BI-RADS Density Research Articles

Abstract PO3-09-01: Integrating pathomic and radiomic images to classify risk of subsequent events among women with DCIS

Abstract Background Multiple sources of ~omic data can be generated from women at different stages of developing breast cancer, the leading cancer diagnosed in women worldwide. Traditionally interrogation of risk factors to study associations and develop prediction models for future breast events has been limited to one or few risk factors, or summary scores of clinical and tumor characteristics. Methods to bring mammography images and breast biopsies of precancer lesions together to summarize risk of cancer developing in the breast are urgently needed. Integration of these two sources has not been performed to date, but has potential to increase accuracy of risk prediction. Approach The Repository of Archival Human Breast Tissue (RAHBT) was established in 2007 at Washington University School of Medicine (WUSM) and maintains biospecimens and medical record data of women treated with breast-conserving surgery (BCS) or mastectomy for breast cancer at WUSM and six other St Louis metropolitan hospitals between 1981-2016. Prospective follow-up of cases is achieved through health records and Siteman tumor registry. Among 1831 patients with pathologically confirmed DCIS who had no prior cancer, 174 developed breast events at least six months after initial DCIS diagnosis. For each case diagnosed between January 1998 and March 2016 with subsequent breast events, two DCIS controls were matched on race, year of diagnosis (±5 years), age (±5 years), and type of surgery. Tissue micro arrays (TMAs) are constructed after breast pathology review and processed for H&E and imaged. Breast cancer risk factor data are uniformly retrieved form the medical records at time of processing cases of DCIS. Mammogram images are retrieved for all women. Full-field digital mammograms (FFDM) are all using the same technology (Hologic). Risk factors and outcome variables: ipsilateral breast events (IBE) was defined as the development of invasive cancer or DCIS in the treated breast at least 6 months post-diagnosis. Follow-up time was calculated from the date of DCIS diagnosis to the date of first IBE, death, or last follow-up, whichever occurred first. The predictors included commonly used clinicopathological factors: age, tumor grade (high vs. low/intermediate), comedonecrosis, surgical margins (close (≤2mm) vs. negative ( >2mm)), local treatment (BCS only and mastectomy vs. BCS+radiation), and endocrine therapy. BMI, menopausal status, and ER were also available for evaluation as predictors. We limited this analysis to women with digital mammograms immediately prior to diagnosis and include 128 cases and controls. Validation cohort As the RAHBT cohort continues to be followed additional second breast events have been documented after the cut off for events identified through March 2016, and the identical procedures used to review/confirm DCIS and subsequent breast events, construct TMAs, process H&E and images, and assemble risk factor data, and the associated digital mammograms. We have 13 additional ipsilateral breast events with complete data and their controls for independently testing the model performance. Statistical methods Propose novel supervised learning approach to integrate image data from mammograms and TMA slides that accommodates agreement between multi-omics. Results 128 (cases and controls, 21.9% ipsilateral subsequent events) identified for development and internal cross validation. Median age at diagnosis was 52 and median time to subsequent breast event was 63 mo. Controlling for the clinical/pathologic risk factors (age, BMI, BI-RADs density, treatment, tumor grade, parous, menopausal status, ER status, and race) and integrating the whole mammogram and TMA pathology images, we observe a 5-yr ipsilateral AUC = 0.74; 10-yr AUC 0.75. The independent validation is ongoing. Conclusions Integrating heterogeneous multiomic data sources can generate significant improvement in long term risk prediction after initial DCIS diagnosis. Citation Format: Shih-Ting Huang, Debbie Bennett, Robert West, Graham Colditz, Shu Jiang. Integrating pathomic and radiomic images to classify risk of subsequent events among women with DCIS [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-09-01.

Abstract 5879: The impact of radiomic imaging features in predicting short term risk of breast cancer after a negative mammogram using machine learning

Abstract Introduction & Purpose: While mammography reduces breast cancer mortality, up to 20% of breast cancers are diagnosed within the screening interval of a prior negative mammogram. Advances in breast imaging and machine learning have enabled evaluation of radiomic markers from mammography imaging with respect to breast cancer risk. Identification of radiomic markers associated with short term risk of breast cancer would be useful to direct supplemental screening. The aim of this study was to evaluate the potential of mammographic imaging features beyond breast density to predict screening failure using machine learning. Methods: We utilized full-field digital mammography (FFDM) studies from a cohort of women aged 40-85 who had a negative screening mammogram at the University of Pennsylvania Health System from 2010 - 2015. BRCA1/2 carriers, women with prior breast cancer, and women with breast implants were excluded. We identified 125 breast cancer cases diagnosed within 2 years of a negative mammogram whom we matched to controls not diagnosed with breast cancer within 2 years in a 1:5 ratio on BI-RADS breast density category, age, race, and screening date, resulting in a total of 750 FFDM studies. For each FFDM image, a total of 344 radiomic features were extracted using a previously validated lattice-based computational approach for parenchymal pattern characterization. Radiomic features were summarized across both breasts and across mammographic image views (CC and MLO) using means and standard deviations of radiomic features. Principal component analysis was applied to z-score normalized radiomic features and four principal components (PCs) were retained. Widely used machine learning models, such as Support Vector Machines (SVMs) and Random Forests (RFs), were applied. The performance of the machine learning models in predicting screening failure on the basis of the radiomic PCs was evaluated via cross-validated calculations of the area under the ROC curve (AUC). Results: Upon fine-tuning, SVM models using the mean summaries of radiomic features demonstrated a cross-validated case-control discriminatory capacity of AUC = 0.61 (95% CI 0.52, 0.68) while RFs did not achieve significant performance (AUC = 0.53, 95% CI 0.45, 0.60). Neither SVM models, nor RFs achieved significant predictive performance as measured by the cross-validated AUC when the standard deviation summaries of radiomic features was used. Conclusions: Given that BI-RADS density is the standard measure of breast density used clinically to direct supplemental screening, measuring the performance of radiomic features beyond BI-RADS provides the most clinically useful findings. In this small sample, our results suggest that mammographic radiomic features provided only moderate discriminatory accuracy, when BI-RADS density, age, race, and screening date were controlled for via matching. Citation Format: Aimilia Gastounioti, Sarah Ehsan, Walter Mankowski, Emily F. Conant, Despina Kontos, Anne Marie McCarthy. The impact of radiomic imaging features in predicting short term risk of breast cancer after a negative mammogram using machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5879.

Volumetric breast density evaluation using fully automated Volpara software, its comparison with BIRADS density types and correlation with the risk of malignancy

BackgroundMammography is currently the modality of choice for mass screening of breast cancer, although its sensitivity is low in dense breasts. Besides, higher breast density has been identified as independent risk factor so it has been conceptualized that women with dense breasts should be encouraged for supplemental screening. In this study, we aimed to estimate the distribution of volumetric breast density using fully automated Volpara software and to analyze the level of agreement between volumetric density grades and Breast Imaging Reporting and Data System (BI-RADS) density grades. We also aim to estimate the distribution of breast cancer in different VDG and to find a correlation between VDG and risk of malignancy.ResultsVDG-c was most common followed by VDG-b and BIRADS grade B was commonest followed by grade C. The density distribution was found inversely related to the age. Level of agreement between VDG and BIRADS grades was moderate (κ = 0.5890). Statistically significant correlation was noted between VDG-c and d for risk of malignancy (p < 0.001).ConclusionDifficulties associated with the use of BI-RADS density categories may be avoided if assessed using a fully automated volumetric method. High VDG can be considered as independent risk factor for malignancy. Thus, awareness of a woman’s breast density might be useful in determining the frequency and imaging modality for screening.

Spatial Distribution Analysis of Novel Texture Feature Descriptors for Accurate Breast Density Classification

Breast density has been recognised as an important biomarker that indicates the risk of developing breast cancer. Accurate classification of breast density plays a crucial role in developing a computer-aided detection (CADe) system for mammogram interpretation. This paper proposes a novel texture descriptor, namely, rotation invariant uniform local quinary patterns (RIU4-LQP), to describe texture patterns in mammograms and to improve the robustness of image features. In conventional processing schemes, image features are obtained by computing histograms from texture patterns. However, such processes ignore very important spatial information related to the texture features. This study designs a new feature vector, namely, K-spectrum, by using Baddeley’s K-inhom function to characterise the spatial distribution information of feature point sets. Texture features extracted by RIU4-LQP and K-spectrum are utilised to classify mammograms into BI-RADS density categories. Three feature selection methods are employed to optimise the feature set. In our experiment, two mammogram datasets, INbreast and MIAS, are used to test the proposed methods, and comparative analyses and statistical tests between different schemes are conducted. Experimental results show that our proposed method outperforms other approaches described in the literature, with the best classification accuracy of 92.76% (INbreast) and 86.96% (MIAS).

Abstract 2600: Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography

Abstract Accurate estimation of mammographic breast density could aid in augmenting breast cancer risk assessment for women undergoing breast screening with full-field digital mammography (FFDM). Breast density can be estimated from FFDM and is most commonly assessed in the clinic by visual grading into one of the four categories defined by the American College of Radiology BI-RADS. However, BI-RADS density assessment is highly subjective and does not provide a quantitative, continuous measure of percent density (PD), which would allow for more refined risk stratification and assessment of density changes. Here, we introduce Deep-LIBRA, an artificial intelligence (AI) tool for fully-automated assessment of breast PD from FFDM images. Two key modules form the core of Deep-LIBRA: 1) an implementation of a modified U-Net architecture for breast segmentation and 2) a radiomic machine learning module that performs PD estimation within the segmented breast region. To develop and validate Deep-LIBRA, raw (i.e., "For Processing") FFDM images (Selenia Dimensions, Hologic Inc.) acquired at two breast cancer screening practices were retrospectively analyzed. For the breast segmentation module, we used a total of 12,100 FFDM studies from 2,200 individual women and a 90%-10% split-sample training-validation approach, using the Dice coefficient to evaluate the accuracy of Deep-LIBRA versus ground-truth manual breast segmentation. For the PD estimation module we used a total of 3,304 FFDM images from 1,652 individual women; manual PD scores obtained with the widely used Cumulus software were used as the "gold standard" in a three-fold cross-validation setting to assess the accuracy of Deep-LIBRA in PD estimation. PD estimates from Deep-LIBRA were also compared with breast density estimates from the commercially available Volpara software. Breast segmentation had a Dice coefficient of 95.31% when compared to ground-truth manual breast segmentation in the validation set. Deep-LIBRA average differences from ground-truth PD scores in the three cross-validation folds were 4.91%, 4.65%, and 4.22%, while Volpara had corresponding average differences of 6.20%, 6.01%, and 5.94%. Deep-LIBRA PD scores were also significantly different from Volpara PD (t-test p-value &amp;lt; 0.001 in all three folds). Preliminary evaluation results show that Deep-LIBRA is a promising AI approach for accurately assessing PD from FFDM images. Citation Format: Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. Conant, Celine Vachon, Despina Kontos. Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2600.

Abstract PS7-32: Age of first full term pregnancy and other reproductive factors affect mammographic breast density in postmenopausal women

Abstract The objective of this study is to determine the role of age at first full term pregnancy (FFTP), and mammographic breast density (MBD) in postmenopausal women. For this purpose women, age 50-69y, participating in the Flemish (Belgium) population based breast cancer screening program were invited to participate in the study during their visit at the mammography department in the University Hospital Leuven (UZLeuven) or at the mobile unit of their hometown. A self-administered questionnaire providing detailed information on lifestyle and environmental exposure was completed by the participants. In this analysis 1,034 women were included (mean age of 59.1 years (range: 49.2 to 69.8). The three parameters of MBD were the percentage glandular tissue of the total breast volume (GLAND), the ratio of the glandular tissue volume compared to the whole breast volume or Volumetric Breast Density (VBD) and the BI-RADS density classification (VolparaDataManager®, USA). The first two parameters (GLAND and VBD) were transformed according to the natural logarithm to improve normality. These two parameters were linked with the FFTP via a piecewise linear regression (PLR) model using the NLIN procedure in SAS (version 9.4, SAS Institute, Cary, NC, USA). For the BI-RADS, we used ordinal logistic regression analysis. All models were corrected for a priori chosen variables: age at menarche, the ever use of oral contraceptive pill (OC), hormonal use at menopause, age at MBD measurement, and the body mass index at participation. Estimates are provided as a % change (95% confidence intervals [CI]).The average age at FFTP was 26.1 years (5th-95th percentile: 20 to 34). The PLR model estimated the breakpoint in our analysis at a FFTP of 25.7 years (95% CI: 22.3 to 29.1). For the woman with a FFTP younger than 25.7 years, the association between GLAND or VBD and FFTP is statistically not significant. For woman with a FFTP above 25.7 years, each year increase in FFTP was associated with 1.3% increase in GLAND (95% CI: 0.0% to 2.5%) and 1.5% increase in VBD (95% CI: 0.2% to 2.8%). Analysis of the BI-RADS showed similar results, the odds of belonging to a higher BI-RADS classification (e.g. from class 1 to class 2) increased with 5.4% (95% CI: 0.0% to 11.0%) for each year increase in FFTP age after the age of 25.7 years. Among other reproductive factors such as age at menarche, for every year delay there is 3.5% higher GLAND (95% CI: 1.4 % to 5.6%) and 2.5% higher VBD (95% CI: 0.5% to 4.6%) and the ever use of OC resulted in 10.0 % decrease in GLAND (95% CI: -18.8% to -0.2%) and 10.2% decrease in VBD (95%CI:-19.1% to -0.3%).In conclusion, this study is to our knowledge the first one to show that mammographic breast density is significantly increased when pregnancy takes place after 25.7 years of age. Later age at menarche is increasing and use of oral contraceptive is reducing mammographic breast density at postmenopause. (This study was supported by a Grant of the Breast Cancer Organization Think Pink of Belgium, the Department of Breast Radiology and Mobile Unit of the University Hospital Leuven, the Centre of Environmental Sciences and the Centre of Biostatistics of the Hasselt University, and NIH Grant CA06927 to FCCC, PA, USA). Citation Format: Magda Johanna Vandeloo, Kristof Y. Neven, Liesbeth M. Bruckers, Jose Russo, Liesbeth Vancoillie, Esmee M. Bijnens, Eliane Kellen, Tim S. Nawrot, Chantal Van Ongeval. Age of first full term pregnancy and other reproductive factors affect mammographic breast density in postmenopausal women [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-32.

Abstract 3661: Association of mammographic density with blood DNA methylation

Abstract Background: Mammographic density is a strong risk factor for breast cancer. While numerous genetic variations have been shown to be associated with mammographic density, an epigenetic signature of mammographic density has not yet been identified. The objective of this study was to identify a DNA methylation signature associated with mammographic density. Methods: Blood samples were collected from 197 postmenopausal women 45-65 years old with no personal history of breast cancer. For each participant, mammographic density according to the BI-RADs density classification was obtained from the most recent mammogram report. White blood cell DNA methylation at more than 850,000 CpG sites was measured using the Illumina HumanMethylationEPIC BeadChip. Differential methylation was assessed using genome-wide, probe-level, and regional approaches after filtering and normalization using the recommended steps for DNA methylation microarray data. All analyses were adjusted for appropriate confounding variables. Results: On average, genome-wide DNA methylation was higher in women with elevated mammographic density. Additionally, DNA methylation at 343 individual sites and in 34 regions was significantly associated with mammographic density after false discovery rate correction. Most density-associated sites were hypermethylated. The top differentially methylated sites were located near cell cycle regulatory genes (ICMT, DESI2, TCF21/TARID, ROBO3), as well as those encoding cytoskeletal or plasma membrane proteins (PALLD, ANO7, ATP8B3, MTUS2). We observed hypermethylation near the promoters of genes including RUFY1 and SLFN12 and within the bodies of genes including LCN6, SNED1, and LRFN1. Functional analysis of the differentially methylated sites revealed enrichment of gene ontology categories including angiogenesis and regulation of cell adhesion, as well as apoptotic signaling in response to DNA damage. Conclusions: White blood cell DNA methylation differed according to mammographic density in a sample of postmenopausal women. Probe-based and regional analysis identified differentially methylated regions located near genes implicated in cancer. These differentially methylated regions may serve as a signature of mammographic density, and future studies of the genes implicated may provide a deeper understanding of the connection between mammographic density and breast cancer risk. Citation Format: Rachel McFarland Lucia, Wei-Lin Huang, Andrea Alvarez, Irene Masunaka, Argyrios Ziogas, Deborah Goodman, Andrew O. Odegaard, Trina M. Norden-Krichmar, Hannah Lui Park. Association of mammographic density with blood DNA methylation [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3661.

Abstract P2-08-10: Mammographic breast density changes with endocrine therapy (ET) in women with hormone receptor positive (HR+) breast cancer in a Utah population

Abstract Introduction: Women with extremely dense breast tissue on mammography have a higher risk of breast cancer compared to similarly-aged women with less dense breast tissue. Tamoxifen therapy has been observed to reduce breast density among both pre- and postmenopausal women with HR+ breast cancer and may predict benefit of therapy. Studies of aromatase inhibitor (AI) therapy and breast density change, however, are limited and results inconclusive. Factors associated with breast density change with ET, and effect of duration of ET on breast density change, have not been well studied. Methods: Using the Utah Population Database, women were identified who underwent screening mammography in 2005-2016 and who had no personal history of breast cancer. Those who were subsequently diagnosed with HR+ breast cancer and treated in one of the major health care systems in Utah (Intermountain Healthcare or University of Utah Health) were included in the analysis. ET prescription information and duration of ET were obtained from provider records and the Utah All-Payer Claims Database. Women with mammography results in the electronic medical record both before and within 3 years after the breast cancer diagnosis were included. BI-RADS density classification from mammogram reports 6-12 months, 13-24 months, and 25-36 months after ET initiation were compared to pre-diagnosis BI-RADS density classification. Breast density reduction was defined as a decrease of ≥1 BI-RADS category compared to baseline. Fisher’s exact test was used to examine ET and breast density reduction among women ≥55y and &amp;lt;55y. Results: In total, 1328 women with HR+ breast cancer and baseline BI-RADS breast density categories B-D were included. Women with BI-RADS density category A(almost entirely fatty) were excluded. A majority (61%) were age ≥55y at diagnosis, were non-Hispanic white (90%), and had BMI ≥25 kg/m2 (56%); 15% had a first-degree family history of breast cancer. At baseline, 602 (44%) women had scattered fibroglandular densities (BI-RADS category B), 697 (50%) had heterogeneously dense breasts (BI-RADS category C), and 83 (6%) had extremely dense breasts (BI-RADS category D). Half were treated with AI only (49%), 21% with tamoxifen only, and 29% with both during follow-up. In both the age ≥55y and &amp;lt;55y groups, women with BI-RADS category D at baseline were more likely to experience a reduction in breast density with ET compared to women with either BI-RADS categories B or C (Table 1). There were no significant differences in breast density reduction observed between women treated with AI only compared to those treated with tamoxifen only, irrespective of age and baseline density. Breast cancer family history also had no effect on breast density reduction with ET. Conclusion: Baseline breast density is associated with a demonstrable reduction in breast density during ET. Our findings suggest that AI therapy can reduce breast density similar to that seen with tamoxifen therapy. Table 1 Breast Density (BD) changes among women who underwent mammography 6-12 months, 13-24 months, and 25-36 months after ET initiationBaseline BDN≥55 years oldPTotal&amp;lt;55 years oldPNo BD reduction N(%)BD reduction N(%)No BD reduction N(%)BD reduction N(%)6-12 months after ET initiationScattered157152(97)5(3)5452(96)2(3)Heterogeneous148115(78)33(22)10477(74)27(26)Dense144(29)10(71)&amp;lt;0.0001248(33)16(67)&amp;lt;0.0001Total319271(85)48(15)182137 (75)45(25)13-24 months after ET initiationScattered232214(92)18(8)8473(87)11(13)Heterogeneous211144(68)67(32)155107(69)48(31)Dense172(12)15(88)&amp;lt;0.0001287(25)21(75)&amp;lt;0.0001Total460360(78)100(22)267187(70)80(30)25-36 months after ET initiationScattered248220(89)28(11)8374(89)9 (11)Heterogeneous203133(66)70(35)16097(61)63(39)Dense162(13)14(88)&amp;lt;0.00013213(41)19 (59)&amp;lt;0.0001Total467355(76)112(24)275184(67)91(33) Citation Format: Mei Wei, Lisa Pappas, Emily Guinto, Carol Sweeney, Nicola Camp, Phoebe Freer, Saundra Buys, N. Lynn Henry, Karen Curtin. Mammographic breast density changes with endocrine therapy (ET) in women with hormone receptor positive (HR+) breast cancer in a Utah population [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-08-10.

Abstract P2-02-05: Sensitivity of screening mammography by density and texture: A cohort study from a population based screening programme in Denmark

Abstract Background Screening mammography works better in fatty than in dense breast tissue. Computerized assessment of parenchymal texture is a non-subjective method to obtain a refined description of breast tissue, potentially valuable in addition to breast density scoring for identification of women in need of supplementary imaging. We studied sensitivity of screening mammography by a combination of radiologist assessed BI-RADS density score and computer assessed parenchymal texture marker, MTR, in a population-based screening program, Capital Region, Denmark. Methods Breast density was coded according to the 4th Edition of the BI-RADS density code, and MTR marker was divided into quartiles from 1 to 4. Screening data were followed-up for identification of screen-detected and interval cancers. We calculated sensitivity and specificity with 95% confidence intervals (CI) by BI-RADS density score, MTR marker, and combination hereof. Results In total, 55 350 women were screened from 1 November 2012 to 31 December 2013. Full data were available for 54 997 women, of whom 28% had BI-RADS density score 1, 40% score 2, 27% score 3, and 5% score 4. Density and texture were strongly correlated, but the combination led to identification of subgroups with different sensitivity. Sensitivity was high, about 80%, in women with BI-RADS density scores 1 and MTR markers 1 or 2.Sensitivity was low, 67%, in women with BI-RADS density score 2 and MTR marker 4. For women with BI-RADS density scores 3 and 4, the already low sensitivity was further decreased for women with MTR marker 4. Specificity was 97-99% in all subgroups. Conclusion Our study showed that women with low density constituted a heterogenous group. Classifying women for extra imaging based on density only might be a too crude approach. Screening sensitivity was systematically high in women with fatty and homogeneous breast tissue. Sensitivity and specificity by combination of density score (BI-RADS) and texture marker (MTR), Denmark 2012-13 MeasureSensitivity % (95% CI)Specificity % (95% CI)BI-RADS 178 (69-85)99 (98-99)MTR 181 (70-90)99 (98-99)MTR 271 (54-85)99 (99-99)MTR 382 (48-98)98 (98-99)MTR 4BI-RADS 275 (70-81)97 (97-97)MTR 173 (57-85)98 (97-98)MTR 277 (66-85)97 (97-97)MTR 380 (64-88)97 (97-98)MTR 467 (51-81)97 (97-98)BI-RADS 369 (62-76)97 (97-97)MTR 171 (52-86)97 (96-98)MTR 2MTR 377 (66-87)97 (97-98)MTR 461 (46-72)97 (97-98)BI-RADS 447 (30-65)98 (97-98)MTR 157 (29-82)98 (97-99)MTR 2MTR 3MTR 441 (21-64)97 (97-98) Citation Format: My von Euler-Chelpin. Sensitivity of screening mammography by density and texture: A cohort study from a population based screening programme in Denmark [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-02-05.

Abstract P5-08-02: Association of daily alcohol intake, volumetric density and breast cancer risk

Abstract Background: Alcohol intake and breast density are established risk factors for breast cancer (BC). A few studies suggest breast density is on the causal pathway between alcohol and BC but the literature is not consistent. We examine the interrelationship between alcohol, volumetric breast density and BC risk, specifically whether there is a stronger alcohol and BC association among women with dense breasts and/or whether breast density mediates the alcohol and BC association. Methods: We conducted a nested case-control study within the San Francisco Mammography Registry consisting of four breast screening centers. From 2006-2013, through linkages with the California cancer registry, we identified 2572 women diagnosed with BC who had screening mammograms performed at least six months prior to diagnosis. Controls (5119) were matched to cases on age, date of earliest mammogram, race/ethnicity, facility, and mammography machine. We obtained the raw format of digital mammograms on average 3.1 (standard deviation (SD) =1.7) years prior to diagnosis or corresponding date for controls. We ascertained usual daily alcohol intake and other risk factors from a clinical questionnaire at time of mammography. We obtained BI-RADS density from clinical records and used VolparaTM to assess volumetric percent density, dense volume and non-dense volume from the mammogram. We examined the associations of daily alcohol intake (none, one or less, two or more drinks per day) and volumetric density phenotypes (per 1 SD) with BC risk using logistic regression (odds ratios, OR; 95% confidence intervals, CI; and trend tests). We examined deviation from multiplicative interaction using chi-squared tests. We evaluated mediation of the alcohol and BC association by volumetric density measures using logistic regression to estimate the association between alcohol use and BC with and without adjustment for density measures. Percent mediation was estimated using the differences in the log OR estimates from the two models. All models were adjusted for age, 1/BMI and menopause and matching factors. Analyses were also stratified by menopausal status. Results: Alcohol intake was available on 2233 cases and 4562 controls, 88% of those eligible. BC cases and controls had similar age (57.2 years (SD=11.5) vs. 57.1 years (SD=11.5)), BMI (25.3 kg/m2 (SD=5.3) vs. 24.9 kg/ m2 (SD=5.1)) and race (69.0% vs. 68.2% Caucasian). Cases were more likely to drink alcohol daily than controls (52.1% vs. 49.0%), in particular two or more drinks per day (14.8% vs. 13.2%). Alcohol was associated with increased BC risk (OR=1.14, 95% CI:1.02-1.27, for one or less drinks per day and OR=1.22, 95% CI:1.05-1.42 for 2 or more drinks per day) compared to non-drinkers (p-trend=0.004). Percent volumetric density (OR=1.45 per SD, 95%CI: 1.36-1.56,) and dense volume (OR=1.30, 95% CI: 1.24-1.37) were also positively associated with BC risk; non-dense volume was inversely associated (OR=0.93, 95%CI: 0.86-1.01). Associations were similar by menopausal subgroup. There was no evidence for a differential association of alcohol and breast cancer risk by dense breasts assessed using any of the density phenotypes examined (all P’s&amp;gt;0.1). However, the association between alcohol and overall risk of BC was partially mediated by dense volume among all women (percent mediated=25%, P=0.01) and postmenopausal women (percent mediated=19%, P=0.03). Conclusions: The association of daily alcohol intake and breast cancer risk was similar among women with dense and non-dense breasts. However, dense volume partially mediated the association between alcohol and risk of breast cancer, particularly among postmenopausal women, suggesting that alcohol partially influences breast cancer risk through changes in breast tissue composition. Citation Format: Celine Marie Vachon, Christopher G. Scott, Stacey J Winham, John A Shepherd, Kathleen R Brandt, Matthew R Jensen, Carrie B Hruska, John J Heine, V. Shane Pankratz, Karla Kerlikowske. Association of daily alcohol intake, volumetric density and breast cancer risk [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-08-02.

Abstract 593: Mammographic breast density in postmenopausal women is affected by the age of first full term pregnancy

Abstract Mammographic breast density (MBD) is accepted as a risk factor in breast cancer, whereas age of first full term pregnancy (FFTP) has been demonstrated to be a protective one. The objective of this study is to investigate the association between reproductive history and MBD at menopause. Women, age 50-69y, participating in the Flemish (Belgium) governmental organised breast cancer screening program were invited to participate in the study during their visit at the mammography department in Leuven (hospital and the mobile unit). The study was approved by the ethical committee of the hospital. After signing the informed consent, a self-administered questionnaire (SAQ) providing information on lifestyle and environmental exposure was completed by the participants. In the present work we present the analysis of the first 640 women that completed the SAQ and underwent mammography. Questionnaire data were registered via the Qualtrics software. The three parameters of MBD (VolparaDataManager®, USA) included the percentage glandular tissue of the total breast volume (GLAND), the ratio of the glandular tissue volume compared to the whole breast volume or Volumetric Breast Density (VBD) and the BIRADS density classification. We applied linear regression models to investigate the association between the continuous breast density markers (GLAND and VBD, log transformed) and the following explanatory variables: age of woman, ever pregnant, number of pregnancies, age of FFTP, ever breastfed (BF), amount and duration of BF, and breastfeeding in FFTP. An ordinal logistic regression model was used for the breast density marker BIRADS. The average GLAND and VBD changes with a multiplicative factor of 1.073 (p = 0.013) and 1.072 (p = 0.013) for a 5-year increase of age of FFTP. Thus, an increase of 5 years in age of FFTP is associated with about 7% increase in geometric mean of GLAND and VBD. The odds ratio for a higher versus a lower score of the BIRADS classification is 1.24 (p = 0.023) greater for every 5-year increase in age of FFTP. These results are independent for the inverse association between the age of the woman and the MBD markers. For a 5 year increment in age, geometric GLAND (p = 0.019) and VBD (p = 0.024) are approximately 5% lower and the odds ratio for a higher BIRADS score is 12% higher (p = 0.079). There is no association between the MBD markers and breastfeeding, the number and duration BF, and the number of pregnancies after correcting for age and age of FFTP. In conclusion, this study is to our knowledge the first one to show that MBD is significantly reduced when pregnancy takes place at early age.( This study was supported by a Grant of the Breast Cancer Organization Think Pink of Belgium, the Department of Breast Radiology and Mobile Unit of the University Hospital Leuven, the Center of Environmental Sciences and the Center of Biostatistics of the Hasselt University, and NIH Grant CA06927 to FCCC, PA, USA.) Citation Format: Magda J. Vandeloo, Kristof Y. Neven, Liesbeth Bruckers, Jose Russo, Liesbeth Vancoullie, Esmée Bijnens, Eliane Kellen, Tim S. Nawrot, Chantal Van Ongeval. Mammographic breast density in postmenopausal women is affected by the age of first full term pregnancy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 593.

A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model.

BackgroundAccurate breast cancer risk assessment for women attending routine screening is needed to guide screening and preventive interventions. We evaluated the accuracy of risk predictions from both visual and volumetric mammographic density combined with the Tyrer-Cuzick breast cancer risk model.MethodsA case-control study (474 patient participants and 2243 healthy control participants) of women aged 40–79 years was performed using self-reported classical risk factors. Breast density was measured by using automated volumetric software and Breast Imaging and Reporting Data System (BI-RADS) density categories. Odds ratios (95% CI) were estimated by using logistic regression, adjusted for age, demographic factors, and 10-year risk from the Tyrer-Cuzick model, for a change from the 25th to 75th percentile of the adjusted percent density distribution in control participants (IQ-OR).ResultsAfter adjustment for classical risk factors in the Tyrer-Cuzick model, age, and body mass index (BMI), BI-RADS density had an IQ-OR of 1.55 (95% CI = 1.33 to 1.80) compared with 1.40 (95% CI = 1.21 to 1.60) for volumetric percent density. Fibroglandular volume (IQ-OR = 1.28, 95% CI = 1.12 to 1.47) was a weaker predictor than was BI-RADS density (Pdiff = 0.014) or volumetric percent density (Pdiff = 0.065). In this setting, 4.8% of women were at high risk (8% + 10-year risk), using the Tyrer-Cuzick model without density, and 7.1% (BI-RADS) compared with 6.8% (volumetric) when combined with density.ConclusionThe addition of volumetric and visual mammographic density measures to classical risk factors improves risk stratification. A combined risk could be used to guide precision medicine, through risk-adapted screening and prevention strategies.

Prediction of Cancer Masking in Screening Mammography Using Density and Textural Features

High mammographic density reduces the diagnostic accuracy of screening mammography due to masking of tumors, resulting in possible delayed diagnosis and missed cancers. Women with high masking risk could be preselected for alternative screening regimens less susceptible to masking. In this study, various models to predict masking status are presented based on biometric and image-based parameters. For a cohort of 67 nonscreen-detected (cancers detected via other means after a negative mammogram) and 147 screen-detected invasive cancers, quantitative volumetric breast density, BI-RADS density, and the distribution and appearance of dense tissue through statistical and texture metrics were measured. Age and Body Mass Index were recorded. Stepwise multivariate logistic regressions were computed to select those parameters that predicted nonscreen-detected cancers. Accuracy of the models was evaluated using the area under receiver operator characteristic curve (AUC). Using BI-RADS density alone to predict masking risk yielded an AUC of 0.64 (95% confidence interval [0.57-0.70]). Age-adjusted BI-RADS density or volumetric breast density had AUCs of 0.72 [0.64-0.79] and 0.71 [0.62-0.78], respectively. A model extracted from the full pool of variables had an AUC of 0.75 [0.67-0.82]. The optimal model predicts masking more accurately than density alone, suggesting that texture metrics may be useful in models to guide a stratified screening strategy.

Screening mammography: benefit of double reading by breast density

PurposeThe currently recommended double reading of all screening mammography examinations is an economic burden for screening programs. The sensitivity of screening is higher for women with low breast density than for women with high density. One may therefore ask whether single reading could replace double reading at least for women with low density. We addressed this question using data from a screening program where the radiologists coded their readings independently.MethodsData include all screening mammography examinations in the Capital Region of Denmark from 1 November 2012 to 31 December 2013. Outcome of screening was assessed by linkage to the Danish Pathology Register. We calculated sensitivity, specificity, number of interval cancers, and false positive-tests per 1000 screened women by both single reader and consensus BI-RADS density code.ResultsIn total 54,808 women were included. The overall sensitivity of double reading was 72%, specificity was 97.6%, 3 women per 1000 screened experienced an interval cancer, and 24 a false-positive test. Across all BI-RADS density codes, single reading consistently decreased sensitivity as compared with consensus reading. The same was true for specificity, apart from results across BI-RADS density codes set by reader 2.ConclusionsSingle reading decreased sensitivity as compared with double reading across all BI-RADS density codes. This included results based on consensus BI-RADS density codes. This means that replacement of double with single reading would have negative consequences for the screened women, even if density could be assessed automatically calibrated to the usual consensus level.

Abstract C82: Breast density notification in a racially diverse and immigrant sample of women: Evidence for social disparities in awareness, knowledge, and follow-up

Abstract As mandated by legislation in 31 states, clinical mammographic breast density (MBD) information is currently disclosed to women identified as having heterogeneously or extremely dense breasts on routine mammograms (Breast Imaging-Reporting and Data System [BI-RADS] density categories of C/D, hereafter referred to as high MBD). The New York State legislation, enacted in 2013, informs women about the lower sensitivity of mammography and increased breast cancer risk associated with having dense breasts and advises them to seek risk assessment and additional screening; women with low MBD (BI-RADS density categories of A/B) do not receive density information. The legally mandated MBD information provided to women is ambiguous and written at a high literacy level. Additionally, any follow-up actions on this information taken by women require personal resources, including high educational attainment, health insurance coverage, and access to regular health care provider versed in breast cancer risk assessment and screening guidelines. We investigated the impact of this legislation on women's awareness of MBD, knowledge of its effect on breast cancer risk and detection, and communication with health care providers in a predominately Hispanic and foreign-born sample of 466 women, recruited from a mammography clinic in the Northern Manhattan community of New York City (age range: 40-60 years; 79% Hispanic, 12% African American; 81% foreign-born; 26% less than high school education). Overall 88% of the sample had obtained a prior mammogram since 2013 and 31% had heard about MBD. Awareness of MBD did not differ by risk factors for breast cancer (e.g., family history of breast cancer, breast biopsy, parity-related factors) or psychological factors related to breast cancer screening (breast cancer risk perception and worry). Non-Hispanic white women were significantly more likely to have heard of MBD (81%) than non-Hispanic African American (54%), and Hispanic women (13%). In multivariable logistic regression models, odds of MBD awareness were significantly lower in women who were Hispanic (OR=0.1, 95% CI: 0.04, 0.3 relative to white women), foreign-born (OR=0.3, 95% CI: 0.2, 0.6 relative to U.S.-born), or had lower educational attainment (e.g., OR=0.3, 95% CI: 0.2-0.7 for high school or less vs. college or higher degree). Women with personal high MBD were twice as likely as women with low MBD to be aware of MBD, but adjusting for personal MBD did not substantially alter the associations of race/ethnicity, education, and nativity with MBD awareness. In women who reported awareness of MBD, 47% and 76%, respectively, were able to correctly identify the higher risk and masking potential associated with high MBD, and 31% reported following on MBD information with their health care providers. Racial/ethnic and educational differences in knowledge of MBD impact and health care provider communication were smaller and less consistent. In conclusion, awareness of MBD remains low in women from racial/ethnic minority and lower socioeconomic backgrounds, even in a regularly screened population. These findings underscore the importance of understanding and averting potential unintended disparate consequences of breast density notification. Citation Format: Parisa Tehranifar, Carmen B. Rodriguez, Mariangela Agovino, Karen M. Schmitt. Breast density notification in a racially diverse and immigrant sample of women: Evidence for social disparities in awareness, knowledge, and follow-up [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr C82.

Comparison between software volumetric breast density estimates in breast tomosynthesis and digital mammography images in a large public screening cohort

ObjectivesTo compare software estimates of volumetric breast density (VBD) based on breast tomosynthesis (BT) projections to those based on digital mammography (DM) images in a large screening cohort, the Malmö Breast Tomosynthesis Screening Trial (MBTST).MethodsDM and BT images of 9909 women (enrolled 2010–2015) were retrospectively analysed with prototype software to estimate VBD. Software calculation is based on a physics model of the image acquisition process and incorporates the effect of masking in DM based on accumulated dense tissue areas. VBD (continuously and categorically) was compared between BT [central projection (mediolateral oblique view (MLO)] and two-view DM, and with radiologists’ BI-RADS density 4th ed. scores. Agreement and correlation were investigated with weighted kappa (κ), Spearman’s correlation coefficient (r), and Bland–Altman analysis.ResultsThere was a high correlation (r = 0.83) between VBD in DM and BT and substantial agreement between the software breast density categories [observed agreement, 61.3% and 84.8%; κ = 0.61 and ĸ = 0.69 for four (a/b/c/d) and two (fat involuted vs. dense) density categories, respectively]. There was moderate agreement between radiologists’ BI-RADS scores and software density categories in DM (ĸ = 0.55) and BT (ĸ = 0.47).ConclusionsIn a large public screening setting, we report a substantial agreement between VBD in DM and BT using software with special focus on masking effect. This automated and objective mode of measuring VBD may be of value to radiologists and women when BT is used as the primary breast cancer screening modality.Key Points• There was a high correlation between continuous volumetric breast density in DM and BT.• There was substantial agreement between software breast density categories (four groups) in DM and BT; with clinically warranted binary software breast density categories, the agreement increased markedly.• There was moderate agreement between radiologists’ BI-RADS scores and software breast density categories in DM and BT.

Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breast Tissue.

The purpose of this study was to compare diagnostic accuracy and interpretation time of screening automated breast ultrasound (ABUS) for women with dense breast tissue without and with use of a recently U.S. Food and Drug Administration-approved computer-aided detection (CAD) system for concurrent read. In a retrospective observer performance study, 18 radiologists interpreted a cancer-enriched set (i.e., cancer prevalence higher than in the original screening cohort) of 185 screening ABUS studies (52 with and 133 without breast cancer). These studies were from a large cohort of ABUS-screened patients interpreted as BI-RADS density C or D. Each reader interpreted each case twice in a counterbalanced study, once without the CAD system and once with it, separated by 4 weeks. For each case, each reader identified abnormal findings and reported BI-RADS assessment category and level of suspicion for breast cancer. Interpretation time was recorded. Level of suspicion data were compared to evaluate diagnostic accuracy by means of the Dorfman-Berbaum-Metz method of jackknife with ANOVA ROC analysis. Interpretation times were compared by ANOVA. The ROC AUC was 0.848 with the CAD system, compared with 0.828 without it, for a difference of 0.020 (95% CI, -0.011 to 0.051) and was statistically noninferior to the AUC without the CAD system with respect to a margin of -0.05 (p = 0.000086). The mean interpretation time was 3 minutes 33 seconds per case without the CAD system and 2 minutes 24 seconds with it, for a difference of 1 minute 9 seconds saved (95% CI, 44-93 seconds; p = 0.000014), or a reduction in interpretation time to 67% of the time without the CAD system. Use of the concurrent-read CAD system for interpretation of screening ABUS studies of women with dense breast tissue who do not have symptoms is expected to make interpretation significantly faster and produce noninferior diagnostic accuracy compared with interpretation without the CAD system.

Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic.

Ultimately, the incorporation of automated quantitative measures of breast density will lead to more effective clinical care and more robust outcomes research than the current, subjective assignment of Breast Imaging and Reporting Data System density categories, by providing reproducible estimates of both the risk of masking a cancer as well as the risk of developing breast cancer—two important factors in determining personalized breast cancer screening algorithms.

Abstract A02: Comparative analysis of breast density among Black, White, and Hispanic women presenting for screening mammography

Abstract Background: Data on ethnic variations in breast density are limited and often not inclusive of underrepresented minorities, including Black and Hispanic women. As breast density is associated with elevated breast cancer risk, investigating racial and ethnic difference may elucidate the observed differences in breast cancer risk among different populations. Methods: We reviewed breast density recorded at the initial mammographic screening study in women presenting to the Capital Breast Care Center (CBCC) and Georgetown University Hospital (GUH) from 2010 to 2014. Patient demographics including race, age at screening, education and menopausal status were abstracted in addition to body mass index (BMI). From imaging reports, we recorded the BI-RADS density categories: 1-fatty, 2-scattered fibroglandular densities, 3-heterogeneously dense and 4-extremely dense. Multivariable unconditional logistic regression was used to identify predictors of breast density. Results: Density categorization was recorded for 2,146 women over the five-year period, with 940 (43.8%) Black, 893 (41.6%), Hispanic and 314 (14.6%) White. Analysis of subject characteristics by low density (categories 1 and 2) and high density (categories 3 and 4) show that high category is observed in younger, Hispanic, nulliparous, premenopausal and non-obese women (P-values &amp;lt;.0001). Obese women are 70% less likely to have high breast density compared to non-obese women. Being Hispanic, premenopausal, and non-obese were predictive of high mammographic density on logistic regression. Conclusion: In this analysis of density distribution in a diverse sample of women presenting for mammography, Hispanic women have the highest breast density, followed by Black women with Whites having the lowest. Unique in our findings is women who identify as Hispanic having the highest breast density and lower rates of obesity. Further investigation of the impact of obesity on breast density in minority women, especially in the understudied Hispanic group is needed. Citation Format: Bridget A. Oppong, Chiranjeev Dash, Suzanne Oneill, Kepher Makambi, Tesha Coleman, Lucile L. Adams-Campbell. Comparative analysis of breast density among Black, White, and Hispanic women presenting for screening mammography [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr A02.

Abstract 5299: Validation of the BCSC model within the Mayo Benign Breast Disease Cohort

Abstract Background: The Breast Cancer Surveillance Consortium (BCSC) model predicts invasive breast cancer risk in women with Benign Breast Disease (BBD), and recently incorporated BBD histology into the model. The BCSC has been validated in the Mayo Mammography Health Study, but has yet to be examined in Mayo Clinic’s BBD cohort. The Benign Breast Disease to Breast Cancer (BBD-BC) model predicts risk of both invasive and in situ breast cancer. Here we compare the performance of the BCSC and BBD-BC in Mayo Clinic’s BBD cohort. Methods: Eligible women underwent a breast biopsy with benign findings at the Mayo Clinic between years 1997-2001 and had a 4-view screening mammogram within six months of biopsy. Clinical BI-RADS density assessments using the 4th edition American College of Radiology were available on all mammograms, and were coded as almost entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense. Risk at 5 and 10 years of invasive cancer only (BCSC model) or both invasive and in situ cancer (BBD-BC model) were estimated. In situ cancers were censored at time of diagnosis for both models. Concordance statistics, i.e. model discrimination (higher is better), for each model were calculated using a Cox proportional hazards model with predicted risk as the sole predictor, and were compared using permutation tests. The ratio of total predicted (sum of predicted risk) to observed number of invasive breast cancers was used to assess model calibration. Calibration was not formally compared across models due to differences in how DCIS was treated in the development of each (BCSC censored; BBD-BC event). Results: 999 women met inclusion criteria. BI-RADS density was categorized as fatty in 46 (4.6%), scattered densities in 372 (37.2%), heterogeneously dense in 423 (42.3%), and extremely dense in 158 (15.8%). 62 invasive cancers occurred over a median 13.3yrs of follow-up, with 16 (25.8%) and 48 (77.4%) of the cancers occurring with-in 5 and 10 years. The concordance of the BCSC at 5 years was 0.550 (95% CI 0.409—0.691), compared to 0.719 (95% CI 0.578—0.860) for the BBD-BC (p-value=0.005). At 10 years the BCSC concordance was not significantly different from the BBD-BC, at 0.624 (95% CI 0.542—0.706) and 0.662 (95% CI 0.580—0.744), respectively (p-value=0.306). The BCSC over predicted the number invasive cancers in the BBD cohort at 5 years (predicted-to-observed=1.54; 95% CI 1.01—2.70), but was well calibrated at 10 years (1.06; 95% CI 0.83, 1.47). Conclusions: The BCSC model performed reasonably well in the BBD Cohort 10 years post-biopsy, but overestimated risk at 5 years. Additional study is needed to improve models for prediction of breast cancer risk among women with BBD. Citation Format: Ryan D. Frank, Celine M. Vachon, Stacey J. Winham, Robert A. Vierkant, Marlene H. Frost, Derek C. Radisky, Daniel W. Visscher, Amy C. Degnim. Validation of the BCSC model within the Mayo Benign Breast Disease Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5299. doi:10.1158/1538-7445.AM2017-5299