Comparison of Danish dichotomous and BI-RADS classifications of mammographic density.

PubMed

Hodge, Rebecca; Hellmann, Sophie Sell; von Euler-Chelpin, My; Vejborg, Ilse; Andersen, Zorana Jovanovic

2014-06-01

In the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before. To compare the Danish dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system. The study sample consisted of 120 mammograms taken in Copenhagen in 1991-2001, which tested false positive, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic. Of the 120 women, 32 (26.7%) were classified as having fatty and 88 (73.3%) as mixed/dense mammographic density, according to Danish dichotomous classification. According to BI-RADS density classification, 12 (10.0%) women were classified as having predominantly fatty (BI-RADS code 1), 46 (38.3%) as having scattered fibroglandular (BI-RADS code 2), 57 (47.5%) as having heterogeneously dense (BI-RADS 3), and five (4.2%) as having extremely dense (BI-RADS code 4) mammographic density. The inter-rater variability assessed by weighted kappa statistic showed a substantial agreement (0.75). The dichotomous mammographic density classification system utilized in early years of Copenhagen's mammographic screening program (1991-2001) agreed well with the BI-RADS density classification system.

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.

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.

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.

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

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

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

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.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

PubMed Central

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-01-01

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography (FFDM) images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793±0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions. PMID:27997380

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

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 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

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

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.

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.

Dietary vitamin D and calcium intake and mammographic density in postmenopausal women.

PubMed

Bertone-Johnson, Elizabeth R; Chlebowski, Rowan T; Manson, Joann E; Wactawski-Wende, Jean; Aragaki, Aaron K; Tamimi, Rulla M; Rexrode, Kathryn M; Thomson, Cynthia A; Rohan, Thomas E; Peck, Jennifer D; Pisano, Etta D; Martin, Christopher F; Sarto, Gloria; McTiernan, Anne

2010-01-01

Dietary intake of vitamin D and calcium may be related to risk of breast cancer, possibly by affecting mammographic density. However, the few studies that have evaluated the association between these nutrients and mammographic density in postmenopausal women have had inconsistent results. We conducted a cross-sectional analysis in 808 participants of the Mammogram Density Ancillary Study of the Women's Health Initiative. Mammographic percent density was measured using baseline mammograms taken before randomization of participants in the intervention trials. Vitamin D and calcium intake was assessed with a validated food frequency questionnaire and an inventory of current supplement use, both completed at baseline. After adjustment for age, body mass index, regional solar irradiance, and other factors, we did not find a relationship between vitamin D or calcium intake and mammographic density. Mean mammographic percent densities in women reporting total vitamin D intakes of less than 100, 100 to 199, 200 to 399, 400 to 599, and 600 or greater IU/day were 5.8%, 10.4%, 6.2%, 3.8%, and 5.1%, respectively (P trend = 0.67). Results in women reporting a total calcium intake of less than 500, 500 to 749, 750 to 999, 1,000 to 1,199, and 1,200 or greater mg/day were 7.3%, 4.9%, 7.3%, 6.9%, and 7.11%, respectively (P trend = 0.51). We did not observe any effect modification by overall level of mammographic density or solar irradiance, but supplemental vitamin D use was associated with lower density in younger women (P interaction = 0.009). These findings do not support a relationship between dietary vitamin D or calcium intake and mammographic density in postmenopausal women. Additional studies should explore these associations in women of different ages and in relation to serum vitamin D levels.

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.

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

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.

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.

Residential particulate matter and distance to roadways in relation to mammographic density: results from the Nurses' Health Studies.

PubMed

DuPre, Natalie C; Hart, Jaime E; Bertrand, Kimberly A; Kraft, Peter; Laden, Francine; Tamimi, Rulla M

2017-11-23

High mammographic density is a strong, well-established breast cancer risk factor. Three studies conducted in various smaller geographic settings reported inconsistent findings between air pollution and mammographic density. We assessed whether particulate matter (PM) exposures (PM 2.5 , PM 2.5-10 , and PM 10 ) and distance to roadways were associated with mammographic density among women residing across the United States. The Nurses' Health Studies are prospective cohorts for whom a subset has screening mammograms from the 1990s (interquartile range 1990-1999). PM was estimated using spatio-temporal models linked to residential addresses. Among 3258 women (average age at mammogram 52.7 years), we performed multivariable linear regression to assess associations between square-root-transformed mammographic density and PM within 1 and 3 years before the mammogram. For linear regression estimates of PM in relation to untransformed mammographic density outcomes, bootstrapped robust standard errors are used to calculate 95% confidence intervals (CIs). Analyses were stratified by menopausal status and region of residence. Recent PM and distance to roadways were not associated with mammographic density in premenopausal women (PM 2.5 within 3 years before mammogram β = 0.05, 95% CI -0.16, 0.27; PM 2.5-10 β = 0, 95%, CI -0.15, 0.16; PM 10 β = 0.02, 95% CI -0.10, 0.13) and postmenopausal women (PM 2.5 within 3 years before mammogram β = -0.05, 95% CI -0.27, 0.17; PM 2.5-10 β = -0.01, 95% CI -0.16, 0.14; PM 10 β = -0.02, 95% CI -0.13, 0.09). Largely null associations were observed within regions. Suggestive associations were observed among postmenopausal women in the Northeast (n = 745), where a 10-μg/m 3 increase in PM 2.5 within 3 years before the mammogram was associated with 3.4 percentage points higher percent mammographic density (95% CI -0.5, 7.3). These findings do not support that recent PM or roadway exposures influence mammographic density. Although PM was largely not associated with mammographic density, we cannot rule out the role of PM during earlier exposure time windows and possible associations among northeastern postmenopausal women.

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.

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.

Biological and Computational Modeling of Mammographic Density and Stromal Patterning

DTIC Science & Technology

2010-07-01

clumping Score Monolayer Absent Many Absent Absent Absent 1 Nucl. overlap Mild Moderate Mild Micro- nucleoli Rare 2 Clustering Moderate...Few Moderate Micro- nucleoli Occasional 3 Loss cohesion Conspicuous Absent Frequent Macro- nucleoli Frequent 4 We performed serial RPFNA

Metabolic syndrome and mammographic density in Mexican women

PubMed Central

Rice, Megan; Biessy, Carine; Lajous, Martin; Bertrand, Kimberly A.; Tamimi, Rulla M.; Torres-Mejía, Gabriela; López-Ridaura, Ruy; Romieu, Isabelle

2014-01-01

Background Metabolic syndrome has been associated with an increased risk of breast cancer; however little is known about the association between metabolic syndrome and percent mammographic density, a strong predictor of breast cancer. Methods We analyzed cross-sectional data from 789 premenopausal and 322 postmenopausal women in the Mexican Teacher's Cohort (ESMaestras). Metabolic syndrome was defined according to the harmonized definition. We measured percent density on mammograms using a computer-assisted thresholding method. Multivariable linear regression was used to estimate the association between density and metabolic syndrome, as well as its components by state (Jalisco, Veracruz) and menopausal status (premenopausal, postmenopausal). Results Among premenopausal women in Jalisco, women with metabolic syndrome had higher percent density compared to those without after adjusting for potential confounders including BMI (difference = 4.76, 95%CI: 1.72, 7.81). Among the metabolic syndrome components, only low high-density lipoprotein levels (<50mg/dl) were associated with significantly higher percent density among premenopausal women in Jalisco (difference=4.62, 95%CI: 1.73, 7.52). Metabolic syndrome was not associated with percent density among premenopausal women in Veracruz (difference=-2.91, 95% CI: -7.19, 1.38), nor among postmenopausal women in either state. Conclusion Metabolic syndrome was associated with higher percent density among premenopausal women in Jalisco, Mexico, but was not associated with percent density among premenopausal women in Veracruz, Mexico or among postmenopausal women in either Jalisco or Veracruz. These findings provide some support for a possible role of metabolic syndrome in mammographic density among premenopausal women; however results were inconsistent across states and require further confirmation in larger studies. PMID:23682074

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

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.

Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, growth hormone, and mammographic density in the Nurses' Health Studies.

PubMed

Rice, Megan S; Tworoger, Shelley S; Rosner, Bernard A; Pollak, Michael N; Hankinson, Susan E; Tamimi, Rulla M

2012-12-01

Higher circulating insulin-like growth factor I (IGF-1) levels have been associated with higher mammographic density among women in some, but not all studies. Also, few studies have examined the association between mammographic density and circulating growth hormone (GH) in premenopausal women. We conducted a cross-sectional study among 783 premenopausal women and 436 postmenopausal women who were controls in breast cancer case-control studies nested in the Nurses' Health Study (NHS) and NHSII. Participants provided blood samples in 1989-1990 (NHS) or in 1996-1999 (NHSII), and mammograms were obtained near the time of blood draw. Generalized linear models were used to assess the associations of IGF-1, IGF-binding protein-3 (IGFBP-3), IGF-1:IGFBP-3 ratio, and GH with percent mammographic density, total dense area, and total non-dense area. Models were adjusted for potential confounders including age and body mass index (BMI), among others. We also assessed whether the associations varied by age or BMI. In both pre- and postmenopausal women, percent mammographic density was not associated with plasma levels of IGF-1, IGFBP-3, or the IGF-1:IGFBP-3 ratio. In addition, GH was not associated with percent density among premenopausal women in the NHSII. Similarly, total dense area and non-dense area were not significantly associated with any of these analytes. In postmenopausal women, IGF-1 was associated with higher percent mammographic density among women with BMI <25 kg/m(2), but not among overweight/obese women. Overall, plasma IGF-1, IGFBP-3, and GH levels were not associated with mammographic density in a sample of premenopausal and postmenopausal women.

Vitamin D and calcium supplementation and one-year change in mammographic density in the Women’s Health Initiative Calcium and Vitamin D Trial

PubMed Central

Bertone-Johnson, Elizabeth R.; McTiernan, Anne; Thomson, Cynthia A.; Wactawski-Wende, Jean; Aragaki, Aaron K.; Rohan, Thomas E.; Vitolins, Mara Z.; Tamimi, Rulla M.; Johnson, Karen C.; Lane, Dorothy; Rexrode, Kathryn M.; Peck, Jennifer D.; Chlebowski, Rowan T.; Sarto, Gloria; Manson, JoAnn E.

2012-01-01

Background Calcium and vitamin D may be inversely related to breast cancer risk, in part by affecting mammographic density. However, results from previous, mostly cross-sectional studies have been mixed, and there have been few randomized clinical trials of the effect of calcium and vitamin D supplementation on change in mammographic density. Methods We assessed the effect of one year of supplementation on mammographic density in 330 postmenopausal women enrolled in the Women’s Health Initiative Hormone Therapy (HT) and Calcium and Vitamin D (CaD) trials. Women were randomized to receive 1000 mg/day of elemental calcium carbonate plus 400 IU/day of vitamin D3 or placebo. Results After approximately one year, mammographic density decreased 2% in the CaD supplementation group and increased 1% in the placebo group (ratio of means = 0.97; 95% confidence interval (CI) = 0.81–1.17). Results suggested potential interaction by HT use (P = 0.08). Among women randomized to HT placebo, the ratio of mean density comparing CaD supplementation and placebo groups was 0.82 (95%CI = 0.61–1.11) vs. 1.16 (95%CI = 0.92–1.45) in women randomized to active HT. In sensitivity analyses limited to women taking ≥80% of study supplements, ratios were 0.67 (95%CI = 0.41–1.07) in women not assigned to HT and 1.07 (95%CI = 0.79–1.47) women assigned to HT. Conclusions We observed no overall effect of vitamin D and calcium supplementation on mammographic density after one year. Impact Potential interaction between these nutrients and estrogen as related to mammographic density warrants further study. PMID:22253296

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.

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.

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.

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.

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.

A new approach to develop computer-aided detection schemes of digital mammograms

NASA Astrophysics Data System (ADS)

Tan, Maxine; Qian, Wei; Pu, Jiantao; Liu, Hong; Zheng, Bin

2015-06-01

The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779   ±   0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

Biologic and Computational Modeling of Mammographic Density and Stromal Patterning

DTIC Science & Technology

2008-07-01

Moderate Mild Micro- nucleoli Rare 2 Clustering Moderate Few Moderate Micro- nucleoli Occasional 3 Loss cohesion Conspicuous Absent Frequent Macro... nucleoli Frequent 4 M asood scores: 6-10 Normal; 11-13 Hyperplasia; 14-15 Atypia; 16-17 High-grade atypia; >17 Suspicious for cancer. 4 We

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.

Sleep patterns, sleep disorders and mammographic density in spanish women: The DDM-Spain/Var-DDM study.

PubMed

Pedraza-Flechas, Ana María; Lope, Virginia; Moreo, Pilar; Ascunce, Nieves; Miranda-García, Josefa; Vidal, Carmen; Sánchez-Contador, Carmen; Santamariña, Carmen; Pedraz-Pingarrón, Carmen; Llobet, Rafael; Aragonés, Nuria; Salas-Trejo, Dolores; Pollán, Marina; Pérez-Gómez, Beatriz

2017-05-01

We explored the relationship between sleep patterns and sleep disorders and mammographic density (MD), a marker of breast cancer risk. Participants in the DDM-Spain/var-DDM study, which included 2878 middle-aged Spanish women, were interviewed via telephone and asked questions on sleep characteristics. Two radiologists assessed MD in their left craneo-caudal mammogram, assisted by a validated semiautomatic-computer tool (DM-scan). We used log-transformed percentage MD as the dependent variable and fitted mixed linear regression models, including known confounding variables. Our results showed that neither sleeping patterns nor sleep disorders were associated with MD. However, women with frequent changes in their bedtime due to anxiety or depression had higher MD (e β :1.53;95%CI:1.04-2.26). Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Association between mammogram density and background parenchymal enhancement of breast MRI

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Wang, Yunzhi; Zarafshani, Ali; Qian, Wei; Liu, Hong; Zheng, Bin

2018-02-01

Breast density has been widely considered as an important risk factor for breast cancer. The purpose of this study is to examine the association between mammogram density results and background parenchymal enhancement (BPE) of breast MRI. A dataset involving breast MR images was acquired from 65 high-risk women. Based on mammography density (BIRADS) results, the dataset was divided into two groups of low and high breast density cases. The Low-Density group has 15 cases with mammographic density (BIRADS 1 and 2), while the High-density group includes 50 cases, which were rated by radiologists as mammographic density BIRADS 3 and 4. A computer-aided detection (CAD) scheme was applied to segment and register breast regions depicted on sequential images of breast MRI scans. CAD scheme computed 20 global BPE features from the entire two breast regions, separately from the left and right breast region, as well as from the bilateral difference between left and right breast regions. An image feature selection method namely, CFS method, was applied to remove the most redundant features and select optimal features from the initial feature pool. Then, a logistic regression classifier was built using the optimal features to predict the mammogram density from the BPE features. Using a leave-one-case-out validation method, the classifier yields the accuracy of 82% and area under ROC curve, AUC=0.81+/-0.09. Also, the box-plot based analysis shows a negative association between mammogram density results and BPE features in the MRI images. This study demonstrated a negative association between mammogram density and BPE of breast MRI images.

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

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.

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 +/- 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.

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

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.

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

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.

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.

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

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.

[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.

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

Various doses of soy isoflavones do not modify mammographic density in postmenopausal women

USDA-ARS?s Scientific Manuscript database

Soy isoflavones have functional similarity to human estrogens and may protect against breast cancer as a result of their antiestrogenic activity or increase risk as a result of their estrogen-like properties. We examined the relation between isoflavone supplementation and mammographic density, a str...

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

Ethnic Differences in Mammographic Densities: An Asian Cross-Sectional Study

PubMed Central

Mariapun, Shivaani; Li, Jingmei; Yip, Cheng Har; Taib, Nur Aishah Mohd; Teo, Soo-Hwang

2015-01-01

Background Mammographic density is a strong risk factor for breast cancer and is highly variable, but, to date, few studies have examined density in Asian women, particularly those in low and middle-income Asian countries where genetic and lifestyle determinants may be significantly different. Methods A total of 1,240 women who attended an opportunistic mammogram screening programme were eligible for analysis. Mammographic density was estimated using a fully-automated thresholding method and differences across ethnic groups were examined using linear regression in 205 randomly selected Chinese women, 138 Malay and 199 Indian women. Results Percent density was significantly higher in Chinese women (28.5%; 95% CI 27.0%, 30.0%) compared to Malay (24.2%; 95% CI 22.5%, 26.0%) and Indian (24.3%; 95% CI 22.8%, 25.7%) women (p<0.001), after adjustment for age, BMI, menopausal status, parity and age at first full term pregnancy. Correspondingly, adjusted nondense area was significantly lower in Chinese (72.2cm2; 95% CI 67.9cm2, 76.5cm2) women compared to Malay (92.1cm2; 95% CI 86.9cm2, 97.2cm2) and Indian (97.7cm2; 95% CI 93.4cm2, 101.9cm2) women (p<0.001), but dense area did not differ across the three ethnic groups. Conclusions Our study shows that higher percent density and lower nondense area reflect the higher incidence of breast cancer in Chinese compared to Malay and Indian women in Malaysia. Known lifestyle determinants of mammographic density do not fully account for the ethnic variations observed in mammographic density in this Asian cohort. PMID:25659139

Ethnic differences in mammographic densities: an Asian cross-sectional study.

PubMed

Mariapun, Shivaani; Li, Jingmei; Yip, Cheng Har; Taib, Nur Aishah Mohd; Teo, Soo-Hwang

2015-01-01

Mammographic density is a strong risk factor for breast cancer and is highly variable, but, to date, few studies have examined density in Asian women, particularly those in low and middle-income Asian countries where genetic and lifestyle determinants may be significantly different. A total of 1,240 women who attended an opportunistic mammogram screening programme were eligible for analysis. Mammographic density was estimated using a fully-automated thresholding method and differences across ethnic groups were examined using linear regression in 205 randomly selected Chinese women, 138 Malay and 199 Indian women. Percent density was significantly higher in Chinese women (28.5%; 95% CI 27.0%, 30.0%) compared to Malay (24.2%; 95% CI 22.5%, 26.0%) and Indian (24.3%; 95% CI 22.8%, 25.7%) women (p<0.001), after adjustment for age, BMI, menopausal status, parity and age at first full term pregnancy. Correspondingly, adjusted nondense area was significantly lower in Chinese (72.2cm2; 95% CI 67.9cm2, 76.5cm2) women compared to Malay (92.1cm2; 95% CI 86.9cm2, 97.2cm2) and Indian (97.7cm2; 95% CI 93.4cm2, 101.9cm2) women (p<0.001), but dense area did not differ across the three ethnic groups. Our study shows that higher percent density and lower nondense area reflect the higher incidence of breast cancer in Chinese compared to Malay and Indian women in Malaysia. Known lifestyle determinants of mammographic density do not fully account for the ethnic variations observed in mammographic density in this Asian cohort.

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).

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

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

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.

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.

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.

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.

Computer-Aided Diagnosis of Breast Cancer: A Multi-Center Demonstrator

DTIC Science & Technology

1998-10-01

Artificial Neural Network (ANN) approach to computer aided diagnosis of breast cancer from mammographic findings. An ANN has been developed to provide support for the clinical decision to perform breast biopsy. The system is designed to aid in the decision to biopsy those patients who have suspicious mammographic findings. The decision to biopsy can be viewed as a two stage process: 1)the mammographer views the mammogram and determines the presence or absence of image features such as calcifications and masses, 2) the presence and description of these features

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

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.

Radiologist Agreement for Mammographic Recall by Case Difficulty and Finding Type.

PubMed

Onega, Tracy; Smith, Megan; Miglioretti, Diana L; Carney, Patricia A; Geller, Berta A; Kerlikowske, Karla; Buist, Diana S M; Rosenberg, Robert D; Smith, Robert A; Sickles, Edward A; Haneuse, Sebastien; Anderson, Melissa L; Yankaskas, Bonnie

2016-11-01

The aim of this study was to assess agreement of mammographic interpretations by community radiologists with consensus interpretations of an expert radiology panel to inform approaches that improve mammographic performance. From 6 mammographic registries, 119 community-based radiologists were recruited to assess 1 of 4 randomly assigned test sets of 109 screening mammograms with comparison studies for no recall or recall, giving the most significant finding type (mass, calcifications, asymmetric density, or architectural distortion) and location. The mean proportion of agreement with an expert radiology panel was calculated by cancer status, finding type, and difficulty level of identifying the finding at the patient, breast, and lesion level. Concordance in finding type between study radiologists and the expert panel was also examined. For each finding type, the proportion of unnecessary recalls, defined as study radiologist recalls that were not expert panel recalls, was determined. Recall agreement was 100% for masses and for examinations with obvious findings in both cancer and noncancer cases. Among cancer cases, recall agreement was lower for lesions that were subtle (50%) or asymmetric (60%). Subtle noncancer findings and benign calcifications showed 33% agreement for recall. Agreement for finding responsible for recall was low, especially for architectural distortions (43%) and asymmetric densities (40%). Most unnecessary recalls (51%) were asymmetric densities. Agreement in mammographic interpretation was low for asymmetric densities and architectural distortions. Training focused on these interpretations could improve the accuracy of mammography and reduce unnecessary recalls. Copyright © 2012 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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.

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

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.

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 appearances of male breast disease.

PubMed

Appelbaum, A H; Evans, G F; Levy, K R; Amirkhan, R H; Schumpert, T D

1999-01-01

Various male breast diseases have characteristic mammographic appearances that can be correlated with their pathologic diagnoses. Male breast cancer is usually subareolar and eccentric to the nipple. Margins of the lesions are more frequently well defined, and calcifications are rarer and coarser than those occurring in female breast cancer. Gynecomastia usually appears as a fan-shaped density emanating from the nipple, gradually blending into surrounding fat. It may have prominent extensions into surrounding fat and, in some cases, an appearance similar to that of a heterogeneously dense female breast. Although there are characteristic mammographic features that allow breast cancer in men to be recognized, there is substantial overlap between these features and the mammographic appearance of benign nodular lesions. The mammographic appearance of gynecomastia is not similar to that of male breast cancer, but in rare cases, it can mask malignancy. Gynecomastia can be mimicked by chronic inflammation. All mammographically lucent lesions of the male breast appear to be benign, similar to such lesions in the female breast.

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.

Imaging Breast Density: Established and Emerging Modalities1

PubMed Central

Chen, Jeon-Hor; Gulsen, Gultekin; Su, Min-Ying

2015-01-01

Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and three-dimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature. PMID:26692524

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.

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.

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

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.

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.

Using speed of sound imaging to characterize breast density

PubMed Central

Sak, Mark; Duric, Neb; Littrup, Peter; Bey-Knight, Lisa; Ali, Haythem; Vallieres, Patricia; Sherman, Mark E.; Gierach, Gretchen L.

2017-01-01

A population of 165 women with negative mammographic screens also received an ultrasound tomography (UST) exam at the Karmanos Cancer Institute (KCI) in Detroit, MI. Standard statistical techniques were employed to measure the associations between the various mammographic and UST related density measures and various participant characteristics such as age, weight and height. The Mammographic percent density (MPD) was found to have similar strength associations with UST mean sound speed (Spearman coefficient, rs = 0.722, p < 0.001) and UST median sound speed (rs = 0.737, p < 0.001). Both were stronger than the associations between MPD with two separate measures of UST percent density, a k-means (rs = 0.568, p < 0.001) or a threshold (rs = 0.715, p < 0.001) measure. Segmentation of the UST sound speed images into dense and non-dense volumes showed weak to moderate associations with the mammographically equivalent measures. Relationships were found to be inversely and weakly associated between age and the UST mean sound speed (rs = −0.239, p = 0.002), UST median sound speed (rs = −0.226, p= 0.004) and MPD (rs = −0.204, p= 0.008). Relationships were found to be inversely and moderately associated between BMI and the UST mean sound speed (rs = −0.429, p < 0.001), UST median sound speed (rs = −0.447, p < 0.001) and MPD (rs = −0.489, p < 0.001). The results confirm and strengthen findings presented in previous work indicating that UST sound speed imaging yields viable markers of breast density in a manner consistent with mammography, the current clinical standard. These results lay the groundwork for further studies to assess the role of sound speed imaging in risk prediction. PMID:27692872

Mammographic findings of breast cancer screening in patients with positive family history in South-East Nigeria.

PubMed

Ebubedike, U R; Umeh, E O; C Anyanwu, S N

2018-06-01

A positive family history of breast cancer is an important risk factor associated with the development of breast cancer in women. Early detection required regular screening in these women. To determine the mammographic findings of breast cancer screening in patients with a positive family history in Iyienu, Southeast Nigeria. Forty-three consenting females with a positive family history of breast cancer who underwent mammographic screening at Radiology Department, Iyienu Mission Hospital, Anambra State, were enrolled in the study. Mammographic findings were compared with those of females with a negative family history. The mean age was 49.6 years with a range of 35-69 years. The mammographic findings were asymmetric density, nipple retraction, tissue retraction, skin thickening, lymphadenopathy, and calcification within a mass with varying frequency for the right and left breasts. A significant statistical difference was found in lymphadenopathy and calcification for the right and left breasts, respectively, when compared with those without positive family history.

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.

Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.

PubMed

Cheng, Qingsu; Bilgin, Cemal Cagatay; Fontenay, Gerald; Chang, Hang; Henderson, Matthew; Han, Ju; Parvin, Bahram

2016-07-07

The effects of the stiffness of the microenvironment on the molecular response of 3D colony organization, at the maximum level of mammographic density (MD), are investigated. Phenotypic profiling reveals that 3D colony formation is heterogeneous and increased stiffness of the microenvironment, within the range of the MD, correlates with the increased frequency of aberrant 3D colony formation. Further integrative analysis of the genome-wide transcriptome and phenotypic profiling hypothesizes overexpression of ERBB2 in the premalignant MCF10A cell lines at a stiffness value that corresponds to the collagen component at high mammographic density. Subsequently, ERBB2 overexpression has been validated in the same cell line. Similar experiments with a more genetically stable cell line of 184A1 also revealed an increased frequency of aberrant colony formation with the increased stiffness; however, 184A1 did not demonstrate overexpression of ERBB2 at the same stiffness value of the high MD. These results suggest that stiffness exacerbates premalignant cell line of MCF10A.

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.

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.

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.

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.

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.

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.

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

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

Identification of a novel percent mammographic density locus at 12q24.

PubMed

Stevens, Kristen N; Lindstrom, Sara; Scott, Christopher G; Thompson, Deborah; Sellers, Thomas A; Wang, Xianshu; Wang, Alice; Atkinson, Elizabeth; Rider, David N; Eckel-Passow, Jeanette E; Varghese, Jajini S; Audley, Tina; Brown, Judith; Leyland, Jean; Luben, Robert N; Warren, Ruth M L; Loos, Ruth J F; Wareham, Nicholas J; Li, Jingmei; Hall, Per; Liu, Jianjun; Eriksson, Louise; Czene, Kamila; Olson, Janet E; Pankratz, V Shane; Fredericksen, Zachary; Diasio, Robert B; Lee, Adam M; Heit, John A; DeAndrade, Mariza; Goode, Ellen L; Vierkant, Robert A; Cunningham, Julie M; Armasu, Sebastian M; Weinshilboum, Richard; Fridley, Brooke L; Batzler, Anthony; Ingle, James N; Boyd, Norman F; Paterson, Andrew D; Rommens, Johanna; Martin, Lisa J; Hopper, John L; Southey, Melissa C; Stone, Jennifer; Apicella, Carmel; Kraft, Peter; Hankinson, Susan E; Hazra, Aditi; Hunter, David J; Easton, Douglas F; Couch, Fergus J; Tamimi, Rulla M; Vachon, Celine M

2012-07-15

Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.

Identification of a novel percent mammographic density locus at 12q24

PubMed Central

Stevens, Kristen N.; Lindstrom, Sara; Scott, Christopher G.; Thompson, Deborah; Sellers, Thomas A.; Wang, Xianshu; Wang, Alice; Atkinson, Elizabeth; Rider, David N.; Eckel-Passow, Jeanette E.; Varghese, Jajini S.; Audley, Tina; Brown, Judith; Leyland, Jean; Luben, Robert N.; Warren, Ruth M.L.; Loos, Ruth J.F.; Wareham, Nicholas J.; Li, Jingmei; Hall, Per; Liu, Jianjun; Eriksson, Louise; Czene, Kamila; Olson, Janet E.; Shane Pankratz, V.; Fredericksen, Zachary; Diasio, Robert B.; Lee, Adam M.; Heit, John A.; deAndrade, Mariza; Goode, Ellen L.; Vierkant, Robert A.; Cunningham, Julie M.; Armasu, Sebastian M.; Weinshilboum, Richard; Fridley, Brooke L.; Batzler, Anthony; Ingle, James N.; Boyd, Norman F.; Paterson, Andrew D.; Rommens, Johanna; Martin, Lisa J.; Hopper, John L.; Southey, Melissa C.; Stone, Jennifer; Apicella, Carmel; Kraft, Peter; Hankinson, Susan E.; Hazra, Aditi; Hunter, David J.; Easton, Douglas F.; Couch, Fergus J.; Tamimi, Rulla M.; Vachon, Celine M.

2012-01-01

Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10−8). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10−8). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition. PMID:22532574

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.

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

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.

Modeling error in assessment of mammographic image features for improved computer-aided mammography training: initial experience

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Tourassi, Georgia D.

2011-03-01

In this study we investigate the hypothesis that there exist patterns in erroneous assessment of BI-RADS image features among radiology trainees when performing diagnostic interpretation of mammograms. We also investigate whether these error making patterns can be captured by individual user models. To test our hypothesis we propose a user modeling algorithm that uses the previous readings of a trainee to identify whether certain BI-RADS feature values (e.g. "spiculated" value for "margin" feature) are associated with higher than usual likelihood that the feature will be assessed incorrectly. In our experiments we used readings of 3 radiology residents and 7 breast imaging experts for 33 breast masses for the following BI-RADS features: parenchyma density, mass margin, mass shape and mass density. The expert readings were considered as the gold standard. Rule-based individual user models were developed and tested using the leave one-one-out crossvalidation scheme. Our experimental evaluation showed that the individual user models are accurate in identifying cases for which errors are more likely to be made. The user models captured regularities in error making for all 3 residents. This finding supports our hypothesis about existence of individual error making patterns in assessment of mammographic image features using the BI-RADS lexicon. Explicit user models identifying the weaknesses of each resident could be of great use when developing and adapting a personalized training plan to meet the resident's individual needs. Such approach fits well with the framework of adaptive computer-aided educational systems in mammography we have proposed before.

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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.

A population-based tissue probability map-driven level set method for fully automated mammographic density estimations.

PubMed

Kim, Youngwoo; Hong, Byung Woo; Kim, Seung Ja; Kim, Jong Hyo

2014-07-01

A major challenge when distinguishing glandular tissues on mammograms, especially for area-based estimations, lies in determining a boundary on a hazy transition zone from adipose to glandular tissues. This stems from the nature of mammography, which is a projection of superimposed tissues consisting of different structures. In this paper, the authors present a novel segmentation scheme which incorporates the learned prior knowledge of experts into a level set framework for fully automated mammographic density estimations. The authors modeled the learned knowledge as a population-based tissue probability map (PTPM) that was designed to capture the classification of experts' visual systems. The PTPM was constructed using an image database of a selected population consisting of 297 cases. Three mammogram experts extracted regions for dense and fatty tissues on digital mammograms, which was an independent subset used to create a tissue probability map for each ROI based on its local statistics. This tissue class probability was taken as a prior in the Bayesian formulation and was incorporated into a level set framework as an additional term to control the evolution and followed the energy surface designed to reflect experts' knowledge as well as the regional statistics inside and outside of the evolving contour. A subset of 100 digital mammograms, which was not used in constructing the PTPM, was used to validate the performance. The energy was minimized when the initial contour reached the boundary of the dense and fatty tissues, as defined by experts. The correlation coefficient between mammographic density measurements made by experts and measurements by the proposed method was 0.93, while that with the conventional level set was 0.47. The proposed method showed a marked improvement over the conventional level set method in terms of accuracy and reliability. This result suggests that the proposed method successfully incorporated the learned knowledge of the experts' visual systems and has potential to be used as an automated and quantitative tool for estimations of mammographic breast density levels.

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.

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.

Multi-view information fusion for automatic BI-RADS description of mammographic masses

NASA Astrophysics Data System (ADS)

Narvaez, Fabián; Díaz, Gloria; Romero, Eduardo

2011-03-01

Most CBIR-based CAD systems (Content Based Image Retrieval systems for Computer Aided Diagnosis) identify lesions that are eventually relevant. These systems base their analysis upon a single independent view. This article presents a CBIR framework which automatically describes mammographic masses with the BI-RADS lexicon, fusing information from the two mammographic views. After an expert selects a Region of Interest (RoI) at the two views, a CBIR strategy searches similar masses in the database by automatically computing the Mahalanobis distance between shape and texture feature vectors of the mammography. The strategy was assessed in a set of 400 cases, for which the suggested descriptions were compared with the ground truth provided by the data base. Two information fusion strategies were evaluated, allowing a retrieval precision rate of 89.6% in the best scheme. Likewise, the best performance obtained for shape, margin and pathology description, using a ROC methodology, was reported as AUC = 0.86, AUC = 0.72 and AUC = 0.85, respectively.

Impact of contra-lateral breast reshaping on mammographic surveillance in women undergoing breast reconstruction following mastectomy for breast cancer.

PubMed

Nava, Maurizio B; Rocco, Nicola; Catanuto, Giuseppe; Falco, Giuseppe; Capalbo, Emanuela; Marano, Luigi; Bordoni, Daniele; Spano, Andrea; Scaperrotta, Gianfranco

2015-08-01

The ultimate goal of breast reconstruction is to achieve symmetry with the contra-lateral breast. Contra-lateral procedures with wide parenchymal rearrangements are suspected to impair mammographic surveillance. This study aims to evaluate the impact on mammographic detection of mastopexies and breast reductions for contralateral adjustment in breast reconstruction. We retrospectively evaluated 105 women affected by uni-lateral breast cancer who underwent mastectomy and immediate two-stage reconstruction between 2002 and 2007. We considered three groups according to the contra-lateral reshaping technique: mastopexy or breast reduction with inferior dermoglandular flap (group 1); mastopexy or breast reduction without inferior dermoglandular flap (group 2); no contra-lateral reshaping (group 3). We assessed qualitative mammographic variations and breast density in the three groups. Statistically significant differences have been found when comparing reshaped groups with non reshaped groups regarding parenchymal distortions, skin thickening and stromal edema, but these differences did not affect cancer surveillance. The surveillance mammography diagnostic accuracy in contra-lateral cancer detection was not significantly different between the three groups (p = 0.56), such as the need for MRI for equivocal findings at mammographic contra-lateral breast (p = 0.77) and the need for core-biopsies to confirm mammographic suspect of contra-lateral breast cancer (p = 0.90). This study confirms previous reports regarding the safety of mastopexies and breast reductions when performed in the setting of contra-lateral breast reshaping after breast reconstruction. Mammographic accuracy, sensitivity and specificity are not affected by the glandular re-arrangement. These results provide a further validation of the safety of current reconstructive paradigms. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Estimation of effective x-ray tissue attenuation differences for volumetric breast density measurement

NASA Astrophysics Data System (ADS)

Chen, Biao; Ruth, Chris; Jing, Zhenxue; Ren, Baorui; Smith, Andrew; Kshirsagar, Ashwini

2014-03-01

Breast density has been identified to be a risk factor of developing breast cancer and an indicator of lesion diagnostic obstruction due to masking effect. Volumetric density measurement evaluates fibro-glandular volume, breast volume, and breast volume density measures that have potential advantages over area density measurement in risk assessment. One class of volume density computing methods is based on the finding of the relative fibro-glandular tissue attenuation with regards to the reference fat tissue, and the estimation of the effective x-ray tissue attenuation differences between the fibro-glandular and fat tissue is key to volumetric breast density computing. We have modeled the effective attenuation difference as a function of actual x-ray skin entrance spectrum, breast thickness, fibro-glandular tissue thickness distribution, and detector efficiency. Compared to other approaches, our method has threefold advantages: (1) avoids the system calibration-based creation of effective attenuation differences which may introduce tedious calibrations for each imaging system and may not reflect the spectrum change and scatter induced overestimation or underestimation of breast density; (2) obtains the system specific separate and differential attenuation values of fibroglandular and fat for each mammographic image; and (3) further reduces the impact of breast thickness accuracy to volumetric breast density. A quantitative breast volume phantom with a set of equivalent fibro-glandular thicknesses has been used to evaluate the volume breast density measurement with the proposed method. The experimental results have shown that the method has significantly improved the accuracy of estimating breast density.

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.

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.

Performance of a subsidised mammographic screening programme in Malaysia, a middle-income Asian country.

PubMed

Lee, Marianne; Mariapun, Shivaani; Rajaram, Nadia; Teo, Soo-Hwang; Yip, Cheng-Har

2017-01-28

The incidence of breast cancer in Asia is increasing because of urbanization and lifestyle changes. In the developing countries in Asia, women present at late stages, and mortality is high. Mammographic screening is the only evidence-based screening modality that reduces breast cancer mortality. To date, only opportunistic screening is offered in the majority of Asian countries because of the lack of justification and funding. Nevertheless, there have been few reports on the effectiveness of such programmes. In this study, we describe the cancer detection rate and challenges experienced in an opportunistic mammographic screening programme in Malaysia. From October 2011 to June 2015, 1,778 asymptomatic women, aged 40-74 years, underwent subsidised mammographic screening. All patients had a clinical breast examination before mammographic screening, and women with mammographic abnormalities were referred to a surgeon. The cancer detection rate and variables associated with a recommendation for adjunct ultrasonography were determined. The mean age for screening was 50.8 years and seven cancers (0.39%) were detected. The detection rate was 0.64% in women aged 50 years and above, and 0.12% in women below 50 years old. Adjunct ultrasonography was recommended in 30.7% of women, and was significantly associated with age, menopausal status, mammographic density and radiologist's experience. The main reasons cited for recommendation of an adjunct ultrasound was dense breasts and mammographic abnormalities. The cancer detection rate is similar to population-based screening mammography programmes in high-income Asian countries. Unlike population-based screening programmes in Caucasian populations where the adjunct ultrasonography rate is 2-4%, we report that 3 out of 10 women attending screening mammography were recommended for adjunct ultrasonography. This could be because Asian women attending screening are likely premenopausal and hence have denser breasts. Radiologists who reported more than 360 mammograms were more confident in reporting a mammogram as normal without adjunct ultrasonography compared to those who reported less than 180 mammograms. Our subsidised opportunistic mammographic screening programme is able to provide equivalent cancer detection rates but the high recall for adjunct ultrasonography would make screening less cost-effective.

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.

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.

Mapping 3D breast lesions from full-field digital mammograms using subject-specific finite element models

NASA Astrophysics Data System (ADS)

García, E.; Oliver, A.; Diaz, O.; Diez, Y.; Gubern-Mérida, A.; Martí, R.; Martí, J.

2017-03-01

Patient-specific finite element (FE) models of the breast have received increasing attention due to the potential capability of fusing images from different modalities. During the Magnetic Resonance Imaging (MRI) to X-ray mammography registration procedure, the FE model is compressed mimicking the mammographic acquisition. Subsequently, suspicious lesions in the MRI volume can be projected into the 2D mammographic space. However, most registration algorithms do not provide the reverse information, avoiding to obtain the 3D geometrical information from the lesions localized in the mammograms. In this work we introduce a fast method to localize the 3D position of the lesion within the MRI, using both cranio-caudal (CC) and medio-lateral oblique (MLO) mammographic projections, indexing the tetrahedral elements of the biomechanical model by means of an uniform grid. For each marked lesion in the Full-Field Digital Mammogram (FFDM), the X-ray path from source to the marker is calculated. Barycentric coordinates are computed in the tetrahedrons traversed by the ray. The list of elements and coordinates allows to localize two curves within the MRI and the closest point between both curves is taken as the 3D position of the lesion. The registration errors obtained in the mammographic space are 9.89 +/- 3.72 mm in CC- and 8.04 +/- 4.68 mm in MLO-projection and the error in the 3D MRI space is equal to 10.29 +/- 3.99 mm. Regarding the uniform grid, it is computed spending between 0.1 and 0.7 seconds. The average time spent to compute the 3D location of a lesion is about 8 ms.

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

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.

Birthweight, early life body size and adult mammographic density: a review of epidemiologic studies.

PubMed

Yochum, Laura; Tamimi, Rulla M; Hankinson, Susan E

2014-10-01

To evaluate the association between birth weight and early life body size with adult mammographic density in the peer-reviewed literature. A comprehensive literature search was conducted through January, 2014. English language articles that assessed adult mammographic density (MD) in relation to early life body size (≤18 years old), or birthweight were included. Nine studies reported results for early life body size and %MD. Both exposure and outcome were assessed at different ages using multiple methods. In premenopausal women, findings were inconsistent; two studies reported significant, inverse associations, one reported a non-significant, inverse association, and two observed no association. Reasons for these inconsistencies were not obvious. In postmenopausal women, four of five studies supported an inverse association. Two of three studies that adjusted for menopausal status found significant, inverse associations. Birthweight and %MD was evaluated in nine studies. No association was seen in premenopausal women and two of three studies reported positive associations in postmenopausal women. Three of four studies that adjusted for menopausal status found no association. Early life body size and birthweight appear unrelated to %MD in premenopausal women while an inverse association in postmenopausal women is more likely. Although based on limited data, birthweight and %MD appear positively associated in postmenopausal women. Given the small number of studies, the multiple methods of data collection and analysis, other methodologic issues, and lack of consistency in results, additional research is needed to clarify this complex association and develop a better understanding of the underlying biologic mechanisms.

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).

Development of an imaging-planning program for screen/film and computed radiography mammography for breasts with short chest wall to nipple distance.

PubMed

Dong, S L; Su, J L; Yeh, Y H; Chu, T C; Lin, Y C; Chuang, K S

2011-04-01

Imaging breasts with a short chest wall to nipple distance (CWND) using a traditional mammographic X-ray unit is a technical challenge for mammographers. The purpose of this study is the development of an imaging-planning program to assist in determination of imaging parameters of screen/film (SF) and computed radiography (CR) mammography for short CWND breasts. A traditional mammographic X-ray unit (Mammomat 3000, Siemens, Munich, Germany) was employed. The imaging-planning program was developed by combining the compressed breast thickness correction, the equivalent polymethylmethacrylate thickness assessment for breasts and the tube loading (mAs) measurement. Both phantom exposures and a total of 597 exposures were used for examining the imaging-planning program. Results of the phantom study show that the tube loading rapidly decreased with the CWND when the automatic exposure control (AEC) detector was not fully covered by the phantom. For patient exposures with the AEC fully covered by breast tissue, the average fractional tube loadings, defined as the ratio of the predicted mAs using the imaging-planning program and mAs of the mammogram, were 1.10 and 1.07 for SF and CR mammograms, respectively. The predicted mAs values were comparable to the mAs values, as determined by the AEC. By applying the imaging-planning program in clinical practice, the experiential dependence of the mammographer for determination of the imaging parameters for short CWND breasts is minimised.

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

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

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.

Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

NASA Astrophysics Data System (ADS)

Li, Qingliang; Shi, Weili; Yang, Huamin; Zhang, Huimao; Li, Guoxin; Chen, Tao; Mori, Kensaku; Jiang, Zhengang

2017-03-01

Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is increasing gradually. In this respect, the method of exploiting the vocabulary tree framework and the inverted file in the mammographic masse retrieval have been proved high accuracy and excellent scalability. However it just considered the features in each image as a visual word and had ignored the spatial configurations of features. It greatly affect the retrieval performance. To overcome this drawback, we introduce the geometric verification method to retrieval in mammographic masses. First of all, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. After that, we grasps the main point of local similarity characteristic of deformations in the local regions by constructing the circle regions of corresponding pairs. Meanwhile we segment the circle to express the geometric relationship of local matches in the area and generate the spatial encoding strictly. Finally we judge whether the matched features are correct or not, based on verifying the all spatial encoding are whether satisfied the geometric consistency. Experiments show the promising results of our approach.

Calculated mammographic spectra confirmed with attenuation curves for molybdenum, rhodium, and tungsten targets.

PubMed

Blough, M M; Waggener, R G; Payne, W H; Terry, J A

1998-09-01

A model for calculating mammographic spectra independent of measured data and fitting parameters is presented. This model is based on first principles. Spectra were calculated using various target and filter combinations such as molybdenum/molybdenum, molybdenum/rhodium, rhodium/rhodium, and tungsten/aluminum. Once the spectra were calculated, attenuation curves were calculated and compared to measured attenuation curves. The attenuation curves were calculated and measured using aluminum alloy 1100 or high purity aluminum filtration. Percent differences were computed between the measured and calculated attenuation curves resulting in an average of 5.21% difference for tungsten/aluminum, 2.26% for molybdenum/molybdenum, 3.35% for rhodium/rhodium, and 3.18% for molybdenum/rhodium. Calculated spectra were also compared to measured spectra from the Food and Drug Administration [Fewell and Shuping, Handbook of Mammographic X-ray Spectra (U.S. Government Printing Office, Washington, D.C., 1979)] and a comparison will also be presented.

Quality assurance in mammography: artifact analysis.

PubMed

Hogge, J P; Palmer, C H; Muller, C C; Little, S T; Smith, D C; Fatouros, P P; de Paredes, E S

1999-01-01

Evaluation of mammograms for artifacts is essential for mammographic quality assurance. A variety of mammographic artifacts (i.e., variations in mammographic density not caused by true attenuation differences) can occur and can create pseudolesions or mask true abnormalities. Many artifacts are readily identified, whereas others present a true diagnostic challenge. Factors that create artifacts may be related to the processor (eg, static, dirt or excessive developer buildup on the rollers, excessive roller pressure, damp film, scrapes and scratches, incomplete fixing, power failure, contaminated developer), the technologist (eg, improper film handling and loading, improper use of the mammography unit and related equipment, positioning and darkroom errors), the mammography unit (eg, failure of the collimation mirror to rotate, grid inhomogeneity, failure of the reciprocating grid to move, material in the tube housing, compression failure, improper alignment of the compression paddle with the Bucky tray, defective compression paddle), or the patient (e.g., motion, superimposed objects or substances [jewelry, body parts, clothing, hair, implanted medical devices, foreign bodies, substances on the skin]). Familiarity with the broad range of artifacts and the measures required to eliminate them is vital. Careful attention to darkroom cleanliness, care in film handling, regularly scheduled processor maintenance and chemical replenishment, daily quality assurance activities, and careful attention to detail during patient positioning and mammography can reduce or eliminate most mammographic artifacts.

Training system for digital mammographic diagnoses of breast cancer

NASA Astrophysics Data System (ADS)

Thomaz, R. L.; Nirschl Crozara, M. G.; Patrocinio, A. C.

2013-03-01

As the technology evolves, the analog mammography systems are being replaced by digital systems. The digital system uses video monitors as the display of mammographic images instead of the previously used screen-film and negatoscope for analog images. The change in the way of visualizing mammographic images may require a different approach for training the health care professionals in diagnosing the breast cancer with digital mammography. Thus, this paper presents a computational approach to train the health care professionals providing a smooth transition between analog and digital technology also training to use the advantages of digital image processing tools to diagnose the breast cancer. This computational approach consists of a software where is possible to open, process and diagnose a full mammogram case from a database, which has the digital images of each of the mammographic views. The software communicates with a gold standard digital mammogram cases database. This database contains the digital images in Tagged Image File Format (TIFF) and the respective diagnoses according to BI-RADSTM, these files are read by software and shown to the user as needed. There are also some digital image processing tools that can be used to provide better visualization of each single image. The software was built based on a minimalist and a user-friendly interface concept that might help in the smooth transition. It also has an interface for inputting diagnoses from the professional being trained, providing a result feedback. This system has been already completed, but hasn't been applied to any professional training yet.

Shapelet analysis of pupil dilation for modeling visuo-cognitive behavior in screening mammography

NASA Astrophysics Data System (ADS)

Alamudun, Folami; Yoon, Hong-Jun; Hammond, Tracy; Hudson, Kathy; Morin-Ducote, Garnetta; Tourassi, Georgia

2016-03-01

Our objective is to improve understanding of visuo-cognitive behavior in screening mammography under clinically equivalent experimental conditions. To this end, we examined pupillometric data, acquired using a head-mounted eye-tracking device, from 10 image readers (three breast-imaging radiologists and seven Radiology residents), and their corresponding diagnostic decisions for 100 screening mammograms. The corpus of mammograms comprised cases of varied pathology and breast parenchymal density. We investigated the relationship between pupillometric fluctuations, experienced by an image reader during mammographic screening, indicative of changes in mental workload, the pathological characteristics of a mammographic case, and the image readers' diagnostic decision and overall task performance. To answer these questions, we extract features from pupillometric data, and additionally applied time series shapelet analysis to extract discriminative patterns in changes in pupil dilation. Our results show that pupillometric measures are adequate predictors of mammographic case pathology, and image readers' diagnostic decision and performance with an average accuracy of 80%.

Matching mammographic regions in mediolateral oblique and cranio caudal views: a probabilistic approach

NASA Astrophysics Data System (ADS)

Samulski, Maurice; Karssemeijer, Nico

2008-03-01

Most of the current CAD systems detect suspicious mass regions independently in single views. In this paper we present a method to match corresponding regions in mediolateral oblique (MLO) and craniocaudal (CC) mammographic views of the breast. For every possible combination of mass regions in the MLO view and CC view, a number of features are computed, such as the difference in distance of a region to the nipple, a texture similarity measure, the gray scale correlation and the likelihood of malignancy of both regions computed by single-view analysis. In previous research, Linear Discriminant Analysis was used to discriminate between correct and incorrect links. In this paper we investigate if the performance can be improved by employing a statistical method in which four classes are distinguished. These four classes are defined by the combinations of view (MLO/CC) and pathology (TP/FP) labels. We use distance-weighted k-Nearest Neighbor density estimation to estimate the likelihood of a region combination. Next, a correspondence score is calculated as the likelihood that the region combination is a TP-TP link. The method was tested on 412 cases with a malignant lesion visible in at least one of the views. In 82.4% of the cases a correct link could be established between the TP detections in both views. In future work, we will use the framework presented here to develop a context dependent region matching scheme, which takes the number and likelihood of possible alternatives into account. It is expected that more accurate determination of matching probabilities will lead to improved CAD performance.

High mammographic density is associated with an increase in stromal collagen and immune cells within the mammary epithelium.

PubMed

Huo, Cecilia W; Chew, Grace; Hill, Prue; Huang, Dexing; Ingman, Wendy; Hodson, Leigh; Brown, Kristy A; Magenau, Astrid; Allam, Amr H; McGhee, Ewan; Timpson, Paul; Henderson, Michael A; Thompson, Erik W; Britt, Kara

2015-06-04

Mammographic density (MD), after adjustment for a women's age and body mass index, is a strong and independent risk factor for breast cancer (BC). Although the BC risk attributable to increased MD is significant in healthy women, the biological basis of high mammographic density (HMD) causation and how it raises BC risk remain elusive. We assessed the histological and immunohistochemical differences between matched HMD and low mammographic density (LMD) breast tissues from healthy women to define which cell features may mediate the increased MD and MD-associated BC risk. Tissues were obtained between 2008 and 2013 from 41 women undergoing prophylactic mastectomy because of their high BC risk profile. Tissue slices resected from the mastectomy specimens were X-rayed, then HMD and LMD regions were dissected based on radiological appearance. The histological composition, aromatase immunoreactivity, hormone receptor status and proliferation status were assessed, as were collagen amount and orientation, epithelial subsets and immune cell status. HMD tissue had a significantly greater proportion of stroma, collagen and epithelium, as well as less fat, than LMD tissue did. Second harmonic generation imaging demonstrated more organised stromal collagen in HMD tissues than in LMD tissues. There was significantly more aromatase immunoreactivity in both the stromal and glandular regions of HMD tissues than in those regions of LMD tissues, although no significant differences in levels of oestrogen receptor, progesterone receptor or Ki-67 expression were detected. The number of macrophages within the epithelium or stroma did not change; however, HMD stroma exhibited less CD206(+) alternatively activated macrophages. Epithelial cell maturation was not altered in HMD samples, and no evidence of epithelial-mesenchymal transition was seen; however, there was a significant increase in vimentin(+)/CD45(+) immune cells within the epithelial layer in HMD tissues. We confirmed increased proportions of stroma and epithelium, increased aromatase activity and no changes in hormone receptor or Ki-67 marker status in HMD tissue. The HMD region showed increased collagen deposition and organisation as well as decreased alternatively activated macrophages in the stroma. The HMD epithelium may be a site for local inflammation, as we observed a significant increase in CD45(+)/vimentin(+) immune cells in this area.

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

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.

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.

Mammographic interpretation training in the UK: current difficulties and future outlook

NASA Astrophysics Data System (ADS)

Chen, Yan; Gale, Alastair G.; Scott, Hazel

2009-02-01

In the UK, most mammographic interpretation training needs to be undertaken where there is a mammo-alternator or other suitable light box; consequently limiting the time and places where training can take place. However, the gradual introduction of digital mammography is opening up new opportunities of providing such training without the restriction of current viewing devices. Whilst high-resolution monitors in appropriate viewing environments are de rigour for actual reporting; advantages of the digital image over film are in the flexibility of training opportunity afforded, e.g. training whenever, wherever suits the individual. A previous study indicated the possible potential for reporting mammographic cases utilising handheld devices with suitable interaction techniques. In a pilot study, a group of mammographers (n=4) were questioned in semi-structured interviews in order to help establish current UK film-readers' training profile. On the basis of the pilot study data, 109 Breast Screening Units (601 film readers) were approached to complete a structured questionnaire in order to establish the potential role of smaller computer devices in mammographic interpretation training (given the use of digital mammography). Subsequently, a study of radiologists' visual search behaviour in digital screening has begun. This has highlighted different image manipulations than found in structured experiments in this area and poses new challenges for visualising the inspection process. Overall the results indicate that using different display sizes for training is possible but is also a challenging task requiring novel interaction approaches.

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.

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.

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.

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.

Computed tomography guided localization of clinically occult breast carcinoma-the ''N'' skin guide

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

Kopans, D.B.; Meyer, J.E.

1982-10-01

Standard computed tomography (CT) can be used for the three-dimensional localization of clinically occult suspicious breast lesions whose exact position cannot be determined by standard mammographic views. A method is described that facilitates accurate preoperative needle localization using CT guidance, once the position of these lesions is defined.

Identification of two novel mammographic density loci at 6Q25.1.

PubMed

Brand, Judith S; Li, Jingmei; Humphreys, Keith; Karlsson, Robert; Eriksson, Mikael; Ivansson, Emma; Hall, Per; Czene, Kamila

2015-06-03

Mammographic density (MD) is a strong heritable and intermediate phenotype for breast cancer, but much of its genetic variation remains unexplained. We performed a large-scale genetic association study including 8,419 women of European ancestry to identify MD loci. Participants of three Swedish studies were genotyped on a custom Illumina iSelect genotyping array and percent and absolute mammographic density were ascertained using semiautomated and fully automated methods from film and digital mammograms. Linear regression analysis was used to test for SNP-MD associations, adjusting for age, body mass index, menopausal status and six principal components. Meta-analyses were performed by combining P values taking sample size, study-specific inflation factor and direction of effect into account. Genome-wide significant associations were observed for two previously identified loci: ZNF365 (rs10995194, P = 2.3 × 10(-8) for percent MD and P = 8.7 × 10(-9) for absolute MD) and AREG (rs10034692, P = 6.7 × 10(-9) for absolute MD). In addition, we found evidence of association for two variants at 6q25.1, both of which are known breast cancer susceptibility loci: rs9485370 in the TAB2 gene (P = 4.8 × 10(-9) for percent MD and P = 2.5 × 10(-8) for absolute MD) and rs60705924 in the CCDC170/ESR1 region (P = 2.2 × 10(-8) for absolute MD). Both regions have been implicated in estrogen receptor signaling with TAB2 being a potential regulator of tamoxifen response. We identified two novel MD loci at 6q25.1. These findings underscore the importance of 6q25.1 as a susceptibility region and provide more insight into the mechanisms through which MD influences breast cancer risk.

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.

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.

Vision 20/20: Mammographic breast density and its clinical applications

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

Ng, Kwan-Hoong, E-mail: ngkh@ummc.edu.my; Lau, Susie

2015-12-15

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are “dense” and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believemore » there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.« less

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

PubMed

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

2018-01-30

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

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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.

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.

Computer aided system for segmentation and visualization of microcalcifications in digital mammograms.

PubMed

Reljin, Branimir; Milosević, Zorica; Stojić, Tomislav; Reljin, Irini

2009-01-01

Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph.

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective.

PubMed

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

2017-09-20

Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists, where there is a notable inter- and intra-reader variability. In order to maintain consistency and accuracy in BI-RADS-based breast density assessment, gaining understanding on radiologists' reading behaviors will be educational. In this study, we proposed to leverage the newly emerged deep learning approach to investigate how the MLO and CC view images of a mammogram examination may have been clinically used by radiologists in coming up with a BI-RADS density category. We implemented a convolutional neural network (CNN)-based deep learning model, aimed at distinguishing the breast density categories using a large (15,415 images) set of real-world clinical mammogram images. Our results showed that the classification of density categories (in terms of area under the receiver operating characteristic curve) using MLO view images is significantly higher than that using the CC view. This indicates that most likely it is the MLO view that the radiologists have predominately used to determine the breast density BI-RADS categories. Our study holds a potential to further interpret radiologists' reading characteristics, enhance personalized clinical training to radiologists, and ultimately reduce reader variations in breast density assessment.

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.

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

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

Fractal Analysis of Radiologists Visual Scanning Pattern in Screening Mammography

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

Alamudun, Folami T; Yoon, Hong-Jun; Hudson, Kathy

2015-01-01

Several investigators have investigated radiologists visual scanning patterns with respect to features such as total time examining a case, time to initially hit true lesions, number of hits, etc. The purpose of this study was to examine the complexity of the radiologists visual scanning pattern when viewing 4-view mammographic cases, as they typically do in clinical practice. Gaze data were collected from 10 readers (3 breast imaging experts and 7 radiology residents) while reviewing 100 screening mammograms (24 normal, 26 benign, 50 malignant). The radiologists scanpaths across the 4 mammographic views were mapped to a single 2-D image plane. Then,more » fractal analysis was applied on the derived scanpaths using the box counting method. For each case, the complexity of each radiologist s scanpath was estimated using fractal dimension. The association between gaze complexity, case pathology, case density, and radiologist experience was evaluated using 3 factor fixed effects ANOVA. ANOVA showed that case pathology, breast density, and experience level are all independent predictors of the visual scanning pattern complexity. Visual scanning patterns are significantly different for benign and malignant cases than for normal cases as well as when breast parenchyma density changes.« less

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 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.

Mammographic density and risk of breast cancer according to tumor characteristics and mode of detection: a Spanish population-based case-control study

PubMed Central

2013-01-01

Introduction It is not clear whether high mammographic density (MD) is equally associated with all subtypes of breast cancer (BC). We investigated the association between MD and subsequent BC, considering invasiveness, means of detection, pathologic subtype, and the time elapsed since mammographic exploration and BC diagnosis. Methods BC cases occurring in the population of women who attended screening from 1997 through 2004 in Navarre, a Spanish region with a fully consolidated screening program, were identified via record linkage with the Navarre Cancer Registry (n = 1,172). Information was extracted from the records of their first attendance at screening in that period. For each case, we randomly selected four controls, matched by screening round, year of birth, and place of residence. Cases were classified according to invasiveness (ductal carcinoma in situ (DCIS) versus invasive tumors), pathologic subtype (considering hormonal receptors and HER2), and type of diagnosis (screen-detected versus interval cases). MD was evaluated by a single, experienced radiologist by using a semiquantitative scale. Data on BC risk factors were obtained by the screening program in the corresponding round. The association between MD and tumor subtype was assessed by using conditional logistic regression. Results MD was clearly associated with subsequent BC. The odds ratio (OR) for the highest MD category (MD >75%) compared with the reference category (MD <10%) was similar for DCIS (OR = 3.47; 95% CI = 1.46 to 8.27) and invasive tumors (OR = 2.95; 95% CI = 2.01 to 4.35). The excess risk was particularly high for interval cases (OR = 7.72; 95% CI = 4.02 to 14.81) in comparison with screened detected tumors (OR = 2.17; 95% CI = 1.40 to 3.36). Sensitivity analyses excluding interval cases diagnosed in the first year after MD assessment or immediately after an early recall to screening yielded similar results. No differences were seen regarding pathologic subtypes. The excess risk associated with MD persisted for at least 7 to 8 years after mammographic exploration. Conclusions Our results confirm that MD is an important risk factor for all types of breast cancer. High breast density strongly increases the risk of developing an interval tumor, and this excess risk is not completely explained by a possible masking effect. PMID:23360535

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

Breast epithelium procurement from stereotactic core biopsy washings: flow cytometry-sorted cell count analysis.

PubMed

Stoler, Daniel L; Stewart, Carleton C; Stomper, Paul C

2002-02-01

Molecular studies of breast lesions have been constrained by difficulties in procuring adequate tissues for analyses. Standard procedures are restricted to larger, palpable masses or the use of paraffin-embedded materials, precluding facile procurement of fresh specimens of early lesions. We describe a study to determine the yield and characteristics of sorted cell populations retrieved in core needle biopsy specimen rinses from a spectrum of breast lesions. Cells from 114 consecutive stereotactic core biopsies of mammographic lesions released into saline washes were submitted for flow cytometric analysis. For each specimen, epithelial cells were separated from stromal and blood tissue based on the presence of cytokeratin 8 and 18 markers. Epithelial cell yields based on pathological diagnoses of the biopsy specimen, patient age, and mammographic appearance of the lesion were determined. Biopsies containing malignant lesions yielded significantly higher numbers of cells than were obtained from benign lesion biopsies. Significantly greater cell counts were observed from lesions from women age 50 or above compared with those of younger women. Mammographic density surrounding the biopsy site, the mammographic appearance of the lesion, and the number of cores taken at the time of biopsy appeared to have little effect on the yield of epithelial cells. We demonstrate the use of flow cytometric sorting of stereotactic core needle biopsy washes from lesions spanning the spectrum of breast pathology to obtain epithelial cells in sufficient numbers to meet the requirements of a variety of molecular and genetic analyses.

Mammographic parenchymal texture as an imaging marker of hormonal activity: a comparative study between pre- and post-menopausal women

NASA Astrophysics Data System (ADS)

Daye, Dania; Bobo, Ezra; Baumann, Bethany; Ioannou, Antonios; Conant, Emily F.; Maidment, Andrew D. A.; Kontos, Despina

2011-03-01

Mammographic parenchymal texture patterns have been shown to be related to breast cancer risk. Yet, little is known about the biological basis underlying this association. Here, we investigate the potential of mammographic parenchymal texture patterns as an inherent phenotypic imaging marker of endogenous hormonal exposure of the breast tissue. Digital mammographic (DM) images in the cranio-caudal (CC) view of the unaffected breast from 138 women diagnosed with unilateral breast cancer were retrospectively analyzed. Menopause status was used as a surrogate marker of endogenous hormonal activity. Retroareolar 2.5cm2 ROIs were segmented from the post-processed DM images using an automated algorithm. Parenchymal texture features of skewness, coarseness, contrast, energy, homogeneity, grey-level spatial correlation, and fractal dimension were computed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate feature classification performance in distinguishing between 72 pre- and 66 post-menopausal women. Logistic regression was performed to assess the independent effect of each texture feature in predicting menopause status. ROC analysis showed that texture features have inherent capacity to distinguish between pre- and post-menopausal statuses (AUC>0.5, p<0.05). Logistic regression including all texture features yielded an ROC curve with an AUC of 0.76. Addition of age at menarche, ethnicity, contraception use and hormonal replacement therapy (HRT) use lead to a modest model improvement (AUC=0.78) while texture features maintained significant contribution (p<0.05). The observed differences in parenchymal texture features between pre- and post- menopausal women suggest that mammographic texture can potentially serve as a surrogate imaging marker of endogenous hormonal activity.

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.

The quality mammographic image. A review of its components.

PubMed

Rickard, M T

1989-11-01

Seven major factors resulting in a quality or high contrast and high resolution mammographic image have been discussed. The following is a summary of their key features: 1) Dedicated mammographic equipment. --Molybdenum target material --Molybdenum filter, beryllium window --Low kVp usage, in range of 24 to 30 --Routine contact mammography performed at 25 kVp --Slightly lower kVp for coned compression --Slightly higher kVp for microfocus magnification 2) Film density --Phototimer with adjustable position --Calibration of phototimer to optimal optical density of approx. 1.4 over full kVp range 3) Breast Compression --General and focal (coned compression). --Essential to achieve proper contrast, resolution and breast immobility. --Foot controls preferable. 4) Focal Spot. --Size recommendation for contact work 0.3 mm. --Minimum power output of 100 mA at 25 kVp desirable to avoid movement blurring in contact grid work. --Size recommendation for magnification work 0.1 mm. 5) Grid. --Usage recommended as routine in all but magnification work. 6) Film-screen Combination. --High contrast--high speed film. --High resolution screen. --Specifically designed cassette for close film-screen contact and low radiation absorption. --Use of faster screens for magnification techniques. 7) Dedicated processing. --Increased developing time--40 to 45 seconds. --Increased developer temperature--35 to 38 degrees. --Adjusted replenishment rate and dryer temperature. All seven factors contributing to image contrast and resolution affect radiation dosage to the breast. The risk of increased dosage associated with the use of various techniques needs to be balanced against the risks of incorrect diagnosis associated with their non-use.(ABSTRACT TRUNCATED AT 250 WORDS)

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.

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

Inter-observer variability within BI-RADS and RANZCR mammographic density assessment schemes

NASA Astrophysics Data System (ADS)

Damases, Christine N.; Mello-Thoms, Claudia; McEntee, Mark F.

2016-03-01

This study compares variability associated with two visual mammographic density (MD) assessment methods using two separate samples of radiologists. The image test-set comprised of images obtained from 20 women (age 42-89 years). The images were assessed for their MD by twenty American Board of Radiology (ABR) examiners and twenty-six radiologists registered with the Royal Australian and New Zealand College of Radiologists (RANZCR). Images were assessed using the same technology and conditions, however the ABR radiologists used the BI-RADS and the RANZCR radiologists used the RANZCR breast density synoptic. Both scales use a 4-point assessment. The images were then grouped as low- and high-density; low including BIRADS 1 and 2 or RANZCR 1 and 2 and high including BI-RADS 3 and 4 or RANZCR 3 and 4. Four-point BI-RADS and RANZCR showed no or negligible correlation (ρ=-0.029 p<0.859). The average inter-observer agreement on the BI-RADS scale had a Kappa of 0.565; [95% CI = 0.519 - 0.610], and ranged between 0.328-0.669 while the inter-observer agreement using the RANZCR scale had a Kappa of 0.360; [95% CI = 0.308 - 0.412] and a range of 0.078-0.499. Our findings show a wider range of inter-observer variability among RANZCR registered radiologists than the ABR examiners.

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

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.

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.

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

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

Computed-aided diagnosis (CAD) in the detection of breast cancer.

PubMed

Dromain, C; Boyer, B; Ferré, R; Canale, S; Delaloge, S; Balleyguier, C

2013-03-01

Computer-aided detection (CAD) systems have been developed for interpretation to improve mammographic detection of breast cancer at screening by reducing the number of false-negative interpretation that can be caused by subtle findings, radiologist distraction and complex architecture. They use a digitized mammographic image that can be obtained from both screen-film mammography and full field digital mammography. Its performance in breast cancer detection is dependent on the performance of the CAD itself, the population to which it is applied and the radiologists who use it. There is a clear benefit to the use of CAD in less experienced radiologist and in detecting breast carcinomas presenting as microcalcifications. This review gives a detailed description CAD systems used in mammography and their performance in assistance of reading in screening mammography and as an alternative to double reading. Other CAD systems developed for MRI and ultrasound are also presented and discussed. Copyright © 2012. Published by Elsevier Ireland Ltd.

Radiologic findings of screen-detected cancers in an organized population-based screening mammography program in Turkey

PubMed Central

Kayhan, Arda; Arıbal, Erkin; Şahin, Cennet; Taşçı, Ömür Can; Gürdal, Sibel Özkan; Öztürk, Enis; Hatipoğlu, Hayat Halide; Özaydın, Nilüfer; Cabioğlu, Neslihan; Özçınar, Beyza; Özmen, Vahit

2016-01-01

PURPOSE Bahçeşehir Breast Cancer Screening Program is a population based organized screening program in Turkey, where asymptomatic women aged 40–69 years are screened biannually. In this prospective study, we aimed to determine the mammographic findings of screen-detected cancers and discuss the efficacy of breast cancer screening in a developing country. METHODS A total of 6912 women were screened in three rounds. The radiologic findings were grouped as mass, focal asymmetry, calcification, and architectural distortion. Masses were classified according to shape, border, and density. Calcifications were grouped according to morphology and distribution. Cancers were grouped according to the clinical stage. RESULTS Seventy cancers were detected with an incidence of 4.8/1000. Two cancers were detected in other centers and three were not visualized mammographically. Mammographic presentations of the remaining 65 cancers were mass (47.7%, n=31), calcification (30.8%, n=20), focal asymmetry (16.9%, n=11), architectural distortion (3.1%, n=2), and skin thickening (1.5%, n=1). The numbers of stage 0, 1, 2, 3, and 4 cancers were 13 (20.0%), 34 (52.3%), 14 (21.5%), 3 (4.6%), and 1 (1.5%), respectively. The numbers of interval and missed cancers were 5 (7.4%) and 7 (10.3%), respectively. CONCLUSION A high incidence of early breast cancer has been detected. The incidence of missed and interval cancers did not show major differences from western screening trials. We believe that this study will pioneer implementation of efficient population-based mammographic screenings in developing countries. PMID:27705880

Information-theoretic CAD system in mammography: Entropy-based indexing for computational efficiency and robust performance

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

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee

2007-08-15

We have previously presented a knowledge-based computer-assisted detection (KB-CADe) system for the detection of mammographic masses. The system is designed to compare a query mammographic region with mammographic templates of known ground truth. The templates are stored in an adaptive knowledge database. Image similarity is assessed with information theoretic measures (e.g., mutual information) derived directly from the image histograms. A previous study suggested that the diagnostic performance of the system steadily improves as the knowledge database is initially enriched with more templates. However, as the database increases in size, an exhaustive comparison of the query case with each stored templatemore » becomes computationally burdensome. Furthermore, blind storing of new templates may result in redundancies that do not necessarily improve diagnostic performance. To address these concerns we investigated an entropy-based indexing scheme for improving the speed of analysis and for satisfying database storage restrictions without compromising the overall diagnostic performance of our KB-CADe system. The indexing scheme was evaluated on two different datasets as (i) a search mechanism to sort through the knowledge database, and (ii) a selection mechanism to build a smaller, concise knowledge database that is easier to maintain but still effective. There were two important findings in the study. First, entropy-based indexing is an effective strategy to identify fast a subset of templates that are most relevant to a given query. Only this subset could be analyzed in more detail using mutual information for optimized decision making regarding the query. Second, a selective entropy-based deposit strategy may be preferable where only high entropy cases are maintained in the knowledge database. Overall, the proposed entropy-based indexing scheme was shown to reduce the computational cost of our KB-CADe system by 55% to 80% while maintaining the system's diagnostic performance.« less

Cost-benefit analysis of biopsy methods for suspicious mammographic lesions; discussion 994-5.

PubMed

Fahy, B N; Bold, R J; Schneider, P D; Khatri, V; Goodnight, J E

2001-09-01

Stereotactic core biopsy (SCB) is more cost-effective than needle-localized biopsy (NLB) for evaluation and treatment of mammographic lesions. A computer-generated mathematical model was developed based on clinical outcome modeling to estimate costs accrued during evaluation and treatment of suspicious mammographic lesions. Total costs were determined for evaluation and subsequent treatment of cancer when either SCB or NLB was used as the initial biopsy method. Cost was estimated by the cumulative work relative value units accrued. The risk of malignancy based on the Breast Imaging Reporting Data System (BIRADS) score and mammographic suspicion of ductal carcinoma in situ were varied to simulate common clinical scenarios. Total cost accumulated during evaluation and subsequent surgical therapy (if required). Evaluation of BIRADS 5 lesions (highly suggestive, risk of malignancy = 90%) resulted in equivalent relative value units for both techniques (SCB, 15.54; NLB, 15.47). Evaluation of lesions highly suspicious for ductal carcinoma in situ yielded similar total treatment relative value units (SCB, 11.49; NLB, 10.17). Only for evaluation of BIRADS 4 lesions (suspicious abnormality, risk of malignancy = 34%) was SCB more cost-effective than NLB (SCB, 7.65 vs. NLB, 15.66). No difference in cost-benefit was found when lesions highly suggestive of malignancy (BIRADS 5) or those suspicious for ductal carcinoma in situ were evaluated initially with SCB vs. NLB, thereby disproving the hypothesis. Only for intermediate-risk lesions (BIRADS 4) did initial evaluation with SCB yield a greater cost savings than with NLB.

Immunoassay and Nb2 lymphoma bioassay prolactin levels and mammographic density in premenopausal and postmenopausal women the Nurses' Health Studies.

PubMed

Rice, Megan S; Tworoger, Shelley S; Bertrand, Kimberly A; Hankinson, Susan E; Rosner, Bernard A; Feeney, Yvonne B; Clevenger, Charles V; Tamimi, Rulla M

2015-01-01

Higher circulating prolactin levels have been associated with higher percent mammographic density among postmenopausal women in some, but not all studies. However, few studies have examined associations with dense area and non-dense breast area breast or considered associations with prolactin Nb2 lymphoma cell bioassay levels. We conducted a cross-sectional study among 1,124 premenopausal and 890 postmenopausal women who were controls in breast cancer case-control studies nested in the Nurses' Health Study (NHS) and NHSII. Participants provided blood samples in 1989-1990 (NHS) or 1996-1999 (NHSII) and mammograms were obtained from around the time of blood draw. Multivariable linear models were used to assess the associations between prolactin levels (measured by immunoassay or bioassay) with percent density, dense area, and non-dense area. Among 1,124 premenopausal women, percent density, dense area, and non-dense area were not associated with prolactin immunoassay levels in multivariable models (p trends = 0.10, 0.18, and 0.69, respectively). Among 890 postmenopausal women, those with prolactin immunoassay levels in the highest versus lowest quartile had modestly, though significantly, higher percent density (difference = 3.01 percentage points, 95 % CI 0.22, 5.80) as well as lower non-dense area (p trend = 0.02). Among women with both immunoassay and bioassay levels, there were no consistent differences in the associations with percent density between bioassay and immunoassay levels. Postmenopausal women with prolactin immunoassay levels in the highest quartile had significantly higher percent density as well as lower non-dense area compared to those in the lowest quartile. Future studies should examine the underlying biologic mechanisms, particularly for non-dense area.

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.

Early Life Processes, Endocrine Mediators and Number of Susceptible Cells in Relation to Breast Cancer Risk

DTIC Science & Technology

2008-04-01

Clinical physiology in obstetrics. Oxford: Blackwell Scientific Publications; 1980 . p. 43 –78. 15. Rovinsky JJ, Jaffin H. Cardiovascular hemodynamics...Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin S, Yaffe MJ. Mammographic density and the risk and detection of

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

Clustering microcalcifications techniques in digital mammograms

NASA Astrophysics Data System (ADS)

Díaz, Claudia. C.; Bosco, Paolo; Cerello, Piergiorgio

2008-11-01

Breast cancer has become a serious public health problem around the world. However, this pathology can be treated if it is detected in early stages. This task is achieved by a radiologist, who should read a large amount of mammograms per day, either for a screening or diagnostic purpose in mammography. However human factors could affect the diagnosis. Computer Aided Detection is an automatic system, which can help to specialists in the detection of possible signs of malignancy in mammograms. Microcalcifications play an important role in early detection, so we focused on their study. The two mammographic features that indicate the microcalcifications could be probably malignant are small size and clustered distribution. We worked with density techniques for automatic clustering, and we applied them on a mammography CAD prototype developed at INFN-Turin, Italy. An improvement of performance is achieved analyzing images from a Perugia-Assisi Hospital, in Italy.

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

Cell Growth and Survival in Ovarian Epithelial Cancer Core A

DTIC Science & Technology

2003-08-01

developing sea urchin (Lvtechinus pictus) embryos. 1979 General Scientific Meetings of the Marine Biological Laboratory, Woods Hole, MA. Biol Bull 157...Research Institute. $94,170 (P.I.: John Ruckdeschel) 21 1996-2001 "Computer-Assisted Diagnosis for Mammographic Calcifications ". USPHS-NIH-NCI R29-96PO259

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.

Computerized quantitative evaluation of mammographic accreditation phantom images

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

Lee, Yongbum; Tsai, Du-Yih; Shinohara, Norimitsu

2010-12-15

Purpose: The objective was to develop and investigate an automated scoring scheme of the American College of Radiology (ACR) mammographic accreditation phantom (RMI 156, Middleton, WI) images. Methods: The developed method consisted of background subtraction, determination of region of interest, classification of fiber and mass objects by Mahalanobis distance, detection of specks by template matching, and rule-based scoring. Fifty-one phantom images were collected from 51 facilities for this study (one facility provided one image). A medical physicist and two radiologic technologists also scored the images. The human and computerized scores were compared. Results: In terms of meeting the ACR's criteria,more » the accuracies of the developed method for computerized evaluation of fiber, mass, and speck were 90%, 80%, and 98%, respectively. Contingency table analysis revealed significant association between observer and computer scores for microcalcifications (p<5%) but not for masses and fibers. Conclusions: The developed method may achieve a stable assessment of visibility for test objects in mammographic accreditation phantom image in whether the phantom image meets the ACR's criteria in the evaluation test, although there is room left for improvement in the approach for fiber and mass objects.« less

A meta-analysis of mammographic screening with and without clinical breast examination

PubMed Central

Hamashima, Chisato; Ohta, Koji; Kasahara, Yoshio; Katayama, Takafumi; Nakayama, Tomio; Honjo, Satoshi; Ohnuki, Koji

2015-01-01

Mammographic screening with clinical breast examination has been recommended in Japan since 2000. Although mammographic screening without clinical breast examination has not been recommended, its introduction is anticipated. The efficacies of mammographic screening with and without clinical breast examination were evaluated based on the results of randomized controlled trials. PubMed and other databases for studies published between 1985 and 2014 were searched. The study design was limited to randomized controlled trials to evaluate mortality reduction from breast cancer. Five studies were eligible for meta-analysis of mammographic screening without clinical breast examination. The relative risk for women aged 40–74 years was 0.75 (95% confidence interval, 0.67–0.83). Three studies evaluated the efficacy of mammographic screening with clinical breast examination. The relative risk for women aged 40–64 years was 0.87 (95% confidence interval, 0.77–0.98). The number needed to invite was always lower in mammographic screening without clinical breast examination than in mammographic screening with clinical breast examination. In both screening methods, the number needed to invite was higher in women aged 40–49 years than in women aged 50–70 years. These results suggest that mammographic screening without clinical breast examination can afford higher benefits to women aged 50 years and over. Although evidence of the efficacy of mammographic screening without clinical breast examination was confirmed based on the results of the randomized controlled trials, a Japanese study is needed to resolve local problems. PMID:25959787

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

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.

Comparison of Background Parenchymal Enhancement at Contrast-enhanced Spectral Mammography and Breast MR Imaging.

PubMed

Sogani, Julie; Morris, Elizabeth A; Kaplan, Jennifer B; D'Alessio, Donna; Goldman, Debra; Moskowitz, Chaya S; Jochelson, Maxine S

2017-01-01

Purpose To assess the extent of background parenchymal enhancement (BPE) at contrast material-enhanced (CE) spectral mammography and breast magnetic resonance (MR) imaging, to evaluate interreader agreement in BPE assessment, and to examine the relationships between clinical factors and BPE. Materials and Methods This was a retrospective, institutional review board-approved, HIPAA-compliant study. Two hundred seventy-eight women from 25 to 76 years of age with increased breast cancer risk who underwent CE spectral mammography and MR imaging for screening or staging from 2010 through 2014 were included. Three readers independently rated BPE on CE spectral mammographic and MR images with the ordinal scale: minimal, mild, moderate, or marked. To assess pairwise agreement between BPE levels on CE spectral mammographic and MR images and among readers, weighted κ coefficients with quadratic weights were calculated. For overall agreement, mean κ values and bootstrapped 95% confidence intervals were calculated. The univariate and multivariate associations between BPE and clinical factors were examined by using generalized estimating equations separately for CE spectral mammography and MR imaging. Results Most women had minimal or mild BPE at both CE spectral mammography (68%-76%) and MR imaging (69%-76%). Between CE spectral mammography and MR imaging, the intrareader agreement ranged from moderate to substantial (κ = 0.55-0.67). Overall agreement on BPE levels between CE spectral mammography and MR imaging and among readers was substantial (κ = 0.66; 95% confidence interval: 0.61, 0.70). With both modalities, BPE demonstrated significant association with menopausal status, prior breast radiation therapy, hormonal treatment, breast density on CE spectral mammographic images, and amount of fibroglandular tissue on MR images (P < .001 for all). Conclusion There was substantial agreement between readers for BPE detected on CE spectral mammographic and MR images. © RSNA, 2016.

Computer-Aided Detection of Mammographic Masses in Dense Breast Images

DTIC Science & Technology

2005-06-01

Kinnard, Ph.D. CONTRACTING ORGANIZATION: Howard University Washington, DC 20059 REPORT DATE: June 2005 TYPE OF REPORT: Annual Summary PREPARED FOR: U.S...AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Howard University Washington, DC 20059 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES...34, Preparing for the Postdoctoral Institute, August, 2004, Howard University and The University of Texas at El Paso. 2. "Computer-Aided Diagnosis and Image

Direct measurement of clinical mammographic x-ray spectra using a CdTe spectrometer.

PubMed

Santos, Josilene C; Tomal, Alessandra; Furquim, Tânia A; Fausto, Agnes M F; Nogueira, Maria S; Costa, Paulo R

2017-07-01

To introduce and evaluate a method developed for the direct measurement of mammographic x-ray spectra using a CdTe spectrometer. The assembly of a positioning system and the design of a simple and customized alignment device for this application is described. A positioning system was developed to easily and accurately locate the CdTe detector in the x-ray beam. Additionally, an alignment device to line up the detector with the central axis of the radiation beam was designed. Direct x-ray spectra measurements were performed in two different clinical mammography units and the measured x-ray spectra were compared with computer-generated spectra. In addition, the spectrometer misalignment effect was evaluated by comparing the measured spectra when this device is aligned relatively to when it is misaligned. The positioning and alignment of the spectrometer have allowed the measurements of direct mammographic x-ray spectra in agreement with computer-generated spectra. The most accurate x-ray spectral shape, related with the minimal HVL value, and high photon fluence for measured spectra was found with the spectrometer aligned according to the proposed method. The HVL values derived from both simulated and measured x-ray spectra differ at most 1.3 and 4.5% for two mammography devices evaluated in this study. The experimental method developed in this work allows simple positioning and alignment of a spectrometer for x-ray spectra measurements given the geometrical constraints and maintenance of the original configurations of mammography machines. © 2017 American Association of Physicists in Medicine.

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

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.

A model of primary and scattered photon fluence for mammographic x-ray image quantification

NASA Astrophysics Data System (ADS)

Tromans, Christopher E.; Cocker, Mary R.; Brady, Michael, Sir

2012-10-01

We present an efficient method to calculate the primary and scattered x-ray photon fluence component of a mammographic image. This can be used for a range of clinically important purposes, including estimation of breast density, personalized image display, and quantitative mammogram analysis. The method is based on models of: the x-ray tube; the digital detector; and a novel ray tracer which models the diverging beam emanating from the focal spot. The tube model includes consideration of the anode heel effect, and empirical corrections for wear and manufacturing tolerances. The detector model is empirical, being based on a family of transfer functions that cover the range of beam qualities and compressed breast thicknesses which are encountered clinically. The scatter estimation utilizes optimal information sampling and interpolation (to yield a clinical usable computation time) of scatter calculated using fundamental physics relations. A scatter kernel arising around each primary ray is calculated, and these are summed by superposition to form the scatter image. Beam quality, spatial position in the field (in particular that arising at the air-boundary due to the depletion of scatter contribution from the surroundings), and the possible presence of a grid, are considered, as is tissue composition using an iterative refinement procedure. We present numerous validation results that use a purpose designed tissue equivalent step wedge phantom. The average differences between actual acquisitions and modelled pixel intensities observed across the adipose to fibroglandular attenuation range vary between 5% and 7%, depending on beam quality and, for a single beam quality are 2.09% and 3.36% respectively with and without a grid.

Development of terminology for mammographic techniques for radiological technologists.

PubMed

Yagahara, Ayako; Yokooka, Yuki; Tsuji, Shintaro; Nishimoto, Naoki; Uesugi, Masahito; Muto, Hiroshi; Ohba, Hisateru; Kurowarabi, Kunio; Ogasawara, Katsuhiko

2011-07-01

We are developing a mammographic ontology to share knowledge of the mammographic domain for radiologic technologists, with the aim of improving mammographic techniques. As a first step in constructing the ontology, we used mammography reference books to establish mammographic terminology for identifying currently available knowledge. This study proceeded in three steps: (1) determination of the domain and scope of the terminology, (2) lexical extraction, and (3) construction of hierarchical structures. We extracted terms mainly from three reference books and constructed the hierarchical structures manually. We compared features of the terms extracted from the three reference books. We constructed a terminology consisting of 440 subclasses grouped into 19 top-level classes: anatomic entity, image quality factor, findings, material, risk, breast, histological classification of breast tumors, role, foreign body, mammographic technique, physics, purpose of mammography examination, explanation of mammography examination, image development, abbreviation, quality control, equipment, interpretation, and evaluation of clinical imaging. The number of terms that occurred in the subclasses varied depending on which reference book was used. We developed a terminology of mammographic techniques for radiologic technologists consisting of 440 terms.

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.

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

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.

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.

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.

Ethnicity, Soybean Consumption, and Mammographic Densities

DTIC Science & Technology

1998-09-01

46. Franke. A. A., Custer. L. J.. Cerna, C. M., and Narala, K. Rapid HPLC cleavage of flavonoids by human intestinal bacteria. Appl. Environ...study exploring dietary risk factors for breast cancer. urine, and feces using gas chromatography and HPLC (27, 33-41). Studies among various...01708. 2 To whom requests for reprints should be addressed, at Cancer Research Center 3 The abbreviations used are: DMA, O-desmethylangolensin; HPLC

Minimization of annotation work: diagnosis of mammographic masses via active learning

NASA Astrophysics Data System (ADS)

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-06-01

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

Minimization of annotation work: diagnosis of mammographic masses via active learning.

PubMed

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-05-22

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

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

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.

A completely automated CAD system for mass detection in a large mammographic database.

PubMed

Bellotti, R; De Carlo, F; Tangaro, S; Gargano, G; Maggipinto, G; Castellano, M; Massafra, R; Cascio, D; Fauci, F; Magro, R; Raso, G; Lauria, A; Forni, G; Bagnasco, S; Cerello, P; Zanon, E; Cheran, S C; Lopez Torres, E; Bottigli, U; Masala, G L; Oliva, P; Retico, A; Fantacci, M E; Cataldo, R; De Mitri, I; De Nunzio, G

2006-08-01

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Correlative Feature Analysis for Multimodality Breast CAD

DTIC Science & Technology

2009-09-01

Imaging 20, 1275–1284 2001. 22V. Caselles, R . Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vis. 22, 61–79 1997. 23R. Malladi , J...A. R . Jamieson, C. A. Sennett, and S. A. Jensen, “Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset...Academic Radiology, 15, 1437-1445 (2008). Conference Proceeding Papers [1] Y. Yuan, M. L. Giger, K. Suzuki, H. Li, and A. R . Jamieson, “A

The 2003 Australian Breast Health Survey: survey design and preliminary results.

PubMed

Villanueva, Elmer V; Jones, Sandra; Nehill, Caroline; Favelle, Simone; Steel, David; Iverson, Donald; Zorbas, Helen

2008-01-14

The Breast Health Surveys, conducted by the National Breast Cancer Centre (NBCC) in 1996 and 2003, are designed to gain insight into the knowledge, attitudes and behaviours of a nationally representative sample of Australian women on issues relevant to breast cancer. In this article, we focus on major aspects of the design and present results on respondents' knowledge about mammographic screening. The 2003 BHS surveyed English-speaking Australian women aged 30-69 without a history of breast cancer using computer-assisted telephone interviewing. Questions covered the following themes: knowledge and perceptions about incidence, mortality and risk; knowledge and behaviour regarding early detection, symptoms and diagnosis; mammographic screening; treatment; and accessibility and availability of information and services. Respondents were selected using a complex sample design involving stratification. Sample weights against Australian population benchmarks were used in all statistical analyses. Means and proportions for the entire population and by age group and area of residence were calculated. Statistical tests were conducted using a level of significance of 0.01. Of the 3,144 respondents who consented to being interviewed, 138 (4.4%) had a previous diagnosis of breast cancer and were excluded leaving 3,006 completed interviews eligible for analysis. A majority of respondents (61.1%) reported ever having had a mammogram and 29.1% identified mammography as being the best way of finding breast cancer. A majority of women (85.9%) had heard of the BreastScreen Australia (BSA) program, the national mammographic screening program providing free biennial screening mammograms, with 94.5% believing that BSA attendance was available regardless of the presence or absence of symptoms. There have been substantial gains in women's knowledge about mammographic screening over the seven years between the two surveys. The NBCC Breast Health Surveys provide a valuable picture of the knowledge of Australian women about a range of issues. The present analysis shows significant gains in knowledge and behaviours relating to mammographic screening, while identifying additional areas for targeted improvement, as in the need to better communicate with women about screening and diagnostic services. Further analysis of additional core topic areas (eg., incidence, mortality, risk and treatment) will provide equally noteworthy insight.

Fractal analysis of radiologists' visual scanning pattern in screening mammography

NASA Astrophysics Data System (ADS)

Alamudun, Folami T.; Yoon, Hong-Jun; Hudson, Kathy; Morin-Ducote, Garnetta; Tourassi, Georgia

2015-03-01

Several researchers have investigated radiologists' visual scanning patterns with respect to features such as total time examining a case, time to initially hit true lesions, number of hits, etc. The purpose of this study was to examine the complexity of the radiologists' visual scanning pattern when viewing 4-view mammographic cases, as they typically do in clinical practice. Gaze data were collected from 10 readers (3 breast imaging experts and 7 radiology residents) while reviewing 100 screening mammograms (24 normal, 26 benign, 50 malignant). The radiologists' scanpaths across the 4 mammographic views were mapped to a single 2-D image plane. Then, fractal analysis was applied on the composite 4- view scanpaths. For each case, the complexity of each radiologist's scanpath was measured using fractal dimension estimated with the box counting method. The association between the fractal dimension of the radiologists' visual scanpath, case pathology, case density, and radiologist experience was evaluated using fixed effects ANOVA. ANOVA showed that the complexity of the radiologists' visual search pattern in screening mammography is dependent on case specific attributes (breast parenchyma density and case pathology) as well as on reader attributes, namely experience level. Visual scanning patterns are significantly different for benign and malignant cases than for normal cases. There is also substantial inter-observer variability which cannot be explained only by experience level.

Metabolic syndrome and mammographic density: The Study of Women’s Health Across the Nation (SWAN)

PubMed Central

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

2013-01-01

The metabolic syndrome (MetS) is associated with an increase in breast cancer risk. In this study, we evaluated whether the MetS was associated with an increase in percent mammographic density (MD), a breast cancer risk factor. We used linear regression and mixed models to examine the cross-sectional and longitudinal associations of the MetS and components of the MetS to percent MD in 790 pre- and early perimenopausal women enrolled in the Study of Women’s Health Across the Nation (SWAN). In cross-sectional analyses adjusted for body mass index (BMI), modest inverse associations were observed between percent MD and the MetS (β = −2.5, SE = 1.9, p = 0.19), abdominal adiposity (β = −4.8, SE = 1.9, p = 0.01) and raised glucose (β = −3.7, SE = 2.4, p = 0.12). In longitudinal models adjusted for covariates including age and BMI, abdominal adiposity (β = 0.34, SE = 0.17, p = 0.05) was significantly positively associated with slower annual decline in percent MD with time. In conclusion, our results do not support the hypothesis that the MetS increases breast cancer risk via a mechanism reflected by an increase in percent MD. PMID:21105041

Application of the Minkowski-functionals for automated pattern classification of breast parenchyma depicted by digital mammography

NASA Astrophysics Data System (ADS)

Boehm, Holger F.; Fischer, Tanja; Riosk, Dororthea; Britsch, Stefanie; Reiser, Maximilian

2008-03-01

With an estimated life-time-risk of about 10%, breast cancer is the most common cancer among women in western societies. Extensive mammography-screening programs have been implemented for diagnosis of the disease at an early stage. Several algorithms for computer-aided detection (CAD) have been proposed to help radiologists manage the increasing number of mammographic image-data and identify new cases of cancer. However, a major issue with most CAD-solutions is the fact that performance strongly depends on the structure and density of the breast tissue. Prior information about the global tissue quality in a patient would be helpful for selecting the most effective CAD-approach in order to increase the sensitivity of lesion-detection. In our study, we propose an automated method for textural evaluation of digital mammograms using the Minkowski Functionals in 2D. 80 mammograms are consensus-classified by two experienced readers as fibrosis, involution/atrophy, or normal. For each case, the topology of graylevel distribution is evaluated within a retromamillary image-section of 512 x 512 pixels. In addition, we obtain parameters from the graylevel-histogram (20th percentile, median and mean graylevel intensity). As a result, correct classification of the mammograms based on the densitometic parameters is achieved in between 38 and 48%, whereas topological analysis increases the rate to 83%. The findings demonstrate the effectiveness of the proposed algorithm. Compared to features obtained from graylevel histograms and comparable studies, we draw the conclusion that the presented method performs equally good or better. Our future work will be focused on the characterization of the mammographic tissue according to the Breast Imaging Reporting and Data System (BI-RADS). Moreover, other databases will be tested for an in-depth evaluation of the efficiency of our proposal.

A feasibility study of X-ray phase-contrast mammographic tomography at the Imaging and Medical beamline of the Australian Synchrotron.

PubMed

Nesterets, Yakov I; Gureyev, Timur E; Mayo, Sheridan C; Stevenson, Andrew W; Thompson, Darren; Brown, Jeremy M C; Kitchen, Marcus J; Pavlov, Konstantin M; Lockie, Darren; Brun, Francesco; Tromba, Giuliana

2015-11-01

Results are presented of a recent experiment at the Imaging and Medical beamline of the Australian Synchrotron intended to contribute to the implementation of low-dose high-sensitivity three-dimensional mammographic phase-contrast imaging, initially at synchrotrons and subsequently in hospitals and medical imaging clinics. The effect of such imaging parameters as X-ray energy, source size, detector resolution, sample-to-detector distance, scanning and data processing strategies in the case of propagation-based phase-contrast computed tomography (CT) have been tested, quantified, evaluated and optimized using a plastic phantom simulating relevant breast-tissue characteristics. Analysis of the data collected using a Hamamatsu CMOS Flat Panel Sensor, with a pixel size of 100 µm, revealed the presence of propagation-based phase contrast and demonstrated significant improvement of the quality of phase-contrast CT imaging compared with conventional (absorption-based) CT, at medically acceptable radiation doses.

MO-F-CAMPUS-I-01: Accuracy of Radiologists Interpretation of Mammographic Breast Density

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

Vedantham, S; Shi, L; Karellas, A

2015-06-15

Purpose: Several commercial and non-commercial software and techniques are available for determining breast density from mammograms. However, where mandated by law the breast density information communicated to the subject/patient is based on radiologist’s interpretation of breast density from mammograms. Several studies have reported on the concordance among radiologists in interpreting mammographic breast density. In this work, we investigated the accuracy of radiologist’s interpretation of breast density. Methods: Volumetric breast density (VBD) determined from 134 unilateral dedicated breast CT scans from 134 subjects was considered the truth. An MQSA-qualified study radiologist with more than 20 years of breast imaging experience reviewedmore » the DICOM “for presentation” standard 2-view mammograms of the corresponding breasts and assigned BIRADS breast density categories. For statistical analysis, the breast density categories were dichotomized in two ways; fatty vs. dense breasts where “fatty” corresponds to BIRADS breast density categories A/B, and “dense” corresponds to BIRADS breast density categories C/D, and extremely dense vs. fatty to heterogeneously dense breasts, where extremely dense corresponds to BIRADS breast density category D and BIRADS breast density categories A through C were grouped as fatty to heterogeneously dense breasts. Logistic regression models (SAS 9.3) were used to determine the association between radiologist’s interpretation of breast density and VBD from breast CT, from which the area under the ROC (AUC) was determined. Results: Both logistic regression models were statistically significant (Likelihood Ratio test, p<0.0001). The accuracy (AUC) of the study radiologist for classification of fatty vs. dense breasts was 88.4% (95% CI: 83–94%) and for classification of extremely dense breast was 94.3% (95% CI: 90–98%). Conclusion: The accuracy of the radiologist in classifying dense and extremely dense breasts is high. Considering the variability in VBD estimates from commercial software, the breast density information communicated to the patient should be based on radiologist’s interpretation. This work was supported in part by NIH R21 CA176470 and R21 CA134128. The contents are solely the responsibility of the authors and do not reflect the official views of the NIH or NCI.« 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.

Cigarette smoking and mammographic density in the Danish Diet, Cancer and Health cohort.

PubMed

Jacobsen, Katja Kemp; Lynge, Elsebeth; Vejborg, Ilse; Tjønneland, Anne; von Euler-Chelpin, My; Andersen, Zorana J

2016-02-01

Smoking before first childbirth increases breast cancer risk, but the biological mechanism remains unknown and may involve mammographic density (MD), one of the strongest biomarkers of breast cancer risk. We aimed to examine whether active smoking and passive smoking were associated with MD. For the 5,356 women (4,489 postmenopausal) from the Danish Diet, Cancer and Health cohort (1993-1997) who attended mammographic screening in Copenhagen (1993-2001), we used MD (mixed/dense or fatty) assessed at the first screening after cohort entry. Active smoking (status, duration, and intensity) and passive smoking were assessed at cohort baseline (1993-1997) via questionnaire, together with other breast cancer risk factors. Logistic regression was used to estimate associations (odds ratios, 95 % confidence intervals) between smoking and MD, adjusting for confounders. Two thousand and twenty-six (56.5 %) women had mixed/dense MD, 2,214 (41.4 %) were current, and 1,175 (21.9 %) former smokers. Current smokers had significantly lower odds (0.86, 0.75-0.99) of having mixed/dense MD compared to never smokers, while former smoking was not associated with MD. Inverse association between smoking and MD was strongest in women who initiated smoking before age of 16 years (0.79, 0.64-0.96), smoked ≥15 cigarettes/day (0.83, 0.71-0.98), smoked ≥5 pack-years (0.62, 0.43-0.89), smoked >30 years (0.86, 0.75-0.99), and smoked ≥11 years before first childbirth (0.70, 0.51-0.96). Association between smoking and MD diminished after smoking cessation, with increased odds of having mixed/dense breasts in women who quit smoking >20 years ago as compared to current smokers (1.37, 1.01-1.67). There was no association between passive smoking and MD. We found an inverse association between active smoking and MD.

A content-based retrieval of mammographic masses using the curvelet descriptor

NASA Astrophysics Data System (ADS)

Narváez, Fabian; Díaz, Gloria; Gómez, Francisco; Romero, Eduardo

2012-03-01

Computer-aided diagnosis (CAD) that uses content based image retrieval (CBIR) strategies has became an important research area. This paper presents a retrieval strategy that automatically recovers mammography masses from a virtual repository of mammographies. Unlike other approaches, we do not attempt to segment masses but instead we characterize the regions previously selected by an expert. These regions are firstly curvelet transformed and further characterized by approximating the marginal curvelet subband distribution with a generalized gaussian density (GGD). The content based retrieval strategy searches similar regions in a database using the Kullback-Leibler divergence as the similarity measure between distributions. The effectiveness of the proposed descriptor was assessed by comparing the automatically assigned label with a ground truth available in the DDSM database.1 A total of 380 masses with different shapes, sizes and margins were used for evaluation, resulting in a mean average precision rate of 89.3% and recall rate of 75.2% for the retrieval task.

Prevalence scaling: applications to an intelligent workstation for the diagnosis of breast cancer.

PubMed

Horsch, Karla; Giger, Maryellen L; Metz, Charles E

2008-11-01

Our goal was to investigate the effects of changes that the prevalence of cancer in a population have on the probability of malignancy (PM) output and an optimal combination of a true-positive fraction (TPF) and a false-positive fraction (FPF) of a mammographic and sonographic automatic classifier for the diagnosis of breast cancer. We investigate how a prevalence-scaling transformation that is used to change the prevalence inherent in the computer estimates of the PM affects the numerical and histographic output of a previously developed multimodality intelligent workstation. Using Bayes' rule and the binormal model, we study how changes in the prevalence of cancer in the diagnostic breast population affect our computer classifiers' optimal operating points, as defined by maximizing the expected utility. Prevalence scaling affects the threshold at which a particular TPF and FPF pair is achieved. Tables giving the thresholds on the scaled PM estimates that result in particular pairs of TPF and FPF are presented. Histograms of PMs scaled to reflect clinically relevant prevalence values differ greatly from histograms of laboratory-designed PMs. The optimal pair (TPF, FPF) of our lower performing mammographic classifier is more sensitive to changes in clinical prevalence than that of our higher performing sonographic classifier. Prevalence scaling can be used to change computer PM output to reflect clinically more appropriate prevalence. Relatively small changes in clinical prevalence can have large effects on the computer classifier's optimal operating point.

Measurement of compressed breast thickness by optical stereoscopic photogrammetry.

PubMed

Tyson, Albert H; Mawdsley, Gordon E; Yaffe, Martin J

2009-02-01

The determination of volumetric breast density (VBD) from mammograms requires accurate knowledge of the thickness of the compressed breast. In attempting to accurately determine VBD from images obtained on conventional mammography systems, the authors found that the thickness reported by a number of mammography systems in the field varied by as much as 15 mm when compressing the same breast or phantom. In order to evaluate the behavior of mammographic compression systems and to be able to predict the thickness at different locations in the breast on patients, they have developed a method for measuring the local thickness of the breast at all points of contact with the compression paddle using optical stereoscopic photogrammetry. On both flat (solid) and compressible phantoms, the measurements were accurate to better than 1 mm with a precision of 0.2 mm. In a pilot study, this method was used to measure thickness on 108 volunteers who were undergoing mammography examination. This measurement tool will allow us to characterize paddle surface deformations, deflections and calibration offsets for mammographic units.

Comparison of Background Parenchymal Enhancement at Contrast-enhanced Spectral Mammography and Breast MR Imaging

PubMed Central

Morris, Elizabeth A.; Kaplan, Jennifer B.; D’Alessio, Donna; Goldman, Debra; Moskowitz, Chaya S.

2017-01-01

Purpose To assess the extent of background parenchymal enhancement (BPE) at contrast material–enhanced (CE) spectral mammography and breast magnetic resonance (MR) imaging, to evaluate interreader agreement in BPE assessment, and to examine the relationships between clinical factors and BPE. Materials and Methods This was a retrospective, institutional review board–approved, HIPAA-compliant study. Two hundred seventy-eight women from 25 to 76 years of age with increased breast cancer risk who underwent CE spectral mammography and MR imaging for screening or staging from 2010 through 2014 were included. Three readers independently rated BPE on CE spectral mammographic and MR images with the ordinal scale: minimal, mild, moderate, or marked. To assess pairwise agreement between BPE levels on CE spectral mammographic and MR images and among readers, weighted κ coefficients with quadratic weights were calculated. For overall agreement, mean κ values and bootstrapped 95% confidence intervals were calculated. The univariate and multivariate associations between BPE and clinical factors were examined by using generalized estimating equations separately for CE spectral mammography and MR imaging. Results Most women had minimal or mild BPE at both CE spectral mammography (68%–76%) and MR imaging (69%–76%). Between CE spectral mammography and MR imaging, the intrareader agreement ranged from moderate to substantial (κ = 0.55–0.67). Overall agreement on BPE levels between CE spectral mammography and MR imaging and among readers was substantial (κ = 0.66; 95% confidence interval: 0.61, 0.70). With both modalities, BPE demonstrated significant association with menopausal status, prior breast radiation therapy, hormonal treatment, breast density on CE spectral mammographic images, and amount of fibroglandular tissue on MR images (P < .001 for all). Conclusion There was substantial agreement between readers for BPE detected on CE spectral mammographic and MR images. © RSNA, 2016 PMID:27379544

High Mammographic Density in Long-Term Night-Shift Workers: DDM-Spain/Var-DDM.

PubMed

Pedraza-Flechas, Ana María; Lope, Virginia; Sánchez-Contador, Carmen; Santamariña, Carmen; Pedraz-Pingarrón, Carmen; Moreo, Pilar; Ederra, María; Miranda-García, Josefa; Vidal, Carmen; Llobet, Rafael; Aragonés, Nuria; Salas-Trejo, Dolores; Pollán, Marina; Pérez-Gómez, Beatriz

2017-06-01

Background: Night-shift work (NSW) has been suggested as a possible cause of breast cancer, and its association with mammographic density (MD), one of the strongest risk factors for breast cancer, has been scarcely addressed. This study examined NSW and MD in Spanish women. Methods: The study covered 2,752 women aged 45-68 years recruited in 2007-2008 in 7 population-based public breast cancer screening centers, which included 243 women who had performed NSW for at least one year. Occupational data and information on potential confounders were collected by personal interview. Two trained radiologist estimated the percentage of MD assisted by a validated semiautomatic computer tool (DM-scan). Multivariable mixed linear regression models with random screening center-specific intercepts were fitted using log-transformed percentage of MD as the dependent variable and adjusting by known confounding variables. Results: Having ever worked in NSW was not associated with MD [Formula: see text]:0.96; 95% confidence interval (CI), 0.86-1.06]. However, the adjusted geometric mean of the percentage of MD in women with NSW for more than 15 years was 25% higher than that of those without NSW history (MD >15 years :20.7% vs. MD never :16.5%;[Formula: see text]:1.25; 95% CI,1.01-1.54). This association was mainly observed in postmenopausal participants ([Formula: see text]:1.28; 95% CI, 1.00-1.64). Among NSW-exposed women, those with ≤2 night-shifts per week had higher MD than those with 5 to 7 nightshifts per week ([Formula: see text]:1.42; 95% CI, 1.10-1.84). Conclusions: Performing NSW was associated with higher MD only in women with more than 15 years of cumulated exposure. These findings warrant replication in futures studies. Impact: Our findings suggest that MD could play a role in the pathway between long-term NSW and breast cancer. Cancer Epidemiol Biomarkers Prev; 26(6); 905-13. ©2017 AACR . ©2017 American Association for Cancer Research.

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

Bone mineral density and mammographic density in Mexican women.

PubMed

Moseson, Heidi; Rice, Megan S; López-Ridaura, Ruy; Bertrand, Kimberly A; Torres, Gabriela; Blanco, Margarita; Tamayo-Orozco, Juan Alfredo; Lajous, Martin; Romieu, Isabelle

2016-01-01

Bone mineral density (BMD) is a putative marker for lifetime exposure to estrogen. Studies that have explored whether BMD is a determinant of mammographic density (MD) have observed inconsistent results. Therefore,we examined this potential association in a sample of women (n = 1,516) from the clinical sub-cohort in the Mexican teachers’ cohort (n = 115,315). We used multivariable linear regression to assess the association between quartiles of BMD and percent MD, as well as total dense and non-dense area of the breast, stratified by menopausal status. We also examined the associations by body mass index (BMI) (< 30 kg/m(2), ≥ 30 kg/m(2)). Overall, there was no association between BMD and MD among premenopausal women. However, when we stratified by BMI, there was a modest inverse association between BMD and percent MD (difference between extreme quartiles = -2.8, 95 % CI -5.9, 0.27, p trend = 0.04) among women with BMI < 30 kg/m(2), but a positive association among obese women (comparable difference = 5.1, 95 % CI 0.02, 10.1, p trend = 0.03;p interaction < 0.01). Among postmenopausal women, BMD and percent MD were positively associated after adjustment for BMI (p trend < 0.01). Postmenopausal women in the highest two quartiles of BMD had 4–5 % point higher percent MD compared to women in the lowest quartile. The association did not differ by BMI in postmenopausal women (p interaction = 0.76). Among obese premenopausal women as well as postmenopausal women, BMD was positively associated with percent MD. Among leaner premenopausal women, BMD and percent MD were modestly inversely associated. These findings support the hypothesis that cumulative exposure to estrogen (as measured by BMD) may influence MD.

Bone mineral density and mammographic density in Mexican women

PubMed Central

Moseson, Heidi; Rice, Megan S.; López-Ridaura, Ruy; Bertrand, Kimberly A.; Torres, Gabriela; Blanco, Margarita; Tamayo-Orozco, Juan Alfredo; Lajous, Martin; Romieu, Isabelle

2016-01-01

Background Bone mineral density (BMD) is a putative marker for lifetime exposure to estrogen. Studies that have explored whether BMD is a determinant of mammographic density (MD) have observed inconsistent results. Therefore, we examined this potential association in a sample of women (N=1,516) from the clinical sub-cohort in the Mexican Teachers’ Cohort (N=115,315). Methods We used multivariable linear regression to assess the association between quartiles of BMD and percent MD, as well as total dense and non-dense area of the breast, stratified by menopausal status. We also examined the associations by body mass index (BMI) (<30kg/m2,, ≥30kg/m2). Results Overall, there was no association between BMD and MD among premenopausal women. However, when we stratified by BMI, there was a modest inverse association between BMD and percent MD (difference between extreme quartiles= −2.8, 95%CI: −5.9, 0.27, p-trend=0.04) among women with BMI <30 kg/m2, but a positive association among obese women (comparable difference=5.1, 95%CI: 0.02, 10.1, p-trend=0.03; p-interaction<0.01). Among postmenopausal women, BMD and percent MD were positively associated after adjustment for BMI (p-trend<0.01). Postmenopausal women in the highest two quartiles of BMD had 4–5 percentage point higher percent MD compared to women in the lowest quartile. The association did not differ by BMI in postmenopausal women (p-interaction=0.76). Conclusion Among obese premenopausal women as well as postmenopausal women, BMD was positively associated with percent MD. Among leaner premenopausal women, BMD and percent MD were modestly inversely associated. These findings support the hypothesis that cumulative exposure to estrogen (as measured by BMD) may influence MD. PMID:26463740

Automated assessment of bilateral breast volume asymmetry as a breast cancer biomarker during mammographic screening

NASA Astrophysics Data System (ADS)

Williams, Alex C.; Hitt, Austin; Voisin, Sophie; Tourassi, Georgia

2013-03-01

The biological concept of bilateral symmetry as a marker of developmental stability and good health is well established. Although most individuals deviate slightly from perfect symmetry, humans are essentially considered bilaterally symmetrical. Consequently, increased fluctuating asymmetry of paired structures could be an indicator of disease. There are several published studies linking bilateral breast size asymmetry with increased breast cancer risk. These studies were based on radiologists' manual measurements of breast size from mammographic images. We aim to develop a computerized technique to assess fluctuating breast volume asymmetry in screening mammograms and investigate whether it correlates with the presence of breast cancer. Using a large database of screening mammograms with known ground truth we applied automated breast region segmentation and automated breast size measurements in CC and MLO views using three well established methods. All three methods confirmed that indeed patients with breast cancer have statistically significantly higher fluctuating asymmetry of their breast volumes. However, statistically significant difference between patients with cancer and benign lesions was observed only for the MLO views. The study suggests that automated assessment of global bilateral asymmetry could serve as a breast cancer risk biomarker for women undergoing mammographic screening. Such biomarker could be used to alert radiologists or computer-assisted detection (CAD) systems to exercise increased vigilance if higher than normal cancer risk is suspected.

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms.

PubMed

Tourassi, Georgia D; Harrawood, Brian; Singh, Swatee; Lo, Joseph Y; Floyd, Carey E

2007-01-01

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms

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

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrievalmore » precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.« less

A prototype of mammography CADx scheme integrated to imaging quality evaluation techniques

NASA Astrophysics Data System (ADS)

Schiabel, Homero; Matheus, Bruno R. N.; Angelo, Michele F.; Patrocínio, Ana Claudia; Ventura, Liliane

2011-03-01

As all women over the age of 40 are recommended to perform mammographic exams every two years, the demands on radiologists to evaluate mammographic images in short periods of time has increased considerably. As a tool to improve quality and accelerate analysis CADe/Dx (computer-aided detection/diagnosis) schemes have been investigated, but very few complete CADe/Dx schemes have been developed and most are restricted to detection and not diagnosis. The existent ones usually are associated to specific mammographic equipment (usually DR), which makes them very expensive. So this paper describes a prototype of a complete mammography CADx scheme developed by our research group integrated to an imaging quality evaluation process. The basic structure consists of pre-processing modules based on image acquisition and digitization procedures (FFDM, CR or film + scanner), a segmentation tool to detect clustered microcalcifications and suspect masses and a classification scheme, which evaluates as the presence of microcalcifications clusters as well as possible malignant masses based on their contour. The aim is to provide enough information not only on the detected structures but also a pre-report with a BI-RADS classification. At this time the system is still lacking an interface integrating all the modules. Despite this, it is functional as a prototype for clinical practice testing, with results comparable to others reported in literature.

Towards the use of computationally inserted lesions for mammographic CAD assessment

NASA Astrophysics Data System (ADS)

Ghanian, Zahra; Pezeshk, Aria; Petrick, Nicholas; Sahiner, Berkman

2018-03-01

Computer-aided detection (CADe) devices used for breast cancer detection on mammograms are typically first developed and assessed for a specific "original" acquisition system, e.g., a specific image detector. When CADe developers are ready to apply their CADe device to a new mammographic acquisition system, they typically assess the CADe device with images acquired using the new system. Collecting large repositories of clinical images containing verified cancer locations and acquired by the new image acquisition system is costly and time consuming. Our goal is to develop a methodology to reduce the clinical data burden in the assessment of a CADe device for use with a different image acquisition system. We are developing an image blending technique that allows users to seamlessly insert lesions imaged using an original acquisition system into normal images or regions acquired with a new system. In this study, we investigated the insertion of microcalcification clusters imaged using an original acquisition system into normal images acquired with that same system utilizing our previously-developed image blending technique. We first performed a reader study to assess whether experienced observers could distinguish between computationally inserted and native clusters. For this purpose, we applied our insertion technique to clinical cases taken from the University of South Florida Digital Database for Screening Mammography (DDSM) and the Breast Cancer Digital Repository (BCDR). Regions of interest containing microcalcification clusters from one breast of a patient were inserted into the contralateral breast of the same patient. The reader study included 55 native clusters and their 55 inserted counterparts. Analysis of the reader ratings using receiver operating characteristic (ROC) methodology indicated that inserted clusters cannot be reliably distinguished from native clusters (area under the ROC curve, AUC=0.58±0.04). Furthermore, CADe sensitivity was evaluated on mammograms with native and inserted microcalcification clusters using a commercial CADe system. For this purpose, we used full field digital mammograms (FFDMs) from 68 clinical cases, acquired at the University of Michigan Health System. The average sensitivities for native and inserted clusters were equal, 85.3% (58/68). These results demonstrate the feasibility of using the inserted microcalcification clusters for assessing mammographic CAD devices.

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.

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.

Characterization of difference of Gaussian filters in the detection of mammographic regions

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

Catarious, David M. Jr.; Baydush, Alan H.; Floyd, Carey E. Jr.

2006-11-15

In this article, we present a characterization of the effect of difference of Gaussians (DoG) filters in the detection of mammographic regions. DoG filters have been used previously in mammographic mass computer-aided detection (CAD) systems. As DoG filters are constructed from the subtraction of two bivariate Gaussian distributions, they require the specification of three parameters: the size of the filter template and the standard deviations of the constituent Gaussians. The influence of these three parameters in the detection of mammographic masses has not been characterized. In this work, we aim to determine how the parameters affect (1) the physical descriptorsmore » of the detected regions (2) the true and false positive rates, and (3) the classification performance of the individual descriptors. To this end, 30 DoG filters are created from the combination of three template sizes and four values for each of the Gaussians' standard deviations. The filters are used to detect regions in a study database of 181 craniocaudal-view mammograms extracted from the Digital Database for Screening Mammography. To describe the physical characteristics of the identified regions, morphological and textural features are extracted from each of the detected regions. Differences in the mean values of the features caused by altering the DoG parameters are examined through statistical and empirical comparisons. The parameters' effects on the true and false positive rate are determined by examining the mean malignant sensitivities and false positives per image (FPpI). Finally, the effect on the classification performance is described by examining the variation in FPpI at the point where 81% of the malignant masses in the study database are detected. Overall, the findings of the study indicate that increasing the standard deviations of the Gaussians used to construct a DoG filter results in a dramatic decrease in the number of regions identified at the expense of missing a small number of malignancies. The sharp reduction in the number of identified regions allowed the identification of textural differences between large and small mammographic regions. We find that the classification performances of the features that achieve the lowest average FPpI are influenced by all three of the parameters.« less

Collaborating with Mammographers to Address Their Work-Related Musculoskeletal Discomfort

PubMed Central

Sommerich, Carolyn M.; Lavender, Steven A.; Evans, Kevin D.; Sanders, Elizabeth; Joines, Sharon; Lamar, Sabrina; Umar, Radin Zaid Radin; Yen, Wei-Ting; Park, SangHyun

2017-01-01

Mammographers are an understudied group of healthcare workers, yet the prevalence of musculoskeletal (MSK) symptoms in mammographers appears to be elevated, similar to many occupations in healthcare. In this study, we used a participatory approach to identify needs and opportunities for developing interventions to reduce mammographers’ exposures to risk factors that lead to development of MSK symptoms. In this paper, we present a number of those needs and several intervention concepts along with evaluations of those concepts from experienced mammographers. We include findings from a preliminary field test of a novel intervention concept to reduce the need to adopt awkward postures while positioning patients for a screening or diagnostic mammogram. PMID:26794257

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.

The implementation of an AR (augmented reality) approach to support mammographic interpretation training: an initial feasibility study

NASA Astrophysics Data System (ADS)

Tang, Qiang; Chen, Yan; Gale, Alastair G.

2017-03-01

Appropriate feedback plays an important role in optimising mammographic interpretation training whilst also ensuring good interpretation performance. The traditional keyboard, mouse and workstation technical approach has a critical limitation in providing supplementary image-related information and providing complex feedback in real time. Augmented Reality (AR) provides a possible superior approach in this situation, as feedback can be provided directly overlaying the displayed mammographic images so making a generic approach which can also be vendor neutral. In this study, radiological feedback was dynamically remapped virtually into the real world, using perspective transformation, in order to provide a richer user experience in mammographic interpretation training. This is an initial attempt of an AR approach to dynamically superimpose pre-defined feedback information of a DICOM image on top of a radiologist's view, whilst the radiologist is examining images on a clinical workstation. The study demonstrates the feasibility of the approach, although there are limitations on interactive operations which are due to the hardware used. The results of this fully functional approach provide appropriate feedback/image correspondence in a simulated mammographic interpretation environment. Thus, it is argued that employing AR is a feasible way to provide rich feedback in the delivery of mammographic interpretation training.

Correlating the ground truth of mammographic histology with the success or failure of imaging.

PubMed

Tot, Tibor

2005-02-01

Detailed and systematic mammographic-pathologic correlation is essential for evaluation of the advantages and disadvantages of mammography as an imaging method as well as for establishing the role of additional methods or alternatives. Two- and three-dimensional large section histopathology represents an ideal tool for this correlation. This kind of interdisciplinary approach ("mammographic histology") is slowly but irrevocably becoming accepted as the new golden standard in diagnosing breast abnormalities. In this review, upon summarizing the theoretical background and our practical experience in routine diagnostic use of these advantageous techniques, we report on the accuracy of the preoperative radiological diagnosis. As compared to the final diagnostic outcome, stellate lesions on the mammogram and microcalcifications of casting type indicate malignancy with very high accuracy while predicting malignancy in cases of powdery and crushed stone type microcalcifications is problematic. The extent of the disease is regularly underestimated on the mammogram by the radiologist. Combining different radiological signs, and comparing repeated static images taken in regular intervals in screening or postoperative follow-up, the mammographer may type and grade the lesions properly in a considerable number of cases. Regular mammographic-pathologic correlation may increase the specificity and sensitivity of mammographic diagnosis. This correlation is essential for establishing the proper pre- and postoperative histological diagnosis, too.

The relative effect of mammographic screening on breast cancer mortality by socioeconomic status

PubMed Central

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

2016-01-01

Abstract Breast cancer incidence and mortality are higher in women with a high socioeconomic status (SES). The potential to prevent death from breast cancer is therefore greater in the high SES group. This does, however, require that the effectiveness of screening in the high SES group is equal to or greater than the effectiveness in the low SES group. The aim of this study is to assess the relative effectiveness of mammographic screening on breast cancer mortality by SES. In Nijmegen, the Netherlands, women are invited to participate in biennial mammographic screening since 1975. Postal code is collected at each round and is used to calculate the SES of each woman based on the SES indicator of the Netherlands Institute for Social Research. The Dutch average was used to classify the SES score of each woman as either high or low. We designed a case-control study to investigate the effect of mammographic screening in women aged 50 to 75, 40 to 75, and 50 to 69 years, and calculated the odds ratios (ORs) and 95% confidence intervals (CIs). Among the women invited to the mammographic screening program in Nijmegen, 10% had a high SES. In women aged 50 to 75 years, the breast cancer death rate was 38% lower in screened women than in unscreened women. The ORs for women with high SES (OR 0.82, 95% CI 0.31–2.19) and low SES did not differ significantly (OR 0.61, 95% CI 0.47–0.78). Mammographic screening reduces breast cancer mortality, but we did not observe a significant difference in the relative effectiveness of screening by SES. If the effectiveness of mammographic screening is indeed not dependent on SES status, the absolute number of breast cancer deaths prevented by mammographic screening will be greater in the high SES than low SES group, because women with a high SES have a greater risk of breast cancer death. PMID:27495038

Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses

NASA Astrophysics Data System (ADS)

Kooi, Thijs; Karssemeijer, Nico

2017-03-01

When humans identify objects in images, context is an important cue; a cheetah is more likely to be a domestic cat when a television set is recognised in the background. Similar principles apply to the analysis of medical images. The detection of diseases that manifest unilaterally in symmetrical organs or organ pairs can in part be facilitated by a search for symmetrical discrepancies in or between the organs in question. During a mammographic exam, images are recorded of each breast and absence of a certain structure around the same location in the contralateral image will render the area under scrutiny more suspicious and conversely, the presence of similar tissue less so. In this paper, we present a fusion scheme for a deep Convolutional Neural Network (CNN) architecture with the goal to optimally capture such asymmetries. The method is applied to the domain of mammography CAD, but can be relevant to other medical image analysis tasks where symmetry is important such as lung, prostate or brain images.

Morphological filtering and multiresolution fusion for mammographic microcalcification detection

NASA Astrophysics Data System (ADS)

Chen, Lulin; Chen, Chang W.; Parker, Kevin J.

1997-04-01

Mammographic images are often of relatively low contrast and poor sharpness with non-stationary background or clutter and are usually corrupted by noise. In this paper, we propose a new method for microcalcification detection using gray scale morphological filtering followed by multiresolution fusion and present a unified general filtering form called the local operating transformation for whitening filtering and adaptive thresholding. The gray scale morphological filters are used to remove all large areas that are considered as non-stationary background or clutter variations, i.e., to prewhiten images. The multiresolution fusion decision is based on matched filter theory. In addition to the normal matched filter, the Laplacian matched filter which is directly related through the wavelet transforms to multiresolution analysis is exploited for microcalcification feature detection. At the multiresolution fusion stage, the region growing techniques are used in each resolution level. The parent-child relations between resolution levels are adopted to make final detection decision. FROC is computed from test on the Nijmegen database.

Clinical evaluation of JPEG2000 compression for digital mammography

NASA Astrophysics Data System (ADS)

Sung, Min-Mo; Kim, Hee-Joung; Kim, Eun-Kyung; Kwak, Jin-Young; Yoo, Jae-Kyung; Yoo, Hyung-Sik

2002-06-01

Medical images, such as computed radiography (CR), and digital mammographic images will require large storage facilities and long transmission times for picture archiving and communications system (PACS) implementation. American College of Radiology and National Equipment Manufacturers Association (ACR/NEMA) group is planning to adopt a JPEG2000 compression algorithm in digital imaging and communications in medicine (DICOM) standard to better utilize medical images. The purpose of the study was to evaluate the compression ratios of JPEG2000 for digital mammographic images using peak signal-to-noise ratio (PSNR), receiver operating characteristic (ROC) analysis, and the t-test. The traditional statistical quality measures such as PSNR, which is a commonly used measure for the evaluation of reconstructed images, measures how the reconstructed image differs from the original by making pixel-by-pixel comparisons. The ability to accurately discriminate diseased cases from normal cases is evaluated using ROC curve analysis. ROC curves can be used to compare the diagnostic performance of two or more reconstructed images. The t test can be also used to evaluate the subjective image quality of reconstructed images. The results of the t test suggested that the possible compression ratios using JPEG2000 for digital mammographic images may be as much as 15:1 without visual loss or with preserving significant medical information at a confidence level of 99%, although both PSNR and ROC analyses suggest as much as 80:1 compression ratio can be achieved without affecting clinical diagnostic performance.

Cone-beam volume CT mammographic imaging: feasibility study

NASA Astrophysics Data System (ADS)

Chen, Biao; Ning, Ruola

2001-06-01

X-ray projection mammography, using a film/screen combination or digital techniques, has proven to be the most effective imaging modality for early detection of breast cancer currently available. However, the inherent superimposition of structures makes small carcinoma (a few millimeters in size) difficult to detect in the occultation case or in dense breasts, resulting in a high false positive biopsy rate. The cone-beam x-ray projection based volume imaging using flat panel detectors (FPDs) makes it possible to obtain three-dimensional breast images. This may benefit diagnosis of the structure and pattern of the lesion while eliminating hard compression of the breast. This paper presents a novel cone-beam volume CT mammographic imaging protocol based on the above techniques. Through computer simulation, the key issues of the system and imaging techniques, including the x-ray imaging geometry and corresponding reconstruction algorithms, x-ray characteristics of breast tissues, x-ray setting techniques, the absorbed dose estimation and the quantitative effect of x-ray scattering on image quality, are addressed. The preliminary simulation results support the proposed cone-beam volume CT mammographic imaging modality in respect to feasibility and practicability for mammography. The absorbed dose level is comparable to that of current two-view mammography and would not be a prominent problem for this imaging protocol. Compared to traditional mammography, the proposed imaging protocol with isotropic spatial resolution will potentially provide significantly better low contrast detectability of breast tumors and more accurate location of breast lesions.

Measurement of compressed breast thickness by optical stereoscopic photogrammetry

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

Tyson, Albert H.; Mawdsley, Gordon E.; Yaffe, Martin J.

2009-02-15

The determination of volumetric breast density (VBD) from mammograms requires accurate knowledge of the thickness of the compressed breast. In attempting to accurately determine VBD from images obtained on conventional mammography systems, the authors found that the thickness reported by a number of mammography systems in the field varied by as much as 15 mm when compressing the same breast or phantom. In order to evaluate the behavior of mammographic compression systems and to be able to predict the thickness at different locations in the breast on patients, they have developed a method for measuring the local thickness of themore » breast at all points of contact with the compression paddle using optical stereoscopic photogrammetry. On both flat (solid) and compressible phantoms, the measurements were accurate to better than 1 mm with a precision of 0.2 mm. In a pilot study, this method was used to measure thickness on 108 volunteers who were undergoing mammography examination. This measurement tool will allow us to characterize paddle surface deformations, deflections and calibration offsets for mammographic units.« less

Occult Breast Cancer: Scintimammography with High-Resolution Breast-specific Gamma Camera in Women at High Risk for Breast Cancer

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

Rachel F. Brem; Jocelyn A. Rapelyea; , Gilat Zisman

2005-08-01

To prospectively evaluate a high-resolution breast-specific gamma camera for depicting occult breast cancer in women at high risk for breast cancer but with normal mammographic and physical examination findings. MATERIALS AND METHODS: Institutional Review Board approval and informed consent were obtained. The study was HIPAA compliant. Ninety-four high-risk women (age range, 36-78 years; mean, 55 years) with normal mammographic (Breast Imaging Reporting and Data System [BI-RADS] 1 or 2) and physical examination findings were evaluated with scintimammography. After injection with 25-30 mCi (925-1110 MBq) of technetium 99m sestamibi, patients were imaged with a high-resolution small-field-of-view breast-specific gamma camera in craniocaudalmore » and mediolateral oblique projections. Scintimammograms were prospectively classified according to focal radiotracer uptake as normal (score of 1), with no focal or diffuse uptake; benign (score of 2), with minimal patchy uptake; probably benign (score of 3), with scattered patchy uptake; probably abnormal (score of 4), with mild focal radiotracer uptake; and abnormal (score of 5), with marked focal radiotracer uptake. Mammographic breast density was categorized according to BI-RADS criteria. Patients with normal scintimammograms (scores of 1, 2, or 3) were followed up for 1 year with an annual mammogram, physical examination, and repeat scintimammography. Patients with abnormal scintimammograms (scores of 4 or 5) underwent ultrasonography (US), and those with focal hypoechoic lesions underwent biopsy. If no lesion was found during US, patients were followed up with scintimammography. Specific pathologic findings were compared with scintimammographic findings. RESULTS: Of 94 women, 78 (83%) had normal scintimammograms (score of 1, 2, or 3) at initial examination and 16 (17%) had abnormal scintimammograms (score of 4 or 5). Fourteen (88%) of the 16 patients had either benign findings at biopsy or no focal abnormality at US; in two (12%) patients, invasive carcinoma was diagnosed at US-guided biopsy (9 mm each at pathologic examination). CONCLUSION: High-resolution breast-specific scintimammography can depict small (<1-cm), mammographically occult, nonpalpable lesions in women at increased risk for breast cancer not otherwise identified at mammography or physical examination.« less

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.

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

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

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.

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.

Computer Simulation of Breast Cancer Screening

DTIC Science & Technology

2001-07-01

21. Tompkins PA, Abreu CC, Carroll FE, Xiao therapeutic medical physics. Med Phys 14. Gentry JR, DeWerd LA. TLD measure- QF, MacDonald CA. Use of...capillary op- 1996; 23:1997-2005. ments of in vivo mammographic expo- tics as a beam intensifier for a Compton 28. Hammerstein GR, Miller DW, White DR...cm), and was only poorly correlated thicker slices. with breast thickness (r2 0.159). The For comparison images and dosimetry , magnification factor

Effects of Tamoxifen and oestrogen on histology and radiographic density in high and low mammographic density human breast tissues maintained in murine tissue engineering chambers.

PubMed

Chew, G L; Huo, C W; Huang, D; Blick, T; Hill, P; Cawson, J; Frazer, H; Southey, M C; Hopper, J L; Britt, K; Henderson, M A; Haviv, I; Thompson, E W

2014-11-01

Mammographic density (MD) is a strong risk factor for breast cancer. It is altered by exogenous endocrine treatments, including hormone replacement therapy and Tamoxifen. Such agents also modify breast cancer (BC) risk. However, the biomolecular basis of how systemic endocrine therapy modifies MD and MD-associated BC risk is poorly understood. This study aims to determine whether our xenograft biochamber model can be used to study the effectiveness of therapies aimed at modulating MD, by examine the effects of Tamoxifen and oestrogen on histologic and radiographic changes in high and low MD tissues maintained within the biochamber model. High and low MD human tissues were precisely sampled under radiographic guidance from prophylactic mastectomy fresh specimens of high-risk women, then inserted into separate vascularized murine biochambers. The murine hosts were concurrently implanted with Tamoxifen, oestrogen or placebo pellets, and the high and low MD biochamber tissues maintained in the murine host environment for 3 months, before the high and low MD biochamber tissues were harvested for histologic and radiographic analyses. The radiographic density of high MD tissue maintained in murine biochambers was decreased in Tamoxifen-treated mice compared to oestrogen-treated mice (p = 0.02). Tamoxifen treatment of high MD tissue in SCID mice led to a decrease in stromal (p = 0.009), and an increase in adipose (p = 0.023) percent areas, compared to placebo-treated mice. No histologic or radiographic differences were observed in low MD biochamber tissue with any treatment. High MD biochamber tissues maintained in mice implanted with Tamoxifen, oestrogen or placebo pellets had dynamic and measurable histologic compositional and radiographic changes. This further validates the dynamic nature of the MD xenograft model, and suggests the biochamber model may be useful for assessing the underlying molecular pathways of Tamoxifen-reduced MD, and in testing of other pharmacologic interventions in a preclinical model of high MD.

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.

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.

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.

Fractal dimension and lacunarity analysis of mammographic patterns in assessing breast cancer risk related to HRT treated population: a longitudinal and cross-sectional study

NASA Astrophysics Data System (ADS)

Karemore, Gopal; Nielsen, Mads

2009-02-01

Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and Lacunarity contribute to asses breast cancer risk. FD represents the complexity while the Lacunarity characterize the gappiness of a fractal. Our cross-sectional case-control study includes mammograms of 50 patients diagnosed with breast cancer in the subsequent 2-4 years and 50 matched controls. The longitudinal double blind placebo controlled HRT study includes 39 placebo and 36 HRT treated volunteers for two years. ROIs with same dimension (250*150 pixels) were created behind the nipple region on these radiographs. Box counting method was used to calculate the fractal dimension (FD) and the Lacunarity. Paired t-test and Pearson correlation coefficient were calculated. It was found that there were no differences between cancer and control group for FD (P=0.8) and Lacunarity (P=0.8) in crosssectional study whereas earlier published heterogeneity examination of radiographs (BC-HER) breast cancer risk score separated groups (p=0.002). In the longitudinal study, FD decreased significantly (P<0.05) in the HRT treated population while Lacunarity remained insignificant (P=0.2). FD is negatively correlated to Lacunarity (-0.74, P<0.001), BIRADS (-0.34, P<0.001) and Percentage Density (-0.41, P<0.001). FD is invariant to the mammographic texture change from control to cancer population but marginally varying in HRT treated population. This study yields no evidence that lacunarity or FD are suitable surrogate markers of mammographic heterogeneity as they neither pick up breast cancer risk, nor show good sensitivity to HRT.

Longitudinal study of mammographic density measures that predict breast cancer risk

PubMed Central

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

2016-01-01

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

Long-term effect of oncoplastic breast-conserving surgery using latissimus dorsi miniflaps on mammographic surveillance and the detection of local recurrence.

PubMed

Mele, S; Wright, D; Paramanathan, N; Laws, S; Peiris, L; Rainsbury, R

2017-09-01

Latissimus dorsi miniflap is a breast-conserving volume replacement technique for the reconstruction of large breast defects. While mammographic features of miniflap reconstruction have been described, little is known about the incidence, mode of presentation and size of local recurrence after this procedure. This study aimed to investigate the impact of latissimus dorsi miniflap reconstruction on the frequency, presentation and detection of local recurrence. Clinical, radiological and pathological data were reviewed in 261 patients. Complete records were available for 11 patients developing local recurrence, including mode, time of presentation and size of the recurrent tumours. All mammograms before and after local recurrence were assessed in relation to a range of specific characteristics including parenchymal density, flap visibility, architectural distortion, mass, calcifications, fat necrosis, skin thickening and breast oedema. Twenty-one patients developed local recurrence at 10.4 years following reconstruction (mean age 49 years, resection weight 182 g and tumour size 33 mm). Following radiotherapy, 0.5% of patients developed local recurrence each year, which increased five-fold when radiotherapy was omitted (HR 4.99). Local recurrences were diagnosed in five patients by mammography alone, in three by mammography and palpable lump, and in three by palpable lump alone. They were detected when small (15 mm) and were associated with new mammographic abnormalities in 10 patients. Long follow-up demonstrates that latissimus dorsi miniflap reconstruction allows oncologically safe breast conservation when combined with postoperative radiotherapy. Local recurrences are detected early, either by mammography, clinical examination or both, and detection is not compromised by the presence of a flap. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

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.

Analysis of confidence level scores from an ROC study: comparison of three mammographic systems for detection of simulated calcifications

NASA Astrophysics Data System (ADS)

Lai, Chao-Jen; Shaw, Chris C.; Whitman, Gary J.; Yang, Wei T.; Dempsey, Peter J.

2005-04-01

The purpose of this study is to compare the detection performance of three different mammography systems: screen/film (SF) combination, a-Si/CsI flat-panel (FP-), and charge-coupled device (CCD-) based systems. A 5-cm thick 50% adipose/50% glandular breast tissue equivalent slab phantom was used to provide an uniform background. Calcium carbonate grains of three different size groups were used to simulate microcalcifications (MCs): 112-125, 125-140, and 140-150 μm overlapping with the uniform background. Calcification images were acquired with the three mammography systems. Digital images were printed on hardcopy films. All film images were displayed on a mammographic viewer and reviewed by 5 mammographers. The visibility of the MC was rated with a 5-point confidence rating scale for each detection task, including the negative controls. Scores were averaged over all readers for various detectors and size groups. Receiver operating characteristic (ROC) analysis was performed and the areas under the ROC curves (Az"s) were computed for various imaging conditions. The results shows that (1) the FP-based system performed significantly better than the SF and CCD-based systems for individual size groups using ROC analysis (2) the FP-based system also performed significantly better than the SF and CCD-based systems for individual size groups using averaged confidence scale, and (3) the results obtained from the Az"s were largely correlated with these from confidence level scores. However, the correlation varied slightly among different imaging conditions.

Computing the scatter component of mammographic images.

PubMed

Highnam, R P; Brady, J M; Shepstone, B J

1994-01-01

The authors build upon a technical report (Tech. Report OUEL 2009/93, Engng. Sci., Oxford Uni., Oxford, UK, 1993) in which they proposed a model of the mammographic imaging process for which scattered radiation is a key degrading factor. Here, the authors propose a way of estimating the scatter component of the signal at any pixel within a mammographic image, and they use this estimate for model-based image enhancement. The first step is to extend the authors' previous model to divide breast tissue into "interesting" (fibrous/glandular/cancerous) tissue and fat. The scatter model is then based on the idea that the amount of scattered radiation reaching a point is related to the energy imparted to the surrounding neighbourhood. This complex relationship is approximated using published empirical data, and it varies with the size of the breast being imaged. The approximation is further complicated by needing to take account of extra-focal radiation and breast edge effects. The approximation takes the form of a weighting mask which is convolved with the total signal (primary and scatter) to give a value which is input to a "scatter function", approximated using three reference cases, and which returns a scatter estimate. Given a scatter estimate, the more important primary component can be calculated and used to create an image recognizable by a radiologist. The images resulting from this process are clearly enhanced, and model verification tests based on an estimate of the thickness of interesting tissue present proved to be very successful. A good scatter model opens the was for further processing to remove the effects of other degrading factors, such as beam hardening.

The Mammographic Head Demonstrator Developed in the Framework of the “IMI” Project:. First Imaging Tests Results

NASA Astrophysics Data System (ADS)

Bisogni, Maria Giuseppina

2006-04-01

In this paper we report on the performances and the first imaging test results of a digital mammographic demonstrator based on GaAs pixel detectors. The heart of this prototype is the X-ray detection unit, which is a GaAs pixel sensor read-out by the PCC/MEDIPIXI circuit. Since the active area of the sensor is 1 cm2, 18 detectors have been organized in two staggered rows of nine chips each. To cover the typical mammographic format (18 × 24 cm2) a linear scanning is performed by means of a stepper motor. The system is integrated in mammographic equipment comprehending the X-ray tube, the bias and data acquisition systems and the PC-based control system. The prototype has been developed in the framework of the integrated Mammographic Imaging (IMI) project, an industrial research activity aiming to develop innovative instrumentation for morphologic and functional imaging. The project has been supported by the Italian Ministry of Education, University and Research (MIUR) and by five Italian High Tech companies in collaboration with the universities of Ferrara, Roma “La Sapienza”, Pisa and the INFN.

Computer-aided diagnosis of breast cancer via Gabor wavelet bank and binary-class SVM in mammographic images

NASA Astrophysics Data System (ADS)

Torrents-Barrena, Jordina; Puig, Domenec; Melendez, Jaime; Valls, Aida

2016-03-01

Breast cancer is one of the most dangerous diseases that attack women in their 40s worldwide. Due to this fact, it is estimated that one in eight women will develop a malignant carcinoma during their life. In addition, the carelessness of performing regular screenings is an important reason for the increase of mortality. However, computer-aided diagnosis systems attempt to enhance the quality of mammograms as well as the detection of early signs related to the disease. In this paper we propose a bank of Gabor filters to calculate the mean, standard deviation, skewness and kurtosis features by four-sized evaluation windows. Therefore, an active strategy is used to select the most relevant pixels. Finally, a supervised classification stage using two-class support vector machines is utilised through an accurate estimation of kernel parameters. In order to show the development of our methodology based on mammographic image analysis, two main experiments are fulfilled: abnormal/normal breast tissue classification and the ability to detect the different breast cancer types. Moreover, the public screen-film mini-MIAS database is compared with a digitised breast cancer database to evaluate the method robustness. The area under the receiver operating characteristic curve is used to measure the performance of the method. Furthermore, both confusion matrix and accuracy are calculated to assess the results of the proposed algorithm.

Computer-assisted education and interdisciplinary breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Whatmough, Pamela; Gale, Alastair G.; Wilson, A. R. M.

1996-04-01

The diagnosis of breast disease for screening or symptomatic women is largely arrived at by a multi-disciplinary team. We report work on the development and assessment of an inter- disciplinary computer based learning system to support the diagnosis of this disease. The diagnostic process is first modelled from different viewpoints and then appropriate knowledge structures pertinent to the domains of radiologist, pathologist and surgeon are depicted. Initially the underlying inter-relationships of the mammographic diagnostic approach were detailed which is largely considered here. Ultimately a system is envisaged which will link these specialties and act as a diagnostic aid as well as a multi-media educational system.

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.

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.

Use of border information in the classification of mammographic masses

NASA Astrophysics Data System (ADS)

Varela, C.; Timp, S.; Karssemeijer, N.

2006-01-01

We are developing a new method to characterize the margin of a mammographic mass lesion to improve the classification of benign and malignant masses. Towards this goal, we designed features that measure the degree of sharpness and microlobulation of mass margins. We calculated these features in a border region of the mass defined as a thin band along the mass contour. The importance of these features in the classification of benign and malignant masses was studied in relation to existing features used for mammographic mass detection. Features were divided into three groups, each representing a different mass segment: the interior region of a mass, the border and the outer area. The interior and the outer area of a mass were characterized using contrast and spiculation measures. Classification was done in two steps. First, features representing each of the three mass segments were merged into a neural network classifier resulting in a single regional classification score for each segment. Secondly, a classifier combined the three single scores into a final output to discriminate between benign and malignant lesions. We compared the classification performance of each regional classifier and the combined classifier on a data set of 1076 biopsy proved masses (590 malignant and 486 benign) from 481 women included in the Digital Database for Screening Mammography. Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the classifiers. The area under the ROC curve (Az) was 0.69 for the interior mass segment, 0.76 for the border segment and 0.75 for the outer mass segment. The performance of the combined classifier was 0.81 for image-based and 0.83 for case-based evaluation. These results show that the combination of information from different mass segments is an effective approach for computer-aided characterization of mammographic masses. An advantage of this approach is that it allows the assessment of the contribution of regions rather than individual features. Results suggest that the border and the outer areas contained the most valuable information for discrimination between benign and malignant masses.

Future possibilities in the prevention of breast cancer: Luteinizing hormone-releasing hormone agonists

PubMed Central

Spicer, Darcy V; Pike, Malcolm C

2000-01-01

The cyclic production of estrogen and progesterone by the premenopausal ovary accounts for the steep rise in breast cancer risk in premenopausal women. These hormones are breast cell mitogens. By reducing exposure to these ovarian hormones, agonists of luteinizing hormone-releasing hormone (LHRH) given to suppress ovarian function may prove useful in cancer prevention. To prevent deleterious effects of hypoestrogenemia, the addition of low-dose hormone replacement to the LHRH agonist appears necessary. Pilot data with such an approach indicates it is feasible and reduces mammographic densities. PMID:11250719

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.

Dynamic changes in high and low mammographic density human breast tissues maintained in murine tissue engineering chambers during various murine peripartum states and over time.

PubMed

Chew, G L; Huang, D; Huo, C W; Blick, T; Hill, P; Cawson, J; Frazer, H; Southey, M D; Hopper, J L; Henderson, M A; Haviv, I; Thompson, E W

2013-07-01

Mammographic density (MD) is a strong heritable risk factor for breast cancer, and may decrease with increasing parity. However, the biomolecular basis for MD-associated breast cancer remains unclear, and systemic hormonal effects on MD-associated risk is poorly understood. This study assessed the effect of murine peripartum states on high and low MD tissue maintained in a xenograft model of human MD. Method High and low MD human breast tissues were precisely sampled under radiographic guidance from prophylactic mastectomy specimens of women. The high and low MD tissues were maintained in separate vascularised biochambers in nulliparous or pregnant SCID mice for 4 weeks, or mice undergoing postpartum involution or lactation for three additional weeks. High and low MD biochamber material was harvested for histologic and radiographic comparisons during various murine peripartum states. High and low MD biochamber tissues in nulliparous mice were harvested at different timepoints for histologic and radiographic comparisons. Results High MD biochamber tissues had decreased stromal (p = 0.0027), increased adipose (p = 0.0003) and a trend to increased glandular tissue areas (p = 0.076) after murine postpartum involution. Stromal areas decreased (p = 0.042), while glandular (p = 0.001) and adipose areas (p = 0.009) increased in high MD biochamber tissues during lactation. A difference in radiographic density was observed in high (p = 0.0021) or low MD biochamber tissues (p = 0.004) between nulliparous, pregnant and involution groups. No differences in tissue composition were observed in high or low MD biochamber tissues maintained for different durations, although radiographic density increased over time. Conclusion High MD biochamber tissues had measurable histologic changes after postpartum involution or lactation. Alterations in radiographic density occurred in biochamber tissues between different peripartum states and over time. These findings demonstrate the dynamic nature of the human MD xenograft model, providing a platform for studying the biomolecular basis of MD-associated cancer risk.

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.

A citizen science approach to optimising computer aided detection (CAD) in mammography

NASA Astrophysics Data System (ADS)

Ionescu, Georgia V.; Harkness, Elaine F.; Hulleman, Johan; Astley, Susan M.

2018-03-01

Computer aided detection (CAD) systems assist medical experts during image interpretation. In mammography, CAD systems prompt suspicious regions which help medical experts to detect early signs of cancer. This is a challenging task and prompts may appear in regions that are actually normal, whilst genuine cancers may be missed. The effect prompting has on readers performance is not fully known. In order to explore the effects of prompting errors, we have created an online game (Bat Hunt), designed for non-experts, that mirrors mammographic CAD. This allows us to explore a wider parameter space. Users are required to detect bats in images of flocks of birds, with image difficulty matched to the proportions of screening mammograms in different BI-RADS density categories. Twelve prompted conditions were investigated, along with unprompted detection. On average, players achieved a sensitivity of 0.33 for unprompted detection, and sensitivities of 0.75, 0.83, and 0.92 respectively for 70%, 80%, and 90% of targets prompted, regardless of CAD specificity. False prompts distract players from finding unprompted targets if they appear in the same image. Player performance decreases when the number of false prompts increases, and increases proportionally with prompting sensitivity. Median lowest d' was for unprompted condition (1.08) and the highest for sensitivity 90% and 0.5 false prompts per image (d'=4.48).

Methods for Evaluating Mammography Imaging Techniques

DTIC Science & Technology

2000-06-01

independent of disease prevalence . When test outcomes are dichotomous, sensitivity and specificity measure test accuracy. Sensitivity is the...phers were not provided with the disease prevalence in the The model we use accounts for within mammographer test set. Mammographers provided one

External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

PubMed

Benndorf, Matthias; Burnside, Elizabeth S; Herda, Christoph; Langer, Mathias; Kotter, Elmar

2015-08-01

Lesions detected at mammography are described with a highly standardized terminology: the breast imaging-reporting and data system (BI-RADS) lexicon. Up to now, no validated semantic computer assisted classification algorithm exists to interactively link combinations of morphological descriptors from the lexicon to a probabilistic risk estimate of malignancy. The authors therefore aim at the external validation of the mammographic mass diagnosis (MMassDx) algorithm. A classification algorithm like MMassDx must perform well in a variety of clinical circumstances and in datasets that were not used to generate the algorithm in order to ultimately become accepted in clinical routine. The MMassDx algorithm uses a naïve Bayes network and calculates post-test probabilities of malignancy based on two distinct sets of variables, (a) BI-RADS descriptors and age ("descriptor model") and (b) BI-RADS descriptors, age, and BI-RADS assessment categories ("inclusive model"). The authors evaluate both the MMassDx (descriptor) and MMassDx (inclusive) models using two large publicly available datasets of mammographic mass lesions: the digital database for screening mammography (DDSM) dataset, which contains two subsets from the same examinations-a medio-lateral oblique (MLO) view and cranio-caudal (CC) view dataset-and the mammographic mass (MM) dataset. The DDSM contains 1220 mass lesions and the MM dataset contains 961 mass lesions. The authors evaluate discriminative performance using area under the receiver-operating-characteristic curve (AUC) and compare this to the BI-RADS assessment categories alone (i.e., the clinical performance) using the DeLong method. The authors also evaluate whether assigned probabilistic risk estimates reflect the lesions' true risk of malignancy using calibration curves. The authors demonstrate that the MMassDx algorithms show good discriminatory performance. AUC for the MMassDx (descriptor) model in the DDSM data is 0.876/0.895 (MLO/CC view) and AUC for the MMassDx (inclusive) model in the DDSM data is 0.891/0.900 (MLO/CC view). AUC for the MMassDx (descriptor) model in the MM data is 0.862 and AUC for the MMassDx (inclusive) model in the MM data is 0.900. In all scenarios, MMassDx performs significantly better than clinical performance, P < 0.05 each. The authors furthermore demonstrate that the MMassDx algorithm systematically underestimates the risk of malignancy in the DDSM and MM datasets, especially when low probabilities of malignancy are assigned. The authors' results reveal that the MMassDx algorithms have good discriminatory performance but less accurate calibration when tested on two independent validation datasets. Improvement in calibration and testing in a prospective clinical population will be important steps in the pursuit of translation of these algorithms to the clinic.

Correlation of mammographic density and serum calcium levels in patients with primary breast cancer.

PubMed

Hack, Carolin C; Stoll, Martin J; Jud, Sebastian M; Heusinger, Katharina; Adler, Werner; Haeberle, Lothar; Ganslandt, Thomas; Heindl, Felix; Schulz-Wendtland, Rüdiger; Cavallaro, Alexander; Uder, Michael; Beckmann, Matthias W; Fasching, Peter A; Bayer, Christian M

2017-06-01

Percentage mammographic breast density (PMD) is one of the most important risk factors for breast cancer (BC). Calcium, vitamin D, bisphosphonates, and denosumab have been considered and partly confirmed as factors potentially influencing the risk of BC. This retrospective observational study investigated the association between serum calcium level and PMD. A total of 982 BC patients identified in the research database at the University Breast Center for Franconia with unilateral BC, calcium and albumin values, and mammogram at the time of first diagnosis were included. PMD was assessed, using a semiautomated method by two readers. Linear regression analyses were conducted to investigate the impact on PMD of the parameters of serum calcium level adjusted for albumin level, and well-known clinical predictors such as age, body mass index (BMI), menopausal status and confounder for serum calcium like season in which the BC was diagnosed. Increased calcium levels were associated with reduced PMD (P = 0.024). Furthermore, PMD was inversely associated with BMI (P < 0.001) and age (P < 0.001). There was also an association between PMD and menopausal status (P < 0.001). The goodness-of-fit of the regression model was moderate. This is the first study assessing the association between serum calcium level and PMD. An inverse association with adjusted serum calcium levels was observed. These findings add to previously published data relating to vitamin D, bisphosphonates, denosumab, and the RANK/RANKL signaling pathway in breast cancer risk and prevention. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Mobile Versus Fixed Facility: Latinas' Attitudes and Preferences for Obtaining a Mammogram.

PubMed

Scheel, John R; Tillack, Allison A; Mercer, Lauren; Coronado, Gloria D; Beresford, Shirley A A; Molina, Yamile; Thompson, Beti

2018-01-01

Mobile mammographic services have been proposed as a way to reduce Latinas' disproportionate late-stage presentation compared with white women by increasing their access to mammography. The aims of this study were to assess why Latinas may not use mobile mammographic services and to explore their preferences after using these services. Using a mixed-methods approach, a secondary analysis was conducted of baseline survey data (n = 538) from a randomized controlled trial to improve screening mammography rates among Latinas in Washington. Descriptive statistics and bivariate regression were used to characterize mammography location preferences and to test for associations with sociodemographic indices, health care access, and perceived breast cancer risk and beliefs. On the basis of these findings, a qualitative study (n = 18) was used to explore changes in perceptions after using mobile mammographic services. More Latinas preferred obtaining a mammogram at a fixed facility (52.3% [n = 276]) compared with having no preference (46.3% [n = 249]) and preferring mobile mammographic services (1.7% [n = 9]). Concerns about privacy and comfort (15.6% [n = 84]) and about general quality (10.6% [n = 57]) were common reasons for preferring a fixed facility. Those with no history of mammography preferred a fixed facility (P < .05). In the qualitative study, Latinas expressed similar initial concerns but became positive toward the mobile mammographic services after obtaining a mammogram. Although most Latinas preferred obtaining a mammogram at a fixed facility, positive experiences with mobile mammography services changed their attitudes toward them. These findings highlight the need to include community education when using mobile mammographic service to increase screening mammography rates in underserved communities. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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.

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.

21 CFR 892.1710 - Mammographic x-ray system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Mammographic x-ray system. 892.1710 Section 892.1710 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED.... This generic type of device may include signal analysis and display equipment, patient and equipment...

21 CFR 892.1710 - Mammographic x-ray system.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Mammographic x-ray system. 892.1710 Section 892.1710 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED.... This generic type of device may include signal analysis and display equipment, patient and equipment...

21 CFR 892.1710 - Mammographic x-ray system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Mammographic x-ray system. 892.1710 Section 892.1710 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED.... This generic type of device may include signal analysis and display equipment, patient and equipment...

21 CFR 892.1710 - Mammographic x-ray system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Mammographic x-ray system. 892.1710 Section 892.1710 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED.... This generic type of device may include signal analysis and display equipment, patient and equipment...

21 CFR 892.1710 - Mammographic x-ray system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Mammographic x-ray system. 892.1710 Section 892.1710 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED.... This generic type of device may include signal analysis and display equipment, patient and equipment...

Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms.

PubMed

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree

2018-05-01

In this paper, two novel feature extraction methods, using neighborhood structural similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since gray-level distribution of pixels is different in benign and malignant masses, more regular and homogeneous patterns are visible in benign masses compared to malignant masses; the proposed method exploits the similarity between neighboring regions of masses by designing two new features, namely, NSS-I and NSS-II, which capture global similarity at different scales. Complementary to these global features, uniform local binary patterns are computed to enhance the classification efficiency by combining with the proposed features. The performance of the features are evaluated using the images from the mini-mammographic image analysis society (mini-MIAS) and digital database for screening mammography (DDSM) databases, where a tenfold cross-validation technique is incorporated with Fisher linear discriminant analysis, after selecting the optimal set of features using stepwise logistic regression method. The best area under the receiver operating characteristic curve of 0.98 with an accuracy of is achieved with the mini-MIAS database, while the same for the DDSM database is 0.93 with accuracy .

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

Automated recognition of microcalcification clusters in mammograms

NASA Astrophysics Data System (ADS)

Bankman, Isaac N.; Christens-Barry, William A.; Kim, Dong W.; Weinberg, Irving N.; Gatewood, Olga B.; Brody, William R.

1993-07-01

The widespread and increasing use of mammographic screening for early breast cancer detection is placing a significant strain on clinical radiologists. Large numbers of radiographic films have to be visually interpreted in fine detail to determine the subtle hallmarks of cancer that may be present. We developed an algorithm for detecting microcalcification clusters, the most common and useful signs of early, potentially curable breast cancer. We describe this algorithm, which utilizes contour map representations of digitized mammographic films, and discuss its benefits in overcoming difficulties often encountered in algorithmic approaches to radiographic image processing. We present experimental analyses of mammographic films employing this contour-based algorithm and discuss practical issues relevant to its use in an automated film interpretation instrument.

Assessment of mammographic film processor performance in a hospital and mobile screening unit.

PubMed

Murray, J G; Dowsett, D J; Laird, O; Ennis, J T

1992-12-01

In contrast to the majority of mammographic breast screening programmes, film processing at this centre occurs on site in both hospital and mobile trailer units. Initial (1989) quality control (QC) sensitometric tests revealed a large variation in film processor performance in the mobile unit. The clinical significance of these variations was assessed and acceptance limits for processor performance determined. Abnormal mammograms were used as reference material and copied using high definition 35 mm film over a range of exposure settings. The copies were than matched with QC film density variation from the mobile unit. All films were subsequently ranked for spatial and contrast resolution. Optimal values for processing time of 2 min (equivalent to film transit time 3 min and developer time 46 s) and temperature of 36 degrees C were obtained. The widespread anomaly of reporting film transit time as processing time is highlighted. Use of mammogram copies as a means of measuring the influence of film processor variation is advocated. Careful monitoring of the mobile unit film processor performance has produced stable quality comparable with the hospital based unit. The advantages of on site film processing are outlined. The addition of a sensitometric step wedge to all mammography film stock as a means of assessing image quality is recommended.

Incremental clinical value of ultrasound in men with mammographically confirmed gynecomastia.

PubMed

Chen, Po-Hao; Slanetz, Priscilla J

2014-01-01

To determine whether ultrasound is of any value in male patients presenting with focal symptoms who have classic features of gynecomastia but no concerning findings on mammography. Over a 3-year period, all male patients who underwent mammographic evaluation were identified in this retrospective study. Patients with a mammographic diagnosis of gynecomastia and subsequent breast ultrasound at a large tertiary academic medical center comprised the study cohort. Men whose ultrasound diagnosis differed from the initial mammographic evaluation were analyzed for both additional benign findings as well as findings that warranted biopsy. A total of 353 mammograms were obtained from 327 unique patients (ages 18-95, mean 51 years). Of all mammographic examinations, gynecomastia was the sole finding in 73% (259). In those 259 studies, 85% were further evaluated with ultrasound, in which 6 (2.7%) showed additional benign findings, and 4 (1.8%) showed suspicious findings for which biopsy was recommended. No malignancies were detected in those patients. Furthermore, no malignancies were detected in patients whose mammogram revealed only normal fatty parenchyma or only gynecomastia. In all cases of cancer, mammography revealed visible masses. Judicious use of breast ultrasound in men improves outcome. Our data suggest that targeted ultrasound is of limited value in symptomatic male patients where mammography is negative or reveals only gynecomastia and leads to unnecessary benign biopsies in these patients. When mammography reveals concerning findings, ultrasound adds positively to clinical management. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Screening Ultrasound as an Adjunct to Mammography in Women with Mammographically Dense Breasts

PubMed Central

Scheel, John R.; Lee, Janie M.; Sprague, Brian L.; Lee, Christoph I.; Lehman, Constance D.

2015-01-01

There is increasing interest in the potential benefits and harms of screening ultrasound to supplement mammographic screening of women with dense breast tissue. We review the current evidence regarding adjunctive screening breast ultrasound (US) and provide a summary for clinicians who counsel patients with dense breasts. We conducted a comprehensive literature review of published clinical trials and observational cohort studies assessing the efficacy of screening handheld US (HHUS) and automated breast US (ABUS) to supplement mammography among women with dense breasts. From a total of 189 peer-reviewed publications on the performance of screening US, 12 studies were relevant to our analysis. The reporting of breast cancer risk factors varied across studies; however, the study populations tended to be at greater than average risk for developing breast cancer. There is consistent evidence that adjunctive screening US detects more invasive cancers compared to mammography alone, but there is currently no evidence of associated long-term breast cancer mortality reduction. The studies also collectively found that US was associated with an additional 11.7–106.6 biopsies/1,000 examinations (Median 52.2), and detected an additional 0.3–7.7 cancers/1,000 examinations (Median 4.2). The associated number of unnecessary breast biopsies resulting from adjunct US screening exceeds that observed with screening mammography alone by approximately 5-fold. Adjunctive screening with ultrasound should also be considered in the context of screening mammography. It is important for clinicians to be aware that improvements in cancer detection in mammographically dense breasts have been achieved with the transition from film to digital mammography, reducing a limitation of film mammography. Clinicians should discuss breast density as one of several important breast cancer risk factors, consider the potential harms of adjunctive screening, and arrive at a shared decision consistent with each woman’s preferences and values. PMID:24959654

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.

Breast screening: What can the interval cancer review teach us? Are we perhaps being a bit too hard on ourselves?

PubMed

Lekanidi, Katerina; Dilks, Phil; Suaris, Tamara; Kennett, Steffan; Purushothaman, Hema

2017-09-01

The aim of this study was to determine the features that make interval cancers apparent on the preceding screening mammogram and determine whether changes in the ways of performing the interval cancer review will affect the true interval cancer rate. This study was approved by the clinical governance committee. Mammograms of women diagnosed with an interval cancer were included in the study if they had been allocated to either the "suspicious signs" group or "subtle signs" group, during the historic interval cancer review. Three radiologists, individually and blinded to the site of interval cancer, reviewed the mammograms and documented the presence, site, characteristics and classification of any abnormality. Findings were compared with the appearances of the abnormality at the site of subsequent cancer development by a different breast radiologist. The chi-squared test was used in the analysis of the results, seeking associations between recall concordance and cancer mammographic or histological characteristics. 111/590 interval cancers fulfilled the study inclusion criteria. In 17% of the cases none of the readers identified the relevant abnormality on the screening mammogram. 1/3 readers identified the relevant lesion in 22% of the cases, 2/3 readers in 28% of cases and all 3 readers in 33% of cases. The commonest unanimously recalled abnormality was microcalcification and the most challenging mammographic abnormality to detect was asymmetric density. We did not find any statistically significant association between recall concordance and time to interval cancer, position of lesion in the breast, breast density or cancer grade. Even the simple step of performing an independent blinded review of interval cancers reduces the rate of interval cancers classified as missed by up to 39%. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Vitamin D and Mammographic Findings.

PubMed

Riedel, J; Straub, L; Wissing, J; Artmann, A; Schmidmayr, M; Kiechle, M; Seifert-Klauss, V R

2016-05-01

Introduction: Pleiotropic immune-modulatory and anti-proliferative effects of vitamin D and hopes to stop cancerogenesis have led to an increased interest in possible reduction of breast cancer with higher vitamin D levels. Mammographic density is an established risk factor for breast cancer risk, and its association with serum vitamin D is complex, as recent studies have shown. Patients and Methods: In this cross-sectional study, 1103 participants were recruited in the breast diagnostic unit of the Klinikum rechts der Isar, TU Munich. A standardised questionnaire and blood samples for 25-OH-vitamin D were taken on the day of mammography. Histologic results of biopsies in suspicious mammographies were documented. Results: In the 1090 data-sets analysed, vitamin D-deficiency was common among women under 40. Highest vitamin D values were observed in participants aged 60-69 years, but average values for all age cohorts were below 20 ng/ml of vitamin D. 15.6 % of all participants had very low vitamin D values (< 10 ng/ml), 51.3 % were vitamin D-deficient (10-19 ng/ml) and only 5.7 % were above 30 ng/ml, i.e. showed sufficient vitamin D. Patients with malignant results had vitamin D < 10 ng/ml more often (16.9 %; p = 0.61), and only 3.4 % in this group had sufficient vitamin D supply (> 30 ng/ml). There were no significant differences in vitamin D-levels between density groups according to the American College of Radiology (ACR) criteria. Conclusion: Vitamin D values were lower than in comparable US women. Up to now, there is no direct clinical evidence for a relationship between the risk for breast cancer and a specific vitamin D value.

A review of automatic mass detection and segmentation in mammographic images.

PubMed

Oliver, Arnau; Freixenet, Jordi; Martí, Joan; Pérez, Elsa; Pont, Josep; Denton, Erika R E; Zwiggelaar, Reyer

2010-04-01

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis. Copyright 2009 Elsevier B.V. All rights reserved.

A method to determine the mammographic regions that show early changes due to the development of breast cancer

NASA Astrophysics Data System (ADS)

Karemore, Gopal; Nielsen, Mads; Karssemeijer, Nico; Brandt, Sami S.

2014-11-01

It is well understood nowadays that changes in the mammographic parenchymal pattern are an indicator of a risk of breast cancer and we have developed a statistical method that estimates the mammogram regions where the parenchymal changes, due to breast cancer, occur. This region of interest is computed from a score map by utilising the anatomical breast coordinate system developed in our previous work. The method also makes an automatic scale selection to avoid overfitting while the region estimates are computed by a nested cross-validation scheme. In this way, it is possible to recover those mammogram regions that show a significant difference in classification scores between the cancer and the control group. Our experiments suggested that the most significant mammogram region is the region behind the nipple and that can be justified by previous findings from other research groups. This result was conducted on the basis of the cross-validation experiments on independent training, validation and testing sets from the case-control study of 490 women, of which 245 women were diagnosed with breast cancer within a period of 2-4 years after the baseline mammograms. We additionally generalised the estimated region to another, mini-MIAS study and showed that the transferred region estimate gives at least a similar classification result when compared to the case where the whole breast region is used. In all, by following our method, one most likely improves both preclinical and follow-up breast cancer screening, but a larger study population will be required to test this hypothesis.

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

PubMed

Kashyap, Kanchan L; Bajpai, Manish K; Khanna, Pritee; Giakos, George

2018-01-01

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function. Copyright © 2017 John Wiley & Sons, Ltd.

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.

An iterative three-dimensional electron density imaging algorithm using uncollimated compton scattered x rays from a polyenergetic primary pencil beam.

PubMed

Van Uytven, Eric; Pistorius, Stephen; Gordon, Richard

2007-01-01

X-ray film-screen mammography is currently the gold standard for detecting breast cancer. However, one disadvantage is that it projects a three-dimensional (3D) object onto a two-dimensional (2D) image, reducing contrast between small lesions and layers of normal tissue. Another limitation is its reduced sensitivity in women with mammographically dense breasts. Computed tomography (CT) produces a 3D image yet has had a limited role in mammography due to its relatively high dose, low resolution, and low contrast. As a first step towards implementing quantitative 3D mammography, which may improve the ability to detect and specify breast tumors, we have developed an analytical technique that can use Compton scatter to obtain 3D information of an object from a single projection. Imaging material with a pencil beam of polychromatic x rays produces a characteristic scattered photon spectrum at each point on the detector plane. A comparable distribution may be calculated using a known incident x-ray energy spectrum, beam shape, and an initial estimate of the object's 3D mass attenuation and electron density. Our iterative minimization algorithm changes the initially arbitrary electron density voxel matrix to reduce regular differences between the analytically predicted and experimentally measured spectra at each point on the detector plane. The simulated electron density converges to that of the object as the differences are minimized. The reconstruction algorithm has been validated using simulated data produced by the EGSnrc Monte Carlo code system. We applied the imaging algorithm to a cylindrically symmetric breast tissue phantom containing multiple inhomogeneities. A preliminary ROC analysis scores greater than 0.96, which indicate that under the described simplifying conditions, this approach shows promise in identifying and localizing inhomogeneities which simulate 0.5 mm calcifications with an image voxel resolution of 0.25 cm and at a dose comparable to mammography.

The use of lower resolution viewing devices for mammographic interpretation: implications for education and training.

PubMed

Chen, Yan; James, Jonathan J; Turnbull, Anne E; Gale, Alastair G

2015-10-01

To establish whether lower resolution, lower cost viewing devices have the potential to deliver mammographic interpretation training. On three occasions over eight months, fourteen consultant radiologists and reporting radiographers read forty challenging digital mammography screening cases on three different displays: a digital mammography workstation, a standard LCD monitor, and a smartphone. Standard image manipulation software was available for use on all three devices. Receiver operating characteristic (ROC) analysis and ANOVA (Analysis of Variance) were used to determine the significance of differences in performance between the viewing devices with/without the application of image manipulation software. The effect of reader's experience was also assessed. Performance was significantly higher (p < .05) on the mammography workstation compared to the other two viewing devices. When image manipulation software was applied to images viewed on the standard LCD monitor, performance improved to mirror levels seen on the mammography workstation with no significant difference between the two. Image interpretation on the smartphone was uniformly poor. Film reader experience had no significant effect on performance across all three viewing devices. Lower resolution standard LCD monitors combined with appropriate image manipulation software are capable of displaying mammographic pathology, and are potentially suitable for delivering mammographic interpretation training. • This study investigates potential devices for training in mammography interpretation. • Lower resolution standard LCD monitors are potentially suitable for mammographic interpretation training. • The effect of image manipulation tools on mammography workstation viewing is insignificant. • Reader experience had no significant effect on performance in all viewing devices. • Smart phones are not suitable for displaying mammograms.

The influence of mammographic technologists on radiologists’ ability to interpret screening mammograms in community practice

PubMed Central

Henderson, Louise M.; Benefield, Thad; Marsh, Mary W.; Schroeder, Bruce F.; Durham, Danielle; Yankaskas, Bonnie C.; Bowling, J. Michael

2014-01-01

Purpose To determine whether the mammographic technologist has an effect on the radiologists’ interpretative performance of screening mammography in community practice. Materials and Methods In this institutional review board approved retrospective cohort study, we included Carolina Mammography Registry (CMR) data from 372 radiologists and 356 mammographic technologists from 1994 to 2009 who performed 1,003,276 screening mammograms. Measures of interpretative performance (recall rate, sensitivity, specificity, positive predictive value (PPV1), and cancer detection rate (CDR)) were ascertained prospectively with cancer outcomes collected from the state cancer registry and pathology reports. To determine if the mammographic technologist influenced the radiologists’ performance, we employed mixed effects logistic regression models, including a radiologist-specific random effect and taking into account the clustering of examinations across women, separately for screen-film mammography (SFM) and full field digital mammography (FFDM). Results Of the 356 mammographic technologists included, 343 performed 889,347 SFM examinations and 51 performed 113,929 FFDM examinations, and 38 performed both SFM and FFDM. A total of 4,328 cancers were reported for SFM and 564 cancers for FFDM. The technologists had a statistically significant effect on the radiologists’ recall rate, sensitivity, specificity and CDR for both SFM and FFDM (p-values<0.01). For PPV1, variability by technologist was observed for SFM (p-value<0.0001) but not for FFDM (p-value=0.088). Conclusion The interpretative performance of radiologists in screening mammography varies substantially by the technologist performing the examination. Additional studies should aim to identify technologist characteristics that may explain this variation. PMID:25435185

Attenuation characteristics of fiberoptic plates for digital mammography and other X-ray imaging applications.

PubMed

Vedantham, S; Karellas, A; Suryanarayanan, S

2003-01-01

Spatially coherent fiberoptic plates are important components of some charge-coupled device (CCD)-based x-ray imaging systems. These plates efficiently transmit scintillations from the phosphor, and also filter out x-rays not absorbed by the phosphor, thus protecting the CCD from direct x-ray interaction. The thickness of the fiberoptic plate and the CCD package present a significant challenge in the design of a digital x-ray cassette capable of insertion into the existing film-screen cassette holders of digital mammography systems. This study was performed with an aim to optimize fiberoptic plate thickness. Attenuation measurements were performed on nine fiberoptic plates varying in material composition that exhibit desirable optical characteristics such as good coupling efficiency. Mammographic spectra from a clinical mammographic system and an Americium-241 (Am-241) source (59.54 KeV) were used. The spectra were recorded with a high-resolution cadmium zinc telluride (CZT)-based spectrometer and corrected for dead time and pile-up. The linear attenuation coefficients varied by a factor of 3 in the set of tested fiberoptic plates at both mammographic energies and 59.54 keV. Our results suggest that a 3-mm thick high-absorption plate might provide adequate for shielding at mammographic energies. A thickness of 2-mm is feasible for mammographic applications with further optimization of the fiberoptic plate composition by incorporating non-scintillating, high-atomic number material. This would allow more space for cooling components of the cassette and for a more compact device, which is critical for clinical implementation of the technology.

Mammographic screening practices among Chinese-Australian women.

PubMed

Kwok, Cannas; Fethney, Judith; White, Kate

2012-03-01

To report mammographic screening practice among Chinese-Australian women, and to examine the relationship between demographic characteristics, acculturation factors (English proficiency and length of stay in Australia), cultural beliefs, and having a mammogram as recommended. Cross-sectional and descriptive. The study was conducted in 2009 in Sydney, Australia. Of 988 Chinese-Australian women over 18 years of age invited to participate in the study, 785 (79%) completed and returned the questionnaire. Of these women, 320 (40.8%) were in the target age range of 50 to 69 years. The Chinese Breast Cancer Screening Beliefs Questionnaire (CBCSB) was used as a data collection instrument. Analysis included descriptive statistics, bivariate analysis using chi-square and t tests, and logistic regression. Of the 320 women in the targeted age range of 50 to 69 years, 238 (74.4%) had a mammogram as recommended biannually. Being married-de facto, in the 60 to 69 age group, and speaking Cantonese at home were positively associated with women's mammographic screening practice. However, no statistically significant differences in acculturation factors and having a mammogram as recommended were found. In terms of CBCSB score, women who had mammograms as recommended had more positive attitudes toward health checkups and perceived fewer barriers to mammographic screening. Effort should be focused on specific subgroups of Chinese-Australian women in order to fully understand the barriers involved in participating in mammographic screening. Nurses can use the findings from the present study to design culturally sensitive breast cancer screening programs to encourage women's participation in mammography. © 2011 Sigma Theta Tau International.

Afimoxifene in Reducing the Risk of Breast Cancer in Women With Mammographically Dense Breast | Division of Cancer Prevention

Cancer.gov

This randomized phase II trial studies how well afimoxifene works in reducing the risk of breast cancer in women with mammographically dense breast. Estrogen can cause the growth of breast cancer cells. Hormone therapy using afimoxifene may fight breast cancer by blocking the use of estrogen by the tumor cells. |

Influence of scatter reduction method and monochromatic beams on image quality and dose in mammography.

PubMed

Moeckli, Raphaël; Verdun, Francis R; Fiedler, Stefan; Pachoud, Marc; Bulling, Shelley; Schnyder, Pierre; Valley, Jean-François

2003-12-01

In mammography, the image contrast and dose delivered to the patient are determined by the x-ray spectrum and the scatter to primary ratio S/P. Thus the quality of the mammographic procedure is highly dependent on the choice of anode and filter material and on the method used to reduce the amount of scattered radiation reaching the detector. Synchrotron radiation is a useful tool to study the effect of beam energy on the optimization of the mammographic process because it delivers a high flux of monochromatic photons. Moreover, because the beam is naturally flat collimated in one direction, a slot can be used instead of a grid for scatter reduction. We have measured the ratio S/P and the transmission factors for grids and slots for monoenergetic synchrotron radiation. In this way the effect of beam energy and scatter rejection method were separated, and their respective importance for image quality and dose analyzed. Our results show that conventional mammographic spectra are not far from optimum and that the use of a slot instead of a grid has an important effect on the optimization of the mammographic process. We propose a simple numerical model to quantify this effect.

Economic savings and costs of periodic mammographic screening in the workplace.

PubMed

Griffiths, R I; McGrath, M M; Vogel, V G

1996-03-01

This article discusses the costs and benefits of mammographic screening in the workplace. The cost of mammography itself and of diagnostic work-up are two of the largest costs involved. Therefore, the most efficient approach to providing mammography depends on the number of employees receiving mammography; and the diagnostic accuracy of mammography and underlying incidence of breast cancer in the screened population strongly influence the number of suspicious mammograms that are not associated with breast cancer. The health benefit of mammographic screening is due to reduced mortality and morbidity through early detection and more effective treatment, which may also result in economic savings if early-stage cancer is less expensive to treat. However, the total lifetime cost of treating early-stage cancer may be greater than treating late-stage cancer because of improved survival of early-stage patients. Thus, although periodic mammographic screening is not likely to result in overall economic savings, in many populations of working-age women, especially those with identifiable risk factors, screening is cost-effective because the expenditure required to save a year of life through early detection of breast cancer is low compared to other types of health services for which employers commonly pay.

Mammographic x-ray unit kilovoltage test tool based on k-edge absorption effect.

PubMed

Napolitano, Mary E; Trueblood, Jon H; Hertel, Nolan E; David, George

2002-09-01

A simple tool to determine the peak kilovoltage (kVp) of a mammographic x-ray unit has been designed. Tool design is based on comparing the effect of k-edge discontinuity of the attenuation coefficient for a series of element filters. Compatibility with the mammography accreditation phantom (MAP) to obtain a single quality control film is a second design objective. When the attenuation of a series of sequential elements is studied simultaneously, differences in the absorption characteristics due to the k-edge discontinuities are more evident. Specifically, when the incident photon energy is higher than the k-edge energy of a number of the elements and lower than the remainder, an inflection may be seen in the resulting attenuation data. The maximum energy of the incident photon spectra may be determined based on this inflection point for a series of element filters. Monte Carlo photon transport analysis was used to estimate the photon transmission probabilities for each of the sequential k-edge filter elements. The photon transmission corresponds directly to optical density recorded on mammographic x-ray film. To observe the inflection, the element filters chosen must have k-edge energies that span a range greater than the expected range of the end point energies to be determined. For the design, incident x-ray spectra ranging from 25 to 40 kVp were assumed to be from a molybdenum target. Over this range, the k-edge energy changes by approximately 1.5 keV between sequential elements. For this design 21 elements spanning an energy range from 20 to 50 keV were chosen. Optimum filter element thicknesses were calculated to maximize attenuation differences at the k-edge while maintaining optical densities between 0.10 and 3.00. Calculated relative transmission data show that the kVp could be determined to within +/-1 kV. To obtain experimental data, a phantom was constructed containing 21 different elements placed in an acrylic holder. MAP images were used to determine appropriate exposure techniques for a series of end point energies from 25 to 35 kVp. The average difference between the kVp determination and the calibrated dial setting was 0.8 and 1.0 kV for a Senographe 600 T and a Senographe DMR, respectively. Since the k-edge absorption energies of the filter materials are well known, independent calibration or a series of calibration curves is not required.

Nodular Fasciitis in the Axillary Tail of the Breast

PubMed Central

Samardzic, Dejan; Chetlen, Alison; Malysz, Jozef

2014-01-01

Nodular fasciitis is a benign proliferation of myofibroblasts which presents clinically as a rapidly growing mass with nonspecific features on imaging and high cellular activity on histopathology. Nodular fasciitis can be mistaken for malignant fibrous lesions such as soft tissue sarcoma or breast carcinoma when located within breast tissue. This presents a problem for appropriate treatment planning as the natural history of nodular fasciitis is spontaneous regression. We present the mammographic, sonographic, computed tomography, and histopathologic characteristics of nodular fasciitis in a 68 year female initially presenting with a rapidly enlarging right axillary mass. PMID:25426226

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.

Feasibility of telemammography as biomedical application for breast imaging

NASA Astrophysics Data System (ADS)

Beckerman, Barbara G.; Batsell, Stephen G.; MacIntyre, Lawrence P.; Sarraf, Hamed S.; Gleason, Shaun S.; Schnall, Mitchell D.

1999-07-01

Mammographic screening is an important tool in the early detection of breast cancer. The migration of mammography from the current mode of x-ray mammography using a film screen image detector and display to a digital technology provides an opportunity to improve access and performance of breast cancer screening. The sheer size and volume of the typical screening exam, the need to have previous screening data readily available, and the need to view other breast imaging data together to provide a common consensus and to plan treatment, make telemammography an ideal application for breast imaging. For telemammography to be a viable option, it must overcome the technical challenges related to transmission, archiving, management, processing and retrieval of large data sets. Researchers from the University of Pennsylvania, the University of Chicago and Lockheed Martin Energy Systems/Oak Ridge National Laboratory have developed a framework for transmission of large-scale medical images over high-speed networks, leveraged existing high-speed networks between research and medical facilities; tested the feasibility of point-to-point transmission of mammographic images in a near-real time environment; evaluated network performance and transmission scenarios; and investigated the impact of image preprocessing on an experimental computer-aided diagnosis system. Results of the initial study are reported here.

Modelling breast cancer tumour growth for a stable disease population.

PubMed

Isheden, Gabriel; Humphreys, Keith

2017-01-01

Statistical models of breast cancer tumour progression have been used to further our knowledge of the natural history of breast cancer, to evaluate mammography screening in terms of mortality, to estimate overdiagnosis, and to estimate the impact of lead-time bias when comparing survival times between screen detected cancers and cancers found outside of screening programs. Multi-state Markov models have been widely used, but several research groups have proposed other modelling frameworks based on specifying an underlying biological continuous tumour growth process. These continuous models offer some advantages over multi-state models and have been used, for example, to quantify screening sensitivity in terms of mammographic density, and to quantify the effect of body size covariates on tumour growth and time to symptomatic detection. As of yet, however, the continuous tumour growth models are not sufficiently developed and require extensive computing to obtain parameter estimates. In this article, we provide a detailed description of the underlying assumptions of the continuous tumour growth model, derive new theoretical results for the model, and show how these results may help the development of this modelling framework. In illustrating the approach, we develop a model for mammography screening sensitivity, using a sample of 1901 post-menopausal women diagnosed with invasive breast cancer.

Mammographic features of isolated tuberculous mastitis.

PubMed

Al-Marri, Mohammed R; Aref, Essam; Omar, Ahamed J

2005-04-01

To present the mammography findings in 8 patients with tuberculosis (TB) of the breast, with a review of the literature. This study is a retrospective data collection. Each chart with confirmed breast TB based on bacteriology or pathologic findings was analyzed for clinical presentation, gender, nationality, demographic data, prior history of TB, investigation, management, mammographic findings and ultrasound, when available. Mammograms were reviewed by 2 consultant radiologists without knowing the previous diagnosis or the nature of the study. The study was carried out at The State Tuberculosis Registry and Radiology Department, Hamad General Hospital, State of Qatar, from 1990 to 2002. Out of 13 females with TB mastitis, only 8 cases had mammograms preoperatively. The incidence of breast TB in Qatar is rare (1/1000 mammograms per year). Three types of TB mastitis were identified in our study; the nodular (50%), the diffuse (37.5%) of which 77% were limited to one sector of the breast and the sclerosing (12.5%) mastitis. Three patients (43%) were reported as carcinoma. Although mammography identified 3 types of TB, it was not helpful in differentiating TB from carcinoma of the breast. However, the careful evaluation of the degree of density and trabecular thickening of the mass in relation to it size might reduce the number of false positive cases of carcinoma diagnosed with mammograms. Biopsy specimen remains the best diagnostic tool in TB mastitis.

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.

Predictors of invasive breast cancer in mammographically detected microcalcification in patients with a core biopsy diagnosis of flat epithelial atypia, atypical ductal hyperplasia or ductal carcinoma in situ and recommendations for a selective approach to sentinel lymph node biopsy.

PubMed

Catteau, Xavier; Simon, Philippe; Noël, Jean-Christophe

2012-04-15

15±30% of malignancies detected through screening programs are ductal carcinoma in situ (DCIS), and the majority of DCIS cases present in the form of mammographic microcalcification. This study was performed in order to determine the value of features in predicting invasive disease in patients with mammographic calcification and to help determine which patients (with, Core Needle Biopsy-diagnosed DCIS) are the most appropriate candidates for Sentinel Lymph Node (SLN) biopsy. The original aspect of this study was to select patients with mammographic microcalcification but without an associated mass. The factor that we identified to be associated with invasive disease at final surgical excision was the presence of necrosis at core histology. SLN biopsy or complete axillary lymph node dissection was performed in 22 (40%) patients of whom only one (4.5%) had a micrometastasis. Further larger studies are needed to see if it would be interesting to propose a SLN biopsy in case of necrosis on CNB-diagnosed DCIS with microcalcifications but not associated with a mass. Copyright © 2012 Elsevier GmbH. All rights reserved.

Comparison of two portable solid state detectors with an improved collimation and alignment device for mammographic x-ray spectroscopy.

PubMed

Bottigli, U; Golosio, B; Masala, G L; Oliva, P; Stumbo, S; Delogu, P; Fantacci, M E; Abbene, L; Fauci, F; Raso, G

2006-09-01

We describe a portable system for mammographic x-ray spectroscopy, based on a 2 X 2 X 1 mm3 cadmium telluride (CdTe) solid state detector, that is greatly improved over a similar system based on a 3 X 3 X 2 mm3 cadmium zinc telluride (CZT) solid state detector evaluated in an earlier work. The CdTe system utilized new pinhole collimators and an alignment device that facilitated measurement of mammographic x-ray spectra. Mammographic x-ray spectra acquired by each system were comparable. Half value layer measurements obtained using an ion chamber agreed closely with those derived from the x-ray spectra measured by either detector. The faster electronics and other features of the CdTe detector allowed its use with a larger pinhole collimator than could be used with the CZT detector. Additionally, the improved pinhole collimator and alignment features of the apparatus permitted much more rapid setup for acquisition of x-ray spectra than was possible on the system described in the earlier work. These improvements in detector technology, collimation and ease of alignment, as well as low cost, make this apparatus attractive as a tool for both laboratory research and advanced mammography quality control.

Mammographic screening: measurement of the cost in a population based programme in Victoria, Australia.

PubMed

Hurley, S F; Livingston, P M; Thane, N; Quang, L

1994-08-01

To estimate the cost per woman participating in a mammographic screening programme, and to describe methods for measuring costs. Expenditure, resource usage, and throughput were monitored over a 12 month period. Unit costs for each phase of the screening process were estimated and linked with the probabilities of each screening outcome to obtain the cost per woman screened and the cost per breast cancer detected. A pilot, population based Australian programme offering free two-view mammographic screening. A total of 5986 women aged 50-69 years who lived in the target area, were listed on the electoral roll, had no previous breast cancer, and attended the programme. Unit costs for recruitment, screening, and recall mammography were $17.54, $60.04, and $175.54, respectively. The costs of clinical assessment for women with subsequent clear, benign, malignant (palpable), and malignant (impalpable) diagnoses were $173.71, $527.29, $436.62, and $567.22, respectively. The cost per woman screened was $117.70, and the cost per breast cancer detected was $11,550. The cost per woman screened is a key variable in assessment of the cost effectiveness of mammographic screening, and is likely to vary between health care settings. Its measurement is justified if decisions about health care services are to be based on cost effectiveness criteria.

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.

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.

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

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.

Optimum processing of mammographic film.

PubMed

Sprawls, P; Kitts, E L

1996-03-01

Underprocessing of mammographic film can result in reduced contrast and visibility of breast structures and an unnecessary increase in radiation dose to the patient. Underprocessing can be caused by physical factors (low developer temperature, inadequate development time, insufficient developer agitation) or chemical factors (developer not optimized for film type; overdiluted, underreplenished, contaminated, or frequently changed developer). Conventional quality control programs are designed to produce consistent processing but do not address the issue of optimum processing. Optimum processing is defined as the level of processing that produces the film performance characteristics (contrast and sensitivity) specified by the film manufacturer. Optimum processing of mammographic film can be achieved by following a two-step protocol. The first step is to set up the processing conditions according to recommendations from the film and developer chemistry manufacturers. The second step is to verify the processing results by comparing them with sensitometric data provided by the film manufacturer.

Audit feedback on reading performance of screening mammograms: An international comparison.

PubMed

Hofvind, S; Bennett, R L; Brisson, J; Lee, W; Pelletier, E; Flugelman, A; Geller, B

2016-09-01

Providing feedback to mammography radiologists and facilities may improve interpretive performance. We conducted a web-based survey to investigate how and why such feedback is undertaken and used in mammographic screening programmes. The survey was sent to representatives in 30 International Cancer Screening Network member countries where mammographic screening is offered. Seventeen programmes in 14 countries responded to the survey. Audit feedback was aimed at readers in 14 programmes, and facilities in 12 programmes. Monitoring quality assurance was the most common purpose of audit feedback. Screening volume, recall rate, and rate of screen-detected cancers were typically reported performance measures. Audit reports were commonly provided annually, but more frequently when target guidelines were not reached. The purpose, target audience, performance measures included, form and frequency of the audit feedback varied amongst mammographic screening programmes. These variations may provide a basis for those developing and improving such programmes. © The Author(s) 2016.

Mammographic density estimation with automated volumetric breast density measurement.

PubMed

Ko, Su Yeon; Kim, Eun-Kyung; Kim, Min Jung; Moon, Hee Jung

2014-01-01

To compare automated volumetric breast density measurement (VBDM) with radiologists' evaluations based on the Breast Imaging Reporting and Data System (BI-RADS), and to identify the factors associated with technical failure of VBDM. In this study, 1129 women aged 19-82 years who underwent mammography from December 2011 to January 2012 were included. Breast density evaluations by radiologists based on BI-RADS and by VBDM (Volpara Version 1.5.1) were compared. The agreement in interpreting breast density between radiologists and VBDM was determined based on four density grades (D1, D2, D3, and D4) and a binary classification of fatty (D1-2) vs. dense (D3-4) breast using kappa statistics. The association between technical failure of VBDM and patient age, total breast volume, fibroglandular tissue volume, history of partial mastectomy, the frequency of mass > 3 cm, and breast density was analyzed. The agreement between breast density evaluations by radiologists and VBDM was fair (k value = 0.26) when the four density grades (D1/D2/D3/D4) were used and moderate (k value = 0.47) for the binary classification (D1-2/D3-4). Twenty-seven women (2.4%) showed failure of VBDM. Small total breast volume, history of partial mastectomy, and high breast density were significantly associated with technical failure of VBDM (p = 0.001 to 0.015). There is fair or moderate agreement in breast density evaluation between radiologists and VBDM. Technical failure of VBDM may be related to small total breast volume, a history of partial mastectomy, and high breast density.

Using tumor phenotype, histological tumor distribution, and mammographic appearance to explain the survival differences between screen-detected and clinically detected breast cancers.

PubMed

Chuang, Shu-Lin; Chen, Sam Li-Sheng; Yu, Cheng-Ping; Chang, King-Jen; Yen, Amy Ming-Fang; Chiu, Sherry Yueh-Hsia; Fann, Jean Ching-Yuan; Tabár, László; Stephen, Duffy W; Smith, Robert A; Chen, Hsiu-Hsi

2014-08-01

In the era of mass screening for breast cancer with mammography, it has been noted that conventional tumor attributes and mammographic appearance are insufficient to account for the better prognosis of screen-detected tumors. Such prognostication may require additional updated pathological information regarding tumor phenotype (e.g., basal status) and histological tumor distribution (focality). We investigated this hypothesis using a Bayesian approach to analyze breast cancer data from Dalarna County, Sweden. We used data for tumors diagnosed in the Swedish Two-County Trial and early service screening period, 1977-1995, and from the mature service screening period, 1996-1998. In the early period of mammographic screening (1977-1995), the crude hazard ratio (HR) of breast cancer death for screen-detected cases compared with symptomatic ones was 0.22 (95% CI: 0.17-0.29) compared with 0.53 (95% CI: 0.34-0.76) when adjusted for conventional tumor attributes only. Using the data from the mature service screening period, 1996-1998, the HR was 0.23 (95% CI: 0.08-0.44) unadjusted and 0.71 (95% CI: 0.26-1.47) after adjustment for tumor phenotype, mammographic appearance, histological tumor distribution, and conventional tumor attributes. The area under the ROC curve (AUC) for the prediction of breast cancer deaths using these variables without the detection mode was 0.82, only slightly less than that observed when additionally including the detection mode (AUC=0.83). Using Freedman statistics, conventional tumor attributes and mammographic appearances explained 58% (95% CI: 57.5-58.6%) of the difference of breast cancer survival between the screen-detected and the clinically detected breast cancers, whereas the corresponding figure was increased to 77% (95% CI: 75.6-77.6%) when adding the two information on tumor phenotype and histological tumor distribution. The results indicated that conventional tumor attributes and mammographic appearance are not sufficient to be interim markers for explaining the survival difference between screen-detected and clinically detected cancers in the era marked by the widespread use of mammography. Additional information on tumor phenotype and histological distribution may be added as effective interim markers for explaining the benefit of the early detection of breast cancer with mammography. © 2014 APMIS. Published by John Wiley & Sons Ltd.

An alignment method for mammographic X-ray spectroscopy under clinical conditions.

PubMed

Miyajima, S; Imagawa, K; Matsumoto, M

2002-09-01

This paper describes an alignment method for mammographic X-ray spectroscopy under clinical conditions. A pinhole, a fluorescent screen, a laser device and the case for a detector are used for alignment of the focal spot, a collimator and a detector. The method determines the line between the focal spot and the point of interest in an X-ray field radiographically. The method allows alignment for both central axis and off-axis directions.

Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

PubMed

Li, Zhiming; Yu, Lan; Wang, Xin; Yu, Haiyang; Gao, Yuanxiang; Ren, Yande; Wang, Gang; Zhou, Xiaoming

2017-11-09

The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Using component technologies for web based wavelet enhanced mammographic image visualization.

PubMed

Sakellaropoulos, P; Costaridou, L; Panayiotakis, G

2000-01-01

The poor contrast detectability of mammography can be dealt with by domain specific software visualization tools. Remote desktop client access and time performance limitations of a previously reported visualization tool are addressed, aiming at more efficient visualization of mammographic image resources existing in web or PACS image servers. This effort is also motivated by the fact that at present, web browsers do not support domain-specific medical image visualization. To deal with desktop client access the tool was redesigned by exploring component technologies, enabling the integration of stand alone domain specific mammographic image functionality in a web browsing environment (web adaptation). The integration method is based on ActiveX Document Server technology. ActiveX Document is a part of Object Linking and Embedding (OLE) extensible systems object technology, offering new services in existing applications. The standard DICOM 3.0 part 10 compatible image-format specification Papyrus 3.0 is supported, in addition to standard digitization formats such as TIFF. The visualization functionality of the tool has been enhanced by including a fast wavelet transform implementation, which allows for real time wavelet based contrast enhancement and denoising operations. Initial use of the tool with mammograms of various breast structures demonstrated its potential in improving visualization of diagnostic mammographic features. Web adaptation and real time wavelet processing enhance the potential of the previously reported tool in remote diagnosis and education in mammography.

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.

The human mammary gland as a target for isoflavones: how does the relation vary in individuals with different ethnicity?

PubMed

Maskarinec, Gertraud

2013-05-01

Based on observational studies, it appears that soy food consumption provides protection against breast cancer primarily in Asian but not in Western populations. Given the problems in examining the effects of isoflavones directly in the human mammary gland, this review describes epidemiologic studies that investigated the association with biomarkers reflecting hormonal activity of isoflavones, in particular sex steroid levels, mammographic densities, nipple aspirate fluid, and tissue specimens from biopsies or surgeries. Three possible mechanisms that may be responsible for ethnic-specific health effects from these compounds are discussed: genetic variation in metabolic enzymes, timing of exposure, and intestinal metabolism by microbiota. Only a limited number of comparative studies and even fewer nutritional interventions have examined effects and addressed differences in biomarkers between Asian and Western populations. Investigations that looked at estrogens and mammographic densities as endpoints observed some associations in Asian women that were not seen in Caucasians. On the other hand, the low rate of nipple aspirate fluid production and a lack of breast tissue studies make it impossible to evaluate effects of isoflavones on these biomarkers in Asian women. Based on the current evidence, it appears likely that the timing of exposure is the most important determinant of beneficial health effects from soy foods. This may be the result of gut microbiota, which colonize the intestine during childhood and facilitates the hydrolysis of glycosides and the formation of equol from dadzein, a pathway that may result in beneficial health effects. The current evidence is insufficient to answer the question whether women of diverse ethnic groups experience distinct effects from soy isoflavones in breast tissue, but as knowledge about the role of early life nutrition and the development of gut microbiota increases, the potential for diverse metabolic pathways of isoflavones in individuals with different ethnic backgrounds and dietary exposures may be clarified. Georg Thieme Verlag KG Stuttgart · New York.

Mammographic Density Phenotypes and Risk of Breast Cancer: A Meta-analysis

PubMed Central

Graff, Rebecca E.; Ursin, Giske; dos Santos Silva, Isabel; McCormack, Valerie; Baglietto, Laura; Vachon, Celine; Bakker, Marije F.; Giles, Graham G.; Chia, Kee Seng; Czene, Kamila; Eriksson, Louise; Hall, Per; Hartman, Mikael; Warren, Ruth M. L.; Hislop, Greg; Chiarelli, Anna M.; Hopper, John L.; Krishnan, Kavitha; Li, Jingmei; Li, Qing; Pagano, Ian; Rosner, Bernard A.; Wong, Chia Siong; Scott, Christopher; Stone, Jennifer; Maskarinec, Gertraud; Boyd, Norman F.; van Gils, Carla H.

2014-01-01

Background Fibroglandular breast tissue appears dense on mammogram, whereas fat appears nondense. It is unclear whether absolute or percentage dense area more strongly predicts breast cancer risk and whether absolute nondense area is independently associated with risk. Methods We conducted a meta-analysis of 13 case–control studies providing results from logistic regressions for associations between one standard deviation (SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects models to calculate pooled odds ratios and 95% confidence intervals (CIs). All tests were two-sided with P less than .05 considered to be statistically significant. Results Among premenopausal women (n = 1776 case patients; n = 2834 control subjects), summary odds ratios were 1.37 (95% CI = 1.29 to 1.47) for absolute dense area, 0.78 (95% CI = 0.71 to 0.86) for absolute nondense area, and 1.52 (95% CI = 1.39 to 1.66) for percentage dense area when pooling estimates adjusted for age, body mass index, and parity. Corresponding odds ratios among postmenopausal women (n = 6643 case patients; n = 11187 control subjects) were 1.38 (95% CI = 1.31 to 1.44), 0.79 (95% CI = 0.73 to 0.85), and 1.53 (95% CI = 1.44 to 1.64). After additional adjustment for absolute dense area, associations between absolute nondense area and breast cancer became attenuated or null in several studies and summary odds ratios became 0.82 (95% CI = 0.71 to 0.94; P heterogeneity = .02) for premenopausal and 0.85 (95% CI = 0.75 to 0.96; P heterogeneity < .01) for postmenopausal women. Conclusions The results suggest that percentage dense area is a stronger breast cancer risk factor than absolute dense area. Absolute nondense area was inversely associated with breast cancer risk, but it is unclear whether the association is independent of absolute dense area. PMID:24816206

Polymorphisms in a Putative Enhancer at the 10q21.2 Breast Cancer Risk Locus Regulate NRBF2 Expression

PubMed Central

Darabi, Hatef; McCue, Karen; Beesley, Jonathan; Michailidou, Kyriaki; Nord, Silje; Kar, Siddhartha; Humphreys, Keith; Thompson, Deborah; Ghoussaini, Maya; Bolla, Manjeet K.; Dennis, Joe; Wang, Qin; Canisius, Sander; Scott, Christopher G.; Apicella, Carmel; Hopper, John L.; Southey, Melissa C.; Stone, Jennifer; Broeks, Annegien; Schmidt, Marjanka K.; Scott, Rodney J.; Lophatananon, Artitaya; Muir, Kenneth; Beckmann, Matthias W.; Ekici, Arif B.; Fasching, Peter A.; Heusinger, Katharina; dos-Santos-Silva, Isabel; Peto, Julian; Tomlinson, Ian; Sawyer, Elinor J.; Burwinkel, Barbara; Marme, Frederik; Guénel, Pascal; Truong, Thérèse; Bojesen, Stig E.; Flyger, Henrik; Benitez, Javier; González-Neira, Anna; Anton-Culver, Hoda; Neuhausen, Susan L.; Arndt, Volker; Brenner, Hermann; Engel, Christoph; Meindl, Alfons; Schmutzler, Rita K.; Arnold, Norbert; Brauch, Hiltrud; Hamann, Ute; Chang-Claude, Jenny; Khan, Sofia; Nevanlinna, Heli; Ito, Hidemi; Matsuo, Keitaro; Bogdanova, Natalia V.; Dörk, Thilo; Lindblom, Annika; Margolin, Sara; Kosma, Veli-Matti; Mannermaa, Arto; Tseng, Chiu-chen; Wu, Anna H.; Floris, Giuseppe; Lambrechts, Diether; Rudolph, Anja; Peterlongo, Paolo; Radice, Paolo; Couch, Fergus J.; Vachon, Celine; Giles, Graham G.; McLean, Catriona; Milne, Roger L.; Dugué, Pierre-Antoine; Haiman, Christopher A.; Maskarinec, Gertraud; Woolcott, Christy; Henderson, Brian E.; Goldberg, Mark S.; Simard, Jacques; Teo, Soo H.; Mariapun, Shivaani; Helland, Åslaug; Haakensen, Vilde; Zheng, Wei; Beeghly-Fadiel, Alicia; Tamimi, Rulla; Jukkola-Vuorinen, Arja; Winqvist, Robert; Andrulis, Irene L.; Knight, Julia A.; Devilee, Peter; Tollenaar, Robert A.E.M.; Figueroa, Jonine; García-Closas, Montserrat; Czene, Kamila; Hooning, Maartje J.; Tilanus-Linthorst, Madeleine; Li, Jingmei; Gao, Yu-Tang; Shu, Xiao-Ou; Cox, Angela; Cross, Simon S.; Luben, Robert; Khaw, Kay-Tee; Choi, Ji-Yeob; Kang, Daehee; Hartman, Mikael; Lim, Wei Yen; Kabisch, Maria; Torres, Diana; Jakubowska, Anna; Lubinski, Jan; McKay, James; Sangrajrang, Suleeporn; Toland, Amanda E.; Yannoukakos, Drakoulis; Shen, Chen-Yang; Yu, Jyh-Cherng; Ziogas, Argyrios; Schoemaker, Minouk J.; Swerdlow, Anthony; Borresen-Dale, Anne-Lise; Kristensen, Vessela; French, Juliet D.; Edwards, Stacey L.; Dunning, Alison M.; Easton, Douglas F.; Hall, Per; Chenevix-Trench, Georgia

2015-01-01

Genome-wide association studies have identified SNPs near ZNF365 at 10q21.2 that are associated with both breast cancer risk and mammographic density. To identify the most likely causal SNPs, we fine mapped the association signal by genotyping 428 SNPs across the region in 89,050 European and 12,893 Asian case and control subjects from the Breast Cancer Association Consortium. We identified four independent sets of correlated, highly trait-associated variants (iCHAVs), three of which were located within ZNF365. The most strongly risk-associated SNP, rs10995201 in iCHAV1, showed clear evidence of association with both estrogen receptor (ER)-positive (OR = 0.85 [0.82–0.88]) and ER-negative (OR = 0.87 [0.82–0.91]) disease, and was also the SNP most strongly associated with percent mammographic density. iCHAV2 (lead SNP, chr10: 64,258,684:D) and iCHAV3 (lead SNP, rs7922449) were also associated with ER-positive (OR = 0.93 [0.91–0.95] and OR = 1.06 [1.03–1.09]) and ER-negative (OR = 0.95 [0.91–0.98] and OR = 1.08 [1.04–1.13]) disease. There was weaker evidence for iCHAV4, located 5′ of ADO, associated only with ER-positive breast cancer (OR = 0.93 [0.90–0.96]). We found 12, 17, 18, and 2 candidate causal SNPs for breast cancer in iCHAVs 1–4, respectively. Chromosome conformation capture analysis showed that iCHAV2 interacts with the ZNF365 and NRBF2 (more than 600 kb away) promoters in normal and cancerous breast epithelial cells. Luciferase assays did not identify SNPs that affect transactivation of ZNF365, but identified a protective haplotype in iCHAV2, associated with silencing of the NRBF2 promoter, implicating this gene in the etiology of breast cancer. PMID:26073781

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

Radiographic features of the limbs of juvenile and subadult loggerhead sea turtles (Caretta caretta)

PubMed Central

Valente, Ana Luisa; Marco, Ignasi; Zamora, Maria Angeles; Parga, Maria Luz; Lavín, Santiago; Alegre, Ferran; Cuenca, Rafaela

2007-01-01

This study aimed to provide the normal radiographic anatomic appearance of the limbs of the loggerhead sea turtle, Caretta caretta. Dorsopalmar and dorsoplantar radiographs were taken of the forelimbs and hindlimbs of 15 juvenile and 15 subadult loggerhead sea turtles, 17 alive and 13 dead. For comparison, computed tomographic, gross anatomic, osteologic, and histologic studies were performed on the limbs of 5 of the sea turtles. Bones from the distal part of the fore and hind flippers were seen in detail with a mammographic film–screen combination. The pectoral and pelvic girdles, superimposed by the carapace, were better seen on standard radiographs with the use of rare-earth intensifying screens. Mammographic radiographs of the manus of 5 small juvenile turtles showed active growth zones. Visualization of bone contours in the distal part of the limbs was clearer than in mammals owing to the very few superimpositions. The presence of a substantial amount of cartilage in the epiphyses produced better visibility of limb ends. We conclude that use of a mammography film–screen combination is the best way to evaluate the bony and joint structures of the limbs of sea turtles. Radiography provides reliable images for clinical purposes. Considering the low cost and logistics of this technique, it is a practical ancillary test for marine animal rehabilitation centers to use. PMID:17955906

Investigating the Association of Eye Gaze Pattern and Diagnostic Error in Mammography

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

Voisin, Sophie; Pinto, Frank M; Xu, Songhua

2013-01-01

The objective of this study was to investigate the association between eye-gaze patterns and the diagnostic accuracy of radiologists for the task of assessing the likelihood of malignancy of mammographic masses. Six radiologists (2 expert breast imagers and 4 Radiology residents of variable training) assessed the likelihood of malignancy of 40 biopsy-proven mammographic masses (20 malignant and 20 benign) on a computer monitor. Eye-gaze data were collected using a commercial remote eye-tracker. Upon reviewing each mass, the radiologists were also asked to provide their assessment regarding the probability of malignancy of the depicted mass as well as a rating regardingmore » the perceived difficulty of the diagnostic task. The collected data were analyzed using established algorithms and various quantitative metrics were extracted to characterize the recorded gaze patterns. The extracted metrics were correlated with the radiologists diagnostic decisions and perceived complexity scores. Results showed that the visual gaze pattern of radiologists varies substantially, not only depending on their experience level but also among individuals. However, some eye gaze metrics appear to correlate with diagnostic error and perceived complexity more consistently. These results suggest that although gaze patterns are generally associated with diagnostic error and the human perceived difficulty of the diagnostic task, there are substantially individual differences that are not explained simply by the experience level of the individual performing the diagnostic task.« less

Prognosis for Mammographically Occult, Early-Stage Breast Cancer Patients Treated With Breast-Conservation Therapy

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

Yang, Tzu-I. J.; Yang Qifeng; Department of Breast Surgery, Qilu Hospital, Shandong University, Jinan

2010-01-15

Purpose: To compare mammographically occult (MamOcc) and mammographically positive (MamPos) early-stage breast cancer patients treated with breast-conservation therapy (BCT), to analyze differences between the two cohorts. Methods and Materials: Our two cohorts consisted of 214 MamOcc and 2168 MamPos patients treated with BCT. Chart reviews were conducted to assess mammogram reports and method of detection. All clinical-pathologic and outcome parameters were analyzed to detect differences between the two cohorts. Results: Median follow-up was 7 years. There were no differences in final margins, T stage, nodal status, estrogen/progesterone receptor status, or 'triple-negative' status. Significant differences included younger age at diagnosis (pmore » < 0.0001), more positive family history (p = 0.0033), less HER-2+ disease (p = 0.0294), and 1{sup o} histology (p < 0.0001). At 10 years, the differences in overall survival, cause-specific survival, and distant relapse between the two groups did not differ significantly. The MamOcc cohort had more breast relapses (15% vs. 8%; p = 0.0357), but on multivariate analysis this difference was not significant (hazard ratio 1.0, 95% confidence interval 0.993-1.007, p = 0.9296). Breast relapses were mammographically occult in 32% of the MamOcc and 12% of the MamPos cohorts (p = 0.0136). Conclusions: Although our study suggests that there are clinical-pathologic variations for the MamOcc cohort vs. MamPos patients that may ultimately affect management, breast relapse after BCT was not significantly different. Breast recurrences were more often mammographically occult in the MamOcc cohort; consideration should be given to closer follow-up and alternative imaging strategies (ultrasound, breast MRI) for routine posttreatment examination. To our knowledge, this represents the largest series addressing the prognostic significance of MamOcc cancers treated with BCT.« less

Risk of Breast Cancer in Women with False-Positive Results according to Mammographic Features.

PubMed

Castells, Xavier; Torá-Rocamora, Isabel; Posso, Margarita; Román, Marta; Vernet-Tomas, Maria; Rodríguez-Arana, Ana; Domingo, Laia; Vidal, Carmen; Baré, Marisa; Ferrer, Joana; Quintana, María Jesús; Sánchez, Mar; Natal, Carmen; Espinàs, Josep A; Saladié, Francina; Sala, María

2016-08-01

Purpose To assess the risk of breast cancer in women with false-positive screening results according to radiologic classification of mammographic features. Materials and Methods Review board approval was obtained, with waiver of informed consent. This retrospective cohort study included 521 200 women aged 50-69 years who underwent screening as part of the Spanish Breast Cancer Screening Program between 1994 and 2010 and who were observed until December 2012. Cox proportional hazards regression analysis was used to estimate the age-adjusted hazard ratio (HR) of breast cancer and the 95% confidence interval (CI) in women with false-positive mammograms as compared with women with negative mammograms. Separate models were adjusted for screen-detected and interval cancers and for screen-film and digital mammography. Time without a breast cancer diagnosis was plotted by using Kaplan-Meier curves. Results When compared with women with negative mammograms, the age-adjusted HR of cancer in women with false-positive results was 1.84 (95% CI: 1.73, 1.95; P < .001). The risk was higher in women who had calcifications, whether they were (HR, 2.73; 95% CI: 2.28, 3.28; P < .001) or were not (HR, 2.24; 95% CI: 2.02, 2.48; P < .001) associated with masses. Women in whom mammographic features showed changes in subsequent false-positive results were those who had the highest risk (HR, 9.13; 95% CI: 8.28, 10.07; P < .001). Conclusion Women with false-positive results had an increased risk of breast cancer, particularly women who had calcifications at mammography. Women who had more than one examination with false-positive findings and in whom the mammographic features changed over time had a highly increased risk of breast cancer. Previous mammographic features might yield useful information for further risk-prediction models and personalized follow-up screening protocols. (©) RSNA, 2016 Online supplemental material is available for this article.

Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification

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

Drukker, Karen, E-mail: kdrukker@uchicago.edu; Giger, Maryellen L.; Li, Hui

2014-03-15

Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy. Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, “QIA alone,” (2) the three-compartment breast (3CB) composition measure—derived from the dual-energy mammography—of water, lipid, and protein thickness were assessed, “3CBmore » alone”, and (3) information from QIA and 3CB was combined, “QIA + 3CB.” Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, Bland–Altman plots, and Receiver Operating Characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the “QIA alone” method, 0.72 (0.07) for “3CB alone” method, and 0.86 (0.04) for “QIA+3CB” combined. The difference in AUC was 0.043 between “QIA + 3CB” and “QIA alone” but failed to reach statistical significance (95% confidence interval [–0.17 to + 0.26]). Conclusions: In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.« less

Increased breast density correlates with the proliferation-seeking radiotracer (99m)Tc(V)-DMSA uptake in florid epithelial hyperplasia and in mixed ductal carcinoma in situ with invasive ductal carcinoma but not in pure invasive ductal carcinoma or in mild epithelial hyperplasia.

PubMed

Papantoniou, Vassilios; Valsamaki, Pipitsa; Sotiropoulou, Evangelia; Tsaroucha, Angeliki; Tsiouris, Spyridon; Sotiropoulou, Maria; Marinopoulos, Spyridon; Kounadi, Evangelia; Karianos, Theodore; Fothiadaki, Athina; Archontaki, Aikaterini; Syrgiannis, Konstantinos; Ptohis, Nikolaos; Makris, Nikolaos; Limouris, Georgios; Antsaklis, Aris

2011-10-01

The purpose of this study was to assess the relationship of mammographic breast density (BD) and cell proliferation/focal adhesion kinase activation-seeking radiotracer technetium 99m pentavalent dimercaptosuccinic acid (99mTc(V)-DMSA) uptake in women with different breast histologies, that is, mild epithelial hyperplasia (MEH), florid epithelial hyperplasia (FEH), mixed ductal carcinoma in situ with invasive ductal carcinoma (DCIS + IDC), and pure IDC. Fifty-five women with histologically confirmed mammary pathologies were submitted preoperatively to mammography and 99mTc(V)-DMSA scintimammography. The percentage and intensity of 99mTc(V)-DMSA uptake and the percentage of BD were calculated by computer-assisted methods and compared (t-test) between the breast pathologies. In breasts with increased BD, FEH and DCIS + IDC were found. On the contrary, pure IDC and MEH were identified in breasts with significantly lower BD values. In breasts with increased 99mTc(V)-DMSA area and intensity of uptake, FEH was the main lesion found compared to all other histologies. Linear regression analysis between BD and 99mTc(V)-DMSA uptake area and intensity revealed significant coefficients of correlation (r  =  .689, p < .001 and r  =  .582, p < .001, respectively). Increased BD correlates with the presence of FEH and mixed DCIS + IDC but not with pure IDC or MEH. Its close relationship to 99mTc(V)-DMSA, which also showed an affinity to FEH, indicates that stromal microenvironment may constitute a specific substrate leading to progression to different subtypes of cancerous lesions originating from different pathways.

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

Multiscale wavelet representations for mammographic feature analysis

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu

1992-12-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

Deep learning in mammography and breast histology, an overview and future trends.

PubMed

Hamidinekoo, Azam; Denton, Erika; Rampun, Andrik; Honnor, Kate; Zwiggelaar, Reyer

2018-07-01

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Wavelet Compression of Satellite-Transmitted Digital Mammograms

NASA Technical Reports Server (NTRS)

Zheng, Yuan F.

2001-01-01

Breast cancer is one of the major causes of cancer death in women in the United States. The most effective way to treat breast cancer is to detect it at an early stage by screening patients periodically. Conventional film-screening mammography uses X-ray films which are effective in detecting early abnormalities of the breast. Direct digital mammography has the potential to improve the image quality and to take advantages of convenient storage, efficient transmission, and powerful computer-aided diagnosis, etc. One effective alternative to direct digital imaging is secondary digitization of X-ray films. This technique may not provide as high an image quality as the direct digital approach, but definitely have other advantages inherent to digital images. One of them is the usage of satellite-transmission technique for transferring digital mammograms between a remote image-acquisition site and a central image-reading site. This technique can benefit a large population of women who reside in remote areas where major screening and diagnosing facilities are not available. The NASA-Lewis Research Center (LeRC), in collaboration with the Cleveland Clinic Foundation (CCF), has begun a pilot study to investigate the application of the Advanced Communications Technology Satellite (ACTS) network to telemammography. The bandwidth of the T1 transmission is limited (1.544 Mbps) while the size of a mammographic image is huge. It takes a long time to transmit a single mammogram. For example, a mammogram of 4k by 4k pixels with 16 bits per pixel needs more than 4 minutes to transmit. Four images for a typical screening exam would take more than 16 minutes. This is too long a time period for a convenient screening. Consequently, compression is necessary for making satellite-transmission of mammographic images practically possible. The Wavelet Research Group of the Department of Electrical Engineering at The Ohio State University (OSU) participated in the LeRC-CCF collaboration by providing advanced compression technology using wavelet transform. OSU developed a time-efficient software package with various wavelets to compress a serious of mammographic images. This documents reports the result of the compression activities.

Can computer-aided diagnosis (CAD) help radiologists find mammographically missed screening cancers?

NASA Astrophysics Data System (ADS)

Nishikawa, Robert M.; Giger, Maryellen L.; Schmidt, Robert A.; Papaioannou, John

2001-06-01

We present data from a pilot observer study whose goal is design a study to test the hypothesis that computer-aided diagnosis (CAD) can improve radiologists' performance in reading screening mammograms. In a prospective evaluation of our computer detection schemes, we have analyzed over 12,000 clinical exams. Retrospective review of the negative screening mammograms for all cancer cases found an indication of the cancer in 23 of these negative cases. The computer found 54% of these in our prospective testing. We added to these cases normal exams to create a dataset of 75 cases. Four radiologists experienced in mammography read the cases and gave their BI-RADS assessment and their confidence that the patient should be called back for diagnostic mammography. They did so once reading the films only and a second time reading with the computer aid. Three radiologists had no change in area under the ROC curve (mean Az of 0.73) and one improved from 0.73 to 0.78, but this difference failed to reach statistical significance (p equals 0.23). These data are being used to plan a larger more powerful study.

Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Zhang, Jing; Lo, Joseph Y.; Kuzmiak, Cherie M.; Ghate, Sujata V.; Yoon, Sora

2014-03-01

Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that utilizes trainee models, which relate human-assessed image characteristics to interpretation error. We proposed that these models be used to identify the most difficult and therefore the most educationally useful cases for each trainee. In this study, as a next step in our research, we propose to build trainee models that utilize features that are automatically extracted from images using computer vision algorithms. To predict error, we used a logistic regression which accepts imaging features as input and returns error as output. Reader data from 3 experts and 3 trainees were used. Receiver operating characteristic analysis was applied to evaluate the proposed trainee models. Our experiments showed that, for three trainees, our models were able to predict error better than chance. This is an important step in the development of adaptive computer-aided education systems since computer-extracted features will allow for faster and more extensive search of imaging databases in order to identify the most educationally beneficial cases.

Effects of processing conditions on mammographic image quality.

PubMed

Braeuning, M P; Cooper, H W; O'Brien, S; Burns, C B; Washburn, D B; Schell, M J; Pisano, E D

1999-08-01

Any given mammographic film will exhibit changes in sensitometric response and image resolution as processing variables are altered. Developer type, immersion time, and temperature have been shown to affect the contrast of the mammographic image and thus lesion visibility. The authors evaluated the effect of altering processing variables, including film type, developer type, and immersion time, on the visibility of masses, fibrils, and speaks in a standard mammographic phantom. Images of a phantom obtained with two screen types (Kodak Min-R and Fuji) and five film types (Kodak Min-R M, Min-R E, Min-R H; Fuji UM-MA HC, and DuPont Microvision-C) were processed with five different developer chemicals (Autex SE, DuPont HSD, Kodak RP, Picker 3-7-90, and White Mountain) at four different immersion times (24, 30, 36, and 46 seconds). Processor chemical activity was monitored with sensitometric strips, and developer temperatures were continuously measured. The film images were reviewed by two board-certified radiologists and two physicists with expertise in mammography quality control and were scored based on the visibility of calcifications, masses, and fibrils. Although the differences in the absolute scores were not large, the Kodak Min-R M and Fuji films exhibited the highest scores, and images developed in White Mountain and Autex chemicals exhibited the highest scores. For any film, several processing chemicals may be used to produce images of similar quality. Extended processing may no longer be necessary.

Mammographic images segmentation based on chaotic map clustering algorithm

PubMed Central

2014-01-01

Background This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. Results The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. Conclusions We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI. PMID:24666766

A GaAs pixel detectors-based digital mammographic system: Performances and imaging tests results

NASA Astrophysics Data System (ADS)

Annovazzi, A.; Amendolia, S. R.; Bigongiari, A.; Bisogni, M. G.; Catarsi, F.; Cesqui, F.; Cetronio, A.; Colombo, F.; Delogu, P.; Fantacci, M. E.; Gilberti, A.; Lanzieri, C.; Lavagna, S.; Novelli, M.; Passuello, G.; Paternoster, G.; Pieracci, M.; Poletti, M.; Quattrocchi, M.; Rosso, V.; Stefanini, A.; Testa, A.; Venturelli, L.

2007-06-01

The prototype presented in this paper is based on GaAs pixel detectors read-out by the PCC/MEDIPIX I circuit. The active area of a sensor is about 1 cm 2 therefore to cover the typical irradiation field used in mammography (18×24 cm 2), 18 GaAs detection units have been organized in two staggered rows of nine chips each and moved by a stepper motor in the orthogonal direction. The system is integrated in a mammographic equipment which comprehends the X-ray tube, the bias and data acquisition systems and the PC-based control system. The prototype has been developed in the framework of the Integrated Mammographic Imaging (IMI) project, an industrial research activity aiming to develop innovative instrumentation for morphologic and functional imaging. The project has been supported by the Italian Ministry of Education, University and Research (MIUR) and by five Italian High Tech companies, Alenia Marconi Systems (AMS), CAEN, Gilardoni, LABEN and Poli.Hi.Tech., in collaboration with the universities of Ferrara, Roma "La Sapienza", Pisa and the Istituto Nazionale di Fisica Nucleare (INFN). In this paper, we report on the electrical characterization and the first imaging test results of the digital mammographic system. To assess the imaging capability of such a detector we have built a phantom, which simulates the breast tissue with malignancies. The radiographs of the phantom, obtained by delivering an entrance dose of 4.8 mGy, have shown particulars with a measured contrast below 1%.

Individualized grid-enabled mammographic training system

NASA Astrophysics Data System (ADS)

Yap, M. H.; Gale, A. G.

2009-02-01

The PERFORMS self-assessment scheme measures individuals skills in identifying key mammographic features on sets of known cases. One aspect of this is that it allows radiologists' skills to be trained, based on their data from this scheme. Consequently, a new strategy is introduced to provide revision training based on mammographic features that the radiologist has had difficulty with in these sets. To do this requires a lot of random cases to provide dynamic, unique, and up-to-date training modules for each individual. We propose GIMI (Generic Infrastructure in Medical Informatics) middleware as the solution to harvest cases from distributed grid servers. The GIMI middleware enables existing and legacy data to support healthcare delivery, research, and training. It is technology-agnostic, data-agnostic, and has a security policy. The trainee examines each case, indicating the location of regions of interest, and completes an evaluation form, to determine mammographic feature labelling, diagnosis, and decisions. For feedback, the trainee can choose to have immediate feedback after examining each case or batch feedback after examining a number of cases. All the trainees' result are recorded in a database which also contains their trainee profile. A full report can be prepared for the trainee after they have completed their training. This project demonstrates the practicality of a grid-based individualised training strategy and the efficacy in generating dynamic training modules within the coverage/outreach of the GIMI middleware. The advantages and limitations of the approach are discussed together with future plans.

Grid-enabled mammographic auditing and training system

NASA Astrophysics Data System (ADS)

Yap, M. H.; Gale, A. G.

2008-03-01

Effective use of new technologies to support healthcare initiatives is important and current research is moving towards implementing secure grid-enabled healthcare provision. In the UK, a large-scale collaborative research project (GIMI: Generic Infrastructures for Medical Informatics), which is concerned with the development of a secure IT infrastructure to support very widespread medical research across the country, is underway. In the UK, there are some 109 breast screening centers and a growing number of individuals (circa 650) nationally performing approximately 1.5 million screening examinations per year. At the same, there is a serious, and ongoing, national workforce issue in screening which has seen a loss of consultant mammographers and a growth in specially trained technologists and other non-radiologists. Thus there is a need to offer effective and efficient mammographic training so as to maintain high levels of screening skills. Consequently, a grid based system has been proposed which has the benefit of offering very large volumes of training cases that the mammographers can access anytime and anywhere. A database, spread geographically across three university systems, of screening cases is used as a test set of known cases. The GIMI mammography training system first audits these cases to ensure that they are appropriately described and annotated. Subsequently, the cases are utilized for training in a grid-based system which has been developed. This paper briefly reviews the background to the project and then details the ongoing research. In conclusion, we discuss the contributions, limitations, and future plans of such a grid based approach.

Mammographic Density Estimation with Automated Volumetric Breast Density Measurement

PubMed Central

Ko, Su Yeon; Kim, Eun-Kyung; Kim, Min Jung

2014-01-01

Objective To compare automated volumetric breast density measurement (VBDM) with radiologists' evaluations based on the Breast Imaging Reporting and Data System (BI-RADS), and to identify the factors associated with technical failure of VBDM. Materials and Methods In this study, 1129 women aged 19-82 years who underwent mammography from December 2011 to January 2012 were included. Breast density evaluations by radiologists based on BI-RADS and by VBDM (Volpara Version 1.5.1) were compared. The agreement in interpreting breast density between radiologists and VBDM was determined based on four density grades (D1, D2, D3, and D4) and a binary classification of fatty (D1-2) vs. dense (D3-4) breast using kappa statistics. The association between technical failure of VBDM and patient age, total breast volume, fibroglandular tissue volume, history of partial mastectomy, the frequency of mass > 3 cm, and breast density was analyzed. Results The agreement between breast density evaluations by radiologists and VBDM was fair (k value = 0.26) when the four density grades (D1/D2/D3/D4) were used and moderate (k value = 0.47) for the binary classification (D1-2/D3-4). Twenty-seven women (2.4%) showed failure of VBDM. Small total breast volume, history of partial mastectomy, and high breast density were significantly associated with technical failure of VBDM (p = 0.001 to 0.015). Conclusion There is fair or moderate agreement in breast density evaluation between radiologists and VBDM. Technical failure of VBDM may be related to small total breast volume, a history of partial mastectomy, and high breast density. PMID:24843235

Racial Differences in Quantitative Measures of Area and Volumetric Breast Density

PubMed Central

McCarthy, Anne Marie; Keller, Brad M.; Pantalone, Lauren M.; Hsieh, Meng-Kang; Synnestvedt, Marie; Conant, Emily F.; Armstrong, Katrina; Kontos, Despina

2016-01-01

Abstract Background: Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures. Methods: Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists’ reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided. Results: There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race. Conclusions: After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening. PMID:27130893

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.

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

Drukker, Karen, E-mail: kdrukker@uchicago.edu; Sennett, Charlene A.; Giger, Maryellen L.

Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patientsmore » (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.« less

Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

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

Drukker, Karen, E-mail: kdrukker@uchicago.edu; Sennett, Charlene A.; Giger, Maryellen L.

2014-01-15

Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patientsmore » (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.« less

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

Li, H; Lan, L; Sennett, C

Purpose: To gain insight into the role of parenchyma stroma in the characterization of breast tumors by incorporating computerized mammographic parenchyma assessment into breast CADx in the task of distinguishing between malignant and benign lesions. Methods: This study was performed on 182 biopsy-proven breast mass lesions, including 76 benign and 106 malignant lesions. For each full-field digital mammogram (FFDM) case, our quantitative imaging analysis was performed on both the tumor and a region-of-interest (ROI) from the normal contralateral breast. The lesion characterization includes automatic lesion segmentation and feature extraction. Radiographic texture analysis (RTA) was applied on the normal ROIs tomore » assess the mammographic parenchymal patterns of these contralateral normal breasts. Classification performance of both individual computer extracted features and the output from a Bayesian artificial neural network (BANN) were evaluated with a leave-one-lesion-out method using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the figure of merit. Results: Lesion characterization included computer-extracted phenotypes of spiculation, size, shape, and margin. For parenchymal pattern characterization, five texture features were selected, including power law beta, contrast, and edge gradient. Merging of these computer-selected features using BANN classifiers yielded AUC values of 0.79 (SE=0.03) and 0.67 (SE=0.04) in the task of distinguishing between malignant and benign lesions using only tumor phenotypes and texture features from the contralateral breasts, respectively. Incorporation of tumor phenotypes with parenchyma texture features into the BANN yielded improved classification performance with an AUC value of 0.83 (SE=0.03) in the task of differentiating malignant from benign lesions. Conclusion: Combining computerized tumor and parenchyma phenotyping was found to significantly improve breast cancer diagnostic accuracy highlighting the need to consider both tumor and stroma in decision making. Funding: University of Chicago Dean Bridge Fund, NCI U24-CA143848-05, P50-CA58223 Breast SPORE program, and Breast Cancer Research Foundation. COI: MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a cofounder and stockholder in Quantitative Insights.« less

Mammographic breast density and risk of breast cancer in women with atypical hyperplasia: an observational cohort study from the Mayo Clinic Benign Breast Disease (BBD) cohort.

PubMed

Vierkant, Robert A; Degnim, Amy C; Radisky, Derek C; Visscher, Daniel W; Heinzen, Ethan P; Frank, Ryan D; Winham, Stacey J; Frost, Marlene H; Scott, Christopher G; Jensen, Matthew R; Ghosh, Karthik; Manduca, Armando; Brandt, Kathleen R; Whaley, Dana H; Hartmann, Lynn C; Vachon, Celine M

2017-01-31

Atypical hyperplasia (AH) and mammographic breast density (MBD) are established risk factors for breast cancer (BC), but their joint contributions are not well understood. We examine associations of MBD and BC by histologic impression, including AH, in a subcohort of women from the Mayo Clinic Benign Breast Disease Cohort. Women with a diagnosis of BBD and mammogram between 1985 and 2001 were eligible. Histologic impression was assessed via pathology review and coded as non-proliferative disease (NP), proliferative disease without atypia (PDWA) and AH. MBD was assessed clinically using parenchymal pattern (PP) or BI-RADS criteria and categorized as low, moderate or high. Percent density (PD) was also available for a subset of women. BC and clinical information were obtained by questionnaires, medical records and the Mayo Clinic Tumor Registry. Women were followed from date of benign biopsy to BC, death or last contact. Standardized incidence ratios (SIRs) compared the observed number of BCs to expected counts. Cox regression estimated multivariate-adjusted MBD hazard ratios. Of the 6271 women included in the study, 1132 (18.0%) had low MBD, 2921 (46.6%) had moderate MBD, and 2218 (35.4%) had high MBD. A total of 3532 women (56.3%) had NP, 2269 (36.2%) had PDWA and 470 (7.5%) had AH. Over a median follow-up of 14.3 years, 528 BCs were observed. The association of MBD and BC risk differed by histologic impression (p-interaction = 0.03), such that there was a strong MBD and BC association among NP (p < 0.001) but non-significant associations for PDWA (p = 0.27) and AH (p = 0.96). MBD and BC associations for AH women were not significant within subsets defined by type of MBD measure (PP vs. BI-RADS), age at biopsy, number of foci of AH, type of AH (lobular vs. ductal) and body mass index, and after adjustment for potential confounding variables. Women with atypia who also had high PD (>50%) demonstrated marginal evidence of increased BC risk (SIR 4.98), but results were not statistically significant. We found no evidence of an association between MBD and subsequent BC risk in women with AH.

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

PubMed

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

2018-01-01

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. 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. 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%. 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.

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

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

Opportunistic mammography screening provides effective detection rates in a limited resource healthcare system.

PubMed

Teh, Yew-Ching; Tan, Gie-Hooi; Taib, Nur Aishah; Rahmat, Kartini; Westerhout, Caroline Judy; Fadzli, Farhana; See, Mee-Hoong; Jamaris, Suniza; Yip, Cheng-Har

2015-05-15

Breast cancer is the leading cause of cancer deaths in women world-wide. In low and middle income countries, where there are no population-based mammographic screening programmes, late presentation is common, and because of inadequate access to optimal treatment, survival rates are poor. Mammographic screening is well-studied in high-income countries in western populations, and because it has been shown to reduce breast cancer mortality, it has become part of the healthcare systems in such countries. However the performance of mammographic screening in a developing country is largely unknown. This study aims to evaluate the performance of mammographic screening in Malaysia, a middle income country, and to compare the stage and surgical treatment of screen-detected and symptomatic breast cancer. A retrospective review of 2510 mammograms performed from Jan to Dec 2010 in a tertiary medical centre is carried out. The three groups identified are the routine (opportunistic) screening group, the targeted (high risk) screening group and the diagnostic group. The performance indicators of each group is calculated, and stage at presentation and treatment between the screening and diagnostic group is analyzed. The cancer detection rate in the opportunistic screening group, targeted screening group, and the symptomatic group is 0.5 %, 1.25 % and 26 % respectively. The proportion of ductal carcinoma in situ is 23.1 % in the two screening groups compared to only 2.5 % in the diagnostic group. Among the opportunistic screening group, the cancer detection rate was 0.2 % in women below 50 years old compared to 0.65 % in women 50 years and above. The performance indicators are within international standards. Early-staged breast cancer (Stage 0-2) were 84.6 % in the screening groups compared to 61.1 % in the diagnostic group. From the results, in a setting with resource constraints, targeted screening of high risk individuals will give a higher yield, and if more resources are available, population-based screening of women 50 and above is effective. Opportunistic mammographic screening is feasible and effective in a middle income country with performance indicators within international standards. Waiting until women are symptomatic will lead to more advanced cancers.

Comparison of Mammographic Changes Across Three Different Fractionation Schedules for Early-Stage Breast Cancer

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

Tian, Sibo; Paster, Lina F.; Kim, Sinae

Purpose: As the use of hypofractionated breast radiation therapy (RT) increases, so will the need for long-term data on post-RT mammographic changes. The purpose of the present study was to longitudinally compare the incidence of common mammographic sequelae seen after breast conserving surgery and RT in patients treated with accelerated partial breast irradiation (APBI), hypofractionated whole breast irradiation (HWBI), and conventionally fractionated whole breast irradiation (WBI). Methods and Materials: Patients treated with either APBI or HWBI after breast conserving therapy and with ≥3 mammograms of the treated breast were identified. They were matched 1:1 by age ±5 years to patients treatedmore » with WBI. The mammograms were evaluated for common post-RT breast findings by a mammographer who was unaware of the treatment. The outcomes were analyzed using a cumulative logistic regression model; P<.05 indicated statistically significance. Results: Of 89 patients treated with RT from 2006 to 2011, 29 had received APBI, 30 had received HWBI, and 30 had received WBI. Their median age was 60 years (range 33-83). A total of 605 mammograms were evaluated, with a median follow-up of 48 months. The treatment technique did not affect the severity of architectural distortion when the groups were evaluated longitudinally. The likelihood of finding skin thickening decreased with increasing follow-up duration (odds ratio 0.6; P<.001) adjusted for fractionation schemes. No differences were seen with respect to changes in skin thickening, fluid collections, or calcifications among the treatment groups, after adjustment for the follow-up time. The clinical characteristics, including age, race, T stage, and chemotherapy use, were not linked to the likelihood of finding several mammographic phenomena over time. Conclusions: Although specific post-treatment imaging findings evolved over time, RT fractionation did not alter the relative incidence or severity of architectural distortion, skin thickening, fluid collections, or calcifications. These findings will be useful to both radiologists and radiation oncologists when counseling patients regarding follow-up studies after RT.« less

Are Mammographically Occult Additional Tumors Identified More Than 2 Cm Away From the Primary Breast Cancer on MRI Clinically Significant?

PubMed

Goodman, Sarah; Mango, Victoria; Friedlander, Lauren; Desperito, Elise; Wynn, Ralph; Ha, Richard

2018-06-08

To evaluate the clinical significance of mammographically occult additional tumors identified more than 2cm away from the primary breast cancer on preoperative magnetic resonance imaging (MRI). An Institutional Review Board approved review of consecutive preoperative breast MRIs performed from 1/1/08 to 12/31/14, yielded 667 patients with breast cancer. These patients underwent further assessment to identify biopsy proven mammographically occult breast tumors located more than 2cm away from the edge of the primary tumor. Additional MRI characteristics of the primary and secondary tumors and pathology were reviewed. Statistical analysis was performed using SPSS (v. 24). Of 667 patients with breast cancer, 129 patients had 150 additional ipsilateral mammographically occult tumors that were more than 2cm away from the edge of the primary tumor. One hundred twelve of 129 (86.8%) patients had one additional tumor and 17/129 (13.2%) had two or more additional tumors. In 71/129 (55.0%), additional tumors were located in a different quadrant and in 58/129 (45.0%) additional tumors were in the same quadrant but ≥2cm away. Overall, primary tumor size was significantly larger (mean 1.87± 1.25 cm) than the additional tumors (mean 0.79 ± 0.61cm, p < 0.001). However, in 20/129 (15.5%) the additional tumor was larger and in 26/129 (20.2%) the additional tumor was ≥1cm. The primary tumor was significantly more likely to be invasive (81.4%, 105/129) compared to additional tumors (70%, 105/150, p = 0.03). In 9/129 (7.0%) patients, additional tumors yielded unsuspected invasive cancer orhigher tumor grade. The additional tumor was more likely to be nonmass lesion type (37.3% vs 24% p = 0.02) and focus lesion type (10% vs 0.08%, p < 0.001) compared to primary tumor. Mammographically occult additional tumors identified more than 2cm away from the primary breast tumor on MRI are unlikely to be surgically treated if undiagnosed and may be clinically significant. Copyright © 2018 Academic Radiology. Published by Elsevier Inc. All rights reserved.

Mammographic Screening at Age 40 or 45? What Difference Does It Make? The Potential Impact of American Cancer Society Mammography Screening Guidelines.

PubMed

Fancher, Crystal E; Scott, Anthony; Allen, Ahkeel; Dale, Paul

2017-08-01

this is a 10-year retrospective chart review evaluating the potential impact of the most recent American Cancer Society mammography screening guidelines which excludes female patients aged 40 to 44 years from routine annual screening mammography. Instead they recommend screening mammography starting at age 45 with the option to begin screening earlier if the patient desires. The institutional cancer registry was systematically searched to identify all women aged 40 to 44 years treated for breast cancer over a 10-year period. These women were separated into two cohorts: screening mammography detected cancer (SMDC) and nonscreening mammography detected cancer (NSMDC). Statistical analysis of the cohorts was performed for lymph node status (SLN), five-year disease-free survival, and five-year overall survival. Women with SMDC had a significantly lower incidence of SLN positive cancer than the NSMDC group, 9 of 63 (14.3%) versus 36 of 81 (44 %; P < 0.001). The five-year disease-free survival for both groups was 84 per cent for SMDC and 80 per cent for NSMDC; this was not statistically significant. The five-year overall survival was statistically significant at 94 per cent for the SMDC group and 80 per cent for the NSMDC group (P < 0.05). This review demonstrates the significance of mammographic screening for early detection and treatment of breast cancer. Mammographic screening in women aged 40 to 44 detected tumors with fewer nodal metastases, resulting in improved survival and reaffirming the need for annual mammographic screening in this age group.

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist's assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.

Varying performance in mammographic interpretation across two countries: Do results indicate reader or population variances?

NASA Astrophysics Data System (ADS)

Soh, BaoLin P.; Lee, Warwick B.; Wong, Jill; Sim, Llewellyn; Hillis, Stephen L.; Tapia, Kriscia A.; Brennan, Patrick C.

2016-03-01

Aim: To compare the performance of Australian and Singapore breast readers interpreting a single test-set that consisted of mammographic examinations collected from the Australian population. Background: In the teleradiology era, breast readers are interpreting mammographic examinations from different populations. The question arises whether two groups of readers with similar training backgrounds, demonstrate the same level of performance when presented with a population familiar only to one of the groups. Methods: Fifty-three Australian and 15 Singaporean breast radiologists participated in this study. All radiologists were trained in mammogram interpretation and had a median of 9 and 15 years of experience in reading mammograms respectively. Each reader interpreted the same BREAST test-set consisting of sixty de-identified mammographic examinations arising from an Australian population. Performance parameters including JAFROC, ROC, case sensitivity as well as specificity were compared between Australian and Singaporean readers using a Mann Whitney U test. Results: A significant difference (P=0.036) was demonstrated between the JAFROC scores of the Australian and Singaporean breast radiologists. No other significant differences were observed. Conclusion: JAFROC scores for Australian radiologists were higher than those obtained by the Singaporean counterparts. Whilst it is tempting to suggest this is down to reader expertise, this may be a simplistic explanation considering the very similar training and audit backgrounds of the two populations of radiologists. The influence of reading images that are different from those that radiologists normally encounter cannot be ruled out and requires further investigation, particularly in the light of increasing international outsourcing of radiologic reporting.

Impact of full field digital mammography on the classification and mammographic characteristics of interval breast cancers.

PubMed

Knox, Mark; O'Brien, Angela; Szabó, Endre; Smith, Clare S; Fenlon, Helen M; McNicholas, Michelle M; Flanagan, Fidelma L

2015-06-01

Full field digital mammography (FFDM) is increasingly replacing screen film mammography (SFM) in breast screening programs. Interval breast cancers are an issue in all screening programs and the purpose of our study is to assess the impact of FFDM on the classification of interval breast cancers at independent blind review and to compare the mammographic features of interval cancers at FFDM and SFM. This study included 138 cases of interval breast cancer, 76 following an FFDM screening examination and 62 following screening with SFM. The prior screening mammogram was assessed by each of five consultant breast radiologists who were blinded to the site of subsequent cancer. Subsequent review of the diagnostic mammogram was performed and cases were classified as missed, minimal signs, occult or true interval. Mammographic features of the interval cancer at diagnosis and any abnormality identified on the prior screening mammogram were recorded. The percentages of cancers classified as missed at FFDM and SFM did not differ significantly, 10.5% (8 of 76) at FFDM and 8.1% (5 of 62) at SFM (p=.77). There were significantly less interval cancers presenting as microcalcifications (alone or in association with another abnormality) following screening with FFDM, 16% (12 of 76) than following a SFM examination, 32% (20 of 62) (p=.02). Interval breast cancers continue to pose a problem at FFDM. The switch to FFDM has changed the mammographic presentation of interval breast cancer, with less interval cancers presenting in association with microcalcifications. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Optical pre-screening in breast screening programs: Can we identify women who benefit most from limited mammography resources?

NASA Astrophysics Data System (ADS)

Walter, Jane; Loshchenov, Maxim; Zhilkin, Vladimir; Peake, Rachel; Stone, Jennifer; Lilge, Lothar

2017-04-01

Background: In excess of 60% of all cancers are detected in low and middle-income countries, with breast cancer (BC) the dominant malignancy for women. Incidence rates continue to climb, most noticeably in the less than 50-year-old population. Expansion of mammography infrastructure and resources is lacking, resulting in over 60% of women diagnosed with stage III/IV BC in the majority of these countries. Optical Breast Spectroscopy (OBS) was shown to correlate well with mammographic breast density (MBD). OBS could aid breast screening programs in low- and middle-income countries by lowering the number of mammographs required for complete population coverage. However, its performance needs to be tested in large population trails to ensure high sensitivity and acceptable specificity. Methods: For the planned studies in low- and middle-income countries in different continents, online methods need to be implemented to monitor the performance and data collection by these devices, operated by trained nurses. Based on existing datasets, procedures were developed to validate an individual woman's data integrity and to identify operator errors versus system malfunctions. Results: Using a dataset comprising spectra from 360 women collected by 2 instruments in different locations and with 3 different trained operators, automated methods were developed to identify 100% of the source or photodetector malfunctions as well as incorrect calibrations and 96% of instances of insufficient tissue contact. Conclusions: Implementing the dataset validation locally in each instrument and tethered to a cloud database will allow the planned clinical trials to proceed.

The classification of normal screening mammograms

NASA Astrophysics Data System (ADS)

Ang, Zoey Z. Y.; Rawashdeh, Mohammad A.; Heard, Robert; Brennan, Patrick C.; Lee, Warwick; Lewis, Sarah J.

2016-03-01

Rationale and objectives: To understand how breast screen readers classify the difficulty of normal screening mammograms using common lexicon describing normal appearances. Cases were also assessed on their suitability for a single reader strategy. Materials and Methods: 15 breast readers were asked to interpret a test set of 29 normal screening mammogram cases and classify them by rating the difficulty of the case on a five-point Likert scale, identifying the salient features and assessing their suitability for single reading. Using the False Positive Fractions from a previous study, the 29 cases were classified into 10 "low", 10 "medium" and nine "high" difficulties. Data was analyzed with descriptive statistics. Spearman's correlation was used to test the strength of association between the difficulty of the cases and the readers' recommendation for single reading strategy. Results: The ratings from readers in this study corresponded to the known difficulty level of cases for the 'low' and 'high' difficulty cases. Uniform ductal pattern and density, symmetrical mammographic features and the absence of micro-calcifications were the main reasons associated with 'low' difficulty cases. The 'high' difficulty cases were described as having `dense breasts'. There was a statistically significant negative correlation between the difficulty of the cases and readers' recommendation for single reading (r = -0.475, P = 0.009). Conclusion: The findings demonstrated potential relationships between certain mammographic features and the difficulty for readers to classify mammograms as 'normal'. The standard Australian practice of double reading was deemed more suitable for most cases. There was an inverse moderate association between the difficulty of the cases and the recommendations for single reading.

Simulation of image detectors in radiology for determination of scatter-to-primary ratios using Monte Carlo radiation transport code MCNP/MCNPX.

PubMed

Smans, Kristien; Zoetelief, Johannes; Verbrugge, Beatrijs; Haeck, Wim; Struelens, Lara; Vanhavere, Filip; Bosmans, Hilde

2010-05-01

The purpose of this study was to compare and validate three methods to simulate radiographic image detectors with the Monte Carlo software MCNP/MCNPX in a time efficient way. The first detector model was the standard semideterministic radiography tally, which has been used in previous image simulation studies. Next to the radiography tally two alternative stochastic detector models were developed: A perfect energy integrating detector and a detector based on the energy absorbed in the detector material. Validation of three image detector models was performed by comparing calculated scatter-to-primary ratios (SPRs) with the published and experimentally acquired SPR values. For mammographic applications, SPRs computed with the radiography tally were up to 44% larger than the published results, while the SPRs computed with the perfect energy integrating detectors and the blur-free absorbed energy detector model were, on the average, 0.3% (ranging from -3% to 3%) and 0.4% (ranging from -5% to 5%) lower, respectively. For general radiography applications, the radiography tally overestimated the measured SPR by as much as 46%. The SPRs calculated with the perfect energy integrating detectors were, on the average, 4.7% (ranging from -5.3% to -4%) lower than the measured SPRs, whereas for the blur-free absorbed energy detector model, the calculated SPRs were, on the average, 1.3% (ranging from -0.1% to 2.4%) larger than the measured SPRs. For mammographic applications, both the perfect energy integrating detector model and the blur-free energy absorbing detector model can be used to simulate image detectors, whereas for conventional x-ray imaging using higher energies, the blur-free energy absorbing detector model is the most appropriate image detector model. The radiography tally overestimates the scattered part and should therefore not be used to simulate radiographic image detectors.

Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.

PubMed

Vikhe, P S; Thool, V R

2016-04-01

Detection of mass in mammogram for early diagnosis of breast cancer is a significant assignment in the reduction of the mortality rate. However, in some cases, screening of mass is difficult task for radiologist, due to variation in contrast, fuzzy edges and noisy mammograms. Masses and micro-calcifications are the distinctive signs for diagnosis of breast cancer. This paper presents, a method for mass enhancement using piecewise linear operator in combination with wavelet processing from mammographic images. The method includes, artifact suppression and pectoral muscle removal based on morphological operations. Finally, mass segmentation for detection using adaptive threshold technique is carried out to separate the mass from background. The proposed method has been tested on 130 (45 + 85) images with 90.9 and 91 % True Positive Fraction (TPF) at 2.35 and 2.1 average False Positive Per Image(FP/I) from two different databases, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The obtained results show that, the proposed technique gives improved diagnosis in the early breast cancer detection.

Mammographic Screening of Women Attending a Reference Service Center in Southern Brazil.

PubMed

Romeiro Lopes, Tiara Cristina; Franca Gravena, Angela Andreia; Demitto, Marcela de Oliveira; Brischiliari, Sheila Cristina Rocha; Borghesan, Deise Helena Pelloso; Dell Agnolo, Catia Millene; Carvalho, Maria Dalva de Barros; Pelloso, Sandra Marisa

2016-01-01

To investigate the prevalence of and factors associated with performance of annual mammography by women above 40 years of age. This cross-sectional retrospective study was conducted at an oncology reference service in Southern Brazil from October 2013 to October 2014 with 525 women aged 40 years or older. The prevalence of annual mammography was 54.1%; annual mammographic screening was performed for women without private medical insurance, who were under hormone replacement therapy and who had used contraception in the past. An association was found between non-performance of breast clinical and self-examination and non-performance of mammographic screening. Use of mammography for breast cancer screening in the public health care setting proved to be accessible; nevertheless, the proportion of screened women was low, and they exhibited poor adherence to the basic measures of care recommended for breast assessment. Thus, control of breast cancer requires implementing actions targeting the population most vulnerable to non-adherence to screening in addition to continuously monitoring and assessing that population to reduce the prevalence of this disease.

Pre-operative factors indicating risk of multiple operations versus a single operation in women undergoing surgery for screen detected breast cancer.

PubMed

O'Flynn, E A M; Currie, R J; Mohammed, K; Allen, S D; Michell, M J

2013-02-01

We aim to identify preoperative factors at diagnosis which could predict whether women undergoing wide local excision (WLE) would require further operations. 1593 screen-detected invasive and non-invasive breast cancers were reviewed. Age, presence of ductal carcinoma in situ (DCIS), invasive cancer size on mammography, mammographic sign, tumour type, grade and confidence of the radiologist in malignancy were compared. 83%(1315/1593) of women had a WLE. Of these, 70%(919/1315) had a single operation, and 30%(396/1315) multiple operations. These included repeat WLE to clear margins (60%(238/396)), mastectomy (34%(133/396)) and axillary dissection (6%(25/396)). The presence of mammographic microcalcification, lobular carcinoma and grade 2 malignancy on core biopsy were independent risk factors for multiple operations on multivariate analysis. Women with mammographic DCIS >30 mm were 3.4 times more likely to undergo repeat surgery than those with smaller foci. The multidisciplinary team should pay particular attention to these factors when planning surgery. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170.

PubMed

Dunning, Alison M; Michailidou, Kyriaki; Kuchenbaecker, Karoline B; Thompson, Deborah; French, Juliet D; Beesley, Jonathan; Healey, Catherine S; Kar, Siddhartha; Pooley, Karen A; Lopez-Knowles, Elena; Dicks, Ed; Barrowdale, Daniel; Sinnott-Armstrong, Nicholas A; Sallari, Richard C; Hillman, Kristine M; Kaufmann, Susanne; Sivakumaran, Haran; Moradi Marjaneh, Mahdi; Lee, Jason S; Hills, Margaret; Jarosz, Monika; Drury, Suzie; Canisius, Sander; Bolla, Manjeet K; Dennis, Joe; Wang, Qin; Hopper, John L; Southey, Melissa C; Broeks, Annegien; Schmidt, Marjanka K; Lophatananon, Artitaya; Muir, Kenneth; Beckmann, Matthias W; Fasching, Peter A; Dos-Santos-Silva, Isabel; Peto, Julian; Sawyer, Elinor J; Tomlinson, Ian; Burwinkel, Barbara; Marme, Frederik; Guénel, Pascal; Truong, Thérèse; Bojesen, Stig E; Flyger, Henrik; González-Neira, Anna; Perez, Jose I A; Anton-Culver, Hoda; Eunjung, Lee; Arndt, Volker; Brenner, Hermann; Meindl, Alfons; Schmutzler, Rita K; Brauch, Hiltrud; Hamann, Ute; Aittomäki, Kristiina; Blomqvist, Carl; Ito, Hidemi; Matsuo, Keitaro; Bogdanova, Natasha; Dörk, Thilo; Lindblom, Annika; Margolin, Sara; Kosma, Veli-Matti; Mannermaa, Arto; Tseng, Chiu-Chen; Wu, Anna H; Lambrechts, Diether; Wildiers, Hans; Chang-Claude, Jenny; Rudolph, Anja; Peterlongo, Paolo; Radice, Paolo; Olson, Janet E; Giles, Graham G; Milne, Roger L; Haiman, Christopher A; Henderson, Brian E; Goldberg, Mark S; Teo, Soo H; Yip, Cheng Har; Nord, Silje; Borresen-Dale, Anne-Lise; Kristensen, Vessela; Long, Jirong; Zheng, Wei; Pylkäs, Katri; Winqvist, Robert; Andrulis, Irene L; Knight, Julia A; Devilee, Peter; Seynaeve, Caroline; Figueroa, Jonine; Sherman, Mark E; Czene, Kamila; Darabi, Hatef; Hollestelle, Antoinette; van den Ouweland, Ans M W; Humphreys, Keith; Gao, Yu-Tang; Shu, Xiao-Ou; Cox, Angela; Cross, Simon S; Blot, William; Cai, Qiuyin; Ghoussaini, Maya; Perkins, Barbara J; Shah, Mitul; Choi, Ji-Yeob; Kang, Daehee; Lee, Soo Chin; Hartman, Mikael; Kabisch, Maria; Torres, Diana; Jakubowska, Anna; Lubinski, Jan; Brennan, Paul; Sangrajrang, Suleeporn; Ambrosone, Christine B; Toland, Amanda E; Shen, Chen-Yang; Wu, Pei-Ei; Orr, Nick; Swerdlow, Anthony; McGuffog, Lesley; Healey, Sue; Lee, Andrew; Kapuscinski, Miroslav; John, Esther M; Terry, Mary Beth; Daly, Mary B; Goldgar, David E; Buys, Saundra S; Janavicius, Ramunas; Tihomirova, Laima; Tung, Nadine; Dorfling, Cecilia M; van Rensburg, Elizabeth J; Neuhausen, Susan L; Ejlertsen, Bent; Hansen, Thomas V O; Osorio, Ana; Benitez, Javier; Rando, Rachel; Weitzel, Jeffrey N; Bonanni, Bernardo; Peissel, Bernard; Manoukian, Siranoush; Papi, Laura; Ottini, Laura; Konstantopoulou, Irene; Apostolou, Paraskevi; Garber, Judy; Rashid, Muhammad Usman; Frost, Debra; Izatt, Louise; Ellis, Steve; Godwin, Andrew K; Arnold, Norbert; Niederacher, Dieter; Rhiem, Kerstin; Bogdanova-Markov, Nadja; Sagne, Charlotte; Stoppa-Lyonnet, Dominique; Damiola, Francesca; Sinilnikova, Olga M; Mazoyer, Sylvie; Isaacs, Claudine; Claes, Kathleen B M; De Leeneer, Kim; de la Hoya, Miguel; Caldes, Trinidad; Nevanlinna, Heli; Khan, Sofia; Mensenkamp, Arjen R; Hooning, Maartje J; Rookus, Matti A; Kwong, Ava; Olah, Edith; Diez, Orland; Brunet, Joan; Pujana, Miquel Angel; Gronwald, Jacek; Huzarski, Tomasz; Barkardottir, Rosa B; Laframboise, Rachel; Soucy, Penny; Montagna, Marco; Agata, Simona; Teixeira, Manuel R; Park, Sue Kyung; Lindor, Noralane; Couch, Fergus J; Tischkowitz, Marc; Foretova, Lenka; Vijai, Joseph; Offit, Kenneth; Singer, Christian F; Rappaport, Christine; Phelan, Catherine M; Greene, Mark H; Mai, Phuong L; Rennert, Gad; Imyanitov, Evgeny N; Hulick, Peter J; Phillips, Kelly-Anne; Piedmonte, Marion; Mulligan, Anna Marie; Glendon, Gord; Bojesen, Anders; Thomassen, Mads; Caligo, Maria A; Yoon, Sook-Yee; Friedman, Eitan; Laitman, Yael; Borg, Ake; von Wachenfeldt, Anna; Ehrencrona, Hans; Rantala, Johanna; Olopade, Olufunmilayo I; Ganz, Patricia A; Nussbaum, Robert L; Gayther, Simon A; Nathanson, Katherine L; Domchek, Susan M; Arun, Banu K; Mitchell, Gillian; Karlan, Beth Y; Lester, Jenny; Maskarinec, Gertraud; Woolcott, Christy; Scott, Christopher; Stone, Jennifer; Apicella, Carmel; Tamimi, Rulla; Luben, Robert; Khaw, Kay-Tee; Helland, Åslaug; Haakensen, Vilde; Dowsett, Mitch; Pharoah, Paul D P; Simard, Jacques; Hall, Per; García-Closas, Montserrat; Vachon, Celine; Chenevix-Trench, Georgia; Antoniou, Antonis C; Easton, Douglas F; Edwards, Stacey L

2016-04-01

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER(+) or ER(-)) and human ERBB2 (HER2(+) or HER2(-)) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER(-) tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

PubMed Central

Dunning, Alison M; Michailidou, Kyriaki; Kuchenbaecker, Karoline B; Thompson, Deborah; French, Juliet D; Beesley, Jonathan; Healey, Catherine S; Kar, Siddhartha; Pooley, Karen A; Lopez-Knowles, Elena; Dicks, Ed; Barrowdale, Daniel; Sinnott-Armstrong, Nicholas A; Sallari, Richard C; Hillman, Kristine M; Kaufmann, Susanne; Sivakumaran, Haran; Marjaneh, Mahdi Moradi; Lee, Jason S; Hills, Margaret; Jarosz, Monika; Drury, Suzie; Canisius, Sander; Bolla, Manjeet K; Dennis, Joe; Wang, Qin; Hopper, John L; Southey, Melissa C; Broeks, Annegien; Schmidt, Marjanka K; Lophatananon, Artitaya; Muir, Kenneth; Beckmann, Matthias W; Fasching, Peter A; dos-Santos-Silva, Isabel; Peto, Julian; Sawyer, Elinor J; Tomlinson, Ian; Burwinkel, Barbara; Marme, Frederik; Guénel, Pascal; Truong, Thérèse; Bojesen, Stig E; Flyger, Henrik; González-Neira, Anna; Perez, Jose I A; Anton-Culver, Hoda; Eunjung, Lee; Arndt, Volker; Brenner, Hermann; Meindl, Alfons; Schmutzler, Rita K; Brauch, Hiltrud; Hamann, Ute; Aittomäki, Kristiina; Blomqvist, Carl; Ito, Hidemi; Matsuo, Keitaro; Bogdanova, Natasha; Dörk, Thilo; Lindblom, Annika; Margolin, Sara; Kosma, Veli-Matti; Mannermaa, Arto; Tseng, Chiu-chen; Wu, Anna H; Lambrechts, Diether; Wildiers, Hans; Chang-Claude, Jenny; Rudolph, Anja; Peterlongo, Paolo; Radice, Paolo; Olson, Janet E; Giles, Graham G; Milne, Roger L; Haiman, Christopher A; Henderson, Brian E; Goldberg, Mark S; Teo, Soo H; Yip, Cheng Har; Nord, Silje; Borresen-Dale, Anne-Lise; Kristensen, Vessela; Long, Jirong; Zheng, Wei; Pylkäs, Katri; Winqvist, Robert; Andrulis, Irene L; Knight, Julia A; Devilee, Peter; Seynaeve, Caroline; Figueroa, Jonine; Sherman, Mark E; Czene, Kamila; Darabi, Hatef; Hollestelle, Antoinette; van den Ouweland, Ans M W; Humphreys, Keith; Gao, Yu-Tang; Shu, Xiao-Ou; Cox, Angela; Cross, Simon S; Blot, William; Cai, Qiuyin; Ghoussaini, Maya; Perkins, Barbara J; Shah, Mitul; Choi, Ji-Yeob; Kang, Daehee; Lee, Soo Chin; Hartman, Mikael; Kabisch, Maria; Torres, Diana; Jakubowska, Anna; Lubinski, Jan; Brennan, Paul; Sangrajrang, Suleeporn; Ambrosone, Christine B; Toland, Amanda E; Shen, Chen-Yang; Wu, Pei-Ei; Orr, Nick; Swerdlow, Anthony; McGuffog, Lesley; Healey, Sue; Lee, Andrew; Kapuscinski, Miroslav; John, Esther M; Terry, Mary Beth; Daly, Mary B; Goldgar, David E; Buys, Saundra S; Janavicius, Ramunas; Tihomirova, Laima; Tung, Nadine; Dorfling, Cecilia M; van Rensburg, Elizabeth J; Neuhausen, Susan L; Ejlertsen, Bent; Hansen, Thomas V O; Osorio, Ana; Benitez, Javier; Rando, Rachel; Weitzel, Jeffrey N; Bonanni, Bernardo; Peissel, Bernard; Manoukian, Siranoush; Papi, Laura; Ottini, Laura; Konstantopoulou, Irene; Apostolou, Paraskevi; Garber, Judy; Rashid, Muhammad Usman; Frost, Debra; Izatt, Louise; Ellis, Steve; Godwin, Andrew K; Arnold, Norbert; Niederacher, Dieter; Rhiem, Kerstin; Bogdanova-Markov, Nadja; Sagne, Charlotte; Stoppa-Lyonnet, Dominique; Damiola, Francesca; Sinilnikova, Olga M; Mazoyer, Sylvie; Isaacs, Claudine; Claes, Kathleen B M; De Leeneer, Kim; de la Hoya, Miguel; Caldes, Trinidad; Nevanlinna, Heli; Khan, Sofia; Mensenkamp, Arjen R; Hooning, Maartje J; Rookus, Matti A; Kwong, Ava; Olah, Edith; Diez, Orland; Brunet, Joan; Pujana, Miquel Angel; Gronwald, Jacek; Huzarski, Tomasz; Barkardottir, Rosa B; Laframboise, Rachel; Soucy, Penny; Montagna, Marco; Agata, Simona; Teixeira, Manuel R; Park, Sue Kyung; Lindor, Noralane; Couch, Fergus J; Tischkowitz, Marc; Foretova, Lenka; Vijai, Joseph; Offit, Kenneth; Singer, Christian F; Rappaport, Christine; Phelan, Catherine M; Greene, Mark H; Mai, Phuong L; Rennert, Gad; Imyanitov, Evgeny N; Hulick, Peter J; Phillips, Kelly-Anne; Piedmonte, Marion; Mulligan, Anna Marie; Glendon, Gord; Bojesen, Anders; Thomassen, Mads; Caligo, Maria A; Yoon, Sook-Yee; Friedman, Eitan; Laitman, Yael; Borg, Ake; von Wachenfeldt, Anna; Ehrencrona, Hans; Rantala, Johanna; Olopade, Olufunmilayo I; Ganz, Patricia A; Nussbaum, Robert L; Gayther, Simon A; Nathanson, Katherine L; Domchek, Susan M; Arun, Banu K; Mitchell, Gillian; Karlan, Beth Y; Lester, Jenny; Maskarinec, Gertraud; Woolcott, Christy; Scott, Christopher; Stone, Jennifer; Apicella, Carmel; Tamimi, Rulla; Luben, Robert; Khaw, Kay-Tee; Helland, Åslaug; Haakensen, Vilde; Dowsett, Mitch; Pharoah, Paul D P; Simard, Jacques; Hall, Per; García-Closas, Montserrat; Vachon, Celine; Chenevix-Trench, Georgia; Antoniou, Antonis C; Easton, Douglas F; Edwards, Stacey L

2016-01-01

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER+ or ER−) and human ERBB2 (HER2+ or HER2−) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER− tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression. PMID:26928228

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.

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.

Implementation of the qualities of radiodiagnostic: mammography

NASA Astrophysics Data System (ADS)

Pacífico, L. C.; Magalhães, L. A. G.; Peixoto, J. G. P.; Fernandes, E.

2018-03-01

The objective of the present study was to evaluate the expanded uncertainty of the mammographic calibration process and present the result of the internal audit performed at the Laboratory of Radiological Sciences (LCR). The qualities of the mammographic beans that are references in the LCR, comprises two irradiation conditions: no-attenuated beam and attenuated beam. Both had satisfactory results, with an expanded uncertainty equals 2,1%. The internal audit was performed, and the degree of accordance with the ISO/IEC 17025 was evaluated. The result of the internal audit was satisfactory. We conclude that LCR can perform calibrations on mammography qualities for end users.

Triple Negative Breast Cancer Team Project — EDRN Public Portal

Cancer.gov

Triple negative breast cancers (TNBC), comprise 15-20% of breast cancers, and are associated with later stage at diagnosis, increased mortality, and occur more frequently in younger women where mammographic screening is less reliable. TNBCs are more likely to be diagnosed by physical exam than by mammographic screening. There is an unmet clinical need for biomarkers for the early detection of TNBC. Here, we are proposing the development of a plasma-based biomarker panel for the routine screening of women over the age of 40 for TNBC that can be used to identify women for further imaging.

Evaluation of mammography equipment performance, dose and image quality in five Latin American countries

NASA Astrophysics Data System (ADS)

Brandan, M.-E.; Ruiz-Trejo, C.; Caspani, C. E. M.; Fleitas, I.; de-la-Mora, R.; Miranda, A. A.; Plazas, M.-C.; Betancourt, C.-M.; Borras, C.

2001-10-01

Under the auspices of PAHO/WHO, a multicentric investigation is carried out in five Latin American countries. Its aim is to correlate quality indicators of radiology services with the accuracy of the radiological interpretation as determined by a panel of radiology experts. We present preliminary results from mammographic imaging facilities. Evaluation of the equipment performance and dose measurements in 21 mammographic units show that, on the average, 75% of the units comply with recommendations issued by various organizations. An independent evaluation of the quality of the clinical images show strong variations among the different radiological services.

Polymorphisms in a Putative Enhancer at the 10q21.2 Breast Cancer Risk Locus Regulate NRBF2 Expression.

PubMed

Darabi, Hatef; McCue, Karen; Beesley, Jonathan; Michailidou, Kyriaki; Nord, Silje; Kar, Siddhartha; Humphreys, Keith; Thompson, Deborah; Ghoussaini, Maya; Bolla, Manjeet K; Dennis, Joe; Wang, Qin; Canisius, Sander; Scott, Christopher G; Apicella, Carmel; Hopper, John L; Southey, Melissa C; Stone, Jennifer; Broeks, Annegien; Schmidt, Marjanka K; Scott, Rodney J; Lophatananon, Artitaya; Muir, Kenneth; Beckmann, Matthias W; Ekici, Arif B; Fasching, Peter A; Heusinger, Katharina; Dos-Santos-Silva, Isabel; Peto, Julian; Tomlinson, Ian; Sawyer, Elinor J; Burwinkel, Barbara; Marme, Frederik; Guénel, Pascal; Truong, Thérèse; Bojesen, Stig E; Flyger, Henrik; Benitez, Javier; González-Neira, Anna; Anton-Culver, Hoda; Neuhausen, Susan L; Arndt, Volker; Brenner, Hermann; Engel, Christoph; Meindl, Alfons; Schmutzler, Rita K; Arnold, Norbert; Brauch, Hiltrud; Hamann, Ute; Chang-Claude, Jenny; Khan, Sofia; Nevanlinna, Heli; Ito, Hidemi; Matsuo, Keitaro; Bogdanova, Natalia V; Dörk, Thilo; Lindblom, Annika; Margolin, Sara; Kosma, Veli-Matti; Mannermaa, Arto; Tseng, Chiu-Chen; Wu, Anna H; Floris, Giuseppe; Lambrechts, Diether; Rudolph, Anja; Peterlongo, Paolo; Radice, Paolo; Couch, Fergus J; Vachon, Celine; Giles, Graham G; McLean, Catriona; Milne, Roger L; Dugué, Pierre-Antoine; Haiman, Christopher A; Maskarinec, Gertraud; Woolcott, Christy; Henderson, Brian E; Goldberg, Mark S; Simard, Jacques; Teo, Soo H; Mariapun, Shivaani; Helland, Åslaug; Haakensen, Vilde; Zheng, Wei; Beeghly-Fadiel, Alicia; Tamimi, Rulla; Jukkola-Vuorinen, Arja; Winqvist, Robert; Andrulis, Irene L; Knight, Julia A; Devilee, Peter; Tollenaar, Robert A E M; Figueroa, Jonine; García-Closas, Montserrat; Czene, Kamila; Hooning, Maartje J; Tilanus-Linthorst, Madeleine; Li, Jingmei; Gao, Yu-Tang; Shu, Xiao-Ou; Cox, Angela; Cross, Simon S; Luben, Robert; Khaw, Kay-Tee; Choi, Ji-Yeob; Kang, Daehee; Hartman, Mikael; Lim, Wei Yen; Kabisch, Maria; Torres, Diana; Jakubowska, Anna; Lubinski, Jan; McKay, James; Sangrajrang, Suleeporn; Toland, Amanda E; Yannoukakos, Drakoulis; Shen, Chen-Yang; Yu, Jyh-Cherng; Ziogas, Argyrios; Schoemaker, Minouk J; Swerdlow, Anthony; Borresen-Dale, Anne-Lise; Kristensen, Vessela; French, Juliet D; Edwards, Stacey L; Dunning, Alison M; Easton, Douglas F; Hall, Per; Chenevix-Trench, Georgia

2015-07-02

Genome-wide association studies have identified SNPs near ZNF365 at 10q21.2 that are associated with both breast cancer risk and mammographic density. To identify the most likely causal SNPs, we fine mapped the association signal by genotyping 428 SNPs across the region in 89,050 European and 12,893 Asian case and control subjects from the Breast Cancer Association Consortium. We identified four independent sets of correlated, highly trait-associated variants (iCHAVs), three of which were located within ZNF365. The most strongly risk-associated SNP, rs10995201 in iCHAV1, showed clear evidence of association with both estrogen receptor (ER)-positive (OR = 0.85 [0.82-0.88]) and ER-negative (OR = 0.87 [0.82-0.91]) disease, and was also the SNP most strongly associated with percent mammographic density. iCHAV2 (lead SNP, chr10: 64,258,684:D) and iCHAV3 (lead SNP, rs7922449) were also associated with ER-positive (OR = 0.93 [0.91-0.95] and OR = 1.06 [1.03-1.09]) and ER-negative (OR = 0.95 [0.91-0.98] and OR = 1.08 [1.04-1.13]) disease. There was weaker evidence for iCHAV4, located 5' of ADO, associated only with ER-positive breast cancer (OR = 0.93 [0.90-0.96]). We found 12, 17, 18, and 2 candidate causal SNPs for breast cancer in iCHAVs 1-4, respectively. Chromosome conformation capture analysis showed that iCHAV2 interacts with the ZNF365 and NRBF2 (more than 600 kb away) promoters in normal and cancerous breast epithelial cells. Luciferase assays did not identify SNPs that affect transactivation of ZNF365, but identified a protective haplotype in iCHAV2, associated with silencing of the NRBF2 promoter, implicating this gene in the etiology of breast cancer. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Do mammographic technologists affect radiologists’ diagnostic mammography interpretative performance?

PubMed Central

Henderson, Louise M.; Benefield, Thad; Bowling, J. Michael; Durham, Danielle; Marsh, Mary W.; Schroeder, Bruce F.; Yankaskas, Bonnie C.

2015-01-01

Objective The purpose of this study was to determine whether the technologist has an effect on the radiologists’ interpretative performance of diagnostic mammography. Materials and Methods Using data from a community based mammography registry from 1994 to 2009, we identified 162,755 diagnostic mammograms interpreted by 286 radiologists and performed by 303 mammographic technologists. We calculated sensitivity, false positive rate, and positive predictive value of biopsy (PPV2) for examinations performed (images taken) by each mammographic technologist, separately for film and digital modalities. We assessed the variability of these performance measures among mammographic technologists, using mixed effects logistic regression and taking into account the clustering of examinations within women, radiologists, and radiology practice. Results Among the 291 technologists performing film examinations, mean sensitivity of the examinations they performed was 83.0% (95% Confidence Interval (CI)=80.8–85.2%), mean false positive rate was 8.5 per 1000 examinations (95%CI: 8.0–9.0%), and mean PPV2 was 27.1% (95%CI: 24.8–29.4). For the 45 technologists performing digital examinations, mean sensitivity of the examinations they performed was 79.6% (95%CI: 73.1–86.2%), mean false positive rate was 8.8 (95%CI: 7.5–10.0%), and mean PPV2 was 23.6% (95%CI: 18.8–28.4%). We found significant variation by technologist in the sensitivity, false positive rate, and PPV2 for film but not digital mammography (p<0.0001 for all 3 film performance measures). Conclusions Our results suggest that the technologist has an influence on radiologists’ performance of diagnostic film mammography but not digital. Future work should examine why this difference by modality exists and determine if similar patterns are observed for screening mammography. PMID:25794085

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.

PubMed

Patel, Tejal A; Puppala, Mamta; Ogunti, Richard O; Ensor, Joe E; He, Tiancheng; Shewale, Jitesh B; Ankerst, Donna P; Kaklamani, Virginia G; Rodriguez, Angel A; Wong, Stephen T C; Chang, Jenny C

2017-01-01

A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society. © 2016 American Cancer Society.

The Japanese Guidelines for Breast Cancer Screening.

PubMed

Hamashima, Chisato; Hamashima C, Chisato; Hattori, Masakazu; Honjo, Satoshi; Kasahara, Yoshio; Katayama, Takafumi; Nakai, Masahiro; Nakayama, Tomio; Morita, Takako; Ohta, Koji; Ohnuki, Koji; Sagawa, Motoyasu; Saito, Hiroshi; Sasaki, Seiju; Shimada, Tomoyuki; Sobue, Tomotaka; Suto, Akihiko

2016-05-01

The incidence of breast cancer has progressively increased, making it the leading cause of cancer deaths in Japan. Breast cancer accounts for 20.4% of all new cancers with a reported age-standardized rate of 63.6 per 100 000 women. The Japanese guidelines for breast cancer screening were developed based on a previously established method. The efficacies of mammography with and without clinical breast examination, clinical breast examination and ultrasonography with and without mammography were evaluated. Based on the balance of the benefits and harms, recommendations for population-based and opportunistic screenings were formulated. Five randomized controlled trials of mammographic screening without clinical breast examination were identified for mortality reduction from breast cancer. The overall relative risk for women aged 40-74 years was 0.75 (95% CI: 0.67-0.83). Three randomized controlled trials of mammographic screening with clinical breast examination served as eligible evidence for mortality reduction from breast cancer. The overall relative risk for women aged 40-64 years was 0.87 (95% confidence interval: 0.77-0.98). The major harms of mammographic screening were radiation exposure, false-positive cases and overdiagnosis. Although two case-control studies evaluating mortality reduction from breast cancer were found for clinical breast examination, there was no study assessing the effectiveness of ultrasonography for breast cancer screening. Mammographic screening without clinical breast examination for women aged 40-74 years and with clinical breast examination for women aged 40-64 years is recommended for population-based and opportunistic screenings. Clinical breast examination and ultrasonography are not recommended for population-based screening because of insufficient evidence regarding their effectiveness. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mammographic screening in women with a family history of breast cancer: some results from the Swedish two-county trial.

PubMed

Nixon, R M; Pharoah, P; Tabar, L; Krusemo, U B; Duffy, S W; Prevost, T C; Chen, H H

2000-08-01

The objective of this study is to compare the effectiveness of mammographic screening in women with a family history of breast cancer to those without. In the invited arm of a randomised trial of breast cancer screening, data on family history of breast cancer were available on 29.179 women aged 40-74 attending for screening. Among those women, 358 were diagnosed with breast cancer during the trial. Those with and without a family history were compared with respect to mammographic parenchymal pattern, interval cancer rates, mean sojourn time and sensitivity of screening. In the 358 cancers, the effect of family history was estimated on survival, incidence of advanced cancers and their relationship to screen detection. A significantly higher proportion of high risk mammographic patterns was observed in association with family history among women aged 40-49. Interval cancer rates were higher in women with a family history, and in older women at least, mean sojourn time was shortened in women with a family history (1.89 years compared to 2.70). Survival was better (although not significantly so) in cancers in women with a family history (relative hazard=0.52) independently of detection mode and was significantly poorer in interval cancers then screen detected cancers (relative hazard=2.72) independently of family history. Similarly, interval cancers tended to be larger, and worse malignancy grade in those with and without a family history of breast cancer. These results suggest that the policy often adopted of annual screening for woman aged 40-49, with a family history of breast cancer, is a reasonable one, and that it may also be necessary to shorten the inter-screening interval to one year in women aged over 50 but with a positive family history.

Size assessment of breast lesions by means of a computer-aided detection (CAD) system for magnetic resonance mammography.

PubMed

Levrini, G; Sghedoni, R; Mori, C; Botti, A; Vacondio, R; Nitrosi, A; Iori, M; Nicoli, F

2011-10-01

The aim of this study was to investigate the efficacy of a dedicated software tool for automated volume measurement of breast lesions in contrast-enhanced (CE) magnetic resonance mammography (MRM). The size of 52 breast lesions with a known histopathological diagnosis (three benign, 49 malignant) was automatically evaluated using different techniques. The volume of all lesions was measured automatically (AVM) from CE 3D MRM examinations by means of a computer-aided detection (CAD) system and compared with the size estimates based on maximum diameter measurement (MDM) on MRM, ultrasonography (US), mammography and histopathology. Compared with histopathology as the reference method, AVM understimated lesion size by 4% on average. This result was similar to MDM (3% understimation, not significantly different) but significantly better than US and mammographic lesion measurements (24% and 33% size underestimation, respectively). AVM is as accurate as MDM but faster. Both methods are more accurate for size assessment of breast lesions compared with US and mammography.

Bi-model processing for early detection of breast tumor in CAD system

NASA Astrophysics Data System (ADS)

Mughal, Bushra; Sharif, Muhammad; Muhammad, Nazeer

2017-06-01

Early screening of skeptical masses in mammograms may reduce mortality rate among women. This rate can be further reduced upon developing the computer-aided diagnosis system with decrease in false assumptions in medical informatics. This method highlights the early tumor detection in digitized mammograms. For improving the performance of this system, a novel bi-model processing algorithm is introduced. It divides the region of interest into two parts, the first one is called pre-segmented region (breast parenchyma) and other is the post-segmented region (suspicious region). This system follows the scheme of the preprocessing technique of contrast enhancement that can be utilized to segment and extract the desired feature of the given mammogram. In the next phase, a hybrid feature block is presented to show the effective performance of computer-aided diagnosis. In order to assess the effectiveness of the proposed method, a database provided by the society of mammographic images is tested. Our experimental outcomes on this database exhibit the usefulness and robustness of the proposed method.

Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study

NASA Astrophysics Data System (ADS)

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

2016-03-01

Although mammography is the only clinically acceptable imaging modality used in the population-based breast cancer screening, its efficacy is quite controversy. One of the major challenges is how to help radiologists more accurately classify between benign and malignant lesions. The purpose of this study is to investigate a new mammographic mass classification scheme based on a deep learning method. In this study, we used an image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms, which includes 280 malignant and 280 benign mass ROIs, respectively. An eight layer deep learning network was applied, which employs three pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perception (MLP) classifier for feature categorization. In order to improve robustness of selected features, each convolution layer is connected with a max-pooling layer. A number of 20, 10, and 5 feature maps were utilized for the 1st, 2nd and 3rd convolution layer, respectively. The convolution networks are followed by a MLP classifier, which generates a classification score to predict likelihood of a ROI depicting a malignant mass. Among 560 ROIs, 420 ROIs were used as a training dataset and the remaining 140 ROIs were used as a validation dataset. The result shows that the new deep learning based classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.810+/-0.036. This study demonstrated the potential superiority of using a deep learning based classifier to distinguish malignant and benign breast masses without segmenting the lesions and extracting the pre-defined image features.

SU-E-I-58: Objective Models of Breast Shape Undergoing Mammography and Tomosynthesis Using Principal Component Analysis.

PubMed

Feng, Ssj; Sechopoulos, I

2012-06-01

To develop an objective model of the shape of the compressed breast undergoing mammographic or tomosynthesis acquisition. Automated thresholding and edge detection was performed on 984 anonymized digital mammograms (492 craniocaudal (CC) view mammograms and 492 medial lateral oblique (MLO) view mammograms), to extract the edge of each breast. Principal Component Analysis (PCA) was performed on these edge vectors to identify a limited set of parameters and eigenvectors that. These parameters and eigenvectors comprise a model that can be used to describe the breast shapes present in acquired mammograms and to generate realistic models of breasts undergoing acquisition. Sample breast shapes were then generated from this model and evaluated. The mammograms in the database were previously acquired for a separate study and authorized for use in further research. The PCA successfully identified two principal components and their corresponding eigenvectors, forming the basis for the breast shape model. The simulated breast shapes generated from the model are reasonable approximations of clinically acquired mammograms. Using PCA, we have obtained models of the compressed breast undergoing mammographic or tomosynthesis acquisition based on objective analysis of a large image database. Up to now, the breast in the CC view has been approximated as a semi-circular tube, while there has been no objectively-obtained model for the MLO view breast shape. Such models can be used for various breast imaging research applications, such as x-ray scatter estimation and correction, dosimetry estimates, and computer-aided detection and diagnosis. © 2012 American Association of Physicists in Medicine.

Combined Optical Imaging and Mammography of the Healthy Breast: Optical Contrast Derived From Breast Structure and Compression

PubMed Central

Fang, Qianqian; Carp, Stefan A.; Selb, Juliette; Boverman, Greg; Zhang, Quan; Kopans, Daniel B.; Moore, Richard H.; Miller, Eric L.; Brooks, Dana H.; Boas, David A.

2009-01-01

In this paper, we report new progress in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system. Particularly, we focus on system validation using a series of balloon phantom experiments and the optical image analysis of 49 healthy patients. Using the finite-element method for forward modeling and a regularized Gauss-Newton method for parameter reconstruction, we recovered the inclusions inside the phantom and the hemoglobin images of the human breasts. An enhanced coupling coefficient estimation scheme was also incorporated to improve the accuracy and robustness of the reconstructions. The recovered average total hemoglobin concentration (HbT) and oxygen saturation (SO2) from 68 breast measurements are 16.2 μm and 71%, respectively, where the HbT presents a linear trend with breast density. The low HbT value compared to literature is likely due to the associated mammographic compression. From the spatially co-registered optical/X-ray images, we can identify the chest-wall muscle, fatty tissue, and fibroglandular regions with an average HbT of 20.1±6.1 μm for fibroglandular tissue, 15.4±5.0 μm for adipose, and 22.2±7.3 μm for muscle tissue. The differences between fibroglandular tissue and the corresponding adipose tissue are significant (p < 0.0001). At the same time, we recognize that the optical images are influenced, to a certain extent, by mammographical compression. The optical images from a subset of patients show composite features from both tissue structure and pressure distribution. We present mechanical simulations which further confirm this hypothesis. PMID:19116186

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.

A web-based personalized risk communication and decision-making tool for women with dense breasts: Design and methods of a randomized controlled trial within an integrated health care system.

PubMed

Knerr, Sarah; Wernli, Karen J; Leppig, Kathleen; Ehrlich, Kelly; Graham, Amanda L; Farrell, David; Evans, Chalanda; Luta, George; Schwartz, Marc D; O'Neill, Suzanne C

2017-05-01

Mammographic breast density is one of the strongest risk factors for breast cancer after age and family history. Mandatory breast density disclosure policies are increasing nationally without clear guidance on how to communicate density status to women. Coupling density disclosure with personalized risk counseling and decision support through a web-based tool may be an effective way to allow women to make informed, values-consistent risk management decisions without increasing distress. This paper describes the design and methods of Engaged, a prospective, randomized controlled trial examining the effect of online personalized risk counseling and decision support on risk management decisions in women with dense breasts and increased breast cancer risk. The trial is embedded in a large integrated health care system in the Pacific Northwest. A total of 1250 female health plan members aged 40-69 with a recent negative screening mammogram who are at increased risk for interval cancer based on their 5-year breast cancer risk and BI-RADS® breast density will be randomly assigned to access either a personalized web-based counseling and decision support tool or standard educational content. Primary outcomes will be assessed using electronic health record data (i.e., chemoprevention and breast MRI utilization) and telephone surveys (i.e., distress) at baseline, six weeks, and twelve months. Engaged will provide evidence about whether a web-based personalized risk counseling and decision support tool is an effective method for communicating with women about breast density and risk management. An effective intervention could be disseminated with minimal clinical burden to align with density disclosure mandates. Clinical Trials Registration Number:NCT03029286. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis

PubMed Central

Mazurowski, Maciej A; Lo, Joseph Y; Harrawood, Brian P; Tourassi, Georgia D

2011-01-01

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust intermodality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied “as-is” to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible. PMID:21554985

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

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.

Region-growing approach to detect microcalcifications in digital mammograms

NASA Astrophysics Data System (ADS)

Shin, Jin-Wook; Chae, Soo-Ik; Sook, Yoon M.; Park, Dong-Sun

2001-09-01

Detecting early symptoms of breast cancer is very important to enhance the possibility of cure. There have been active researches to develop computer-aided diagnosis(CAD) systems detecting early symptoms of breast cancer in digital mammograms. An expert or a CAD system can recognize the early symptoms based on microcalcifications appeared in digital mammographic images. Microcalcifications have higher gray value than surrounding regions, so these can be detected by expanding a region from a local maximum. However the resultant image contains unnecessary elements such as noise, holes and valleys. Mathematical morphology is a good solution to delete regions that are affected by the unnecessary elements. In this paper, we present a method that effectively detects microcalcifications in digital mammograms using a combination of local maximum operation and the region-growing operation.

Intensity variation study of the radiation field in a mammographic system using thermoluminescent dosimeters TLD-900 (CaSO4:Dy)

NASA Astrophysics Data System (ADS)

Corrêa, E. L.; Silva, J. O.; Vivolo, V.; Potiens, M. P. A.; Daros, K. A. C.; Medeiros, R. B.

2014-02-01

This study presents the results of the intensity variation of the radiation field in a mammographic system using the thermoluminescent dosimeter TLD-900 (CaSO4:Dy). These TLDs were calibrated and characterized in an industrial X-ray system used for instruments calibration, in the energy range used in mammography. They were distributed in a matrix of 19 lines and five columns, covering an area of 18 cm×8 cm in the center of the radiation field on the clinical equipment. The results showed a variation of the intensity probably explained by the non-uniformity of the field due to the heel effect.

Changes in Breast Density Reporting Patterns of Radiologists After Publication of the 5th Edition BI-RADS Guidelines: A Single Institution Experience.

PubMed

Irshad, Abid; Leddy, Rebecca; Lewis, Madelene; Cluver, Abbie; Ackerman, Susan; Pavic, Dag; Collins, Heather

2017-10-01

The objective of our study was to determine the impact of 5th edition BI-RADS breast density assessment guidelines on density reporting patterns in our clinical practice. PenRad reporting system was used to collect mammographic breast density data reported by five radiologists: 16,907 density assignments using 5th edition BI-RADS guidelines were compared with 19,066 density assessments using 4th edition guidelines. Changes in the density assessment pattern were noted between the 4th and 5th edition guidelines, and agreement in density distribution was compared using the intraclass correlation coefficient. A chi-square analysis was conducted for each reader to examine the change in the proportion of dense versus nondense assignments and on each category type to examine specific changes in proportion of density assignments from the 4th to the 5th edition. All reported p values are two-sided, and statistical significance was considered at the p < 0.001 threshold. Using the 5th edition, there was an overall 5.0% decrease in fatty assessments (p < 0.001), 2.8% increase in scattered densities (p < 0.001), 2.6% increase in heterogeneously dense (p < 0.001), and 0.4% decrease in extremely dense assessments (p = 0.15). Comparing the dense with nondense categories, there was a 2.3% overall increase in the dense assessments (p < 0.001) using 5th edition guidelines, mainly in the heterogeneously dense category. Two radiologists showed increased dense assessments (p < 0.001) using the 5th edition, and three radiologists showed no change (p = 0.39, 0.67, and 0.76). There was an overall increase in the dense assessments using the 5th edition, but individual radiologists in our clinical practice showed a variable adaptation to new guidelines.

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.

Radiologist Agreement for Mammographic Recall by Case Difficulty and Finding Type

PubMed Central

Onega, Tracy; Smith, Megan; Miglioretti, Diana L.; Carney, Patricia A.; Geller, Berta; Kerlikowske, Karla; Buist, Diana SM; Rosenberg, Robert D.; Smith, Robert; Sickles, Edward A.; Haneuse, Sebastien; Anderson, Melissa L.; Yankaskas, Bonnie

2012-01-01

INTRODUCTIONS To assess agreement of mammography interpretations by community radiologists with consensus interpretations of an expert radiology panel, to inform approaches that improve mammography performance. METHODS From six mammography registries, 119 community-based radiologists were recruited to assess one of four randomly assigned test sets of 109 screening mammograms with comparison studies for no recall or recall, giving the most significant finding type [mass, calcifications, asymmetric density or architectural distortion] and location. The mean proportion of agreement with an expert radiology panel was calculated by cancer status, finding type, and difficulty level of identifying the finding at the woman, breast, and lesion level. We also examined concordance in finding type between study radiologists and the expert panel. For each finding type, we determined the proportion of unnecessary recalls, defined as study radiologist recalls that were not expert panel recalls. RESULTS Recall agreement was 100% for masses and for exams with obvious findings in both cancer and non-cancer cases. Among cancer cases, recall agreement was lower for lesions that were subtle (50%) or asymmetric (60%). Subtle non-cancer findings and benign calcifications showed 33% agreement for recall. Agreement for finding responsible for recall was low, especially for architectural distortions (43%) and asymmetric densities (40%). Most unnecessary recalls (51%) were asymmetric densities. CONCLUSION Agreement in mammography interpretation was low for asymmetric densities and architectural distortions. Training focused on these interpretations could improve mammography accuracy and reduce unnecessary recalls. PMID:23122345

Radiologists' preferences for digital mammographic display. The International Digital Mammography Development Group.

PubMed

Pisano, E D; Cole, E B; Major, S; Zong, S; Hemminger, B M; Muller, K E; Johnston, R E; Walsh, R; Conant, E; Fajardo, L L; Feig, S A; Nishikawa, R M; Yaffe, M J; Williams, M B; Aylward, S R

2000-09-01

To determine the preferences of radiologists among eight different image processing algorithms applied to digital mammograms obtained for screening and diagnostic imaging tasks. Twenty-eight images representing histologically proved masses or calcifications were obtained by using three clinically available digital mammographic units. Images were processed and printed on film by using manual intensity windowing, histogram-based intensity windowing, mixture model intensity windowing, peripheral equalization, multiscale image contrast amplification (MUSICA), contrast-limited adaptive histogram equalization, Trex processing, and unsharp masking. Twelve radiologists compared the processed digital images with screen-film mammograms obtained in the same patient for breast cancer screening and breast lesion diagnosis. For the screening task, screen-film mammograms were preferred to all digital presentations, but the acceptability of images processed with Trex and MUSICA algorithms were not significantly different. All printed digital images were preferred to screen-film radiographs in the diagnosis of masses; mammograms processed with unsharp masking were significantly preferred. For the diagnosis of calcifications, no processed digital mammogram was preferred to screen-film mammograms. When digital mammograms were preferred to screen-film mammograms, radiologists selected different digital processing algorithms for each of three mammographic reading tasks and for different lesion types. Soft-copy display will eventually allow radiologists to select among these options more easily.

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.

[The effect of mammographic screening on tumor size, axillary node status and the degree of histologic anaplasia].

PubMed

Garami, Zoltán; Benkó, Klára; Kósa, Csaba; Fülöp, Balázs; Lukács, Géza

2006-10-01

Breast cancer is the most frequent malignant tumor in women in Hungary. Significant reduction of mortality has been brought about not only by the increasing efficiency of complex therapy but also by regular mammographic screening. Of the histopathological data of 633 patients operated with primary breast tumor at the 1st Surgical Clinic of the Debrecen Medical University between January 1st 2000 and December 31st 2004, the authors analyzed tumor diameter, axillary node status and the degree of histologic anaplasia and compared them with the data of mammographic screening. Of the "screened"patients, 70.7% were diagnosed with T1 size tumors, 28.5% with T2 size, and 0.8% with tumors bigger than that. In the "unscreened" patients, our findings were 44.3%, 45.9% and 9.8% respectively. Within T1 tumors, Tla tumors were found in 11%, TIb in 37.6% and T1c in 51.4% in the "screened" group of patients, while the "unscreened" group's results were 2.3%, 12.6% and 85% respectively. 72.7% of the "screened" patients and 56.2% of the "unscreened" patients were found to be axillary node-negative. A study of the degree of histologic anaplasia showed G-I tumors in 15.6%, G-IIs in 62.1% and G-IIIs in 22.3% of the "screened" patients. The corresponding values for the "unscreened" patients were 6.1%, 53.8% and 40.1%, respectively. The differences were highly significant (p < 0.001) in all the parameters investigated. The authors have found a significant increase in the proportion of node-negative patients and patients with smaller tumors even after the first round of mammographic screening and at less than 50% participation. It is to be hoped that a 20% reduction in mortality can be achieved by further increasing the rate of participation.

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.

Cross-national comparison of screening mammography accuracy measures in U.S., Norway, and Spain.

PubMed

Domingo, Laia; Hofvind, Solveig; Hubbard, Rebecca A; Román, Marta; Benkeser, David; Sala, Maria; Castells, Xavier

2016-08-01

To compare accuracy measures for mammographic screening in Norway, Spain, and the US. Information from women aged 50-69 years who underwent mammographic screening 1996-2009 in the US (898,418 women), Norway (527,464), and Spain (517,317) was included. Screen-detected cancer, interval cancer, and the false-positive rates, sensitivity, specificity, positive predictive value (PPV) for recalls (PPV-1), PPV for biopsies (PPV-2), 1/PPV-1 and 1/PPV-2 were computed for each country. Analyses were stratified by age, screening history, time since last screening, calendar year, and mammography modality. The rate of screen-detected cancers was 4.5, 5.5, and 4.0 per 1000 screening exams in the US, Norway, and Spain respectively. The highest sensitivity and lowest specificity were reported in the US (83.1 % and 91.3 %, respectively), followed by Spain (79.0 % and 96.2 %) and Norway (75.5 % and 97.1 %). In Norway, Spain and the US, PPV-1 was 16.4 %, 9.8 %, and 4.9 %, and PPV-2 was 39.4 %, 38.9 %, and 25.9 %, respectively. The number of women needed to recall to detect one cancer was 20.3, 6.1, and 10.2 in the US, Norway, and Spain, respectively. Differences were found across countries, suggesting that opportunistic screening may translate into higher sensitivity at the cost of lower specificity and PPV. • Positive predictive value is higher in population-based screening programmes in Spain and Norway. • Opportunistic mammography screening in the US has lower positive predictive value. • Screening settings in the US translate into higher sensitivity and lower specificity. • The clinical burden may be higher for women screened opportunistically.

Comparison of naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

NASA Astrophysics Data System (ADS)

Cary, Theodore W.; Cwanger, Alyssa; Venkatesh, Santosh S.; Conant, Emily F.; Sehgal, Chandra M.

2012-03-01

This study compares the performance of two proven but very different machine learners, Naïve Bayes and logistic regression, for differentiating malignant and benign breast masses using ultrasound imaging. Ultrasound images of 266 masses were analyzed quantitatively for shape, echogenicity, margin characteristics, and texture features. These features along with patient age, race, and mammographic BI-RADS category were used to train Naïve Bayes and logistic regression classifiers to diagnose lesions as malignant or benign. ROC analysis was performed using all of the features and using only a subset that maximized information gain. Performance was determined by the area under the ROC curve, Az, obtained from leave-one-out cross validation. Naïve Bayes showed significant variation (Az 0.733 +/- 0.035 to 0.840 +/- 0.029, P < 0.002) with the choice of features, but the performance of logistic regression was relatively unchanged under feature selection (Az 0.839 +/- 0.029 to 0.859 +/- 0.028, P = 0.605). Out of 34 features, a subset of 6 gave the highest information gain: brightness difference, margin sharpness, depth-to-width, mammographic BI-RADs, age, and race. The probabilities of malignancy determined by Naïve Bayes and logistic regression after feature selection showed significant correlation (R2= 0.87, P < 0.0001). The diagnostic performance of Naïve Bayes and logistic regression can be comparable, but logistic regression is more robust. Since probability of malignancy cannot be measured directly, high correlation between the probabilities derived from two basic but dissimilar models increases confidence in the predictive power of machine learning models for characterizing solid breast masses on ultrasound.

Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

PubMed

Shi, Bibo; Grimm, Lars J; Mazurowski, Maciej A; Baker, Jay A; Marks, Jeffrey R; King, Lorraine M; Maley, Carlo C; Hwang, E Shelley; Lo, Joseph Y

2018-03-01

The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. In this retrospective study, digital mammographic magnification views were collected for 99 subjects with DCIS at biopsy, 25 of which were subsequently upstaged to invasive cancer. A deep convolutional neural network model that was pretrained on nonmedical images (eg, animals, plants, instruments) was used as the feature extractor. Through a statistical pooling strategy, deep features were extracted at different levels of convolutional layers from the lesion areas, without sacrificing the original resolution or distorting the underlying topology. A multivariate classifier was then trained to predict which tumors contain occult invasive disease. This was compared with the performance of traditional "handcrafted" computer vision (CV) features previously developed specifically to assess mammographic calcifications. The generalization performance was assessed using Monte Carlo cross-validation and receiver operating characteristic curve analysis. Deep features were able to distinguish DCIS with occult invasion from pure DCIS, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval, 0.68-0.73). This performance was comparable with the handcrafted CV features (area under the curve = 0.68; 95% confidence interval, 0.66-0.71) that were designed with prior domain knowledge. Despite being pretrained on only nonmedical images, the deep features extracted from digital mammograms demonstrated comparable performance with handcrafted CV features for the challenging task of predicting DCIS upstaging. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Impact of Breast Reader Assessment Strategy on mammographic radiologists' test reading performance.

PubMed

Suleiman, Wasfi I; Rawashdeh, Mohammad A; Lewis, Sarah J; McEntee, Mark F; Lee, Warwick; Tapia, Kriscia; Brennan, Patrick C

2016-06-01

The detection of breast cancer is somewhat limited by human factors, and thus there is a need to improve reader performance. This study assesses whether radiologists who regularly undertake the education in the form of the Breast Reader Assessment Strategy (BREAST) demonstrate any changes in mammography interpretation performance over time. In 2011, 2012 and 2013, 14 radiologists independently assessed a year-specific BREAST mammographic test-set. Radiologists read a different single test-set once each year, with each comprising 60 digital mammogram cases. Radiologists marked the location of suspected lesions without computer-aided diagnosis (CAD) and assigned a confidence rating of 2 for benign and 3-5 for malignant lesions. The mean sensitivity, specificity, location sensitivity, JAFROC FOM and ROC AUC were calculated. A Kruskal-Wallis test was used to compare the readings for the 14 radiologists across the 3 years. Wilcoxon signed rank test was used to assess comparison between pairs of years. Relationships between changes in performance and radiologist characteristics were examined using a Spearman's test. Significant increases were noted in mean sensitivity (P = 0.01), specificity (P = 0.01), location sensitivity (P = 0.001) and JAFROC FOM (P = 0.001) between 2011 and 2012. Between 2012 and 2013, significant improvements were noted in mean sensitivity (P = 0.003), specificity (P = 0.002), location sensitivity (P = 0.02), JAFROC FOM (P = 0.005) and ROC AUC (P = 0.008). No statistically significant correlations were shown between the levels of improvement and radiologists' characteristics. Radiologists' who undertake the BREAST programme demonstrate significant improvements in test-set performance during a 3-year period, highlighting the value of ongoing education through the use of test-set. © 2016 The Royal Australian and New Zealand College of Radiologists.

Spatial recurrence analysis: A sensitive and fast detection tool in digital mammography

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

Prado, T. L.; Galuzio, P. P.; Lopes, S. R.

Efficient diagnostics of breast cancer requires fast digital mammographic image processing. Many breast lesions, both benign and malignant, are barely visible to the untrained eye and requires accurate and reliable methods of image processing. We propose a new method of digital mammographic image analysis that meets both needs. It uses the concept of spatial recurrence as the basis of a spatial recurrence quantification analysis, which is the spatial extension of the well-known time recurrence analysis. The recurrence-based quantifiers are able to evidence breast lesions in a way as good as the best standard image processing methods available, but with amore » better control over the spurious fragments in the image.« less

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.

Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms

NASA Astrophysics Data System (ADS)

Paquerault, Sophie; Jiang, Yulei; Nishikawa, Robert M.; Schmidt, Robert A.; D'Orsi, Carl J.; Vyborny, Carl J.; Newstead, Gillian M.

2003-05-01

We are developing an automated computer technique to describe calcifications in mammograms according to the BI-RADS lexicon. We evaluated this technique by its agreement with radiologists' description of the same lesions. Three expert mammographers reviewed our database of 90 cases of digitized mammograms containing clustered microcalcifications and described the calcifications according to BI-RADS. In our study, the radiologists used only 4 of the 5 calcification distribution descriptors and 5 of the 14 calcification morphology descriptors contained in BI-RADS. Our computer technique was therefore designed specifically for these 4 calcification distribution descriptors and 5 calcification morphology descriptors. For calcification distribution, 4 linear discriminant analysis (LDA) classifiers were developed using 5 computer-extracted features to produce scores of how well each descriptor describes a cluster. Similarly, for calcification morphology, 5 LDAs were designed using 10 computer-extracted features. We trained the LDAs using only the BI-RADS data reported by the first radiologist and compared the computer output to the descriptor data reported by all 3 radiologists (for the first radiologist, the leave-one-out method was used). The computer output consisted of the best calcification distribution descriptor and the best 2 calcification morphology descriptors. The results of the comparison with the data from each radiologist, respectively, were: for calcification distribution, percent agreement, 74%, 66%, and 73%, kappa value, 0.44, 0.36, and 0.46; for calcification morphology, percent agreement, 83%, 77%, and 57%, kappa value, 0.78, 0.70, and 0.44. These results indicate that the proposed computer technique can select BI-RADS descriptors in good agreement with radiologists.

Double reading.

PubMed

Kopans, D B

2000-07-01

Clearly, the cost of double reading varies with the approach used. The Massachusetts General Hospital method can only lead to an increase in recalls and the costs that these engender (anxiety for the women recalled, trauma from any biopsies obtained, and the actual monetary costs of additional imaging and interventions). It is of interest that one potential cost, the concern that women recalled may be reluctant to participate again in screening, does not seem to be the case. Women who are recalled appear to be more likely to participate in future screening. Double interpretation where there must be a consensus between the interpreting radiologists, and if this cannot be reached a third arbiter, is the most labor intensive, but can reduce the number of recalls in a double reading system. Computer systems have been developed to act as a second reader. The films must be digitized and then fed through the reader, but studies suggest that the computer can identify cancers that may be overlooked by a human reader. The challenge is to do this without too many false-positive calls. If the radiologist finds the false-positives are too numerous and distracting, then the system is not used. As digital mammographic systems proliferate, and computer algorithms become more sophisticated, the second human reader will likely be replaced by a computer-aided detection system and double reading will become the norm.

Bismuth Sulfide Nanoflowers for Detection of X-rays in the Mammographic Energy Range

PubMed Central

Nambiar, Shruti; Osei, Ernest K.; Yeow, John T. W.

2015-01-01

The increased use of diagnostic x-rays, especially in the field of medical radiology, has necessitated a significant demand for high resolution, real-time radiation detectors. In this regard, the photoresponse of bismuth sulfide (Bi2S3), an n-type semiconducting metal chalcogenide, to low energy x-rays has been investigated in this study. In recent years, several types of nanomaterials of Bi2S3 have been widely studied for optoelectronic and thermoelectric applications. However, photoresponse of Bi2S3 nanomaterials for dosimetric applications has not yet been reported. The photosensitivity of Bi2S3 with nanoscale “flower-like” structures was characterized under x-ray tube-potentials typically used in mammographic procedures. Both dark current and photocurrent were measured under varying x-ray doses, field sizes, and bias voltages for each of the tube potentials – 20, 23, 26 and 30 kV. Results show that the Bi2S3 nanoflowers instantaneously responded to even minor changes in the dose delivered. The photoresponse was found to be relatively high (few nA) at bias voltage as low as +1 V, and fairly repeatable for both short and long exposures to mammographic x-rays with minimal or no loss in sensitivity. The overall dose-sensitivity of the Bi2S3 nanoflowers was found to be similar to that of a micro-ionization chamber. PMID:25801531

Increased incidence of breast carcinoma in patients with irradiation for post-partum mastitis: a screening situation. [X-radiation

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

Logan, W.W.; Mansur, P.S.; Cullinan, A.

1979-01-01

In Rochester, New York, 606 women were treated with ionizing radiation for post-partum mastitis, mostly between 1940 and 1955. Two-thirds of all breasts were treated, the average dose per breast being 377 rads (at 2.5 cm breast depth). Mammographic examinations were performed on 265 of these women still residing in this vicinity. Two nonpalpable carcinomas (with no axillary node metastases) were found in the twelve breast lesions that have been biopsied. Some of the biopsies revealed premalignant changes. It is recommended that women in this high-risk category have close medical supervision, as well as periodic mammographic evaluation, and that themore » importance of periodic breast self-examinations should be emphasized.« less

Mammographic and sonographic findings of steatocystoma multiplex presenting as breast lumps.

PubMed

Wan, John Mun Chin; Wong, Jill Su Lin; Tee, Shang-Ian

2012-12-01

Steatocystoma multiplex (SM) is an uncommon cutaneous disorder characterised by multiple intradermal cysts distributed over the trunk and proximal extremities. This condition affects both genders and is often inherited as an autosomal dominant trait, although sporadic cases have been described. This report describes the mammographic and sonographic features of the cysts, which presented as breast lumps, for evaluation. The cysts appeared as numerous well-circumscribed, radiolucent nodules with thin radiodense rims on mammography. On sonography, the cysts could be hypoechoic, isoechoic or demonstrate mixed echoes containing debris-fluid levels, depending on the amount of clear oily liquid and keratinous material. SM can be diagnosed based on a clinical setting of multiple asymptomatic small intradermal nodules over the trunk and proximal extremities, positive family history and imaging findings.

Breast Reference Set Application: Chris Li-FHCRC (2015) — EDRN Public Portal

Cancer.gov

We propose to evaluate nine candidate biomarkers for ER+ breast cancer in samples from the EDRN Breast Cancer Reference Set. These biomarkers have been preliminarily validated in preclinical samples. The intended clinical applications of these markers are to: 1. Inform timing of a subsequent mammogram in women with a negative screening mammogram; 2. Inform continuation of mammographic screening among women 75-79 years; 3. Prioritize women who should be screened with mammography in areas with limited resources. Testing the reference samples would further expedite addressing these intended clinical applications by providing further validation data to support requests for samples from other sources for further Phase 3 evaluation (e.g., WHI, PLCO, and samples collected at the time of mammographic screening from the University of Toronto and UCSF).

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

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.

Effects of Changes in BI-RADS Density Assessment Guidelines (Fourth Versus Fifth Edition) on Breast Density Assessment: Intra- and Interreader Agreements and Density Distribution.

PubMed

Irshad, Abid; Leddy, Rebecca; Ackerman, Susan; Cluver, Abbie; Pavic, Dag; Abid, Ahad; Lewis, Madelene C

2016-12-01

The objective of our study was to determine intra- and interreader agreements for density assessment using the fifth edition of the BI-RADS guidelines and to compare with those for density assessment using the fourth edition of the BI-RADS guidelines. Five radiologists assessed breast density four times in 104 mammographic examinations: twice using the fourth edition of the BI-RADS guidelines and twice using the fifth edition. The intra- and interreader agreements for density assessment based on each guideline were determined and compared. The density distribution pattern under each of the four BI-RADS density categories using each guideline was also noted and compared. The intrareader agreement for density assessment using the fifth-edition criteria was lower than that using the fourth-edition criteria (p = 0.0179). The overall intrareader agreement (weighted kappa) using the old criteria was 0.84 (95% CI, 0.80-0.87), and the individual intrareader agreement values in five readers ranged from 0.78 (95% CI, 0.69-0.88) to 0.92 (95% CI, 0.87-0.97). The overall intrareader agreement using the new BI-RADS criteria was 0.77 (95% CI, 0.73-0.81), and the individual intrareader agreement values in five readers ranged from 0.74 (95% CI, 0.64-0.84) to 0.99 (95% CI, 0.98-1.00). The interreader agreement values obtained using the fifth-edition criteria were also lower than those obtained using the fourth-edition criteria (p = 0.006). The overall interreader agreement using the old BI-RADS criteria was 0.65 (95% CI, 0.61-0.69), whereas the overall interreader agreement using the new BI-RADS criteria was 0.57 (95% CI, 0.53-0.61). Overall a higher number of dense assessments were given when the fifth-edition guidelines were used (p < 0.0001). Compared with the intra- and interreader agreements obtained using the fourth edition of the BI-RADS guidelines, the intra- and interreader agreements were lower using the fifth-edition guidelines. An increased number of dense assessments were given when the fifth-edition guidelines were used.

Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm

NASA Astrophysics Data System (ADS)

Lu, Lee-Jane W.; Nishino, Thomas K.; Johnson, Raleigh F.; Nayeem, Fatima; Brunder, Donald G.; Ju, Hyunsu; Leonard, Morton H., Jr.; Grady, James J.; Khamapirad, Tuenchit

2012-11-01

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N = 2 to 7 replicates in six women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N = 95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (1) 0.76-0.86, 0.8-1.1, and 0.87-0.92 for %-gland tissue, (2) 0.72-0.82, 0.64-0.96, and 0.77-0.91, for glandular volume, (3) 0.87-0.98, 0.94-1.07, and 0.89-0.99, for fat volume, and (4) 0.89-0.98, 0.94-1.00, and 0.89-0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI versus mammography, and between each MRI versus MATH data than between each MRI versus HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.

Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Johnson, Raleigh F.; Nayeem, Fatima; Brunder, Donald G.; Ju, Hyunsu; Leonard, Morton H.; Grady, James J.; Khamapirad, Tuenchit

2012-01-01

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a 4- to 6-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from 2-dimensional (2-D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3-D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3-D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N= 2 to 7 replicates in 6 women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N=95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (I) 0.76–0.86, 0.8–1.1, and 0.87–0.92 for %-gland tissue, (II) 0.72–0.82, 0.64–0.96, and 0.77–0.91, for glandular volume, (III) 0.87–0.98, 0.94–1.07, and 0.89–0.99, for fat volume, and (IV) 0.89–0.98, 0.94–1.00, and 0.89–0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI vs. mammography, and between each MRI vs. MATH data than between each MRI vs. HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues. PMID:23044556

Prevalence of ERα-397 PvuII C/T, ERα-351 XbaI A/G and PGR PROGINS polymorphisms in Brazilian breast cancer-unaffected women

PubMed Central

Giacomazzi, J.; Aguiar, E.; Palmero, E.I.; Schmidt, A.V.; Skonieski, G.; Filho, D.D.; Bock, H.; Saraiva-Pereira, M.L.; Ewald, I.P.; Schuler-Faccini, L.; Camey, S.A.; Caleffi, M.; Giugliani, R.; Ashton-Prolla, P.

2012-01-01

Polymorphisms of hormone receptor genes have been linked to modifications in reproductive factors and to an increased risk of breast cancer (BC). In the present study, we have determined the allelic and genotypic frequencies of the ERα-397 PvuII C/T, ERα-351 XbaI A/G and PGR PROGINS polymorphisms and investigated their relationship with mammographic density, body mass index (BMI) and other risk factors for BC. A consecutive and unselected sample of 750 Brazilian BC-unaffected women enrolled in a mammography screening program was recruited. The distribution of PGR PROGINS genotypic frequencies was 72.5, 25.5 and 2.0% for A1A1, A1A2 and A2A2, respectively, which was equivalent to that encountered in other studies with healthy women. The distribution of ERα genotypes was: ERα-397 PvuII C/T: 32.3% TT, 47.5% TC, and 20.2% CC; ERα-351 XbaI A/G: 46.3% AA, 41.7% AG and 12.0% GG. ERα haplotypes were 53.5% PX, 14.3% Px, 0.3% pX, and 32.0% px. These were significantly different from most previously published reports worldwide (P < 0.05). Overall, the PGR PROGINS genotypes A2A2 and A1A2 were associated with fatty and moderately fatty breast tissue. The same genotypes were also associated with a high BMI in postmenopausal women. In addition, the ERα-351 XbaI GG genotype was associated with menarche ≥12 years (P = 0.02). ERα and PGR polymorphisms have a phenotypic effect and may play an important role in BC risk determination. Finally, if confirmed in BC patients, these associations could have important implications for mammographic screening and strategies and may be helpful to identify women at higher risk for the disease. PMID:22584640

Stereotactic (Mammographically Guided) Breast Biopsy

MedlinePlus

... the type of biopsy being performed or the design of the biopsy machine, a biopsy of tissue ... cost information. The costs for specific medical imaging tests, treatments and procedures may vary by geographic region. ...

Phantom evaluation of the effect of film processing on mammographic screen-film combinations.

PubMed

McLean, D; Rickard, M T

1994-08-01

Mammographic image quality should be optimal for diagnosis, and the film contrast can be manipulated by altering development parameters. In this study phantom test objects were radiographed and processed for a given range of developer temperatures and times for four film-screen systems. Radiologists scored the phantom test objects on the resultant films to evaluate the effect on diagnosis of varying image contrast. While for three film-screen systems processing led to appreciable contrast differences, for only one film system did maximum contrast correspond with optimal phantom test object scoring. The inability to show an effect on diagnosis in all cases is possibly due to the variation in radiologist responses found in this study and in normal clinical circumstances. Other technical factors such as changes in film fog, grain and mottle may contribute to the study findings.

Neurofibromatosis and breast cancer: Do we need to revise the mammographic screening schedule in patients of neurofibromatosis?

PubMed

Pradhan, Dinesh; Kaur, Neeraj; Gami, Ashmita; Hura, Kanwaljeet S; Garg, Garima; Mohanty, Sambit K

2017-01-01

Neurofibromatosis type 1 (NF-1) is a neurocutaneous syndrome with autosomal dominant mode of inheritance and has a high propensity to develop benign and malignant nervous system tumors. Although uncommon, case reports describing the association of NF-1 and breast cancer are available in the literature. We illustrate one such case of NF-1, with no family history of the disorder and presenting with multifocal invasive carcinoma of the right breast, in an attempt to describe the association between these two entities. We also attempt to extensively review the current literature on the subject. Since patients with NF-1 are at an increased risk of developing breast cancer, we recommend strict adherence to careful clinical breast examination and annual screening mammographic examination starting at 40 years of age in all patients of NF-1.

Geometry and Gesture-Based Features from Saccadic Eye-Movement as a Biometric in Radiology

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

Hammond, Tracy; Tourassi, Georgia; Yoon, Hong-Jun

In this study, we present a novel application of sketch gesture recognition on eye-movement for biometric identification and estimating task expertise. 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 for this study. Sketch gesture recognition techniques were employed to extract geometric and gesture-based features from saccadic eye-movements. Our results show that saccadic eye-movement, characterizedmore » using sketch-based features, result in more accurate models for predicting individual identity and level of expertise than more traditional eye-tracking features.« less

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

Digital breast tomosynthesis for breast cancer screening and diagnosis in women with dense breasts - a systematic review and meta-analysis.

PubMed

Phi, Xuan-Anh; Tagliafico, Alberto; Houssami, Nehmat; Greuter, Marcel J W; de Bock, Geertruida H

2018-04-03

This study aimed to systematically review and to meta-analyse the accuracy of digital breast tomosynthesis (DBT) versus digital mammography (DM) in women with mammographically dense breasts in screening and diagnosis. Two independent reviewers identified screening or diagnostic studies reporting at least one of four outcomes (cancer detection rate-CDR, recall rate, sensitivity and specificity) for DBT and DM in women with mammographically dense breasts. Study quality was assessed using QUADAS-2. Meta-analysis of CDR and recall rate used a random effects model. Summary ROC curve summarized sensitivity and specificity. Sixteen studies were included (five diagnostic; eleven screening). In diagnosis, DBT increased sensitivity (84%-90%) versus DM alone (69%-86%) but not specificity. DBT improved CDR versus DM alone (RR: 1.16, 95% CI 1.02-1.31). In screening, DBT + DM increased CDR versus DM alone (RR: 1.33, 95% CI 1.20-1.47 for retrospective studies; RR: 1.52, 95% CI 1.08-2.11 for prospective studies). Recall rate was significantly reduced by DBT + DM in retrospective studies (RR: 0.72, 95% CI 0.64-0.80) but not in two prospective studies (RR: 1.12, 95% CI 0.76-1.63). In women with mammographically dense breasts, DBT+/-DM increased CDR significantly (versus DM) in screening and diagnosis. In diagnosis, DBT+/-DM increased sensitivity but not specificity. The effect of DBT + DM on recall rate in screening dense breasts varied between studies.

Study of quality perception in medical images based on comparison of contrast enhancement techniques in mammographic images

NASA Astrophysics Data System (ADS)

Matheus, B.; Verçosa, L. B.; Barufaldi, B.; Schiabel, H.

2014-03-01

With the absolute prevalence of digital images in mammography several new tools became available for radiologist; such as CAD schemes, digital zoom and contrast alteration. This work focuses in contrast variation and how the radiologist reacts to these changes when asked to evaluated image quality. Three contrast enhancing techniques were used in this study: conventional equalization, CCB Correction [1] - a digitization correction - and value subtraction. A set of 100 images was used in tests from some available online mammographic databases. The tests consisted of the presentation of all four versions of an image (original plus the three contrast enhanced images) to the specialist, requested to rank each one from the best up to worst quality for diagnosis. Analysis of results has demonstrated that CCB Correction [1] produced better images in almost all cases. Equalization, which mathematically produces a better contrast, was considered the worst for mammography image quality enhancement in the majority of cases (69.7%). The value subtraction procedure produced images considered better than the original in 84% of cases. Tests indicate that, for the radiologist's perception, it seems more important to guaranty full visualization of nuances than a high contrast image. Another result observed is that the "ideal" scanner curve does not yield the best result for a mammographic image. The important contrast range is the middle of the histogram, where nodules and masses need to be seen and clearly distinguished.

Breast US as primary imaging modality for diagnosing gynecomastia.

PubMed

Telegrafo, M; Introna, T; Coi, L; Cornacchia, I; Rella, L; Stabile Ianora, A A; Angelelli, G; Moschetta, M

2016-01-01

To assess the role of breast US in diagnosing and classifying gynecomastia as the primary imaging modality and to compare US findings and classification system with the mammographic ones. 48 patients suspected of having gynecomastia underwent mammography and US. Two radiologists in consensus retrospectively evaluated mammograms and sonograms. Both US and mammographic images were evaluated categorizing gynecomastia into non-mass, nodular and flame shaped patterns. The two category assignations were compared in order to find any difference. The reference standard for both the classification systems was represented by the cytological examination in 18 out of 44 cases (41%) and the six-month US follow-up in the remaining cases. The US examination revealed pseudo-gynecomastia in 4/48 (8%) and true gynecomastia in the remaining 44 (92%). Gynecomastia was bilateral in 25/44 cases (57%) and unilateral in the remaining 19 (43%). The cases of true gynecomastia included non mass shape in 26/44 cases (59%), nodular shape in 12 (27%) and flame shape in 6 (14%). The mammographic examination revealed the same results as compared with US findings. 18/44 (41%) patients affected by nodular or dendritic gynecomastia underwent cytological examination confirming the presence of glandular tissue and the benign nature of the clinical condition. US could be proposed as the primary imaging tool for diagnosing and classifying gynecomastia, avoiding unnecessary Xray examinations or invasive procedures in case of diffuse gynecomastia. In case of nodular or dendritic patterns, biopsy remains mandatory for a definitive diagnosis.

Consistency of visual assessments of mammographic breast density from vendor-specific "for presentation" images.

PubMed

Abdolell, Mohamed; Tsuruda, Kaitlyn; Lightfoot, Christopher B; Barkova, Eva; McQuaid, Melanie; Caines, Judy; Iles, Sian E

2016-01-01

Discussions of percent breast density (PD) and breast cancer risk implicitly assume that visual assessments of PD are comparable between vendors despite differences in technology and display algorithms. This study examines the extent to which visual assessments of PD differ between mammograms acquired from two vendors. Pairs of "for presentation" digital mammography images were obtained from two mammography units for 146 women who had a screening mammogram on one vendor unit followed by a diagnostic mammogram on a different vendor unit. Four radiologists independently visually assessed PD from single left mediolateral oblique view images from the two vendors. Analysis of variance, intra-class correlation coefficients (ICC), scatter plots, and Bland-Altman plots were used to evaluate PD assessments between vendors. The mean radiologist PD for each image was used as a consensus PD measure. Overall agreement of the PD assessments was excellent between the two vendors with an ICC of 0.95 (95% confidence interval: 0.93 to 0.97). Bland-Altman plots demonstrated narrow upper and lower limits of agreement between the vendors with only a small bias (2.3 percentage points). The results of this study support the assumption that visual assessment of PD is consistent across mammography vendors despite vendor-specific appearances of "for presentation" images.

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.

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.

Evaluation Of Digital Unsharp-Mask Filtering For The Detection Of Subtle Mammographic Microcalcifications

NASA Astrophysics Data System (ADS)

Chan, Heang-Ping; Vyborny, Carl J.; MacMahon, Heber; Metz, Charles E.; Doi, Kunio; Sickles, Edward A.

1986-06-01

We have conducted a study to assess the effects of digitization and unsharp-mask filtering on the ability of observers to detect subtle microcalcifications in mammograms. Thirty-two conventional screen-film mammograms were selected from patient files by two experienced mammographers. Twelve of the mammograms contained a suspicious cluster of microcalcifications in patients who subsequently underwent biopsy. Twenty of the mammograms were normal cases which were initially interpreted as being free of clustered microcalcifications and did not demonstrate such on careful review. The mammograms were digitized with a high-quality Fuji image processing/simulation system. The system consists of two drum scanners with which an original radiograph can be digitized, processed by a minicomputer, and reconstituted on film. In this study, we employed a sampling aperture of 0.1 mm X 0.1 mm and a sampling distance of 0.1 mm. The density range from 0.2 to 2.75 was digitized to 1024 grey levels per pixel. The digitized images were printed on a single emulsion film with a display aperture having the same size as the sampling aperture. The system was carefully calibrated so that the density and contrast of a digitized image were closely matched to those of the original radiograph. Initially, we evaluated the effects of the weighting factor and the mask size of a unsharp-mask filter on the appearance of mammograms for various types of breasts. Subjective visual comparisons suggested that a mask size of 91 X 91 pixels (9.1 mm X 9.1 mm) enhances the visibility of microcalcifications without excessively increasing the high-frequency noise. Further, a density-dependent weighting factor that increases linearly from 1.5 to 3.0 in the density range of 0.2 to 2.5 enhances the contrast of microcalcifications without introducing many potentially confusing artifacts in the low-density areas. An unsharp-mask filter with these parameters was used to process the digitized mammograms. We conducted observer performance experiments to evaluate the detectability of micro-calcifications in three sets of mammograms: the original film images, unprocessed digitized images, and unsharp-masked images. Each set included the same 20 normal cases and 12 abnormal cases. A total of 5 board-certified radiologists and 4 senior radiology residents participated as observers. In the first experiment, the detectability of microcalcifications was measured for the original, unprocessed digitized, and unsharp-masked images. Each observer read all 96 films in one session with the cases arranged in a different random order. A maximum of 15 seconds was allowed to read each image. To facilitate receiver operating character-istic (ROC) analysis, each observer ranked his/her observation regarding the presence or absence of a cluster of 3 or more microcalcifications on a 5-point confidence rating scale (1=definitely no microcalcifications, 2=probably no microcalcifications; 3=microcalcifi-cations possibly present; 4=microcalcifications probably present; 5=microcalcifications definitely present). The observer identified the location of the suspected microcalci-fications when the confidence rating was 2 or greater. In the second experiment, we evaluated whether reading the unsharp-masked image and the unprocessed digitized image side by side for each case would reduce false-positive detection rates for microcalcifications and thus improve overall performance. The observer was again allowed a maximum of 15 seconds to read each pair of images and was instructed to use the unsharp-masked image for primary reading and the unprocessed digitized image for reference. The experimental setting and procedures were otherwise the same as those for the first experiment.

21 CFR 900.12 - Quality standards.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MAMMOGRAPHY... to mammography. The training shall include instruction in radiation physics, including radiation physics specific to mammography, radiation effects, and radiation protection. The mammographic...

Breast gigantism due to D-penicillamine.

PubMed

Desautels, J E

1994-04-01

One of the alarming side effects of D-penicillamine therapy is massive breast hypertrophy. This effect has been observed in nine patients to date. The author presents another case, including the first description of mammographic findings.

Ideal-observer analysis of lesion detectability in planar, conventional SPECT, and dedicated SPECT scintimammography using effective multi-dimensional smoothing

NASA Astrophysics Data System (ADS)

La Riviere, P. J.; Pan, X.; Penney, B. C.

1998-06-01

Scintimammography, a nuclear-medicine imaging technique that relies on the preferential uptake of Tc-99m-sestamibi and other radionuclides in breast malignancies, has the potential to provide differentiation of mammographically suspicious lesions, as well as outright detection of malignancies in women with radiographically dense breasts. In this work we use the ideal-observer framework to quantify the detectability of a 1-cm lesion using three different imaging geometries: the planar technique that is the current clinical standard, conventional single-photon emission computed tomography (SPECT), in which the scintillation cameras rotate around the entire torso, and dedicated breast SPECT, in which the cameras rotate around the breast alone. We also introduce an adaptive smoothing technique for the processing of planar images and of sinograms that exploits Fourier transforms to achieve effective multidimensional smoothing at a reasonable computational cost. For the detection of a 1-cm lesion with a clinically typical 6:1 tumor-background ratio, we find ideal-observer signal-to-noise ratios (SNR) that suggest that the dedicated breast SPECT geometry is the most effective of the three, and that the adaptive, two-dimensional smoothing technique should enhance lesion detectability in the tomographic reconstructions.

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.

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.

Histopathology findings of non-mass cancers on breast ultrasound.

PubMed

Kim, Hye Rin; Jung, Hae Kyoung

2018-06-01

There is little research done on non-mass cancers (NMCs) on breast ultrasound (US). To evaluate large-sectional histopathology findings of NMCs on breast US. The mammographic and histopathology features of biopsy proven 36 breast cancers which showed pure non-mass lesions on US were retrospectively reviewed. The most common mammographic finding was microcalcification (23/35, 65.7%); fine pleomorphic microcalcification was predominant (18/23, 78.3%). The main tumor type was pure ductal carcinoma in situ (DCIS) (14/36, 38.9%) and DCIS with micro- or minimal invasion (11/36, 30.6%). Among the 25 DCIS, histologic grade was high in 15 (60.0%) and intermediate in nine (36%); comedo necrosis was seen in 17 (68%). Immunohistochemical analysis was available in 27 lesions and showed HER2-overexpression in 12 (44.4%) and triple-negative in two (7.4%). According to our limited patient sample, NMCs on breast US were mainly associated with high-grade DCIS.

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spatially filtered to enforce local consensus among neighboring pixels and the spatially filtered image is output.

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spacially filtered to enforce local consensus among neighboring pixels and the spacially filtered image is output.

Mammographic mass classification based on possibility theory

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2017-03-01

Shape and margin features are very important for differentiating between benign and malignant masses in mammographic images. In fact, benign masses are usually round and oval and have smooth contours. However, malignant tumors have generally irregular shape and appear lobulated or speculated in margins. This knowledge suffers from imprecision and ambiguity. Therefore, this paper deals with the problem of mass classification by using shape and margin features while taking into account the uncertainty linked to the degree of truth of the available information and the imprecision related to its content. Thus, in this work, we proposed a novel mass classification approach which provides a possibility based representation of the extracted shape features and builds a possibility knowledge basis in order to evaluate the possibility degree of malignancy and benignity for each mass. For experimentation, the MIAS database was used and the classification results show the great performance of our approach in spite of using simple features.

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology.

PubMed

Wu, Shibin; Yu, Shaode; Yang, Yuhan; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

PubMed Central

Wu, Shibin; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII). PMID:24416072

Construction of mammographic examination process ontology using bottom-up hierarchical task analysis.

PubMed

Yagahara, Ayako; Yokooka, Yuki; Jiang, Guoqian; Tsuji, Shintarou; Fukuda, Akihisa; Nishimoto, Naoki; Kurowarabi, Kunio; Ogasawara, Katsuhiko

2018-03-01

Describing complex mammography examination processes is important for improving the quality of mammograms. It is often difficult for experienced radiologic technologists to explain the process because their techniques depend on their experience and intuition. In our previous study, we analyzed the process using a new bottom-up hierarchical task analysis and identified key components of the process. Leveraging the results of the previous study, the purpose of this study was to construct a mammographic examination process ontology to formally describe the relationships between the process and image evaluation criteria to improve the quality of mammograms. First, we identified and created root classes: task, plan, and clinical image evaluation (CIE). Second, we described an "is-a" relation referring to the result of the previous study and the structure of the CIE. Third, the procedural steps in the ontology were described using the new properties: "isPerformedBefore," "isPerformedAfter," and "isPerformedAfterIfNecessary." Finally, the relationships between tasks and CIEs were described using the "isAffectedBy" property to represent the influence of the process on image quality. In total, there were 219 classes in the ontology. By introducing new properties related to the process flow, a sophisticated mammography examination process could be visualized. In relationships between tasks and CIEs, it became clear that the tasks affecting the evaluation criteria related to positioning were greater in number than those for image quality. We developed a mammographic examination process ontology that makes knowledge explicit for a comprehensive mammography process. Our research will support education and help promote knowledge sharing about mammography examination expertise.

All pure flat atypical atypia lesions of the breast diagnosed using percutaneous vacuum-assisted breast biopsy do not need surgical excision.

PubMed

Ouldamer, Lobna; Poisson, Elodie; Arbion, Flavie; Bonneau, Carole; Vildé, Anne; Body, Gilles; Michenet, Patrick

2018-04-14

The purposes of this study were to evaluate the outcome of women with pure flat atypical atypia (FEA) diagnosed at vacuum-assisted breast biopsy (VABB) targeting microcalcifications and to determine whether clinical, radiological and pathologic parameters are able to predict which lesions will be upgraded to malignancy. 2414 cases of consecutive VABB for microcalcifications using VA 8-, 10- or 11-Gauge stereotactically guided core biopsy performed between January 2005 and December 2011 from two french breast cancer centers were evaluated. Data of women with VABB-diagnosed pure FEA who underwent either excisional surgery or mammographic follow-up were analyzed. Cases with mass lesions or ipsilateral cancers were excluded. Two pathologists (FA,PM) reviewed the results of procedures performed. Clinical, radiological, as well as histological criteria have been studied in order to determine the correlation between these factors and carcinoma underestimation. This study included 70 cases of pure FEA. Twenty women underwent surgical excision and 50 had clinical and mammographic surveillance only. In three women FEA was upgraded to breast cancer on excision. Clinical and mammographic follow-up for a mean of 56 months ± 27 in the group without excision showed two cancers in the same breast (Intermediate grade DCIS, and invasive ductal carcinoma 84 and 48 months respectively after VABB). Three factors were significantly predictive of underestimation or occurence of cancer for pure FEA when the radiologic lesions are calcifications: age≥ 57 years, radiologic size >10 mm and number of FEA foci ≥4. Copyright © 2018. Published by Elsevier Ltd.

Developing a clinical utility framework to evaluate prediction models in radiogenomics

NASA Astrophysics Data System (ADS)

Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

2015-03-01

Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

Breast US as primary imaging modality for diagnosing gynecomastia

PubMed Central

TELEGRAFO, M.; INTRONA, T.; COI, L.; CORNACCHIA, I.; RELLA, L.; IANORA, A.A. STABILE; ANGELELLI, G.; MOSCHETTA, M.

2016-01-01

Aim To assess the role of breast US in diagnosing and classifying gynecomastia as the primary imaging modality and to compare US findings and classification system with the mammographic ones. Patients and methods 48 patients suspected of having gynecomastia underwent mammography and US. Two radiologists in consensus retrospectively evaluated mammograms and sonograms. Both US and mammographic images were evaluated categorizing gynecomastia into non-mass, nodular and flame shaped patterns. The two category assignations were compared in order to find any difference. The reference standard for both the classification systems was represented by the cytological examination in 18 out of 44 cases (41%) and the six-month US follow-up in the remaining cases. Results The US examination revealed pseudo-gynecomastia in 4/48 (8%) and true gynecomastia in the remaining 44 (92%). Gynecomastia was bilateral in 25/44 cases (57%) and unilateral in the remaining 19 (43%). The cases of true gynecomastia included non mass shape in 26/44 cases (59%), nodular shape in 12 (27%) and flame shape in 6 (14%). The mammographic examination revealed the same results as compared with US findings. 18/44 (41%) patients affected by nodular or dendritic gynecomastia underwent cytological examination confirming the presence of glandular tissue and the benign nature of the clinical condition. Conclusions US could be proposed as the primary imaging tool for diagnosing and classifying gynecomastia, avoiding unnecessary X-ray examinations or invasive procedures in case of diffuse gynecomastia. In case of nodular or dendritic patterns, biopsy remains mandatory for a definitive diagnosis. PMID:27734795

Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression

PubMed Central

Macklin, Paul; Edgerton, Mary E.; Thompson, Alastair M.; Cristini, Vittorio

2012-01-01

Ductal carcinoma in situ (DCIS)—a significant precursor to invasive breast cancer—is typically diagnosed as microcalcifications in mammograms. However, the effective use of mammograms and other patient data to plan treatment has been restricted by our limited understanding of DCIS growth and calcification. We develop a mechanistic, agent-based cell model and apply it to DCIS. Cell motion is determined by a balance of biomechanical forces. We use potential functions to model interactions with the basement membrane and amongst cells of unequal size and phenotype. Each cell’s phenotype is determined by genomic/proteomic- and microenvironment-dependent stochastic processes. Detailed “sub-models” describe cell volume changes during proliferation and necrosis; we are the first to account for cell calcification. We introduce the first patient-specific calibration method to fully constrain the model based upon clinically-accessible histopathology data. After simulating 45 days of solid-type DCIS with comedonecrosis, the model predicts: necrotic cell lysis acts as a biomechanical stress relief, and is responsible for the linear DCIS growth observed in mammography; the rate of DCIS advance varies with the duct radius; the tumour grows 7 to 10 mm per year—consistent with mammographic data; and the mammographic and (post-operative) pathologic sizes are linearly correlated—in quantitative agreement with the clinical literature. Patient histopathology matches the predicted DCIS microstructure: an outer proliferative rim surrounds a stratified necrotic core with nuclear debris on its outer edge and calcification in the centre. This work illustrates that computational modelling can provide new insight on the biophysical underpinnings of cancer. It may one day be possible to augment a patient’s mammography and other imaging with rigorously-calibrated models that help select optimal surgical margins based upon the patient’s histopathologic data. PMID:22342935

Search for novel contrast materials in dual-energy x-ray breast imaging using theoretical modeling of contrast-to-noise ratio

NASA Astrophysics Data System (ADS)

Karunamuni, R.; Maidment, A. D. A.

2014-08-01

Contrast-enhanced (CE) dual-energy (DE) x-ray breast imaging uses a low- and high-energy x-ray spectral pair to eliminate soft-tissue signal variation and thereby increase the detectability of exogenous imaging agents. Currently, CEDE breast imaging is performed with iodinated contrast agents. These compounds are limited by several deficiencies, including rapid clearance and poor tumor targeting ability. The purpose of this work is to identify novel contrast materials whose contrast-to-noise ratio (CNR) is comparable or superior to that of iodine in the mammographic energy range. A monoenergetic DE subtraction framework was developed to calculate the DE signal intensity resulting from the logarithmic subtraction of the low- and high-energy signal intensities. A weighting factor is calculated to remove the dependence of the DE signal on the glandularity of the breast tissue. Using the DE signal intensity and weighting factor, the CNR for materials with atomic numbers (Z) ranging from 1 to 79 are computed for energy pairs between 10 and 50 keV. A group of materials with atomic numbers ranging from 42 to 63 were identified to exhibit the highest levels of CNR in the mammographic energy range. Several of these materials have been formulated as nanoparticles for various applications but none, apart from iodine, have been investigated as CEDE breast imaging agents. Within this group of materials, the necessary dose fraction to the LE image decreases as the atomic number increases. By reducing the dose to the LE image, the DE subtraction technique will not provide an anatomical image of sufficient quality to accompany the contrast information. Therefore, materials with Z from 42 to 52 provide nearly optimal values of CNR with energy pairs and dose fractions that provide good anatomical images. This work is intended to inspire further research into new materials for optimized CEDE breast functional imaging.

Search for novel contrast materials in dual-energy x-ray breast imaging using theoretical modeling of contrast-to-noise ratio.

PubMed

Karunamuni, R; Maidment, A D A

2014-08-07

Contrast-enhanced (CE) dual-energy (DE) x-ray breast imaging uses a low- and high-energy x-ray spectral pair to eliminate soft-tissue signal variation and thereby increase the detectability of exogenous imaging agents. Currently, CEDE breast imaging is performed with iodinated contrast agents. These compounds are limited by several deficiencies, including rapid clearance and poor tumor targeting ability. The purpose of this work is to identify novel contrast materials whose contrast-to-noise ratio (CNR) is comparable or superior to that of iodine in the mammographic energy range. A monoenergetic DE subtraction framework was developed to calculate the DE signal intensity resulting from the logarithmic subtraction of the low- and high-energy signal intensities. A weighting factor is calculated to remove the dependence of the DE signal on the glandularity of the breast tissue. Using the DE signal intensity and weighting factor, the CNR for materials with atomic numbers (Z) ranging from 1 to 79 are computed for energy pairs between 10 and 50 keV. A group of materials with atomic numbers ranging from 42 to 63 were identified to exhibit the highest levels of CNR in the mammographic energy range. Several of these materials have been formulated as nanoparticles for various applications but none, apart from iodine, have been investigated as CEDE breast imaging agents. Within this group of materials, the necessary dose fraction to the LE image decreases as the atomic number increases. By reducing the dose to the LE image, the DE subtraction technique will not provide an anatomical image of sufficient quality to accompany the contrast information. Therefore, materials with Z from 42 to 52 provide nearly optimal values of CNR with energy pairs and dose fractions that provide good anatomical images. This work is intended to inspire further research into new materials for optimized CEDE breast functional imaging.

A multi-image approach to CADx of breast cancer with integration into PACS

NASA Astrophysics Data System (ADS)

Elter, Matthias; Wittenberg, Thomas; Schulz-Wendtland, Rüdiger; Deserno, Thomas M.

2009-02-01

While screening mammography is accepted as the most adequate technique for the early detection of breast cancer, its low positive predictive value leads to many breast biopsies performed on benign lesions. Therefore, we have previously developed a knowledge-based system for computer-aided diagnosis (CADx) of mammographic lesions. It supports the radiologist in the discrimination of benign and malignant lesions. So far, our approach operates on the lesion level and employs the paradigm of content-based image retrieval (CBIR). Similar lesions with known diagnosis are retrieved automatically from a library of references. However, radiologists base their diagnostic decisions on additional resources, such as related mammographic projections, other modalities (e.g. ultrasound, MRI), and clinical data. Nonetheless, most CADx systems disregard the relation between the craniocaudal (CC) and mediolateral-oblique (MLO) views of conventional mammography. Therefore, we extend our approach to the full case level: (i) Multi-frame features are developed that jointly describe a lesion in different views of mammography. Taking into account the geometric relation between different images, these features can also be extracted from multi-modal data; (ii) the CADx system architecture is extended appropriately; (iii) the CADx system is integrated into the radiology information system (RIS) and the picture archiving and communication system (PACS). Here, the framework for image retrieval in medical applications (IRMA) is used to support access to the patient's health care record. Of particular interest is the application of the proposed CADx system to digital breast tomosynthesis (DBT), which has the potential to succeed digital mammography as the standard technique for breast cancer screening. The proposed system is a natural extension of CADx approaches that integrate only two modalities. However, we are still collecting a large enough database of breast lesions with images from multiple modalities to evaluate the benefits of the proposed approach on.

21 CFR 900.12 - Quality standards.

Code of Federal Regulations, 2014 CFR

2014-04-01

... to mammography. The training shall include instruction in radiation physics, including radiation physics specific to mammography, radiation effects, and radiation protection. The mammographic... ensure that medical physicists certified by the body are competent to perform physics survey; and (B)(1...

21 CFR 900.12 - Quality standards.

Code of Federal Regulations, 2013 CFR

2013-04-01

... to mammography. The training shall include instruction in radiation physics, including radiation physics specific to mammography, radiation effects, and radiation protection. The mammographic... ensure that medical physicists certified by the body are competent to perform physics survey; and (B)(1...

21 CFR 900.12 - Quality standards.

Code of Federal Regulations, 2012 CFR

2012-04-01

... to mammography. The training shall include instruction in radiation physics, including radiation physics specific to mammography, radiation effects, and radiation protection. The mammographic... ensure that medical physicists certified by the body are competent to perform physics survey; and (B)(1...

21 CFR 900.12 - Quality standards.

Code of Federal Regulations, 2011 CFR

2011-04-01

... to mammography. The training shall include instruction in radiation physics, including radiation physics specific to mammography, radiation effects, and radiation protection. The mammographic... ensure that medical physicists certified by the body are competent to perform physics survey; and (B)(1...

Pictorial essay: Mammography of the male breast

PubMed Central

Popli, Manju Bala; Popli, V; Bahl, P; Solanki, Y

2009-01-01

Mammography is an imaging modality that is widely perceived to be of use only in women for the detection and diagnosis of breast pathologies. Here, we present a pictorial essay on the mammographic spectrum of male breast pathologies. PMID:19881102

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.

Possibilities of electrical impedance tomography in gynecology

NASA Astrophysics Data System (ADS)

V, Trokhanova O.; A, Chijova Y.; B, Okhapkin M.; V, Korjenevsky A.; S, Tuykin T.

2013-04-01

The paper describes results of comprehensive EIT diagnostics of mammary glands and cervix. The data were obtained from examinations of 170 patients by EIT system MEM (multi-frequency electrical impedance mammograph) and EIT system GIT (gynecological impedance tomograph). Mutual dependence is discussed.

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.

A comparison of image interpretation times in full field digital mammography and digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Astley, Susan; Connor, Sophie; Lim, Yit; Tate, Catriona; Entwistle, Helen; Morris, Julie; Whiteside, Sigrid; Sergeant, Jamie; Wilson, Mary; Beetles, Ursula; Boggis, Caroline; Gilbert, Fiona

2013-03-01

Digital Breast Tomosynthesis (DBT) provides three-dimensional images of the breast that enable radiologists to discern whether densities are due to overlapping structures or lesions. To aid assessment of the cost-effectiveness of DBT for screening, we have compared the time taken to interpret DBT images and the corresponding two-dimensional Full Field Digital Mammography (FFDM) images. Four Consultant Radiologists experienced in reading FFDM images (4 years 8 months to 8 years) with training in DBT interpretation but more limited experience (137-407 cases in the past 6 months) were timed reading between 24 and 32 two view FFDM and DBT cases. The images were of women recalled from screening for further assessment and women under surveillance because of a family history of breast cancer. FFDM images were read before DBT, according to local practice. The median time for readers to interpret FFDM images was 17.0 seconds, with an interquartile range of 12.3-23.6 seconds. For DBT, the median time was 66.0 seconds, and the interquartile range was 51.1-80.5 seconds. The difference was statistically significant (p<0.001). Reading times were significantly longer in family history clinics (p<0.01). Although it took approximately four times as long to interpret DBT than FFDM images, the cases were more complex than would be expected for routine screening, and with higher mammographic density. The readers were relatively inexperienced in DBT interpretation and may increase their speed over time. The difference in times between clinics may be due to increased throughput at assessment, or decreased density.

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.

All atypia diagnosed at stereotactic vacuum-assisted breast biopsy do not need surgical excision.

PubMed

de Mascarel, Isabelle; Brouste, Véronique; Asad-Syed, Maryam; Hurtevent, Gabrielle; Macgrogan, Gaëtan

2011-09-01

The necessity of excision is debatable when atypia are diagnosed at stereotactic vacuum-assisted breast biopsy (microbiopsy). Among the 287 surgical excisions performed at Institut Bergonié from 1999 to 2009, we selected a case-control study group of 151 excisions; 52 involving all the diagnosed cancers and 99 randomly selected among the 235 excisions without cancer, following atypical microbiopsy (24 flat epithelial atypia; 50 atypical ductal hyperplasia; 14 lobular neoplasia; 63 mixed lesions). Mammographical calcification (type, extension, complete removal) and histological criteria of epithelial atypia (type, number of foci, size/extension), topography and microcalcification extension at microbiopsy were compared according to the presence or absence of cancer at excision. Factors associated with cancer at excision were Breast Imaging Reporting and Data System (BI-RADS5) lesions, large and/or multiple foci of mammographical calcifications, histological type, number, size and extension of atypical foci. Flat epithelial atypia alone was never associated with cancer at excision. BI-RADS5, atypical ductal hyperplasia (alone or predominant) and >3 foci of atypia were identified as independent pejorative factors. There was never any cancer at excision when these pejorative factors were absent (n=31). Presence of one (n=59), two (n=23) or three (n=14) factors was associated with cancer in 24, 15 and 13 cases with an odds ratio=5.8 (95% CI: 3-11.2) for each additional factor. We recommend that mammographical data and histological characteristics be taken into account in the decision-making process after diagnosis of atypia on microbiopsy. With experienced senologists and strict histological criteria, some patients could be spared surgery resulting in significant patient, financial and time advantages.

Flat epithelial atypia of the breast: pathological-radiological correlation.

PubMed

Solorzano, Silma; Mesurolle, Benoît; Omeroglu, Attila; El Khoury, Mona; Kao, Ellen; Aldis, Ann; Meterissian, Sarkis

2011-09-01

This study was undertaken to determine the prevalence of flat epithelial atypia at ultrasound-guided and stereotactically guided needle biopsies, to describe the mammographic and sonographic features of flat epithelial atypia, and to determine the significance of lesions diagnosed as flat epithelial atypia at imaging-guided needle biopsies. Retrospective review of a database of 1369 consecutive sonographically and stereotactically guided needle biopsies performed during a 12-month period yielded 33 lesions with flat epithelial atypia as the most severe pathologic entity (32 patients). Two radiologists retrospectively reviewed the imaging presentation, by combined consensus, according to the BI-RADS lexicon. Twenty-two of 33 flat epithelial atypia diagnoses (67%) were obtained under stereotactic guidance, and 11 (33%) were obtained under sonographic guidance. Six patients had synchronous breast cancer. Flat epithelial atypia lesions presented mammographically most often as microcalcifications (20/33 [61%]) distributed in a cluster (14/20 [70%]) with amorphous morphology (13/20 [65%]). Sonographically, flat epithelial atypia lesions appeared most often as masses (9/11 [82%]), with an irregular shape (6/9 [67%]), microlobulated margins (5/9 [56%]), and hypoechoic or complex echotexture (7/9 [78%]). Twenty-eight of 33 lesions (85%) were surgically excised, confirming the flat epithelial atypia diagnosis in 11 of the 28 lesions (39%), yielding carcinoma in four (14%) and atypical ductal hyperplasia in six (21%). Columnar cell changes without atypia were diagnosed in four lesions (14%), and lobular carcinoma in situ was diagnosed in three lesions (11%). Mammographic and sonographic presentation of flat epithelial atypia is not specific (clustered amorphous microcalcifications and irregular, hypoechoic or complex masses). Given the underestimation rate of malignancy, surgical excision should be considered when imaging-guided biopsy yields flat epithelial atypia.

Impact of the mode of detection on outcome in breast cancer patients treated with breast-conserving therapy.

PubMed

Kini, V R; Vicini, F A; Victor, S J; Dmuchowski, C F; Rebner, M; Martinez, A A

1999-10-01

The impact of the mode of detection on outcome in patients with early stage breast cancer treated with breast-conserving therapy (BCT) was reviewed. Between January 1980 and December 1987, 400 cases of stage I and II breast cancer were treated with BCT. All patients underwent an excisional biopsy, external beam irradiation (RT) to the whole breast (45-50 Gy), and a boost to 60 Gy to the tumor bed. One hundred twenty-four cases (31%) were mammographically detected, whereas 276 (69%) were clinically detected. Median follow-up was 9.2 years. Patients whose cancers were detected by mammography more frequently had smaller tumors (90% T1 vs. 62%, p < 0.0001), lower overall disease stage (78% stage I vs. 47%, p < 0.0001), were older at diagnosis (78% >50 years vs. 54%, p < 0.001), less frequently received chemotherapy (8% vs. 21%, p = 0.001), and had an improved disease-free survival (DFS) (80% vs. 70%, p = 0.014), overall survival (OS) (82% vs. 70%, p = 0.005), and cause-specific survival (CSS) (88% vs. 77%, p = 0.003) at 10 years. However, controlling for tumor size, nodal status, and age, no statistically significant differences in the 5- and 10-year actuarial rates of local recurrence (LR), DFS, CSS, or OS were seen based on the mode of detection. Initial mode of detection was the strongest predictor of outcome after a LR. The 3-year DFS rate after LR was significantly better in initially mammographically detected versus clinically detected cases (100% vs. 61%, p = 0.011). Patients with mammographically detected breast cancer generally have smaller tumors and lower overall disease stage at presentation. However, the mode of detection does not independently appear to affect the success of BCT in these patients.

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.

PubMed

Benndorf, Matthias; Kotter, Elmar; Langer, Mathias; Herda, Christoph; Wu, Yirong; Burnside, Elizabeth S

2015-06-01

To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.