risk 2, masking [supplemental screening for women with higher breast density], and masking and risk 1) compared with the current UK NBSP and no screening. The model assumed a lifetime horizon, the health service perspective to identify costs (£, 2015), and measured consequences in quality-adjusted life-years (QALYs). Multiple data sources were used: systematic reviews of effectiveness and utility, published studies reporting costs, and cohort studies embedded in existing NBSPs. Model parameter uncertainty was assessed using probabilistic sensitivity analysis and one-way sensitivity analysis. The base-case analysis, supported by probabilistic sensitivity analysis, suggested that the risk stratified NBSPs (risk 1 and risk-2) were relatively cost-effective when compared with the current UK NBSP, with incremental cost-effectiveness ratios of £16,689 per QALY and £23,924 per QALY, respectively. Stratified NBSP including masking approaches (supplemental screening for women with higher breast density) was not a cost-effective alternative, with incremental cost-effectiveness ratios of £212,947 per QALY (masking) and £75,254 per QALY (risk 1 and masking). When compared with no screening, all stratified NBSPs could be considered cost-effective. Key drivers of cost-effectiveness were discount rate, natural history model parameters, mammographic sensitivity, and biopsy rates for recalled cases. A key assumption was that the risk model used in the stratification process was perfectly calibrated to the population. This early model-based cost-effectiveness analysis provides indicative evidence for decision makers to understand the key drivers of costs and QALYs for exemplar stratified NBSP. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Associations of coffee consumption and caffeine intake with mammographic breast density.

PubMed

Yaghjyan, Lusine; Colditz, Graham; Rosner, Bernard; Gasparova, Aleksandra; Tamimi, Rulla M

2018-05-01

Previous studies suggest that coffee and caffeine intake may be associated with reduced breast cancer risk. We investigated the association of coffee and caffeine intake with mammographic breast density by woman's menopausal status and, in postmenopausal women, by hormone therapy (HT). This study included 4130 cancer-free women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD) was measured from digitized film mammograms using a computer-assisted thresholding technique and square root-transformed for the analysis. Average cumulative coffee/caffeine consumption was calculated using data from all food frequency questionnaires preceding the mammogram date. Information regarding breast cancer risk factors was obtained from questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations of regular, decaffeinated, and total coffee, and energy-adjusted caffeine intake with percent density. In multivariable analyses, decaffeinated coffee was positively associated with PD in premenopausal women (2+ cups/day: β = 0.23, p trend = 0.03). In postmenopausal women, decaffeinated and total coffee were inversely associated with PD (decaffeinated 2+ cups/day: β = - 0.24, p trend = 0.04; total 4+ cups/day: β = - 0.16, p trend = 0.02). Interaction of decaffeinated coffee with menopausal status was significant (p-interaction < 0.001). Among current HT users, regular coffee and caffeine were inversely associated with PD (regular coffee 4+ cups/day: β = - 0.29, p trend = 0.01; caffeine 4th vs. 1st quartile: β = - 0.32, p trend = 0.01). Among past users, decaffeinated coffee was inversely associated with PD (2+ cups/day β = - 0.70, p trend = 0.02). Associations of decaffeinated coffee with percent density differ by woman's menopausal status. Associations of regular coffee and caffeine with percent density may differ by HT status.

RAZOR: A Phase II Open Randomized Trial of Screening Plus Goserelin and Raloxifene Versus Screening Alone in Premenopausal Women at Increased Risk of Breast Cancer.

PubMed

Howell, Anthony; Ashcroft, Linda; Fallowfield, Lesley; Eccles, Diana M; Eeles, Rosalind A; Ward, Ann; Brentnall, Adam R; Dowsett, Mitchell; Cuzick, Jack M; Greenhalgh, Rosemary; Boggis, Caroline; Motion, Jamie; Sergeant, Jamie C; Adams, Judith; Evans, D Gareth

2018-01-01

Background: Ovarian suppression in premenopausal women is known to reduce breast cancer risk. This study aimed to assess uptake and compliance with ovarian suppression using the luteinizing hormone releasing hormone (LHRH) analogue, goserelin, with add-back raloxifene, as a potential regimen for breast cancer prevention. Methods: Women at ≥30% lifetime risk breast cancer were approached and randomized to mammographic screening alone (C-Control) or screening in addition to monthly subcutaneous injections of 3.6 mg goserelin and continuous 60 mg raloxifene daily orally (T-Treated) for 2 years. The primary endpoint was therapy adherence. Secondary endpoints were toxicity/quality of life, change in bone density, and mammographic density. Results: A total of 75/950 (7.9%) women approached agreed to randomization. In the T-arm, 20 of 38 (52%) of women completed the 2-year period of study compared with the C-arm (27/37, 73.0%). Dropouts were related to toxicity but also the wish to have established risk-reducing procedures and proven chemoprevention. As relatively few women completed the study, data are limited, but those in the T-arm reported significant increases in toxicity and sexual problems, no change in anxiety, and less cancer worry. Lumbar spine bone density declined by 7.0% and visually assessed mammographic density by 4.7% over the 2-year treatment period. Conclusions: Uptake is somewhat lower than comparable studies with tamoxifen for prevention with higher dropout rates. Raloxifene may preserve bone density, but reduction in mammographic density reversed after treatment was completed. Impact: This study indicates that breast cancer risk reduction may be possible using LHRH agonists, but reducing toxicity and preventing bone changes would make this a more attractive option. Cancer Epidemiol Biomarkers Prev; 27(1); 58-66. ©2017 AACR . ©2017 American Association for Cancer Research.

Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

Treesearch

Rafal Podlaski; Francis A. Roesch

2014-01-01

Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

Mammographic breast density patterns in asymptomatic mexican women.

PubMed

Calderón-Garcidueñas, Ana Laura; Sanabria-Mondragón, Mónica; Hernández-Beltrán, Lourdes; López-Amador, Noé; Cerda-Flores, Ricardo M

2012-01-01

Breast density (BD) is a risk factor for breast cancer. Aims. To describe BD patterns in asymptomatic Mexican women and the pathological mammographic findings. Methods and Material. Prospective, descriptive, and comparative study. Women answered a questionnaire and their mammograms were analyzed according to BI-RADS. Univariate (χ(2)) and conditional logistic regression analyses were performed. Results. In 300 women studied the BD patterns were fat 56.7% (170), fibroglandular 29% (87), heterogeneously dense 5.7% (17), and dense pattern 8.6% (26). Prevalence of fat pattern was significantly different in women under 50 years (37.6%, 44/117) and older than 50 (68.8%, 126/183). Patterns of high breast density (BD) (dense + heterogeneously dense) were observed in 25.6% (30/117) of women ≤50 years and 7.1% (13/183) of women >50. Asymmetry in BD was observed in 22% (66/300). Compression cone ruled out underlying disease in 56 cases. In the remaining 10, biopsy revealed one fibroadenoma, one complex cyst, and 6 invasive and 2 intraductal carcinomas. 2.6% (8/300) of patients had non-palpable carcinomas. Benign lesions were observed in 63.3% (190/300) of cases, vascular calcification in 150 cases (78.9%), and fat necrosis in 38 cases (20%). Conclusions. Mexican women have a low percentage of high-density patterns.

Possible effects of insulin-like growth factor-I, IGF-binding protein-3 and IGF-1/IGFBP-3 molar ratio on mammographic density: a cross-sectional study.

PubMed

Meggiorini, M L; Cipolla, V; Borgoni, G; Nofroni, I; Pala, A; de Felice, C

2012-01-01

The purpose of this study was to examine the possible effects of IGF-1, IGFBP-3 and IGF-1/IGFBP-3 molar ratio on mammographic density and assess whether this relationship was similar in subgroups of pre- and postmenopausal women. A group of 341 Italian women of childbearing age or naturally postmenopausal who had performed mammographic examination at the section of radiology of our department a maximum three months prior to recruitment were enrolled. A blood sample was drawn for determination of IGF-1, IGFBP-3 levels and IGF-1/IGFBP-3 molar ratio was calculated. On the basis of recent mammograms the women were divided into two groups: dense breast (DB) and non-dense breast (NDB). To assess the association between mammographic density and IGF-1, IGFBP-3 and Molar ratio Student's t-test was employed before and after stratified by menopausal status. The analysis of the relationship between mammographic density and plasma levels of IGF-1, IGFBP-3 and IGF-1/IGFBP-3 molar ratio showed that IGF-1 levels and molar ratio varied in the two groups resulting in higher mean values in the DB group whereas IGFBP-3 showed similar values in both groups (DB and NDB). After stratification of the study population by menopausal status, no association was found. Our study provides strong evidence of a crude association between breast density, and plasma levels of IGF-1 and molar ratio. IGF-1 and molar ratio might increase mammographic density and thus the risk of developing breast cancer.

Tradeoffs between hydraulic and mechanical stress responses of mature Norway spruce trunk wood.

PubMed

Rosner, Sabine; Klein, Andrea; Müller, Ulrich; Karlsson, Bo

2008-08-01

We tested the effects of growth characteristics and basic density on hydraulic and mechanical properties of mature Norway spruce (Picea abies (L.) Karst.) wood from six 24-year-old clones, grown on two sites in southern Sweden differing in water availability. Hydraulic parameters assessed were specific hydraulic conductivity at full saturation (ks100) and vulnerability to cavitation (Psi50), mechanical parameters included bending strength (sigma b), modulus of elasticity (MOE), compression strength (sigma a) and Young's modulus (E). Basic density, diameter at breast height, tree height, and hydraulic and mechanical parameters varied considerably among clones. Clonal means of hydraulic and mechanical properties were strongly related to basic density and to growth parameters across sites, especially to diameter at breast height. Compared with stem wood of slower growing clones, stem wood of rapidly growing clones had significantly lower basic density, lower sigma b, MOE, sigma a and E, was more vulnerable to cavitation, but had higher ks100. Basic density was negatively correlated to Psi50 and ks100. We therefore found a tradeoff between Psi50 and ks100. Clones with high basic density had significantly lower hydraulic vulnerability, but also lower hydraulic conductivity at full saturation and thus less rapid growth than clones with low basic density. This tradeoff involved a negative relationship between Psi50 and sigma b as well as MOE, and between ks100 and sigma b, MOE and sigma a. Basic density and Psi50 showed no site-specific differences, but tree height, diameter at breast height, ks100 and mechanical strength and stiffness were significantly lower at the drier site. Basic density had no influence on the site-dependent differences in hydraulic and mechanical properties, but was strongly negatively related to diameter at breast height. Selecting for growth may thus lead not only to a reduction in mechanical strength and stiffness but also to a reduction in hydraulic safety.

Inconsistencies of Breast Cancer Risk Factors between the Northern and Southern Regions of Vietnam

PubMed

Trieu, Phuong Dung (Yun); Mello-Thoms, Claudia; Peat, Jennifer K; Do, Thuan Doan; Brennan, Patrick C

2017-10-26

Background: In recent decades the amount of new breast cancer cases in the southern region has been reported to increase more rapidly than in the northernVietnam. The aim of this study is to compare breast cancer risk factors between the two regions and establish if westernized influences have an impact on any reported differences. Method: Data was collected from the two largest oncology hospitals in the north and the south of Vietnam in 2015. Breast density, demographic, reproductive and lifestyle data of 127 cases and 269 controls were collected in the north and 141 cases and 250 controls were gathered from the south. Baseline differences in factors between cases and age-matched controls in each region were assessed using chi-square tests and independent t-tests. Odds ratios (OR) for independent risk factors for breast cancer were obtained from conditional logistic regression. Results: In northern Vietnam significantly increased risks in developing breast cancer were observed for women with age at first menstrual period less than 14 years old (OR=2.1; P<0.05), post-menopausal status (OR=2.6; P<0.0001), having less than 2 babies (OR=2.1; P<0.05). Southern Vietnamese women having a breast density of more than 75% (OR=2.1; P<0.01), experiencing post-menopause (OR=1.6; P<0.05), having a history of less than 3 pregnancies (OR=2.6; P<0.0001) and drinking more than a cup of coffee per day (OR=1.9; P<0.05) were more likely to be diagnosed with breast cancer. Conclusion: We found that women living in the south had some breast cancer associations, such as increased mammographic density and coffee consumption, which are closer to the risks in westernized populations than women in the north. Creative Commons Attribution License

Family History and Breast Cancer Risk Among Older Women in the Breast Cancer Surveillance Consortium Cohort.

PubMed

Braithwaite, Dejana; Miglioretti, Diana L; Zhu, Weiwei; Demb, Joshua; Trentham-Dietz, Amy; Sprague, Brian; Tice, Jeffrey A; Onega, Tracy; Henderson, Louise M; Buist, Diana S M; Ziv, Elad; Walter, Louise C; Kerlikowske, Karla

2018-04-01

First-degree family history is a strong risk factor for breast cancer, but controversy exists about the magnitude of the association among older women. To determine whether first-degree family history is associated with increased risk of breast cancer among older women, and identify whether the association varies by breast density. Prospective cohort study between 1996 and 2012 from 7 Breast Cancer Surveillance Consortium (BCSC) registries located in New Hampshire, North Carolina, San Francisco Bay area, western Washington state, New Mexico, Colorado, and Vermont. During a mean (SD) follow-up of 6.3 (3.2) years, 10 929 invasive breast cancers were diagnosed in a cohort of 403 268 women 65 years and older with data from 472 220 mammography examinations. We estimated the 5-year cumulative incidence of invasive breast cancer by first-degree family history, breast density, and age groups. Cox proportional hazards models were fit to estimate the association of first-degree family history with risk of invasive breast cancer (after adjustment for breast density, BCSC registry, race/ethnicity, body mass index, postmenopausal hormone therapy use, and benign breast disease for age groups 65 to 74 years and 75 years and older, separately). Data analyses were performed between June 2016 and June 2017. First-degree family history of breast cancer. Incident breast cancer. In 403 268 women 65 years and older, first-degree family history was associated with an increased risk of breast cancer among women ages 65 to 74 years (hazard ratio [HR], 1.48; 95% CI, 1.35-1.61) and 75 years and older (HR, 1.44; 95% CI, 1.28-1.62). Estimates were similar for women 65 to 74 years with first-degree relative's diagnosis age younger than 50 years (HR, 1.47; 95% CI, 1.25-1.73) vs 50 years and older (HR, 1.33; 95% CI, 1.17-1.51) and for women ages 75 years and older with the relative's diagnosis age younger than 50 years (HR, 1.31; 95% CI, 1.05-1.63) vs 50 years and older (HR, 1.55; 95% CI, 1.33-1.81). Among women ages 65 to 74 years, the risk associated with first-degree family history was highest among those with fatty breasts (HR, 1.67; 95% CI, 1.27-2.21), whereas in women 75 years and older the risk associated with family history was highest among those with dense breasts (HR, 1.55; 95% CI, 1.29-1.87). First-degree family history was associated with increased risk of invasive breast cancer in all subgroups of older women irrespective of a relative's age at diagnosis.

Lymphatic and blood vessels in male breast cancer.

PubMed

Niemiec, Joanna; Sas-Korczynska, Beata; Harazin-Lechowska, Agnieszka; Martynow, Dariusz; Adamczyk, Agnieszka

2015-02-01

It is assumed that lymphatic vessels are responsible for breast cancer dissemination. In 32 male breast carcinomas we evaluated the correlation between: (i) lymphatic vessel density (LVD), distribution of podoplanin-immunostained vessels (DPV), blood vessel density (BVD), infiltration of immune cells and (ii) known clinicopathological parameters. Lymphatic and blood vessels were found in 77.8% and 100% of breast carcinomas, respectively. Double-negative estrogen and progesterone receptor tumors (ER-/PR-) presented significantly higher LVD than ER/PR positive cases, while high-grade tumors exhibited significantly higher DPV than low-grade carcinomas. We detected significantly higher frequency of vascular invasion in high-grade and double-negative carcinomas than in low-grade and ER/PR-positive ones, respectively. The relationship between high number of lymphatic vessels and high tumor grade or steroid receptor negativity might confirm the hypothesis regarding the influence of lymphangiogenesis on the formation of a more aggressive phenotype in male breast cancer. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Dedicated breast CT: Fibroglandular volume measurements in a diagnostic population

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

Vedantham, Srinivasan; Shi Linxi; Karellas, Andrew

2012-12-15

Purpose: To determine the mean and range of volumetric glandular fraction (VGF) of the breast in a diagnostic population using a high-resolution flat-panel cone-beam dedicated breast CT system. This information is important for Monte Carlo-based estimation of normalized glandular dose coefficients and for investigating the dependence of VGF on breast dimensions, race, and pathology. Methods: Image data from a clinical trial investigating the role of dedicated breast CT that enrolled 150 women were retrospectively analyzed to determine the VGF. The study was conducted in adherence to a protocol approved by the institutional human subjects review boards and written informed consentmore » was obtained from all study participants. All participants in the study were assigned BI-RADS{sup Registered-Sign} 4 or 5 as per the American College of Radiology assessment categories after standard diagnostic work-up and underwent dedicated breast CT exam prior to biopsy. A Gaussian-kernel based fuzzy c-means algorithm was used to partition the breast CT images into adipose and fibroglandular tissue after segmenting the skin. Upon determination of the accuracy of the algorithm with a phantom, it was applied to 137 breast CT volumes from 136 women. VGF was determined for each breast and the mean and range were determined. Pathology results with classification as benign, malignant, and hyperplasia were available for 132 women, and were used to investigate if the distributions of VGF varied with pathology. Results: The algorithm was accurate to within {+-}1.9% in determining the volume of an irregular shaped phantom. The study mean ({+-} inter-breast SD) for the VGF was 0.172 {+-} 0.142 (range: 0.012-0.719). VGF was found to be negatively correlated with age, breast dimensions (chest-wall to nipple length, pectoralis to nipple length, and effective diameter at chest-wall), and total breast volume, and positively correlated with fibroglandular volume. Based on pathology, pairwise statistical analysis (Mann-Whitney test) indicated that at the 0.05 significance level, there was no significant difference in distributions of VGF without adjustment for age between malignant and nonmalignant breasts (p= 0.41). Pairwise comparisons of the distributions of VGF in increasing order of mammographic breast density indicated all comparisons were statistically significant (p < 0.002). Conclusions: This study used a different clinical prototype breast CT system than that in previous studies to image subjects from a different geographical region, and used a different algorithm for analysis of image data. The mean VGF estimated from this study is within the range reported in previous studies, indicating that the choice of 50% glandular weight fraction to represent an average breast for Monte Carlo-based estimation of normalized glandular dose coefficients in mammography needs revising. In the study, the distributions of VGF did not differ significantly with pathology.« less

Lymphocytic mastopathy mimicking breast malignancy: a case report*

PubMed Central

Campos, Gabriela Couto Possati; Castro, Melissa Vieira Koch e; de Mattos, Viviane Ferreira Esteves; Pinto, Laura Zaiden Ferreira e; Boechat, Marcia Cristina Bastos; dos Santos, Alair Augusto Sarmet Moreira Damas

2014-01-01

Lymphocytic mastopathy affects both young and middle-aged women and is frequently associated with autoimmune diseases. Diagnosis is done by associating clinical (breast tissue thickening or hardened breast lump), radiological (increased breast density, presence of mass and calcifications), sonographic (nodule with posterior acoustic shadowing), histopathological (fibrosis and lymphocytic infiltrate) and immunohistochemical findings. Lymphocytic mastopathy is a benign entity that may mimic carcinoma. The authors report the case of a patient with lymphocytic mastopathy. PMID:25741094

Update on new technologies in digital mammography

PubMed Central

Patterson, Stephanie K; Roubidoux, Marilyn A

2014-01-01

Despite controversy regarding mammography’s efficacy, it continues to be the most commonly used breast cancer-screening modality. With the development of digital mammography, some improved benefit has been shown in women with dense breast tissue. However, the density of breast tissue continues to limit the sensitivity of conventional mammography. We discuss the development of some derivative digital technologies, primarily digital breast tomosynthesis, and their strengths, weaknesses, and potential patient impact. PMID:25152634

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.

PubMed

Ali, H Raza; Dariush, Aliakbar; Provenzano, Elena; Bardwell, Helen; Abraham, Jean E; Iddawela, Mahesh; Vallier, Anne-Laure; Hiller, Louise; Dunn, Janet A; Bowden, Sarah J; Hickish, Tamas; McAdam, Karen; Houston, Stephen; Irwin, Mike J; Pharoah, Paul D P; Brenton, James D; Walton, Nicholas A; Earl, Helena M; Caldas, Carlos

2016-02-16

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy. We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression. Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553). A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer. ClinicalTrials.gov NCT00070278 ; 03/10/2003.

Preliminary evaluation of a fully automated quantitative framework for characterizing general breast tissue histology via color histogram and color texture analysis

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Gastounioti, Aimilia; Batiste, Rebecca C.; Kontos, Despina; Feldman, Michael D.

2016-03-01

Visual characterization of histologic specimens is known to suffer from intra- and inter-observer variability. To help address this, we developed an automated framework for characterizing digitized histology specimens based on a novel application of color histogram and color texture analysis. We perform a preliminary evaluation of this framework using a set of 73 trichrome-stained, digitized slides of normal breast tissue which were visually assessed by an expert pathologist in terms of the percentage of collagenous stroma, stromal collagen density, duct-lobular unit density and the presence of elastosis. For each slide, our algorithm automatically segments the tissue region based on the lightness channel in CIELAB colorspace. Within each tissue region, a color histogram feature vector is extracted using a common color palette for trichrome images generated with a previously described method. Then, using a whole-slide, lattice-based methodology, color texture maps are generated using a set of color co-occurrence matrix statistics: contrast, correlation, energy and homogeneity. The extracted features sets are compared to the visually assessed tissue characteristics. Overall, the extracted texture features have high correlations to both the percentage of collagenous stroma (r=0.95, p<0.001) and duct-lobular unit density (r=0.71, p<0.001) seen in the tissue samples, and several individual features were associated with either collagen density and/or the presence of elastosis (p<=0.05). This suggests that the proposed framework has promise as a means to quantitatively extract descriptors reflecting tissue-level characteristics and thus could be useful in detecting and characterizing histological processes in digitized histology specimens.

Role of Nuclear Morphometry in Breast Cancer and its Correlation with Cytomorphological Grading of Breast Cancer: A Study of 64 Cases

PubMed Central

Kashyap, Anamika; Jain, Manjula; Shukla, Shailaja; Andley, Manoj

2018-01-01

Background: Fine needle aspiration cytology (FNAC) is a simple, rapid, inexpensive, and reliable method of diagnosis of breast mass. Cytoprognostic grading in breast cancers is important to identify high-grade tumors. Computer-assisted image morphometric analysis has been developed to quantitate as well as standardize various grading systems. Aims: To apply nuclear morphometry on cytological aspirates of breast cancer and evaluate its correlation with cytomorphological grading with derivation of suitable cutoff values between various grades. Settings and Designs: Descriptive cross-sectional hospital-based study. Materials and Methods: This study included 64 breast cancer cases (29 of grade 1, 22 of grade 2, and 13 of grade 3). Image analysis was performed on Papanicolaou stained FNAC slides by NIS –Elements Advanced Research software (Ver 4.00). Nuclear morphometric parameters analyzed included 5 nuclear size, 2 shape, 4 texture, and 2 density parameters. Results: Nuclear size parameters showed an increase in values with increasing cytological grades of carcinoma. Nuclear shape parameters were not found to be significantly different between the three grades. Among nuclear texture parameters, sum intensity, and sum brightness were found to be different between the three grades. Conclusion: Nuclear morphometry can be applied to augment the cytology grading of breast cancer and thus help in classifying patients into low and high-risk groups. PMID:29403169

In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium

PubMed Central

Alfonso, J. C. L.; Schaadt, N. S.; Schönmeyer, R.; Brieu, N.; Forestier, G.; Wemmert, C.; Feuerhake, F.; Hatzikirou, H.

2016-01-01

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches. PMID:27659691

In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium

NASA Astrophysics Data System (ADS)

Alfonso, J. C. L.; Schaadt, N. S.; Schönmeyer, R.; Brieu, N.; Forestier, G.; Wemmert, C.; Feuerhake, F.; Hatzikirou, H.

2016-09-01

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2013-10-01

94http : / / www.elsev ier .com/ locate / jeghPerinatal factors and breast cancer risk among HispanicsMaureen Sanderson a,b,*, Adriana Pérez c,d, Mirabel...risk of breast cancer: a systematic review and meta-analysis of current evidence. Lancet Oncol 2007;8:1088–100. [3] Park SK, Kang D, McGlynn KA

Common genetic variation and novel loci associated with volumetric mammographic density.

PubMed

Brand, Judith S; Humphreys, Keith; Li, Jingmei; Karlsson, Robert; Hall, Per; Czene, Kamila

2018-04-17

Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained. We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h 2 SNP ]) in 4948 participants of the cohort. In total, three novel MD loci were identified (at P < 5 × 10 - 8 ): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in available meta-analysis data including 122,977 breast cancer cases and 105,974 control subjects (P < 0.05). h 2 SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h 2 SNP to previously observed narrow-sense h 2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively. These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic variation with h 2 SNP /h 2 ratios varying between dense and nondense MD components.

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

Aima, M; Viscariello, N; Patton, T

Purpose: The aim of this work is to propose a method to optimize radioactive source localization (RSL) for non-palpable breast cancer surgery. RSL is commonly used as a guiding technique during surgery for excision of non-palpable tumors. A collimated hand-held detector is used to localize radioactive sources implanted in tumors. Incisions made by the surgeon are based on maximum observed detector counts, and tumors are subsequently resected based on an arbitrary estimate of the counts expected at the surgical margin boundary. This work focuses on building a framework to predict detector counts expected throughout the procedure to improve surgical margins.more » Methods: A gamma detection system called the Neoprobe GDS was used for this work. The probe consists of a cesium zinc telluride crystal and a collimator. For this work, an I-125 Best Medical model 2301 source was used. The source was placed in three different phantoms, a PMMA, a Breast (25%- glandular tissue/75%- adipose tissue) and a Breast (75-25) phantom with a backscatter thickness of 6 cm. Counts detected by the probe were recorded with varying amounts of phantom thicknesses placed on top of the source. A calibration curve was generated using MATLAB based on the counts recorded for the calibration dataset acquired with the PMMA phantom. Results: The observed detector counts data used as the validation set was accurately predicted to within ±3.2%, ±6.9%, ±8.4% for the PMMA, Breast (75-25), Breast (25–75) phantom respectively. The average difference between predicted and observed counts was −0.4%, 2.4%, 1.4% with a standard deviation of 1.2 %, 1.8%, 3.4% for the PMMA, Breast (75-25), Breast (25–75) phantom respectively. Conclusion: The results of this work provide a basis for characterization of a detector used for RSL. Counts were predicted to within ±9% for three different phantoms without the application of a density correction factor.« less

Sci—Thur AM: YIS - 08: Constructing an Attenuation map for a PET/MR Breast coil

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

Patrick, John C.; Imaging, Lawson Health Research Institute, Knoxville, TN; London Regional Cancer Program, Knoxville, TN

2014-08-15

In 2013, around 23000 Canadian women and 200 Canadian men were diagnosed with breast cancer. An estimated 5100 women and 55 men died from the disease. Using the sensitivity of MRI with the selectivity of PET, PET/MRI combines anatomical and functional information within the same scan and could help with early detection in high-risk patients. MRI requires radiofrequency coils for transmitting energy and receiving signal but the breast coil attenuates PET signal. To correct for this PET attenuation, a 3-dimensional map of linear attenuation coefficients (μ-map) of the breast coil must be created and incorporated into the PET reconstruction process.more » Several approaches have been proposed for building hardware μ-maps, some of which include the use of conventional kVCT and Dual energy CT. These methods can produce high resolution images based on the electron densities of materials that can be converted into μ-maps. However, imaging hardware containing metal components with photons in the kV range is susceptible to metal artifacts. These artifacts can compromise the accuracy of the resulting μ-map and PET reconstruction; therefore high-Z components should be removed. We propose a method for calculating μ-maps without removing coil components, based on megavoltage (MV) imaging with a linear accelerator that has been detuned for imaging at 1.0MeV. Containers of known geometry with F18 were placed in the breast coil for imaging. A comparison between reconstructions based on the different μ-map construction methods was made. PET reconstructions with our method show a maximum of 6% difference over the existing kVCT-based reconstructions.« less

Measurement of pressure-displacement kinetics of hemoglobin in normal breast tissue with near-infrared spectral imaging.

PubMed

Jiang, Shudong; Pogue, Brian W; Laughney, Ashley M; Kogel, Christine A; Paulsen, Keith D

2009-04-01

Applying localized external displacement to the breast surface can change the interstitial fluid pressure such that regional transient microvascular changes occur in oxygenation and vascular volume. Imaging these dynamic responses over time, while different pressures are applied, could provide selective temporal contrast for cancer relative to the surrounding normal breast. In order to investigate this possibility in normal breast tissue, a near-infrared spectral tomography system was developed that can simultaneously acquire data at three wavelengths with a 15 s time resolution per scan. The system was tested first with heterogeneous blood phantoms. Changes in regional blood concentrations were found to be linearly related to recovered mean hemoglobin concentration (Hb(T)) values (R(2)=0.9). In a series of volunteer breast imaging exams, data from 17 asymptomatic subjects were acquired under increasing and decreasing breast compression. Calculations show that a 10 mm displacement applied to the breast results in surface pressures in the range of 0-55 kPa depending on breast density. The recovered human data indicate that Hb(T) was reduced under compression and the normalized change was significantly correlated to the applied pressure with a p value of 0.005. The maximum Hb(T) decreases in breast tissue were associated with body mass index (BMI), which is a surrogate indicator of breast density. No statistically valid correlations were found between the applied pressure and the changes in tissue oxygen saturation (S(t)O(2)) or water percentage (H(2)O) across the range of BMI values studied.

Measurement of pressure-displacement kinetics of hemoglobin in normal breast tissue with near-infrared spectral imaging

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

Jiang, Shudong; Pogue, Brian W.; Laughney, Ashley M.

2009-04-01

Applying localized external displacement to the breast surface can change the interstitial fluid pressure such that regional transient microvascular changes occur in oxygenation and vascular volume. Imaging these dynamic responses over time, while different pressures are applied, could provide selective temporal contrast for cancer relative to the surrounding normal breast. In order to investigate this possibility in normal breast tissue, a near-infrared spectral tomography system was developed that can simultaneously acquire data at three wavelengths with a 15 s time resolution per scan. The system was tested first with heterogeneous blood phantoms. Changes in regional blood concentrations were found tomore » be linearly related to recovered mean hemoglobin concentration (HbT) values (R{sup 2}=0.9). In a series of volunteer breast imaging exams, data from 17 asymptomatic subjects were acquired under increasing and decreasing breast compression. Calculations show that a 10 mm displacement applied to the breast results in surface pressures in the range of 0-55 kPa depending on breast density. The recovered human data indicate that HbT was reduced under compression and the normalized change was significantly correlated to the applied pressure with a p value of 0.005. The maximum HbT decreases in breast tissue were associated with body mass index (BMI), which is a surrogate indicator of breast density. No statistically valid correlations were found between the applied pressure and the changes in tissue oxygen saturation (StO2) or water percentage (H2O) across the range of BMI values studied.« less

Effects of tamoxifen on bone mineral density and metabolism in postmenopausal women with early-stage breast cancer.

PubMed

Zidan, Jamal; Keidar, Zohar; Basher, Walid; Israel, Ora

2004-01-01

At the present time, tamoxifen is the most widely used anti-estrogen for adjuvant therapy and metastatic disease in postmenopausal women with breast cancer, a population at high risk for osteoporosis. This prospective study was designed to evaluate the effect of adjuvant tamoxifen on bone mineral density and all biochemical markers concomitantly in women with early-stage breast cancer in one study. Using dual-energy X-ray absorptiometry, prior to and 12 mo after tamoxifen treatment, bone mineral density in lumbar spine and femoral neck was measured in 44 women with T1-T2N0M0 estrogen-receptor-positive breast cancer receiving adjuvant treatment with tamoxifen 20 mg/d. Biomarkers that can affect bone mineral metabolism were measured before and after 3 and 12 mo of tamoxifen treatment. Bone mineral density was minimally increased in lumbar spine and femoral neck after 12 mo treatment with tamoxifen (p = 0.79 and 0.55, respectively). No differences were found in serum levels of calcium, phosphate, creatinine, ALAT, albumin, LDH, calcitonin, or estradiol. A significant decrease in osteocalcin levels was found after 3 and 12 mo (p < or = 0.01). TSH and PTH levels were increased (p < or = 0.05) after 3 mo, returning to baseline after 12 mo. In conclusion, tamoxifen has an estrogen-like effect on bone metabolism in postmenopausal women and is associated with preservation of bone mineral density in lumbar spine and femoral neck. Changes in serum concentration of biochemical markers may reflect decreased bone turnover or bone remodeling and add to the understanding of tamoxifen's effect on bone mineral density.

Tumor-infiltrating lymphocyte composition, organization and PD-1/ PD-L1 expression are linked in breast cancer

PubMed Central

Garaud, Soizic; de Wind, Alexandre; Van den Eynden, Gert; Boisson, Anais; Gu-Trantien, Chunyan; Naveaux, Céline; Lodewyckx, Jean-Nicolas; Duvillier, Hugues; Craciun, Ligia; Veys, Isabelle; Larsimont, Denis; Piccart-Gebhart, Martine; Stagg, John; Sotiriou, Christos

2017-01-01

ABSTRACT The clinical relevance of tumor-infiltrating lymphocytes (TIL) in breast cancer (BC) has been clearly established by their demonstrated correlation with long-term positive outcomes. Nevertheless, the relationship between protective immunity, observed in some patients, and critical features of the infiltrate remains unresolved. This study examined TIL density, composition and organization together with PD-1 and PD-L1 expression in freshly collected and paraffin-embedded tissues from 125 patients with invasive primary BC. Tumor and normal breast tissues were analyzed using both flow cytometry and immunohistochemistry. TIL density distribution is a continuum with 25% of tumors identified as TIL-negative at a TIL density equivalent to normal breast tissues. TIL-positive tumors (75%) were equally divided into TIL-intermediate and TIL-high. Tumors had higher mean frequencies of CD4+ T cells and CD19+ B cells and a lower mean frequency of CD8+ T cells compare with normal tissues, increasing the CD4+/CD8+ T-cell ratio. Tertiary lymphoid structures (TLS), principally located in the peri-tumoral stroma, were detected in 60% of tumors and correlated with higher TIL infiltration. PD-1 and PD-L1 expression were also associated with higher TIL densities and TLS. TIL density, TLS and PD-L1 expression were correlated with more aggressive tumor characteristics, including higher proliferation and hormone receptor negativity. Our findings reveal an important relationship between PD-1/PD-L1 expression, increased CD4+ T and B-cell infiltration, TIL density and TLS, suggesting that evaluating not only the extent but also the nature and location of the immune infiltrate should be considered when evaluating antitumor immunity and the potential for benefit from immunotherapies. PMID:28197375

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2011-10-01

1.08 relative to ញ years) or birth order (2+ OR 0.99, 95% CI 0.67-1.46 relative to 1) and breast cancer. However, there was a significant...increase in breast cancer risk among women whose mothers smoked during pregnancy (OR 1.73, 95% CI 1.04-2.88). With the exception of birth order , our

Total Xenoestrogen Body Burden in Relation to Mammographic Density, a Marker of Breast Cancer Risk

DTIC Science & Technology

2009-10-01

Conference.1  Funding has been obtained from the Susan Komen Foundation for an ancillary study of sex hormones and breast density. The Komen...Foundation is providing funds to analyze sex hormone levels in the blood samples obtained in this study. The relation between sex hormone levels and...menopause Use of birth control pills or female hormones Hysterectomy (removal of the uterus) Removal of one or both ovaries Other: Please

Hispanic and Immigrant Paradoxes in U.S. Breast Cancer Mortality: Impact of Neighborhood Poverty and Hispanic Density.

PubMed

Pruitt, Sandi L; Tiro, Jasmin A; Xuan, Lei; Lee, Simon J Craddock

2016-12-14

To test the Hispanic and Immigrant Paradoxes-i.e., survival advantages despite a worse risk factor profile-and the modifying role of neighborhood context, we examined associations between patient ethnicity, birthplace, neighborhood Hispanic density and neighborhood poverty among 166,254 female breast cancer patients diagnosed 1995-2009 in Texas, U.S. Of all, 79.9% were non-Hispanic White, 15.8% Hispanic U.S.-born, and 4.2% Hispanic foreign-born. We imputed birthplace for the 60.7% of Hispanics missing birthplace data using multiple imputation. Shared frailty Cox proportional hazard models (patients nested within census tracts) adjusted for age, diagnosis year, stage, grade, histology, urban/rural residence, and local mammography capacity. Whites (vs. U.S.-born Hispanics) had increased all-cause and breast cancer mortality. Foreign-born (vs. U.S.-born) Hispanics had increased all-cause and breast cancer mortality. Living in higher Hispanic density neighborhoods was generally associated with increased mortality, although associations differed slightly in magnitude and significance by ethnicity, birthplace, and neighborhood poverty. We found no evidence of an Immigrant Paradox and some evidence of a Hispanic Paradox where protective effects were limited to U.S.-born Hispanics. Contrary to prior studies, foreign birthplace and residence in higher Hispanic density neighborhoods were associated with increased mortality. More research on intersections between ethnicity, birthplace and neighborhood context are needed.

Correlates of mammographic density in B-mode ultrasound and real time elastography.

PubMed

Jud, Sebastian Michael; Häberle, Lothar; Fasching, Peter A; Heusinger, Katharina; Hack, Carolin; Faschingbauer, Florian; Uder, Michael; Wittenberg, Thomas; Wagner, Florian; Meier-Meitinger, Martina; Schulz-Wendtland, Rüdiger; Beckmann, Matthias W; Adamietz, Boris R

2012-07-01

The aim of our study involved the assessment of B-mode imaging and elastography with regard to their ability to predict mammographic density (MD) without X-rays. Women, who underwent routine mammography, were prospectively examined with additional B-mode ultrasound and elastography. MD was assessed quantitatively with a computer-assisted method (Madena). The B-mode and elastography images were assessed by histograms with equally sized gray-level intervals. Regression models were built and cross validated to examine the ability to predict MD. The results of this study showed that B-mode imaging and elastography were able to predict MD. B-mode seemed to give a more accurate prediction. R for B-mode image and elastography were 0.67 and 0.44, respectively. Areas in the B-mode images that correlated with mammographic dense areas were either dark gray or of intermediate gray levels. Concerning elastography only the gray levels that represent extremely stiff tissue correlated positively with MD. In conclusion, ultrasound seems to be able to predict MD. Easy and cheap utilization of regular breast ultrasound machines encourages the use of ultrasound in larger case-control studies to validate this method as a breast cancer risk predictor. Furthermore, the application of ultrasound for breast tissue characterization could enable comprehensive research concerning breast cancer risk and breast density in young and pregnant women.

Effects of age at first pregnancy and breast-feeding on the development of postmenopausal osteoporosis.

PubMed

Schnatz, Peter F; Barker, Kathaleen G; Marakovits, Kimberly A; O'Sullivan, David M

2010-01-01

Although pregnancy and breast-feeding require adequate calcium mobilization, it is not known if these affect the acquisition of a healthy peak bone mass (PBM) and, hence, postmenopausal osteoporosis (OPS). The objective of this study was to analyze previous pregnancies and/or breast-feeding and their association with OPS. After obtaining institutional review board approval, postmenopausal women (>49 y) presenting for a dual-energy x-ray absorptiometry bone density scan were invited to participate. Risk factors for OPS, including previous fractures, pregnancy information, and dual-energy x-ray absorptiometry results, were collected. OPS was defined as a T score of -2.5 or lower. Data were obtained from 619 women. Of these, 49.8% were smokers, 27.2% used a bisphosphonate, 64.1% used hormone therapy, and 5.5% had used steroids. Based on PBM, ages at first pregnancy were dichotomized to younger than 27 years and 27 years or older. Women with a history of breast-feeding had a lower prevalence of OPS (7.6%) versus women who had never breast-fed (18.7%; P < 0.001). Women with a first pregnancy when they were 27 years or older and a history of breast-feeding had the lowest prevalence of OPS (4.6%) versus women with a first pregnancy when they were younger than 27 years and no history of breast-feeding (16.3%; P = 0.001). Breast-feeding seems to significantly decrease the incidence of postmenopausal OPS. Women whose first pregnancy occurs after PBM (≥27 y of age) and who have a history of breast-feeding had the lowest prevalence of OPS. Thus, an association between OPS and both breast-feeding and age of pregnancy seems to be present.

Positive predictive values by mammographic density and screening mode in the Norwegian Breast Cancer Screening Program.

PubMed

Moshina, Nataliia; Ursin, Giske; Roman, Marta; Sebuødegård, Sofie; Hofvind, Solveig

2016-01-01

To investigate the probability of breast cancer among women recalled due to abnormal findings on the screening mammograms (PPV-1) and among women who underwent an invasive procedure (PPV-2) by mammographic density (MD), screening mode and age. We used information about 28,826 recall examinations from 26,951 subsequently screened women in the Norwegian Breast Cancer Screening Program, 1996-2010. The radiologists who performed the recall examinations subjectively classified MD on the mammograms into three categories: fatty (<30% fibroglandular tissue); medium dense (30-70%) and dense (>70%). Screening mode was defined as screen-film mammography (SFM) and full-field digital mammography (FFDM). We examined trends of PPVs by MD, screening mode and age. We used logistic regression to estimate odds ratio (OR) of screen-detected breast cancer associated with MD among women recalled, adjusting for screening mode and age. PPV-1 and PPV-2 decreased by increasing MD, regardless of screening mode (p for trend <0.05 for both PPVs). PPV-1 and PPV-2 were statistically significantly higher for FFDM compared with SFM for women with fatty breasts. Among women recalled, the adjusted OR of breast cancer decreased with increasing MD. Compared with women with fatty breasts, the OR was 0.90 (95% CI: 0.84-0.96) for those with medium dense breasts and 0.85 (95% CI: 0.76-0.95) for those with dense breasts. PPVs decreased by increasing MD. Fewer women needed to be recalled or undergo an invasive procedure to detect one breast cancer among those with fatty versus dense breasts in the screening program in Norway, 1996-2010. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Can a totally different approach to soft tissue computer aided detection (CADe) result in affecting radiologists' decisions?

NASA Astrophysics Data System (ADS)

Gur, David

2018-03-01

We tested whether a case based CADe scheme, developed only on negatively interpreted screening mammograms, has predictive value for cancer detection during subsequent screening and how this approach may affect radiologists' performances when alerting them to a small subset ( 15%) of exams on which radiologists tend to miss cancers. A series of six parameters case based CADe schemes, using 200 negative mammograms (800 images 100 women with breast cancer at subsequent screening and 100 women who remained negative), carefully matched by age and breast density, were optimized. CADe alone schemes performed at AUC=0.68 (+/- 0.01). Five radiologists and 4 residents interpreted the same cases and performed at AUC =0.71 (experienced radiologists) and AUC= 0.61 (residents). With the "CADe warnings" shown to the interpreters only if they did not recall one of 24 highest CADe scoring cases, assisted performance of radiologists and residents respectively, were 0.71 and 0.63 (p>0.05). However, when the CADe alone performance was raised to an AUC=0.78, by artificially increasing the number of possible warnings from 16 to 24, radiologists' performances significantly improved from an AUC of 0.68 to 0.72 (p<0.05). In conclusion, the use case based information other than breast density could highlight a small fraction of women whose cancers are more likely to be missed by radiologists and later detected during subsequent mammograms, thereby, leading to an assisted approach that improves radiologists' performances. However, to be effective, the performance of the CADe alone should be substantially higher (e.g. ΔAUC >=0.07) than that of the un-assisted radiologist.

Evaluation of the absorbed dose to the breast using radiochromic film in a dedicated CT mammotomography system employing a quasi-monochromatic x-ray beam.

PubMed

Crotty, Dominic J; Brady, Samuel L; Jackson, D'Vone C; Toncheva, Greta I; Anderson, Colin E; Yoshizumi, Terry T; Tornai, Martin P

2011-06-01

A dual modality SPECT-CT prototype system dedicated to uncompressed breast imaging (mammotomography) has been developed. The computed tomography subsystem incorporates an ultrathick K-edge filtration technique producing a quasi-monochromatic x-ray cone beam that optimizes the dose efficiency of the system for lesion imaging in an uncompressed breast. Here, the absorbed dose in various geometric phantoms and in an uncompressed and pendant cadaveric breast using a normal tomographic cone beam imaging protocol is characterized using both thermoluminescent dosimeter (TLD) measurements and ionization chamber-calibrated radiochromic film. Initially, two geometric phantoms and an anthropomorphic breast phantom are filled in turn with oil and water to simulate the dose to objects that mimic various breast shapes having effective density bounds of 100% fatty and glandular breast compositions, respectively. Ultimately, an excised human cadaver breast is tomographically scanned using the normal tomographic imaging protocol, and the dose to the breast tissue is evaluated and compared to the earlier phantom-based measurements. Measured trends in dose distribution across all breast geometric and anthropomorphic phantom volumes indicate lower doses in the medial breast and more proximal to the chest wall, with consequently higher doses near the lateral peripheries and nipple regions. Measured doses to the oil-filled phantoms are consistently lower across all volume shapes due to the reduced mass energy-absorption coefficient of oil relative to water. The mean measured dose to the breast cadaver, composed of adipose and glandular tissues, was measured to be 4.2 mGy compared to a mean whole-breast dose of 3.8 and 4.5 mGy for the oil- and water-filled anthropomorphic breast phantoms, respectively. Assuming rotational symmetry due to the tomographic acquisition exposures, these results characterize the 3D dose distributions in an uncompressed human breast tissue volume for this dedicated breast imaging device and illustrate advantages of using the novel ultrathick K-edge filtered beam to minimize the dose to the breast during fully-3D imaging.

Evaluation of the absorbed dose to the breast using radiochromic film in a dedicated CT mammotomography system employing a quasi-monochromatic x-ray beam

PubMed Central

Crotty, Dominic J.; Brady, Samuel L.; Jackson, D’Vone C.; Toncheva, Greta I.; Anderson, Colin E.; Yoshizumi, Terry T.; Tornai, Martin P.

2011-01-01

Purpose: A dual modality SPECT-CT prototype system dedicated to uncompressed breast imaging (mammotomography) has been developed. The computed tomography subsystem incorporates an ultrathick K-edge filtration technique producing a quasi-monochromatic x-ray cone beam that optimizes the dose efficiency of the system for lesion imaging in an uncompressed breast. Here, the absorbed dose in various geometric phantoms and in an uncompressed and pendant cadaveric breast using a normal tomographic cone beam imaging protocol is characterized using both thermoluminescent dosimeter (TLD) measurements and ionization chamber-calibrated radiochromic film. Methods: Initially, two geometric phantoms and an anthropomorphic breast phantom are filled in turn with oil and water to simulate the dose to objects that mimic various breast shapes having effective density bounds of 100% fatty and glandular breast compositions, respectively. Ultimately, an excised human cadaver breast is tomographically scanned using the normal tomographic imaging protocol, and the dose to the breast tissue is evaluated and compared to the earlier phantom-based measurements. Results: Measured trends in dose distribution across all breast geometric and anthropomorphic phantom volumes indicate lower doses in the medial breast and more proximal to the chest wall, with consequently higher doses near the lateral peripheries and nipple regions. Measured doses to the oil-filled phantoms are consistently lower across all volume shapes due to the reduced mass energy-absorption coefficient of oil relative to water. The mean measured dose to the breast cadaver, composed of adipose and glandular tissues, was measured to be 4.2 mGy compared to a mean whole-breast dose of 3.8 and 4.5 mGy for the oil- and water-filled anthropomorphic breast phantoms, respectively. Conclusions: Assuming rotational symmetry due to the tomographic acquisition exposures, these results characterize the 3D dose distributions in an uncompressed human breast tissue volume for this dedicated breast imaging device and illustrate advantages of using the novel ultrathick K-edge filtered beam to minimize the dose to the breast during fully-3D imaging. PMID:21815398

Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography.

PubMed

Stout, Natasha K; Lee, Sandra J; Schechter, Clyde B; Kerlikowske, Karla; Alagoz, Oguzhan; Berry, Donald; Buist, Diana S M; Cevik, Mucahit; Chisholm, Gary; de Koning, Harry J; Huang, Hui; Hubbard, Rebecca A; Miglioretti, Diana L; Munsell, Mark F; Trentham-Dietz, Amy; van Ravesteyn, Nicolien T; Tosteson, Anna N A; Mandelblatt, Jeanne S

2014-06-01

Compared with film, digital mammography has superior sensitivity but lower specificity for women aged 40 to 49 years and women with dense breasts. Digital has replaced film in virtually all US facilities, but overall population health and cost from use of this technology are unclear. Using five independent models, we compared digital screening strategies starting at age 40 or 50 years applied annually, biennially, or based on density with biennial film screening from ages 50 to 74 years and with no screening. Common data elements included cancer incidence and test performance, both modified by breast density. Lifetime outcomes included mortality, quality-adjusted life-years, and screening and treatment costs. For every 1000 women screened biennially from age 50 to 74 years, switching to digital from film yielded a median within-model improvement of 2 life-years, 0.27 additional deaths averted, 220 additional false-positive results, and $0.35 million more in costs. For an individual woman, this translates to a health gain of 0.73 days. Extending biennial digital screening to women ages 40 to 49 years was cost-effective, although results were sensitive to quality-of-life decrements related to screening and false positives. Targeting annual screening by density yielded similar outcomes to targeting by age. Annual screening approaches could increase costs to $5.26 million per 1000 women, in part because of higher numbers of screens and false positives, and were not efficient or cost-effective. The transition to digital breast cancer screening in the United States increased total costs for small added health benefits. The value of digital mammography screening among women aged 40 to 49 years depends on women's preferences regarding false positives. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Extraction of breast lesions from ultrasound imagery: Bhattacharyya gradient flow approach

NASA Astrophysics Data System (ADS)

Torkaman, Mahsa; Sandhu, Romeil; Tannenbaum, Allen

2018-03-01

Breast cancer is one of the most commonly diagnosed neoplasms among American women and the second leading cause of death among women all over the world. In order to reduce the mortality rate and cost of treatment, early diagnosis and treatment are essential. Accurate and reliable diagnosis is required in order to ensure the most effective treatment and a second opinion is often advisable. In this paper, we address the problem of breast lesion detection from ultrasound imagery by means of active contours, whose evolution is driven by maximizing the Bhattacharyya distance1 between the probability density functions (PDFs). The proposed method was applied to ultrasound breast imagery, and the lesion boundary was obtained by maximizing the distance-based energy functional such that the maximum (optimal contour) is attained at the boundary of the potential lesion. We compared the results of the proposed method quantitatively using the Dice coefficient (similarity index)2 to well-known GrowCut segmentation method3 and demonstrated that Bhattacharyya approach outperforms GrowCut in most of the cases.

Clinical photoacoustic computed tomography of the human breast in vivo within a single breath hold

NASA Astrophysics Data System (ADS)

Lin, Li; Hu, Peng; Shi, Junhui; Appleton, Catherine M.; Maslov, Konstantin; Wang, Lihong V.

2018-03-01

We have developed a single-breath-hold photoacoustic computed tomography (SBH-PACT) system to detect tumors and reveal detailed angiographic information about human breasts. SBH-PACT provides high spatial and temporal resolutions with a deep in vivo penetration depth of over 4 cm. A volumetric breast image can be acquired by scanning the breast within a single breath hold ( 15 sec). We imaged a healthy female volunteer and seven breast cancer patients. SBH-PACT clearly identified all tumors by revealing higher blood vessel densities and lower compliance associated with the tumors

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

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

Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement.

PubMed

Siu, Albert L

2016-02-16

Update of the 2009 U.S. Preventive Services Task Force (USPSTF) recommendation on screening for breast cancer. The USPSTF reviewed the evidence on the following: effectiveness of breast cancer screening in reducing breast cancer-specific and all-cause mortality, as well as the incidence of advanced breast cancer and treatment-related morbidity; harms of breast cancer screening; test performance characteristics of digital breast tomosynthesis as a primary screening strategy; and adjunctive screening in women with increased breast density. In addition, the USPSTF reviewed comparative decision models on optimal starting and stopping ages and intervals for screening mammography; how breast density, breast cancer risk, and comorbidity level affect the balance of benefit and harms of screening mammography; and the number of radiation-induced breast cancer cases and deaths associated with different screening mammography strategies over the course of a woman's lifetime. This recommendation applies to asymptomatic women aged 40 years or older who do not have preexisting breast cancer or a previously diagnosed high-risk breast lesion and who are not at high risk for breast cancer because of a known underlying genetic mutation (such as a BRCA1 or BRCA2 gene mutation or other familial breast cancer syndrome) or a history of chest radiation at a young age. The USPSTF recommends biennial screening mammography for women aged 50 to 74 years. (B recommendation) The decision to start screening mammography in women prior to age 50 years should be an individual one. Women who place a higher value on the potential benefit than the potential harms may choose to begin biennial screening between the ages of 40 and 49 years. (C recommendation) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening mammography in women aged 75 years or older. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the benefits and harms of digital breast tomosynthesis (DBT) as a primary screening method for breast cancer. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of adjunctive screening for breast cancer using breast ultrasonography, magnetic resonance imaging (MRI), DBT, or other methods in women identified to have dense breasts on an otherwise negative screening mammogram. (I statement).

Comparison Between Digital and Synthetic 2D Mammograms in Breast Density Interpretation.

PubMed

Alshafeiy, Taghreed I; Wadih, Antoine; Nicholson, Brandi T; Rochman, Carrie M; Peppard, Heather R; Patrie, James T; Harvey, Jennifer A

2017-07-01

The purpose of this study was to compare assessments of breast density on synthetic 2D images as compared with digital 2D mammograms. This retrospective study included consecutive women undergoing screening with digital 2D mammography and tomosynthesis during May 2015 with a negative or benign outcome. In separate reading sessions, three radiologists with 5-25 years of clinical experience and 1 year of experience with synthetic 2D mammography read digital 2D and synthetic 2D images and assigned breast density categories according to the 5th edition of BI-RADS. Inter- and intrareader agreement was assessed for each BI-RADS density assessment and combined dense and nondense categories using percent agreement and Cohen kappa coefficient for consensus and all reads. A total of 309 patients met study inclusion criteria. Agreement between consensus BI-RADS density categories assigned for digital and synthetic 2D mammography was 80.3% (95% CI, 75.4-84.5%) with κ = 0.73 (95% CI, 0.66-0.79). For combined dense and nondense categories, agreement reached 91.9% (95% CI, 88.2-94.7%). For consensus readings, similar numbers of patients were shifted between nondense and dense categories (11 and 14, respectively) with the synthetic 2D compared with digital 2D mammography. Interreader differences were apparent; assignment to dense categories was greater with digital 2D mammography for reader 1 (odds ratio [OR], 1.26; p = 0.002), the same for reader 2 (OR, 0.91; p = 0.262), and greater with synthetic 2D mammography for reader 3 (OR, 0.86; p = 0.033). Overall, synthetic 2D mammography is comparable with digital 2D mammography in assessment of breast density, though there is some variability by reader. Practices can readily adopt synthetic 2D mammography without concern that it will affect density assessment and subsequent recommendations for supplemental screening.

Cancer treatment-induced bone loss in premenopausal women: a need for therapeutic intervention?

PubMed

Hadji, P; Gnant, M; Body, J J; Bundred, N J; Brufsky, A; Coleman, R E; Guise, T A; Lipton, A; Aapro, M S

2012-10-01

Current clinical treatment guidelines recommend cytotoxic chemotherapy, endocrine therapy, or both (with targeted therapy if indicated) for premenopausal women with early-stage breast cancer, depending on the biologic characteristics of the primary tumor. Some of these therapies can induce premature menopause or are specifically designed to suppress ovarian function and reduce circulating estrogen levels. In addition to bone loss associated with low estrogen levels, cytotoxic chemotherapy may have a direct negative effect on bone metabolism. As a result, cancer treatment-induced bone loss poses a significant threat to bone health in premenopausal women with breast cancer. Clinical trials of antiresorptive therapies, such as bisphosphonates, have demonstrated the ability to slow or prevent bone loss in this setting. Current fracture risk assessment tools are based on data from healthy postmenopausal women and do not adequately address the risks associated with breast cancer therapy, especially in younger premenopausal women. We therefore recommend that all premenopausal women with breast cancer be informed about the potential risk of bone loss prior to beginning anticancer therapy. Women who experience amenorrhea should have bone mineral density assessed by dual-energy X-ray absorptiometry and receive regular follow-up to monitor bone health. Regular exercise and daily calcium and vitamin D supplementation are recommended. Women with a Z-score <-2.0 or Z-score ≤-1.0 and/or a 5-10% annual decrease in bone mineral density should be considered for bisphosphonate therapy in addition to calcium and vitamin D supplements. Copyright © 2012 Elsevier Ltd. All rights reserved.

Can breast cancer patients use soyafoods to help reduce risk of CHD?

PubMed

Messina, Mark; Messina, Virginia; Jenkins, David J A

2012-09-01

Over the past 20 years, the popularity of soyafoods has increased in part because of research suggesting that these foods convey health benefits independent of their nutrient content. For example, in 1999, the US Food and Drug Administration approved a health-claim for soyafoods and CHD based on the hypocholesterolaemic effects of soya protein. However, soyafoods have become controversial in recent years because of concerns that their uniquely rich phyto-oestrogen (isoflavone) content may cause untoward effects in some individuals. Most notable in this regard is the concern that soyafoods are contraindicated for breast cancer patients and women at high risk of developing this disease. Furthermore, the hypocholesterolaemic effects of soya protein have been challenged. However, the results of recently published meta-analyses indicate that soya protein directly lowers circulating LDL-cholesterol levels by approximately 4 %. There is also intriguing evidence that soyafoods reduce CHD risk independent of their effects on lipid levels. In regard to the breast cancer controversy, recently published clinical and epidemiological data do not support observations in rodents that soyabean isoflavones increase breast cancer risk. In postmenopausal women, isoflavone exposure does not adversely affect breast tissue density or breast cell proliferation. Furthermore, both US and Chinese prospective epidemiological studies show that post-diagnosis soya consumption is associated with an improved prognosis. Therefore, soyafoods should be considered by women as healthy foods to include in diets aimed at reducing the risk of CHD regardless of their breast cancer status.

Geographic Disparity in the Use of Hypofractionated Radiation Therapy Among Elderly Women Undergoing Breast Conservation for Invasive Breast Cancer

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

Gillespie, Erin F.; Matsuno, Rayna K.; Xu, Beibei

Purpose: To evaluate geographic heterogeneity in the delivery of hypofractionated radiation therapy (RT) for breast cancer among Medicare beneficiaries across the United States. Methods and Materials: We identified 190,193 patients from the Centers for Medicare and Medicaid Services Chronic Conditions Warehouse. The study included patients aged >65 years diagnosed with invasive breast cancer treated with breast conservation surgery followed by radiation diagnosed between 2000 and 2012. We analyzed data by hospital referral region based on patient residency ZIP code. The proportion of women who received hypofractionated RT within each region was analyzed over the study period. Multivariable logistic regression models identified predictors ofmore » hypofractionated RT. Results: Over the entire study period we found substantial geographic heterogeneity in the use of hypofractionated RT. The proportion of women receiving hypofractionated breast RT in individual hospital referral regions varied from 0% to 61%. We found no correlation between the use of hypofractionated RT and urban/rural setting or general geographic region. The proportion of hypofractionated RT increased in regions with higher density of radiation oncologists, as well as lower total Medicare reimbursements. Conclusions: This study demonstrates substantial geographic heterogeneity in the use of hypofractionated RT among elderly women with invasive breast cancer treated with lumpectomy in the United States. This heterogeneity persists despite clinical data from multiple randomized trials proving efficacy and safety compared with standard fractionation, and highlights possible inefficiency in health care delivery.« less

Occupational exposures and mammographic density in Spanish women.

PubMed

Lope, Virginia; García-Pérez, Javier; Pérez-Gómez, Beatriz; Pedraza-Flechas, Ana María; Alguacil, Juan; González-Galarzo, Mª Carmen; Alba, Miguel Angel; van der Haar, Rudolf; Cortés-Barragán, Rosa Ana; Pedraz-Pingarrón, Carmen; Moreo, Pilar; Santamariña, Carmen; Ederra, María; Vidal, Carmen; Salas-Trejo, Dolores; Sánchez-Contador, Carmen; Llobet, Rafael; Pollán, Marina

2018-02-01

The association between occupational exposures and mammographic density (MD), a marker of breast cancer risk, has not been previously explored. Our objective was to investigate the influence of occupational exposure to chemical, physical and microbiological agents on MD in adult women. This is a population-based cross-sectional study based on 1476 female workers aged 45-65 years from seven Spanish breast cancer screening programmes. Occupational history was surveyed by trained staff. Exposure to occupational agents was assessed using the Spanish job-exposure matrix MatEmESp. Percentage of MD was measured by two radiologists using a semiautomatic computer tool. The association was estimated using mixed log-linear regression models adjusting for age, education, body mass index, menopausal status, parity, smoking, alcohol intake, type of mammography, family history of breast cancer and hormonal therapy use, and including screening centre and professional reader as random effects terms. Although no association was found with most of the agents, women occupationally exposed to perchloroethylene (e β =1.51; 95% CI 1.04 to 2.19), ionising radiation (e β =1.23; 95% CI 0.99 to 1.52) and mould spores (e β =1.44; 95% CI 1.01 to 2.04) tended to have higher MD. The percentage of density increased 12% for every 5 years exposure to perchloroethylene or mould spores, 11% for every 5 years exposure to aliphatic/alicyclic hydrocarbon solvents and 3% for each 5 years exposure to ionising radiation. Exposure to perchloroethylene, ionising radiation, mould spores or aliphatic/alicyclic hydrocarbon solvents in occupational settings could be associated with higher MD. Further studies are needed to clarify the accuracy and the reasons for these findings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Development of array piezoelectric fingers towards in vivo breast tumor detection

NASA Astrophysics Data System (ADS)

Xu, Xin; Chung, Youngsoo; Brooks, Ari D.; Shih, Wei-Heng; Shih, Wan Y.

2016-12-01

We have investigated the development of a handheld 4 × 1 piezoelectric finger (PEF) array breast tumor detector system towards in vivo patient testing, particularly, on how the duration of the DC applied voltage, the depression depth of the handheld unit, and breast density affect the PEF detection sensitivity on 40 patients. The tests were blinded and carried out in four phases: with DC voltage durations 5, 3, 2, to 0.8 s corresponding to scanning a quadrant, a half, a whole breast, and both breasts within 30 min, respectively. The results showed that PEF detection sensitivity was unaffected by shortening the applied voltage duration from 5 to 0.8 s nor was it affected by increasing the depression depth from 2 to 6 mm. Over the 40 patients, PEF detected 46 of the 48 lesions (46/48)—with the smallest lesion detected being 5 mm in size. Of 28 patients (some have more than one lesion) with mammography records, PEF detected 31/33 of all lesions (94%) and 14/15 of malignant lesions (93%), while mammography detected 30/33 of all lesions (91%) and 12/15 of malignant lesions (80%), indicating that PEF could detect malignant lesions not detectable by mammography without significantly increasing false positives. PEF's detection sensitivity is also shown to be independent of breast density, suggesting that PEF could be a potential tool for detecting breast cancer in young women and women with dense breasts.

Clinicopathologic and prognostic implications of progranulin in breast carcinoma.

PubMed

Li, Li-qin; Huang, Hui-lian; Ping, Jin-liang; Wang, Xiao-hong; Zhong, Jing; Dai, Li-cheng

2011-07-05

Progranulin is a newly discovered 88-kDa glycoprotein originally purified from the highly tumorigenic mouse teratoma-derived cell line PC. Its expression is closely correlated with the development and metastasis of several cancers. However, no immunohistochemical evidence currently exists to correlate progranulin expression with clinicopathologic features in breast carcinoma biopsies, and the role of progranulin as a new marker of metastatic risk and prognosis in breast cancer has not yet been studied. The aim of this study was to investigate the clinicopathologic and prognostic implications of progranulin expression in breast carcinoma and its correlation with tumor angiogenesis. Progranulin expression was determined immunohistochemically in 183 surgical specimens from patients with breast cancer and 20 tissue samples from breast fibroadenomas. The tumor angiogenesis-related biomarker, vascular endothelial growth factor was assayed and microvessel density was assessed by counting vascular endothelial cells in tumor tissues labeled with endoglin antibody. The relationship between progranulin expression and the clinicopathologic data were analyzed. Progranulin proteins were overexpressed in breast cancer. The level of progranulin expression was significantly correlated with tumor size (P = 0.004), lymph node metastasis (P < 0.001) and TNM staging (P < 0.001). High progranulin expression was associated with higher tumor angiogenesis, reflected by increased vascular endothelial growth factor expression (P < 0.001) and higher microvessel density (P = 0.002). Progranulin may be a valuable marker for assessing the metastasis and prognosis of breast cancer, and could provide the basis for new combination regimens with antiangiogenic activity.

Potential impact of legislation mandating breast density notification: benefits, harms, and cost effectiveness of supplemental ultrasound screening

PubMed Central

Sprague, Brian L.; Stout, Natasha K.; Schechter, Clyde; van Ravesteyn, Nicolien T.; Cevik, Mucahit; Alagoz, Oguzhan; Lee, Christoph I.; van den Broek, Jeroen J.; Miglioretti, Diana L.; Mandelblatt, Jeanne S.; de Koning, Harry J.; Kerlikowske, Karla; Lehman, Constance D.; Tosteson, Anna N. A.

2014-01-01

Background At least nineteen states have laws that require telling women with dense breasts and a negative screening mammogram to consider supplemental screening. The most readily available supplemental screening modality is ultrasound, yet little is known about its effectiveness. Objective To evaluate the benefits, harms, and cost-effectiveness of supplemental ultrasound screening for women with dense breasts. Design Comparative modeling with 3 validated simulation models. Data Sources Surveillance, Epidemiology, and End Results Program; Breast Cancer Surveillance Consortium; the medical literature. Target Population A contemporary cohort of women eligible for routine screening. Time Horizon Lifetime. Perspective Payer. Interventions Supplemental ultrasound screening for women with dense breasts following a negative screening mammogram. Outcome Measures Breast cancer deaths averted, quality-adjusted life years (QALYs) gained, false positive ultrasound biopsy recommendations, costs, costs per QALY gained. Results of Base-Case Analysis Supplemental ultrasound screening after a negative mammogram for women aged 50–74 with heterogeneously or extremely dense breasts averted 0.36 additional breast cancer deaths (range across models: 0.14–0.75), gained 1.7 QALYs (0.9–4.7), and resulted in 354 false-positive ultrasound biopsy recommendations (345–421) per 1000 women with dense breasts compared with biennial screening by mammography alone. The cost-effectiveness ratio was $325,000 per QALY gained ($112,000-$766,000). Restricting supplemental ultrasound screening to women with extremely dense breasts cost $246,000 per QALY gained ($74,000-$535,000). Results of Sensitivity Analysis The conclusions were not sensitive to ultrasound performance characteristics, screening frequency, or starting age. Limitations Provider costs for coordinating supplemental ultrasound were not considered. Conclusions Supplemental ultrasound screening for women with dense breasts undergoing screening mammography would substantially increase costs while producing relatively small benefits in breast cancer deaths averted and QALYs gained. Primary Funding Source National Institutes of Health PMID:25486550

The potential contribution of dietary factors to breast cancer prevention.

PubMed

Shapira, Niva

2017-09-01

Breast cancer (BC), the leading cancer in women, is increasing in prevalence worldwide, concurrent with western metabolic epidemics, that is, obesity, metabolic syndrome, and diabetes, and shares major risk factors with these diseases. The corresponding potential for nutritional contributions toward BC prevention is reviewed and related to critical stages in the life cycle and their implications for carcinogenic and pathometabolic trajectories. BC initiation potentially involves diet-related pro-oxidative, inflammatory, and procarcinogenic processes, that interact through combined lipid/fatty acid peroxidation, estrogen metabolism, and related DNA-adduct/depurination/mutation formation. The pathometabolic trajectory is affected by high estrogen, insulin, and growth factor cascades and resultant accelerated proliferation/progression. Anthropometric risk factors - high birth weight, adult tallness, adiposity/BMI, and weight gain - are often reflective of these trends. A sex-based nutritional approach targets women's specific risk in western obesogenic environments, associated with increasing fatness, estrogen metabolism, n-6 : n-3 polyunsaturated fatty acid ratio, and n-6 polyunsaturated fatty acid conversion to proinflammatory/carcinogenic eicosanoids, and effects of timing of life events, for example, ages at menarche, full-term pregnancy, and menopause. Recent large-scale studies have confirmed the effectiveness of the evidence-based recommendations against BC risk, emphasizing low-energy density diets, highly nutritious plant-based regimes, physical activity, and body/abdominal adiposity management. Better understanding of dietary inter-relationships with BC, as applied to food intake, selection, combination, and processing/preparation, and recommended patterns, for example, Mediterranean, DASH, plant-based, low energy density, and low glycemic load, with high nutrient/phytonutrient density, would increase public motivation and authoritative support for early/timely prevention, optimally merging with other dietary/health goals, for lifelong BC prevention.

Association of Catechol-O-methyltransferase polymorphism Val158Met and mammographic density: A meta-analysis.

PubMed

Kallionpää, Roope A; Uusitalo, Elina; Peltonen, Juha

2017-08-15

The Val158Met polymorphism in catechol-O-methyltransferase (COMT) enzyme reduces the methylation of catechol estrogens, which may affect mammographic density. High mammographic density is a known risk factor of breast cancer. Our aim was to perform meta-analysis of the effect of COMT Val158Met polymorphism on mammographic density. Original studies reporting data on mammographic density, stratified by the presence of COMT Val158Met polymorphism, were identified and combined using genetic models Met/Val vs. Val/Val, Met/Met vs. Val/Val, Val/Met+Met/Met vs. Val/Val (dominant model) and Met/Met vs. Val/Met+Val/Val (recessive model). Subgroup analyses by breast cancer status, menopausal status and use of hormone replacement therapy (HRT) were also performed. Eight studies were included in the meta-analysis. The overall effect in percent mammographic density was -1.41 (CI -2.86 to 0.05; P=0.06) in the recessive model. Exclusion of breast cancer patients increased the effect size to -1.93 (CI -3.49 to -0.37; P=0.02). The results suggested opposite effect of COMT Val158Met for postmenopausal users of HRT versus premenopausal women or postmenopausal non-users of HRT. COMT Val158Met polymorphism may be associated with mammographic density at least in healthy women. Menopausal status and HRT should be taken into account in future studies to avoid masking of the underlying effects. Copyright © 2017 Elsevier B.V. All rights reserved.

Contrast-enhanced digital mammography (CEDM): imaging modeling, computer simulations, and phantom study

NASA Astrophysics Data System (ADS)

Chen, Biao; Jing, Zhenxue; Smith, Andrew

2005-04-01

Contrast enhanced digital mammography (CEDM), which is based upon the analysis of a series of x-ray projection images acquired before/after the administration of contrast agents, may provide physicians critical physiologic and morphologic information of breast lesions to determine the malignancy of lesions. This paper proposes to combine the kinetic analysis (KA) of contrast agent uptake/washout process and the dual-energy (DE) contrast enhancement together to formulate a hybrid contrast enhanced breast-imaging framework. The quantitative characteristics of materials and imaging components in the x-ray imaging chain, including x-ray tube (tungsten) spectrum, filter, breast tissues/lesions, contrast agents (non-ionized iodine solution), and selenium detector, were systematically modeled. The contrast-noise-ration (CNR) of iodinated lesions and mean absorbed glandular dose were estimated mathematically. The x-ray techniques optimization was conducted through a series of computer simulations to find the optimal tube voltage, filter thickness, and exposure levels for various breast thicknesses, breast density, and detectable contrast agent concentration levels in terms of detection efficiency (CNR2/dose). A phantom study was performed on a modified Selenia full field digital mammography system to verify the simulated results. The dose level was comparable to the dose in diagnostic mode (less than 4 mGy for an average 4.2 cm compressed breast). The results from the computer simulations and phantom study are being used to optimize an ongoing clinical study.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Pectoral muscle segmentation in breast tomosynthesis with deep learning

NASA Astrophysics Data System (ADS)

Rodriguez-Ruiz, Alejandro; Teuwen, Jonas; Chung, Kaman; Karssemeijer, Nico; Chevalier, Margarita; Gubern-Merida, Albert; Sechopoulos, Ioannis

2018-02-01

Digital breast tomosynthesis (DBT) has superior detection performance than mammography (DM) for population-based breast cancer screening, but the higher number of images that must be reviewed poses a challenge for its implementation. This may be ameliorated by creating a twodimensional synthetic mammographic image (SM) from the DBT volume, containing the most relevant information. When creating a SM, it is of utmost importance to have an accurate lesion localization detection algorithm, while segmenting fibroglandular tissue could also be beneficial. These tasks encounter an extra challenge when working with images in the medio-lateral oblique view, due to the presence of the pectoral muscle, which has similar radiographic density. In this work, we present an automatic pectoral muscle segmentation model based on a u-net deep learning architecture, trained with 136 DBT images acquired with a single system (different BIRADS ® densities and pathological findings). The model was tested on 36 DBT images from that same system resulting in a dice similarity coefficient (DSC) of 0.977 (0.967-0.984). In addition, the model was tested on 125 images from two different systems and three different modalities (DBT, SM, DM), obtaining DSCs between 0.947 and 0.970, a range determined visually to provide adequate segmentations. For reference, a resident radiologist independently annotated a mix of 25 cases obtaining a DSC of 0.971. The results suggest the possibility of using this model for inter-manufacturer DBT, DM and SM tasks that benefit from the segmentation of the pectoral muscle, such as SM generation, computer aided detection systems, or patient dosimetry algorithms.

Background parenchymal uptake on molecular breast imaging as a breast cancer risk factor: a case-control study.

PubMed

Hruska, Carrie B; Scott, Christopher G; Conners, Amy Lynn; Whaley, Dana H; Rhodes, Deborah J; Carter, Rickey E; O'Connor, Michael K; Hunt, Katie N; Brandt, Kathleen R; Vachon, Celine M

2016-04-26

Molecular breast imaging (MBI) is a functional test used for supplemental screening of women with mammographically dense breasts. Additionally, MBI depicts variable levels of background parenchymal uptake (BPU) within nonmalignant, dense fibroglandular tissue. We investigated whether BPU is a risk factor for breast cancer. We conducted a retrospective case-control study of 3027 eligible women who had undergone MBI between February 2004 and February 2014. Sixty-two incident breast cancer cases were identified. A total of 179 controls were matched on age, menopausal status, and MBI year. Two radiologists blinded to case status independently assessed BPU as one of four categories: photopenic, minimal to mild, moderate, or marked. Conditional logistic regression analysis was performed to estimate the associations (OR) of BPU categories (moderate or marked vs. minimal to mild or photopenic) and breast cancer risk, adjusted for other risk factors. The median age was 60.2 years (range 38-86 years) for cases vs. 60.2 years (range 38-88 years) for controls (p = 0.88). Women with moderate or marked BPU had a 3.4-fold (95 % CI 1.6-7.3) and 4.8-fold (95 % CI 2.1-10.8) increased risk of breast cancer, respectively, compared with women with photopenic or minimal to mild BPU, for two radiologists. The results were similar after adjustment for BI-RADS density (OR 3.3 [95 % CI 1.6-7.2] and OR 4.6 [95 % CI 2.1-10.5]) or postmenopausal hormone use (OR 3.6 [95 % CI 1.7-7.7] and OR 5.0 [95 % CI 2.2-11.4]). The association of BPU with breast cancer remained in analyses limited to postmenopausal women only (OR 3.8 [95 % CI 1.5-9.3] and OR 4.1 [95 % CI 1.6-10.2]) and invasive breast cancer cases only (OR 3.6 [95 % CI 1.5-8.8] and OR 4.4 [95 % CI 1.7-11.1]). Variable BPU was observed among women with similar mammographic density; the distribution of BPU categories differed across density categories (p < 0.0001). This study provides the first evidence for BPU as a risk factor for breast cancer. Among women with dense breasts, who comprise >40 % of the screening population, BPU may serve as a functional imaging biomarker to identify the subset at greatest risk.

Prognostic Value of Tumor-Infiltrating Lymphocyte Density Assessed Using a Standardized Method Based on Molecular Subtypes and Adjuvant Chemotherapy in Invasive Breast Cancer.

PubMed

Jang, Nuri; Kwon, Hee Jung; Park, Min Hui; Kang, Su Hwan; Bae, Young Kyung

2018-04-01

This study investigated the prognostic value of tumor-infiltrating lymphocyte (TIL) density as determined by molecular subtype and receipt of adjuvant chemotherapy in invasive breast cancer (IBC). Stromal TIL densities were evaluated in 1489 IBC samples using recommendations proposed by the International TILs Working Group. Cases were allocated to high- and low-TIL density groups using a cutoff of 10%. Of the 1489 IBC patients, 427 (28.7%) were assigned to the high-TIL group and 1062 (71.3%) to the low-TIL group. High TIL density was found to be significantly associated with large tumor size (p = 0.001), high histologic grade (p < 0.001), and high Ki-67 labeling index (p < 0.001). Triple-negative and human epidermal growth factor receptor 2 (HER2)-positive subtypes had significantly higher TIL densities than luminal A or B (HER2-negative) subtypes (p < 0.001). High TIL density was significantly associated with prolonged disease-free survival (DFS) by univariate (p < 0.001) and multivariate (p < 0.001) analyses. In the low-TIL-density group, the patients who did not receive adjuvant chemotherapy showed better DFS (p < 0.001), but no such survival difference was observed in the high-TIL group (p = 0.222). For the patients who received adjuvant anthracycline, high-TIL density was found to be an independent prognostic factor of favorable DFS in the luminal B (HER2-negative; p = 0.003), HER2-positive (p = 0.019), and triple-negative (p = 0.017) subtypes. Measurements of TIL density in routine clinical practice could give useful prognostic information for the triple-negative, HER2-positive, and luminal B (HER2-negative) IBC subtypes, especially for patients administered adjuvant anthracycline.

Anthropomorphic breast phantoms for preclinical imaging evaluation with transmission or emission imaging

NASA Astrophysics Data System (ADS)

Tornai, Martin P.; McKinley, Randolph L.; Bryzmialkiewicz, Caryl N.; Cutler, Spencer J.; Crotty, Dominic J.

2005-04-01

With the development of several classes of dedicated emission and transmission imaging technologies utilizing ionizing radiation for improved breast cancer detection and in vivo characterization, it is extremely useful to have available anthropomorphic breast phantoms in a variety of shapes, sizes and malleability prior to clinical imaging. These anthropomorphic phantoms can be used to evaluate the implemented imaging approaches given a known quantity, the phantom, and to evaluate the variability of the measurement due to the imaging system chain. Thus, we have developed a set of fillable and incompressible breast phantoms ranging in volume from 240 to 1730mL with nipple-to-chest distances from 3.8 to 12cm. These phantoms are mountable and exchangeable on either a uniform chest plate or anthropomorphic torso phantom containing tissue equivalent bones and surface tissue. Another fillable ~700mL breast phantom with solid anterior chest plate is intentionally compressible, and can be used for direct comparisons between standard planar imaging approaches using mild-to-severe compression, partially compressed tomosynthesis, and uncompressed computed mammotomography applications. These phantoms can be filled with various fluids (water and oil based liquids) to vary the fatty tissue background composition. Shaped cellulose sponges with two cell densities are fabricated and can be added to the breasts to simulate connective tissue. Additionally, microcalcifications can be simulated by peppering slits in the sponges with oyster shell fragments. These phantoms have a utility in helping to evaluate clinical imaging paradigms with known input object parameters using basic imaging characterization, in an effort to further evaluate contemporary and next generation imaging tools. They may additionally provide a means to collect known data samples for task based optimization studies.

Realistic respiratory motion margins for external beam partial breast irradiation

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

Conroy, Leigh; Quirk, Sarah; Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4

Purpose: Respiratory margins for partial breast irradiation (PBI) have been largely based on geometric observations, which may overestimate the margin required for dosimetric coverage. In this study, dosimetric population-based respiratory margins and margin formulas for external beam partial breast irradiation are determined. Methods: Volunteer respiratory data and anterior–posterior (AP) dose profiles from clinical treatment plans of 28 3D conformal radiotherapy (3DCRT) PBI patient plans were used to determine population-based respiratory margins. The peak-to-peak amplitudes (A) of realistic respiratory motion data from healthy volunteers were scaled from A = 1 to 10 mm to create respiratory motion probability density functions. Dosemore » profiles were convolved with the respiratory probability density functions to produce blurred dose profiles accounting for respiratory motion. The required margins were found by measuring the distance between the simulated treatment and original dose profiles at the 95% isodose level. Results: The symmetric dosimetric respiratory margins to cover 90%, 95%, and 100% of the simulated treatment population were 1.5, 2, and 4 mm, respectively. With patient set up at end exhale, the required margins were larger in the anterior direction than the posterior. For respiratory amplitudes less than 5 mm, the population-based margins can be expressed as a fraction of the extent of respiratory motion. The derived formulas in the anterior/posterior directions for 90%, 95%, and 100% simulated population coverage were 0.45A/0.25A, 0.50A/0.30A, and 0.70A/0.40A. The differences in formulas for different population coverage criteria demonstrate that respiratory trace shape and baseline drift characteristics affect individual respiratory margins even for the same average peak-to-peak amplitude. Conclusions: A methodology for determining population-based respiratory margins using real respiratory motion patterns and dose profiles in the AP direction was described. It was found that the currently used respiratory margin of 5 mm in partial breast irradiation may be overly conservative for many 3DCRT PBI patients. Amplitude alone was found to be insufficient to determine patient-specific margins: individual respiratory trace shape and baseline drift both contributed to the dosimetric target coverage. With respiratory coaching, individualized respiratory margins smaller than the full extent of motion could reduce planning target volumes while ensuring adequate coverage under respiratory motion.« less

Between-race differences in the effects of breast density information and information about new imaging technology on breast-health decision-making.

PubMed

Manning, Mark; Purrington, Kristen; Penner, Louis; Duric, Neb; Albrecht, Terrance L

2016-06-01

Some US states have mandated that women be informed when they have dense breasts; however, little is known about how general knowledge about breast density (BD) affects related health decision-making. We examined the effects of BD information and imaging technology information on 138 African-American (AA) and European-American (EA) women's intentions to discuss breast cancer screening with their physicians. Women were randomly assigned to receive BD information and/or imaging technology information via 2 by 2 factorial design, and completed planned behavior measures (e.g., attitudes, intentions) related to BC screening. Attitudes mediated the effects of BD information, and the mediation was stronger for AA women compared to EA women. Effects were more robust for BD information compared to imaging technology information. Results of moderator analyses revealed suppressor effects of injunctive norms that were moderated by imaging technology information. Information about BD favorably influences women's intentions to engage in relevant breast health behaviors. Stronger attitude mediated-effects for AA women suggest greater scrutiny of BD information. Since BD information may influence women's intentions to discuss BC screening, strategies to effectively present BD information to AA women should be investigated given the likelihood of their increased scrutiny of BD information. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

ApoA-I mimetic administration, but not increased apoA-I-containing HDL, inhibits tumour growth in a mouse model of inherited breast cancer.

PubMed

Cedó, Lídia; García-León, Annabel; Baila-Rueda, Lucía; Santos, David; Grijalva, Victor; Martínez-Cignoni, Melanie Raquel; Carbó, José M; Metso, Jari; López-Vilaró, Laura; Zorzano, Antonio; Valledor, Annabel F; Cenarro, Ana; Jauhiainen, Matti; Lerma, Enrique; Fogelman, Alan M; Reddy, Srinivasa T; Escolà-Gil, Joan Carles; Blanco-Vaca, Francisco

2016-11-03

Low levels of high-density lipoprotein cholesterol (HDLc) have been associated with breast cancer risk, but several epidemiologic studies have reported contradictory results with regard to the relationship between apolipoprotein (apo) A-I and breast cancer. We aimed to determine the effects of human apoA-I overexpression and administration of specific apoA-I mimetic peptide (D-4F) on tumour progression by using mammary tumour virus-polyoma middle T-antigen transgenic (PyMT) mice as a model of inherited breast cancer. Expression of human apoA-I in the mice did not affect tumour onset and growth in PyMT transgenic mice, despite an increase in the HDLc level. In contrast, D-4F treatment significantly increased tumour latency and inhibited the development of tumours. The effects of D-4F on tumour development were independent of 27-hydroxycholesterol. However, D-4F treatment reduced the plasma oxidized low-density lipoprotein (oxLDL) levels in mice and prevented oxLDL-mediated proliferative response in human breast adenocarcinoma MCF-7 cells. In conclusion, our study shows that D-4F, but not apoA-I-containing HDL, hinders tumour growth in mice with inherited breast cancer in association with a higher protection against LDL oxidative modification.

Influences of race and breast density on related cognitive and emotion outcomes before mandated breast density notification.

PubMed

Manning, Mark; Albrecht, Terrance L; Yilmaz-Saab, Zeynep; Shultz, Julie; Purrington, Kristen

2016-11-01

Many states have adopted laws mandating breast density (BD) notification for applicable women; however, very little is known about what women knew or felt about BD and related breast cancer (BC) risk before implementation of BD notification laws. We examined between-race differences in the extent to which having dense breasts was associated with women's related BD cognition and emotion, and with health care providers' communication about BD. We received surveys between May and October of 2015 assessing health care provider (HCP) communication about BD, BD-related knowledge, BD-related anxiety and BC worry from 182 African American (AA) and 113 European American (EA) women in the state of Michigan for whom we had radiologists' assessments of BD. Whereas having dense breasts was not associated with any BD-related cognition or emotion, there were robust effects of race as follows: EA women were more likely to have been told about BD by a HCP, more likely to know their BD status, had greater knowledge of BD and of BC risk, and had greater perceptions of BC risk and worry; AA women had greater BD-related anxieties. EA women's greater knowledge of their own BD status was directly related to the increased likelihood of HCP communication about BD. However, HCP communication about BD attenuated anxiety for AA women only. We present the only data of which we are aware that examines between-race differences in the associations between actual BD, HCP communication and BD related cognition and emotion before the implementation of BD notification laws. Our findings suggest that the BD notification laws could yield positive benefits for disparities in BD-related knowledge and anxiety when the notifications are followed by discussions with health care providers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Aspirin use is associated with lower mammographic density in a large screening cohort.

PubMed

Wood, Marie E; Sprague, Brian L; Oustimov, Andrew; Synnstvedt, Marie B; Cuke, Melissa; Conant, Emily F; Kontos, Despina

2017-04-01

Observational and biologic studies suggest that aspirin is a promising prevention therapy for breast cancer. However, clinical trials to date have not corroborated this evidence, potentially due to study design. We evaluated the effect of aspirin on mammographic density (MD), an established modifiable risk factor for breast cancer. Electronic medical records from the University of Pennsylvania were evaluated for women who underwent screening mammography, saw their primary care provider, and had a confirmed list of medications during 2012-2013. Logistic regression was performed to test for associations between clinically recorded MD and aspirin use, after adjusting for age, body mass index (BMI), and ethnicity. We identified 26,000 eligible women. Mean age was 57.3, mean BMI was 28.9 kg/m 2 , 41% were African American, and 19.7% reported current aspirin use. Aspirin users were significantly older and had higher BMI. There was an independent, inverse association between aspirin use and MD (P trend  < 0.001). Women with extremely dense breasts were less likely to be aspirin users than women with scattered fibroglandular density (OR 0.73; 95% CI 0.57-0.93). This association was stronger for younger women (P = 0.0002) and for African Americans (P = 0.011). The likelihood of having dense breasts decreased with aspirin dose (P trend  = 0.007), suggesting a dose response. We demonstrate an independent association between aspirin use and lower MD in a large, diverse screening cohort. This association was stronger for younger and African American women: two groups at greater risk for ER- breast cancer. These results contribute to the importance of investigating aspirin for breast cancer prevention.

Breast ultrasound tomography with two parallel transducer arrays

NASA Astrophysics Data System (ADS)

Huang, Lianjie; Shin, Junseob; Chen, Ting; Lin, Youzuo; Gao, Kai; Intrator, Miranda; Hanson, Kenneth

2016-03-01

Breast ultrasound tomography is an emerging imaging modality to reconstruct the sound speed, density, and ultrasound attenuation of the breast in addition to ultrasound reflection/beamforming images for breast cancer detection and characterization. We recently designed and manufactured a new synthetic-aperture breast ultrasound tomography prototype with two parallel transducer arrays consisting of a total of 768 transducer elements. The transducer arrays are translated vertically to scan the breast in a warm water tank from the chest wall/axillary region to the nipple region to acquire ultrasound transmission and reflection data for whole-breast ultrasound tomography imaging. The distance of these two ultrasound transducer arrays is adjustable for scanning breasts with different sizes. We use our breast ultrasound tomography prototype to acquire phantom and in vivo patient ultrasound data to study its feasibility for breast imaging. We apply our recently developed ultrasound imaging and tomography algorithms to ultrasound data acquired using our breast ultrasound tomography system. Our in vivo patient imaging results demonstrate that our breast ultrasound tomography can detect breast lesions shown on clinical ultrasound and mammographic images.

3D ultrasound computer tomography: update from a clinical study

NASA Astrophysics Data System (ADS)

Hopp, T.; Zapf, M.; Kretzek, E.; Henrich, J.; Tukalo, A.; Gemmeke, H.; Kaiser, C.; Knaudt, J.; Ruiter, N. V.

2016-04-01

Ultrasound Computer Tomography (USCT) is a promising new imaging method for breast cancer diagnosis. We developed a 3D USCT system and tested it in a pilot study with encouraging results: 3D USCT was able to depict two carcinomas, which were present in contrast enhanced MRI volumes serving as ground truth. To overcome severe differences in the breast shape, an image registration was applied. We analyzed the correlation between average sound speed in the breast and the breast density estimated from segmented MRIs and found a positive correlation with R=0.70. Based on the results of the pilot study we now carry out a successive clinical study with 200 patients. For this we integrated our reconstruction methods and image post-processing into a comprehensive workflow. It includes a dedicated DICOM viewer for interactive assessment of fused USCT images. A new preview mode now allows intuitive and faster patient positioning. We updated the USCT system to decrease the data acquisition time by approximately factor two and to increase the penetration depth of the breast into the USCT aperture by 1 cm. Furthermore the compute-intensive reflectivity reconstruction was considerably accelerated, now allowing a sub-millimeter volume reconstruction in approximately 16 minutes. The updates made it possible to successfully image first patients in our ongoing clinical study.

Conservation status of the buff-breasted sandpiper: Historic and contemporary distribution and abundance in south America

USGS Publications Warehouse

Lanctot, Richard B.; Blanco, D.E.; Dias, Rafael A.; Isacch, Juan P.; Gill, Verena A.; Almeida, Juliana B.; Delhey, Kaspar; Petracci, Pablo F.; Bencke, Glayson A.; Balbueno, Rodrigo A.

2002-01-01

We present historic and contemporary information on the distribution and abundance of Buff-breasted Sandpipers (Tryngites subruficollis) in South America. Historic information was collated from the literature, area ornithologists, and museums, whereas contemporary data were derived from surveys conducted throughout the main wintering range in Argentina, Uruguay, and Brazil during the austral summers of 1999 and 2001. Variable circular plot sampling was used to estimate population densities. During 1999, the highest concentration of Buff-breasted Sandpipers in Argentina was in southern Bahía Samborombón (General Lavalle District) and areas north of Mar Chiquita coastal lagoon. During 2001, the highest concentrations in Brazil were at Ilha da Torotama and Lagoa do Peixe National Park. During 1999 and 2001, the highest concentrations of Buff-breasted Sandpipers in Uruguay were found along three lagoons (Laguna de Rocha, Laguna de Castillos, and Laguna Garzón) bordering the Atlantic Ocean. Population densities (birds/ha) of Buff-breasted Sandpipers were 0.11 (95% C.I. = 0.04–0.31) in Argentina, 1.62 (0.67–3.93) in Brazil, and 1.08 (0.37–3.18) in Uruguay. High turnover rates at survey sites, due to the formation of large, mobile flocks, contributed to moderately large confidence intervals around our population density estimates. Nevertheless, compared with historic accounts of Buff-breasted Sandpipers, our survey data indicate the population size of this species has declined substantially since the late 1800s and contemporary information suggests the species has continued to decline during the past three decades. Buff-breasted Sandpipers were found almost exclusively in pasturelands and appear to depend heavily upon intensive grazing by livestock, which maintain suitable short grass conditions. We discuss the need for protection of critical areas and proper range management to ensure appropriate habitat remains available for the species, and provide suggestions for future research needs.

Mammographic Breast Density in a Cohort of Medically Underserved Women

DTIC Science & Technology

2014-10-01

chronic diseases, adult weight history, diet , and health literacy. A trained radiologic technician completed full- field digital screening mammograms on... Mediterranean population. Int J Cancer 118:1782-1789 12. El-Bastawissi AY, White E, Mandelson MT, Taplin S (2001) Variation in mammographic breast

Detection of masses in mammogram images using CNN, geostatistic functions and SVM.

PubMed

Sampaio, Wener Borges; Diniz, Edgar Moraes; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Gattass, Marcelo

2011-08-01

Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87. Copyright © 2011 Elsevier Ltd. All rights reserved.

Dietary Energy Density and Postmenopausal Breast Cancer Incidence in the Cancer Prevention Study II Nutrition Cohort.

PubMed

Hartman, Terryl J; Gapstur, Susan M; Gaudet, Mia M; Shah, Roma; Flanders, W Dana; Wang, Ying; McCullough, Marjorie L

2016-10-01

Dietary energy density (ED) is a measure of diet quality that estimates the amount of energy per unit of food (kilocalories per gram) consumed. Low-ED diets are generally high in fiber and fruits and vegetables and low in fat. Dietary ED has been positively associated with body mass index (BMI) and other risk factors for postmenopausal breast cancer. We evaluated the associations of total dietary ED and energy-dense (high-ED) foods with postmenopausal breast cancer incidence. Analyses included 56,795 postmenopausal women from the Cancer Prevention Study II Nutrition Cohort with no previous history of breast or other cancers and who provided information on diet, lifestyle, and medical history in 1999. Multivariable-adjusted breast cancer incidence rate ratios (RRs and 95% CIs) were estimated for quintiles of total dietary ED and for the consumption of high-ED foods in Cox proportional hazards regression models. During a median follow-up of 11.7 y, 2509 invasive breast cancer cases were identified, including 1857 estrogen receptor-positive and 277 estrogen receptor-negative tumors. Median dietary ED was 1.5 kcal/g (IQR: 1.3-1.7 kcal/g). After adjusting for age, race, education, reproductive characteristics, and family history, high compared with low dietary ED was associated with a statistically significantly higher risk of breast cancer (RR for fifth quintile compared with first quintile: 1.20; 95% CI: 1.05, 1.36; P-trend = 0.03). The association between the amount of high-ED foods consumed and breast cancer risk was not statistically significant. We observed no differences by estrogen receptor status or effect modification by BMI, age, or physical activity. These results suggest a modest positive association between total dietary ED and risk of postmenopausal breast cancer. © 2016 American Society for Nutrition.

Lumbar Scoliosis in Patients With Breast Cancer: Prevalence and Relationship With Breast Cancer Treatment, Age, Bone Mineral Density, and Body Mass Index.

PubMed

Jung, Sangeun; Kim, Mee Gang; Lee, Jong In

2017-10-01

To identify the prevalence of lumbar scoliosis in breast cancer patients and to investigate the potential risk factors of lumbar scoliosis. A retrospective chart review was performed in breast cancer patients aged more than 40 years who underwent dual energy X-ray absorptiometry (DEXA) scanning between January 2014 and December 2014. We divided the patients into control and experimental groups in order to investigate the influence of breast cancer treatment. The curvature of the lumbar spine was measured by using the Cobb method on a DEXA scan. Scoliosis was defined by the presence of a curvature 10° or larger. The variables, including age, bone mineral density (BMD), body mass index (BMI), and breast cancer treatments, were also obtained from the medical chart. Prevalence of lumbar scoliosis was evaluated, and it was compared between the two groups. The relationships between lumbar scoliosis and these variables were also investigated. Lumbar scoliosis was present in 16 out of our 652 breast cancer patients. There was no difference in the prevalence of lumbar scoliosis between the control group (7/316) and the experimental group (9/336) (p=0.70). According to the logistic regression analysis, lumbar scoliosis had no significant association with operation, chemotherapy, hormone therapy, BMI, and BMD (p>0.05). However, age showed a significant relationship with prevalence of lumbar scoliosis (p<0.001; odds ratio, 1.11; 95% confidence interval, 1.054-1.170). Prevalence of lumbar scoliosis in patients with breast cancer was 2.45%. Lumbar scoliosis had no association with breast cancer treatments, BMD, and BMI. Age was the only factor related to the prevalence of lumbar scoliosis.

Breast cancer research output, 1945-2008: a bibliometric and density-equalizing analysis.

PubMed

Glynn, Ronan W; Scutaru, Cristian; Kerin, Michael J; Sweeney, Karl J

2010-01-01

Breast cancer is the most common form of cancer among women, with an estimated 194,280 new cases diagnosed in the United States in 2009 alone. The primary aim of this work was to provide an in-depth evaluation of research yield in breast cancer from 1945 to 2008, using large-scale data analysis, the employment of bibliometric indicators of production and quality, and density-equalizing mapping. Data were retrieved from the Web of Science (WOS) Science Citation Expanded database; this was searched using the Boolean operator, 'OR', with different terms related to breast cancer, including "breast cancer", "mammary ductal carcinoma" and "breast tumour". Data were then extracted from each file, transferred to Excel charts and visualised as diagrams. Mapping was performed as described by Groneberg-Kloft et al. in 2008. A total of 180,126 breast cancer-associated items were produced over the study period; these had been cited 4,136,224 times. The United States returned the greatest level of output (n = 77,101), followed by the UK (n = 18,357) and Germany (n = 12,529). International cooperation peaked in 2008, with 3,127 entries produced as a result; relationships between the United States and other countries formed the basis for the 10 most common forms of bilateral cooperation. Publications from nations with high levels of international cooperation were associated with greater average citation rates. A total of 4,096 journals published at least one item on breast cancer, although the top 50 most prolific titles together accounted for over 43% (77,517/180,126) of the total output. Breast cancer-associated research output continues to increase annually. In an era when bibliometric indicators are increasingly being employed in performance assessment, these findings should provide useful information for those tasked with improving that performance.

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

Association between bone mineral density and incidence of breast cancer.

PubMed

Fraenkel, Merav; Novack, Victor; Liel, Yair; Koretz, Michael; Siris, Ethel; Norton, Larry; Shafat, Tali; Shany, Shraga; Geffen, David B

2013-01-01

Previous studies have suggested an inverse relationship between bone mineral density (BMD) and breast cancer incidence. The primary objective of this study was to assess whether BMD is associated with risk of subsequent breast cancer occurrence in the female population of southern Israel. The electronic medical charts of women who underwent BMD at the Soroka Medical Center (SMC) between February 2003 and March 2011 were screened for subsequent breast cancer diagnoses. Women were divided by tertiles of BMD at 3 skeletal sites: lumbar spine (LS, L1-4), total hip (TH) and femoral neck (FN). The incidence of breast cancer was calculated. Of 15268 women who underwent BMD testing, 86 were subsequently diagnosed with breast cancer. Most women in the study were older than 50 years (94.2% and 92.7%, respectively; p = 0.597). Women who subsequently developed breast cancer had a higher mean body-mass index (BMI) (30.9 ± 5.5 vs. 29.1 ± 5.7 p = 0.004) and the mean BMD Z-score was significantly higher than in those without breast cancer for all 3 skeletal sites (LS: 0.36 ± 1.58 vs. -0.12 ± 1.42, p = 0.002; TH: 0.37 ± 1.08 vs. 0.03 ± 1.02, p = 0.002; FN: 0.04 ± 0.99 vs. -0.18 ± 0.94; p = 0.026). Women in the highest Z-score tertiles at the FN and TH had a higher chance of developing breast cancer compared to the lowest tertile; odds ratio of 2.15, 2.02, respectively (P = 0.004 and 0.01 respectively). No association was found between the BMD Z-score and the stage, histology, grade or survival from breast cancer. This study provides additional support for an inverse association between BMD and the risk of breast cancer.

Association between Bone Mineral Density and Incidence of Breast Cancer

PubMed Central

Fraenkel, Merav; Novack, Victor; Liel, Yair; Koretz, Michael; Siris, Ethel; Norton, Larry; Shafat, Tali; Shany, Shraga; Geffen, David B.

2013-01-01

Introduction Previous studies have suggested an inverse relationship between bone mineral density (BMD) and breast cancer incidence. The primary objective of this study was to assess whether BMD is associated with risk of subsequent breast cancer occurrence in the female population of southern Israel. Methods The electronic medical charts of women who underwent BMD at the Soroka Medical Center (SMC) between February 2003 and March 2011 were screened for subsequent breast cancer diagnoses. Women were divided by tertiles of BMD at 3 skeletal sites: lumbar spine (LS, L1–4), total hip (TH) and femoral neck (FN). The incidence of breast cancer was calculated. Results Of 15268 women who underwent BMD testing, 86 were subsequently diagnosed with breast cancer. Most women in the study were older than 50 years (94.2% and 92.7%, respectively; p = 0.597). Women who subsequently developed breast cancer had a higher mean body-mass index (BMI) (30.9±5.5 vs. 29.1±5.7 p = 0.004) and the mean BMD Z-score was significantly higher than in those without breast cancer for all 3 skeletal sites (LS: 0.36±1.58 vs. −0.12±1.42, p = 0.002; TH: 0.37±1.08 vs. 0.03±1.02, p = 0.002; FN: 0.04±0.99 vs. −0.18±0.94; p = 0.026). Women in the highest Z-score tertiles at the FN and TH had a higher chance of developing breast cancer compared to the lowest tertile; odds ratio of 2.15, 2.02, respectively (P = 0.004 and 0.01 respectively). No association was found between the BMD Z-score and the stage, histology, grade or survival from breast cancer. Conclusions This study provides additional support for an inverse association between BMD and the risk of breast cancer. PMID:23940680

Explaining between-race differences in African-American and European-American women's responses to breast density notification.

PubMed

Manning, Mark; Albrecht, Terrance L; Yilmaz-Saab, Zeynep; Penner, Louis; Norman, Andria; Purrington, Kristen

2017-12-01

Prior research shows between-race differences in women's knowledge and emotions related to having dense breasts, thus suggesting that between-race differences in behavioral decision-making following receipt of breast density (BD) notifications are likely. Guided by the theory of planned behavior, this study examined differences in emotion-related responses (i.e., anxiety, worry, confusion) and behavioral cognition (e.g., intentions, behavioral attitudes) following receipt of BD notifications among African American (AA) and European American (EA) women. This study also examined whether race-related perceptions (i.e., discrimination, group-based medical mistrust), relevant knowledge and socioeconomic status (SES) explained the between race differences. Michigan women (N = 457) who presented for routine screening mammogram and had dense breasts, no prior breast cancer diagnoses, and had screen-negative mammograms were recruited from July, 2015 to March 2016. MANOVA was used to examine between race differences in psychological responses (i.e., emotional responses and behavioral cognition), and a multi-group structural regression model was used to examine whether race-related constructs, knowledge and SES mediated the effect of race on emotional responses and behavioral cognition. Prior awareness of BD was accounted for in all analyses. AA women generally reported more negative psychological responses to receiving BD notifications regardless of prior BD awareness. AA women had more favorable perceptions related to talking to their physicians about the BD notifications. Generally, race-related perceptions, SES, and related knowledge partially accounted for the effect of race on psychological response. Race-related perceptions and SES partially accounted for the differences in behavioral intentions. Between-race differences in emotional responses to BD notifications did not explain differences in women's intentions to discuss BD notifications with their physicians. Future examinations are warranted to examine whether there are between-race differences in actual post-BD notification behaviors and whether similar race-related variables account for differences. Copyright © 2017. Published by Elsevier Ltd.

A genome-wide linkage study of mammographic density, a risk factor for breast cancer

PubMed Central

2011-01-01

Introduction Mammographic breast density is a highly heritable (h2 > 0.6) and strong risk factor for breast cancer. We conducted a genome-wide linkage study to identify loci influencing mammographic breast density (MD). Methods Epidemiological data were assembled on 1,415 families from the Australia, Northern California and Ontario sites of the Breast Cancer Family Registry, and additional families recruited in Australia and Ontario. Families consisted of sister pairs with age-matched mammograms and data on factors known to influence MD. Single nucleotide polymorphism (SNP) genotyping was performed on 3,952 individuals using the Illumina Infinium 6K linkage panel. Results Using a variance components method, genome-wide linkage analysis was performed using quantitative traits obtained by adjusting MD measurements for known covariates. Our primary trait was formed by fitting a linear model to the square root of the percentage of the breast area that was dense (PMD), adjusting for age at mammogram, number of live births, menopausal status, weight, height, weight squared, and menopausal hormone therapy. The maximum logarithm of odds (LOD) score from the genome-wide scan was on chromosome 7p14.1-p13 (LOD = 2.69; 63.5 cM) for covariate-adjusted PMD, with a 1-LOD interval spanning 8.6 cM. A similar signal was seen for the covariate adjusted area of the breast that was dense (DA) phenotype. Simulations showed that the complete sample had adequate power to detect LOD scores of 3 or 3.5 for a locus accounting for 20% of phenotypic variance. A modest peak initially seen on chromosome 7q32.3-q34 increased in strength when only the 513 families with at least two sisters below 50 years of age were included in the analysis (LOD 3.2; 140.7 cM, 1-LOD interval spanning 9.6 cM). In a subgroup analysis, we also found a LOD score of 3.3 for DA phenotype on chromosome 12.11.22-q13.11 (60.8 cM, 1-LOD interval spanning 9.3 cM), overlapping a region identified in a previous study. Conclusions The suggestive peaks and the larger linkage signal seen in the subset of pedigrees with younger participants highlight regions of interest for further study to identify genes that determine MD, with the goal of understanding mammographic density and its involvement in susceptibility to breast cancer. PMID:22188651

Skin Dosimetry in Breast Teletherapy on a Phantom Anthropomorphic and Anthropometric Phantom

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

Batista Nogueira, Luciana; Lemos Silva, Hugo Leonardo; Donato da Silva, Sabrina

This paper addresses the breast teletherapy dosimetry. The goal is to evaluate and compare absorbed doses in equivalent skin tissue, TE-skin, of an anthropomorphic and anthropometric breast phantom submitted to breast radiotherapy. The methodology involved the reproduction of a set of tomographic images of the phantom; the elaboration of conformational radiotherapy planning in the SOMAVISION and CadPlan (TPS) software; and the synthetic breast irradiation by parallel opposed fields in 3D conformal teletherapy at 6 MV linear accelerator Clinac-2100 C from VARIAN with prescribed dose (PD) of 180 cGy to the target volume (PTV), referent to the glandular tissue. Radiochromic filmsmore » EBT2 were selected as dosimeters. Two independent calibration processes of films with solid water Gammex 457 plates and water filled box were produced. Curves of optical density (OD) versus absorbed dose were produced. Dosimeters were positioned in the external region of the breast phantom in contact with TE-skin, area of 4.0 cm{sup 2} each. The irradiation process was prepared in duplicate to check the reproducibility of the technique. The radiochromic films were scanned and their response in RGB (Red, Green, Blue) analyzed by the ImageJ software. The optical density was obtained and converted to dose based on the calibration curves. Thus, the spatial dose distribution in the skin was reproduced. The absorbed doses measured on the radiochromic films in TE-skin showed values between upper and lower quadrants at 9 o'clock in the range of 54% of PD, between the upper and lower quadrants 3 o'clock in the range of 72% and 6 o'clock at the lower quadrant in the range of 68 % of PD. The values are ±64% (p <0.05) according to the TPS. It is concluded that the depth dose measured in solid water plates or water box reproduce equivalent dose values for both calibration processes of the radiochromic films. It was observed that the skin received doses ranging from 50% to 78% of the prescribed dose after two parallel opposed irradiation fields. (authors)« less

[Prevalence of low bone mineral density in postmenopausal breast cancer survivors].

PubMed

Poloni, Priscila Ferreira; Omodei, Michelle Sako; Nahas-Neto, Jorge; Uemura, Gilberto; Véspoli, Heloisa De Luca; Nahas, Eliana Aguiar Petri

2015-01-01

To evaluate the prevalence of low bone mineral density (BMD) in postmenopausal breast cancer survivors. In this cross-sectional study, 115 breast cancer survivors, seeking healthcare at a University Hospital in Brazil, were evaluated. Eligibility criteria included women with amenorrhea ≥ 12 months and age ≥ 45 years, treated for breast cancer and metastasis-free for at least five years. BMD was measured by DEXA at the lumbar spine (L1-L4) and femoral neck. Low BMD was considered when total-spine and/or femoral-neck T-score values were <-1.0 Delphi Score (DP) (osteopenia and osteoporosis). The risk factors for low BMD were assessed by interview. Data were analyzed statistically by the χ(2) test and Fisher's exact test. The mean age of breast cancer survivors was 61.6 ± 10.1 years and time since menopause was 14.2 ± 5.6 years, with a mean follow-up of 10.1 ± 3.9 years. Considering spine and femoral neck, 60% of breast cancer survivors had low BMD. By evaluating the risk factors for low BMD, a significant difference was found in the percent distribution for age (higher % of women >50 years with low BMD), personal history of previous fracture (11.6% with low BMD versus 0% with normal BMD) and BMI. A higher frequency of obesity was observed among women with normal BMD (63%) compared to those with low BMD (26.1%) (p<0.05). Postmenopausal breast cancer survivors had a high prevalence of osteopenia and osteoporosis.

Adherence to the World Cancer Research Fund/American Institute for Cancer Research recommendations and breast cancer risk.

PubMed

Harris, Holly R; Bergkvist, Leif; Wolk, Alicja

2016-06-01

The World Cancer Research Fund/American Association for Cancer Research (WCRF/AICR) has published eight nutrition-related recommendations for the prevention of cancer. However, few prospective studies have examined these recommendations by breast cancer hormone receptor subtype and only one case-control study has included the dietary supplements recommendation in their evaluation. We investigated whether adherence to the WCRF/AICR cancer prevention recommendations was associated with breast cancer incidence, overall and by hormone receptor subtype, in the Swedish Mammography Cohort. Among 31,514 primarily postmenopausal women diet and lifestyle factors were assessed with a self-administered food frequency questionnaire. A score was constructed based on adherence to the recommendations for body fatness, physical activity, energy density, plant foods, animal foods, alcoholic drinks and dietary supplements (score range 0-7). Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (95% CIs). During 15 years of follow-up 1,388 cases of breast cancer were identified. Women who met six to seven recommendations had a 51% decreased risk of breast cancer compared to women meeting only zero to two recommendations (95% CI = 0.35-0.70). The association between each additional recommendation met and breast cancer risk was strongest for the ER-positive/PR-positive subtype (HR = 0.86; 95% CI = 0.79-0.94), while for the ER-negative/PR-negative subtype the individual recommendations regarding plant and animal foods were most strongly associated with reduced risk. Our findings support that adherence to the WCRF/AICR recommendations reduces breast cancer risk in a population of primarily postmenopausal women. Promoting these recommendations to the public could help reduce breast cancer incidence. © 2016 UICC.

Quantitative contrast-enhanced spectral mammography based on photon-counting detectors: A feasibility study.

PubMed

Ding, Huanjun; Molloi, Sabee

2017-08-01

To investigate the feasibility of accurate quantification of iodine mass thickness in contrast-enhanced spectral mammography. A computer simulation model was developed to evaluate the performance of a photon-counting spectral mammography system in the application of contrast-enhanced spectral mammography. A figure-of-merit (FOM), which was defined as the decomposed iodine signal-to-noise ratio (SNR) with respect to the square root of the mean glandular dose (MGD), was chosen to optimize the imaging parameters, in terms of beam energy, splitting energy, and prefiltrations for breasts of various thicknesses and densities. Experimental phantom studies were also performed using a beam energy of 40 kVp and a splitting energy of 34 keV with 3 mm Al prefiltration. A two-step calibration method was investigated to quantify the iodine mass thickness, and was validated using phantoms composed of a mixture of glandular and adipose materials, for various breast thicknesses and densities. Finally, the traditional dual-energy log-weighted subtraction method was also studied as a comparison. The measured iodine signal from both methods was compared to the known value to characterize the quantification accuracy and precision. The optimal imaging parameters, which lead to the highest FOM, were found at a beam energy between 42 and 46 kVp with a splitting energy at 34 keV. The optimal tube voltage decreased as the breast thickness or the Al prefiltration increased. The proposed quantification method was able to measure iodine mass thickness on phantoms of various thicknesses and densities with high accuracy. The root-mean-square (RMS) error for cm-scale lesion phantoms was estimated to be 0.20 mg/cm 2 . The precision of the technique, characterized by the standard deviation of the measurements, was estimated to be 0.18 mg/cm 2 . The traditional weighted subtraction method also predicted a linear correlation between the measured signal and the known iodine mass thickness. However, the correlation slope and offset values were strongly dependent on the total breast thickness and density. The results of this study suggest that iodine mass thickness for cm-scale lesions can be accurately quantified with contrast-enhanced spectral mammography. The quantitative information can potentially improve the differential power for malignancy. © 2017 American Association of Physicists in Medicine.

Comparison of Microvessel Density with Prognostic Factors in Invasive Ductal Carcinomas of the Breast.

PubMed

Şener, Ebru; Şipal, Sare; Gündoğdu, Cemal

2016-01-01

Angiogenesis plays a key role in tumor growth and metastasis. Determination of microvessel density is the most common technique used to evaluate the amount of the intratumoral angiogenesis in breast cancer. We have aimed to investigate the relationship with tumor angiogenesis and prognostic parameters in breast invasive ductal carcinomas. In this study, a total of 100 invasive ductal carcinoma patients, who were diagnosed at the Department of Pathology, Ataturk University Faculty of Medicine between the years 2003-2008, were re-evaluated. Patient characteristics and clinicopathological findings were obtained from archival records. In the present study, microvessel density was determined by immunohistochemical staining by using anti-CD34 monoclonal antibody in the paraffin blocks. First, the most vascular area was selected in the tumor under a low magnification (40x) by a light microscope and then microvessels were counted under a higher magnification (200x). Patients were classified as low and high microvessel density depending on their microvessel counts. Chi-square test and multivariate linear regression analysis were used for statistical analysis (p≤0.05). We have determined that microvessel density increases as tumor size increases (p=0.001). Microvessel density was higher in patients with at least 10 lymph node metastases compared to those with no metastasis (p=0.05). However, there was no statistically significant difference between microvessel density and other prognostic factors such as histological grade, nuclear grade, patient age, vascular invasion, estrogen, progesterone receptor status, HER2/neu expression. In our study, we have found that microvessel density is associated with tumor size and lymph node metastasis in patients with invasive ductal carcinoma.

Vasohibin 2 promotes human luminal breast cancer angiogenesis in a non-paracrine manner via transcriptional activation of fibroblast growth factor 2.

PubMed

Tu, Min; Lu, Cheng; Lv, Nan; Wei, Jishu; Lu, Zipeng; Xi, Chunhua; Chen, Jianmin; Guo, Feng; Jiang, Kuirong; Li, Qiang; Wu, Junli; Song, Guoxin; Wang, Shui; Gao, Wentao; Miao, Yi

2016-12-28

Vasohibin 2 (VASH2) is an angiogenic factor and cancer-related protein that acts via paracrine mechanisms. Here, we investigated the angiogenic function and mechanism of action of VASH2 in 200 human breast cancer tissues by performing immunohistochemical staining, western blot, indirect sandwich enzyme-linked immunosorbent assay (ELISA), and a semi-quantitative sandwich-based antibody array. Breast cancer cells stably overexpressing VASH2 or with knocked-down VASH2 were established and used for in vivo and in vitro models. In human luminal tissue, but not in HER2-positive or basal-like breast cancer tissues, VASH2 was positively correlated with CD31-positive microvascular density, induced angiogenesis in xenograft tumors, and promoted human umbilical vein endothelial cell tube formation in vitro. VASH2 expression was absent in the concentrated conditioned medium collected from knocked-down VASH2 and VASH2-overexpressing luminal breast cancer cells. Further, VASH2 regulated the expression of fibroblast growth factor 2 (FGF2) in human luminal breast cancer cells, and the pro-angiogenic effect induced by VASH2 overexpression was blocked by FGF2 neutralization in vitro. Additionally, dual luciferase reporter assay and Chromatin immunoprecipitation analysis results showed that FGF2 promoter was transcriptionally activated by VASH2 via histone modifications. In conclusion, VASH2 expression is positively correlated with FGF2 expression and promotes angiogenesis in human luminal breast cancer by transcriptional activation of fibroblast growth factor 2 through non-paracrine mechanisms. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Imaging macrophage distribution and density in mammary tumors and lung metastases using fluorine-19 MRI cell tracking.

PubMed

Makela, Ashley V; Foster, Paula J

2018-09-01

The presence of tumor-associated macrophages (TAMs) correlates with breast cancer progression and metastatic spread. Metastasis-associated macrophages (MAMs) are also recruited to distant sites, where they support metastatic growth. In this study, we demonstrate that in vivo fluorine-19 ( 19 F)-based MRI cell tracking can evaluate the density and distribution of macrophages within murine breast cancer tumors and associated metastases. Three murine breast cancer cell lines with different metastatic potentials (4T1, 168FARN, and 67NR) were implanted into the mammary fat pad in mice. In vivo whole body 19 F MRI was performed on tumor-bearing mice 24 hours post-intravenous injection of a perfluorocarbon (PFC) agent, which was taken up by macrophages in situ. TAMs were detected mainly in the periphery of primary tumors, and higher numbers of TAMs were detected in the more aggressive 4T1 tumors. Tumors had significantly greater 19 F spins/mm 3 when they were smaller, suggesting more TAM infiltration in early-stage tumors. 19 F signal was observed within lung metastases in mice with 4T1 tumors, and fluorescence microscopy confirmed the presence of PFC-positive macrophages. This study shows for the first time proof of the ability to use MRI cell tracking to visualize MAMs in the lungs. The ability to detect and monitor the number of TAMs in individual tumors with 19 F MRI would allow for identification of breast tumors with heavy infiltration of TAMs and could be used as a biomarker for decisions about how to best treat these patients as well as for monitoring responses to therapy. Magn Reson Med 80:1138-1147, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.