Breast Cancer Surveillance Consortium Research Articles

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer.

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.

Abstract GS01-06: Advancing Evidence of the Associations Between Specific Benign Breast Diagnoses and Future Breast Cancer RIsk

Abstract Background: Benign breast disease (BBD) is a common breast biopsy finding encompassing a diverse spectrum of diagnoses. Breast cancer risk associated with many specific BBD diagnoses and their joint associations with breast density have not been extensively studied. We estimate the future risk of invasive breast cancer associated with specific BBD diagnoses typically categorized into the broad category of proliferative lesions without atypia (PWoA) and evaluate whether associations differ by breast density or by presence of calcifications. Methods: We included 1,313,943 women ages 40-79 years who underwent 5,282,063 mammograms in the Breast Cancer Surveillance Consortium from 1996 to 2019 with no prior history of breast cancer. We fit a Cox proportional hazards model to estimate hazard ratios associated with each combination of PWoA BBD diagnoses, whether calcifications were present on biopsy, and breast density. We used classification trees to group combinations of PWoA diagnoses, breast density, age groups, and presence of calcifications with similar magnitudes of associations with 5-year risk of invasive breast cancer. Results: Compared with women with a PWoA BBD diagnosis without calcifications, presence of calcifications significantly elevated risk for breast cancer (HR=1.19, 95% CI = 1.02 to 1.41, P=0.03). Considering specific PWoA diagnoses, classification trees identified groups of women with higher and lower invasive cancer risk than expected risk for all PWoA combined. Among women ages 60-79 years with heterogeneously or extremely dense breasts, the 2.6% of women with any PWoA diagnosis, except papillomatosis, were at high invasive breast cancer risk (5-year risk >2.5%) (Table 1). Among women ages 40-59 years with dense breasts, presence of calcifications with papillomas, usual ductal hyperplasia, columnar cell hyperplasia, or PWoA NOS diagnoses put these women at high risk (5-year risk >2.5%) compared to women with the same BBD diagnoses without calcifications who were at intermediate or average risk (1-2.49%) (Table 1). Among women ages 60-79 years with non-dense breasts, the 1.6% of women with papilloma, usual ductal hyperplasia, radial scar, or PWoA NOS diagnoses were at high breast cancer risk (Table 2). Among women ages 40-59 years with non-dense breasts, presence of calcifications with usual ductal hyperplasia or PwoA NOS diagnoses put these women at intermediate risk (1.67-2.49%) compared to women with these BBD diagnoses without calcifications who were at average risk (1-1.66%) (Table 2). Women 60-79 years with fatty breasts were at intermediate 5-year risk if they had papillomas, usual ductal hyperplasia, or PWoA NOS diagnoses. Women 40-59 years with fatty breasts were at low risk of breast cancer regardless of PWoA diagnosis (Table 2). Conclusion: Specific BBD diagnoses and the presence of calcifications can change a woman’s predicted 5-year breast cancer risk compared to broad BBD categories alone. This information could be incorporated into risk prediction models to improve model accuracy. Citation Format: Olivia Sattayapiwat, Karla Kerlikowske, Donald Weaver, Alexander Borowsky, Theresa Keegan, Brian Sprague, Diana Miglioretti. Advancing Evidence of the Associations Between Specific Benign Breast Diagnoses and Future Breast Cancer RIsk [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 GS01-06.

Breast density knowledge and willingness to delay treatment for pre-operative breast cancer imaging among women with a personal history of breast cancer.

Following a breast cancer diagnosis, it is uncertain whether women's breast density knowledge influences their willingness to undergo pre-operative imaging to detect additional cancer in their breasts. We evaluated women's breast density knowledge and their willingness to delay treatment for pre-operative testing. We surveyed women identified in the Breast Cancer Surveillance Consortium aged ≥ 18 years, with first breast cancer diagnosed within the prior 6-18 months, who had at least one breast density measurement within the 5 years prior to their diagnosis. We assessed women's breast density knowledge and correlates of willingness to delay treatment for 6 or more weeks for pre-operative imaging via logistic regression. Survey participation was 28.3% (969/3,430). Seventy-two percent (469/647) of women with dense and 11% (34/322) with non-dense breasts correctly knew their density (p < 0.001); 69% (665/969) of all women knew dense breasts make it harder to detect cancers on a mammogram; and 29% (285/969) were willing to delay treatment ≥ 6 weeks to undergo pre-operative imaging. Willingness to delay treatment did not differ by self-reported density (OR:0.99 for non-dense vs. dense; 95%CI: 0.50-1.96). Treatment with chemotherapy was associated with less willingness to delay treatment (OR:0.67; 95%CI: 0.46-0.96). Having previously delayed breast cancer treatment more than 3 months was associated with an increased willingness to delay treatment for pre-operative imaging (OR:2.18; 95%CI: 1.26-3.77). Understanding of personal breast density was not associated with willingness to delay treatment 6 or more weeks for pre-operative imaging, but aspects of a woman's treatment experience were. GOV : NCT02980848 registered December 2, 2016.

Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review.

Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.

Abstract 3449: Is AI-enhanced breast ultrasound ready for breast cancer screening in low-resource environments? A systematic review

Abstract Purpose. Screening mammography is unavailable in many low-resource areas. We ask if the state-of-the-art in artificial intelligence (AI)-enhanced breast ultrasound (BUS) is sufficiently accurate to be used for primary breast cancer screening in low-resource regions. Background. Since the 1980s, high-income countries have implemented mammographic screening programs, leading to breast cancer mortality reduction in screened women.1 Mammography is unavailable in many low-resource regions, such as the USAPI. Furthermore, travel difficulties and lack of radiologists hinder implementation. AI combined with portable BUS may address limitations of the high-income paradigm. In this systematic review, we ask if AI-enhanced BUS can detect/segment lesions (Objective 1) and classify lesions as cancerous (Objective 2). Methods. Two reviewers independently assessed articles from 1/1/2016 to 8/6/2023 from PubMed, Google Scholar, and citation searching. Studies which report on AI development and report performance on a patient-wise, held-out test set met the inclusion criteria. Studies were characterized by AI task and clinical application time. Dataset composition and performance were examined via narrative data synthesis. QUADAS-2 bias assessment was performed using criteria for each AI task. Success in (2) is defined by meeting minimum screening performance guidelines.2,3 Results. PubMed yielded 281 studies, Google Scholar yielded 225 studies, and a manual citation search yielded 41 studies. From 382 unique full texts evaluated, 52 articles met all inclusion criteria: 3 frame selection, 2 real-time detection, 2 combination, 14 segmentation-only, and 31 classification-only. Lesion segmentation-only models achieved a 90th percentile Dice similarity coefficient of 0.913 on generally small datasets. The best evidence for lesion cancer classification reported 0.976 area under the curve. All studies faced elevated bias and applicability concerns under QUADAS-2. Conclusion. Reported performance for (1) is insufficient to introduce AI-enhanced BUS for breast cancer screening. Evidence supporting AI-enhanced BUS for (2) is dependent on few studies relying on internal datasets, limiting generalizability. Geographically diverse clinical trials are needed to confirm and improve robustness of performance of AI-enhanced BUS for (1) and (2). References. 1. Marmot MG, et al. The benefits and harms of breast cancer screening: an independent review. British journal of cancer. 2013;108(11):2205-2240. 2. Lehman CD, et al. National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiology. 2017-04-01 2017;283(1):49-58. doi:10.1148/radiol.2016161174 3. Rosenberg RD, et al. Performance Benchmarks for Screening Mammography. Radiology. 2006-10-01 2006;241(1):55-66. doi:10.1148/radiol.2411051504 Citation Format: Arianna Bunnell, Dustin Valdez, Fredrik Strand, Yannik Glaser, Peter Sadowski, John A. Shepherd. Is AI-enhanced breast ultrasound ready for breast cancer screening in low-resource environments? A systematic review [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3449.

Predicting five-year interval second breast cancer risk in women with prior breast cancer.

Annual surveillance mammography is recommended for women with a personal history of breast cancer. Risk prediction models that estimate mammography failures such as interval second breast cancers could help to tailor surveillance imaging regimens to women's individual risk profiles. In a cohort of women with a history of breast cancer receiving surveillance mammography in the Breast Cancer Surveillance Consortium in 1996-2019, we used LASSO-penalized regression to estimate the probability of an interval second cancer (invasive cancer or ductal carcinoma in situ) in the one-year following a negative surveillance mammogram. Based on predicted risks from this one-year risk model, we generated cumulative risks of an interval second cancer for the five-year period following each mammogram. Model performance was evaluated using cross-validation in the overall cohort and within race and ethnicity strata. In 173,290 surveillance mammograms, we observed 496 interval cancers. One-year risk models were well-calibrated (expected/observed ratio = 1.00) with good accuracy (area under the receiver operating characteristic curve = 0.64). Model performance was similar across race and ethnicity groups. The median five-year cumulative risk was 1.20% (interquartile range 0.93-1.63%). Median five-year risks were highest in women who were under age 40 or pre- or peri-menopausal at diagnosis and those with estrogen receptor-negative primary breast cancers. Our risk model identified women at high risk of interval second breast cancers who may benefit from additional surveillance imaging modalities. Risk models should be evaluated to determine if risk-guided supplemental surveillance imaging improves early detection and decreases surveillance failures.

Population Attributable Risk of Advanced-Stage Breast Cancer by Race and Ethnicity

Advanced-stage breast cancer rates vary by race and ethnicity, with Black women having a 2-fold higher rate than White women among regular screeners. Clinical risk factors that explain a large proportion of advanced breast cancers by race and ethnicity are unknown. To evaluate the population attributable risk proportions (PARPs) for advanced-stage breast cancer (prognostic pathologic stage IIA or higher) associated with clinical risk factors among routinely screened premenopausal and postmenopausal women by race and ethnicity. This cohort study used data collected prospectively from Breast Cancer Surveillance Consortium community-based breast imaging facilities from January 2005 to June 2018. Participants were women aged 40 to 74 years undergoing 3 331 740 annual (prior screening within 11-18 months) or biennial (prior screening within 19-30 months) screening mammograms associated with 1815 advanced breast cancers diagnosed within 2 years of screening examinations. Data analysis was performed from September 2022 to August 2023. Heterogeneously or extremely dense breasts, first-degree family history of breast cancer, overweight/obesity (body mass index >25.0), history of benign breast biopsy, and screening interval (biennial vs annual) stratified by menopausal status and race and ethnicity (Asian or Pacific Islander, Black, Hispanic/Latinx, White, other/multiracial). PARPs for advanced breast cancer. Among 904 615 women, median (IQR) age was 57 (50-64) years. Of the 3 331 740 annual or biennial screening mammograms, 10.8% were for Asian or Pacific Islander women; 9.5% were for Black women; 5.3% were for Hispanic/Latinx women; 72.0% were for White women; and 2.0% were for women of other races and ethnicities, including those who were Alaska Native, American Indian, 2 or more reported races, or other. Body mass index PARPs were larger for postmenopausal vs premenopausal women (30% vs 22%) and highest for postmenopausal Black (38.6%; 95% CI, 32.0%-44.8%) and Hispanic/Latinx women (31.8%; 95% CI, 25.3%-38.0%) and premenopausal Black women (30.3%; 95% CI, 17.7%-42.0%), with overall prevalence of having overweight/obesity highest in premenopausal Black (84.4%) and postmenopausal Black (85.1%) and Hispanic/Latinx women (72.4%). Breast density PARPs were larger for premenopausal vs postmenopausal women (37% vs 24%, respectively) and highest among premenopausal Asian or Pacific Islander (46.6%; 95% CI, 37.9%-54.4%) and White women (39.8%; 95% CI, 31.7%-47.3%) whose prevalence of dense breasts was high (62%-79%). For premenopausal and postmenopausal women, PARPs were small for family history of breast cancer (5%-8%), history of breast biopsy (7%-12%), and screening interval (2.1%-2.3%). In this cohort study among routinely screened women, the proportion of advanced breast cancers attributed to biennial vs annual screening was small. To reduce the number of advanced breast cancer diagnoses, primary prevention should focus on interventions that shift patients with overweight and obesity to normal weight.

Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer.

We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening. We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%). The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.

Digital mammography and digital breast tomosynthesis for detecting invasive lobular and ductal carcinoma.

Invasive lobular carcinoma (ILC) is a distinct histological subtype of breast cancer that can make early detection with mammography challenging. We compared imaging performance of digital breast tomosynthesis (DBT) to digital mammography (DM) for diagnoses of ILC, invasive ductal carcinoma (IDC), and invasive mixed carcinoma (IMC) in a screening population. We included screening exams (DM; n = 1,715,249 or DBT; n = 414,793) from 2011 to 2018 among 839,801 women in the Breast Cancer Surveillance Consortium. Examinations were followed for one year to ascertain incident ILC, IDC, or IMC. We measured cancer detection rate (CDR) and interval invasive cancer rate/1000 screening examinations for each histological subtype and stratified by breast density and modality. We calculated relative risk (RR) for DM vs. DBT using log-binomial models to adjust for the propensity of receiving DBT vs. DM. Unadjusted CDR per 1000 mammograms of ILC overall was 0.33 (95%CI: 0.30-0.36) for DM; 0.45 (95%CI: 0.39-0.52) for DBT, and for women with dense breasts- 0.33 (95%CI: 0.29-0.37) for DM and 0.54 (95%CI: 0.43-0.66) for DBT. Similar results were noted for IDC and IMC. Adjusted models showed a significantly increased RR for cancer detection with DBT compared to DM among women with dense breasts for all three histologies (RR; 95%CI: ILC 1.53; 1.09-2.14, IDC 1.21; 1.02-1.44, IMC 1.76; 1.30-2.38), but no significant increase among women with non-dense breasts. DBT was associated with higher CDR for ILC, IDC, and IMC for women with dense breasts. Early detection of ILC with DBT may improve outcomes for this distinct clinical entity.

Racial and Ethnic Variation in Diagnostic Mammography Performance among Women Reporting a Breast Lump.

We evaluated diagnostic mammography among women with a breast lump to determine whether performance varied across racial and ethnic groups. This study included 51,014 diagnostic mammograms performed between 2005 and 2018 in the Breast Cancer Surveillance Consortium among Asian/Pacific Islander (12%), Black (7%), Hispanic/Latina (6%), and White (75%) women reporting a lump. Breast cancers occurring within 1 year were ascertained from cancer registry linkages. Multivariable regression was used to adjust performance statistic comparisons for breast cancer risk factors, mammogram modality, demographics, additional imaging, and imaging facility. Cancer detection rates were highest among Asian/Pacific Islander [per 1,000 exams, 84.2 (95% confidence interval (CI): 72.0-98.2)] and Black women [81.4 (95% CI: 69.4-95.2)] and lowest among Hispanic/Latina women [42.9 (95% CI: 34.2-53.6)]. Positive predictive values (PPV) were higher among Black [37.0% (95% CI: 31.2-43.3)] and White [37.0% (95% CI: 30.0-44.6)] women and lowest among Hispanic/Latina women [22.0% (95% CI: 17.2-27.7)]. False-positive results were most common among Asian/Pacific Islander women [per 1,000 exams, 183.9 (95% CI: 126.7-259.2)] and lowest among White women [112.4 (95% CI: 86.1-145.5)]. After adjustment, false-positive and cancer detection rates remained higher for Asian/Pacific Islander and Black women (vs. Hispanic/Latina and White). Adjusted PPV was highest among Asian/Pacific Islander women. Among women with a lump, Asian/Pacific Islander and Black women were more likely to have cancer detected and more likely to receive a false-positive result compared with White and Hispanic/Latina women. Strategies for optimizing diagnostic mammography among women with a lump may vary by racial/ethnic group, but additional factors that influence performance differences need to be identified. See related In the Spotlight, p. 1479.

Sources of Disparities in Surveillance Mammography Performance and Risk-Guided Recommendations for Supplemental Breast Imaging: A Simulation Study.

Surveillance mammography is recommended for all women with a history of breast cancer. Risk-guided surveillance incorporating advanced imaging modalities based on individual risk of a second cancer could improve cancer detection. However, personalized surveillance may also amplify disparities. In simulated populations using inputs from the Breast Cancer Surveillance Consortium (BCSC), we investigated race- and ethnicity-based disparities. Disparities were decomposed into those due to primary breast cancer and treatment characteristics, social determinants of health (SDOH) and differential error in second cancer ascertainment by modeling populations with or without variation across race and ethnicity in the distribution of these characteristics. We estimated effects of disparities on mammography performance and supplemental imaging recommendations stratified by race and ethnicity. In simulated cohorts based on 65,446 BCSC surveillance mammograms, when only cancer characteristics varied by race and ethnicity, mammograms for Black women had lower sensitivity compared with the overall population (64.1% vs. 71.1%). Differences between Black women and the overall population were larger when both cancer characteristics and SDOH varied by race and ethnicity (53.8% vs. 71.1%). Basing supplemental imaging recommendations on high predicted second cancer risk resulted in less frequent recommendations for Hispanic (6.7%) and Asian/Pacific Islander women (6.4%) compared with the overall population (10.0%). Variation in cancer characteristics and SDOH led to disparities in surveillance mammography performance and recommendations for supplemental imaging. Risk-guided surveillance imaging may exacerbate disparities. Decision-makers should consider implications for equity in cancer outcomes resulting from implementing risk-guided screening programs. See related In the Spotlight, p. 1479.

Breast cancer risk characteristics of women undergoing whole-breast ultrasound screening versus mammography alone.

There are no consensus guidelines for supplemental breast cancer screening with whole-breast ultrasound. However, criteria for women at high risk of mammography screening failures (interval invasive cancer or advanced cancer) have been identified. Mammography screening failure risk was evaluated among women undergoing supplemental ultrasound screening in clinical practice compared with women undergoing mammography alone. A total of 38,166 screening ultrasounds and 825,360 screening mammograms without supplemental screening were identified during 2014-2020 within three Breast Cancer Surveillance Consortium (BCSC) registries. Risk of interval invasive cancer and advanced cancer were determined using BCSC prediction models. High interval invasive breast cancer risk was defined as heterogeneously dense breasts and BCSC 5-year breast cancer risk ≥2.5% or extremely dense breasts and BCSC 5-year breast cancer risk ≥1.67%. Intermediate/high advanced cancer risk was defined as BCSC 6-year advanced breast cancer risk ≥0.38%. A total of 95.3% of 38,166 ultrasounds were among women with heterogeneously or extremely dense breasts, compared with 41.8% of 825,360 screening mammograms without supplemental screening (p<.0001). Among women with dense breasts, high interval invasive breast cancer risk was prevalent in 23.7% of screening ultrasounds compared with 18.5% of screening mammograms without supplemental imaging (adjusted odds ratio,1.35; 95% CI, 1.30-1.39); intermediate/high advanced cancer risk was prevalent in 32.0% of screening ultrasounds versus 30.5% of screening mammograms without supplemental screening (adjusted odds ratio,0.91; 95% CI, 0.89-0.94). Ultrasound screening was highly targeted to women with dense breasts, but only a modest proportion were at high mammography screening failure risk. A clinically significant proportion of women undergoing mammography screening alone were at high mammography screening failure risk.

Impact of BMI on Prevalence of Dense Breasts by Race and Ethnicity.

Density notification laws require notifying women of dense breasts with dense breast prevalence varying by race/ethnicity. We evaluated whether differences in body mass index (BMI) account for differences in dense breasts prevalence by race/ethnicity. Prevalence of dense breasts (heterogeneously or extremely dense) according to Breast Imaging Reporting and Data System and obesity (BMI > 30 kg/m2) were estimated from 2,667,207 mammography examinations among 866,033 women in the Breast Cancer Surveillance Consortium (BCSC) from January 2005 through April 2021. Prevalence ratios (PR) for dense breasts relative to overall prevalence by race/ethnicity were estimated by standardizing race/ethnicity prevalence in the BCSC to the 2020 U.S. population, and adjusting for age, menopausal status, and BMI using logistic regression. Dense breasts were most prevalent among Asian women (66.0%) followed by non-Hispanic/Latina (NH) White (45.5%), Hispanic/Latina (45.3%), and NH Black (37.0%) women. Obesity was most prevalent in Black women (58.4%) followed by Hispanic/Latina (39.3%), NH White (30.6%), and Asian (8.5%) women. The adjusted prevalence of dense breasts was 19% higher [PR = 1.19; 95% confidence interval (CI), 1.19-1.20] in Asian women, 8% higher (PR = 1.08; 95% CI, 1.07-1.08) in Black women, the same in Hispanic/Latina women (PR = 1.00; 95% CI, 0.99-1.01), and 4% lower (PR = 0.96; 95% CI, 0.96-0.97) in NH White women relative to the overall prevalence. Clinically important differences in breast density prevalence are present across racial/ethnic groups after accounting for age, menopausal status, and BMI. If breast density is the sole criterion used to notify women of dense breasts and discuss supplemental screening it may result in implementing inequitable screening strategies across racial/ethnic groups. See related In the Spotlight, p. 1479.

Digital Breast Tomosynthesis versus Digital Mammography Screening Performance on Successive Screening Rounds from the Breast Cancer Surveillance Consortium.

Background Prior cross-sectional studies have observed that breast cancer screening with digital breast tomosynthesis (DBT) has a lower recall rate and higher cancer detection rate compared with digital mammography (DM). Purpose To evaluate breast cancer screening outcomes with DBT versus DM on successive screening rounds. Materials and Methods In this retrospective cohort study, data from 58 breast imaging facilities in the Breast Cancer Surveillance Consortium were collected. Analysis included women aged 40-79 years undergoing DBT or DM screening from 2011 to 2020. Absolute differences in screening outcomes by modality and screening round were estimated during the study period by using generalized estimating equations with marginal standardization to adjust for differences in women's risk characteristics across modality and round. Results A total of 523 485 DBT examinations (mean age of women, 58.7 years ± 9.7 [SD]) and 1 008 123 DM examinations (mean age, 58.4 years ± 9.8) among 504 863 women were evaluated. DBT and DM recall rates decreased with successive screening round, but absolute recall rates in each round were significantly lower with DBT versus DM (round 1 difference, -3.3% [95% CI: -4.6, -2.1] [P < .001]; round 2 difference, -1.8% [95% CI: -2.9, -0.7] [P = .003]; round 3 or above difference, -1.2% [95% CI: -2.4, -0.1] [P = .03]). DBT had significantly higher cancer detection (difference, 0.6 per 1000 examinations [95% CI: 0.2, 1.1]; P = .009) compared with DM only for round 3 and above. There were no significant differences in interval cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.24, 0.30] [P = .96]; round 2 or above difference, 0.04 [95% CI: -0.19, 0.31] [P = .76]) or total advanced cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.15, 0.19] [P = .94]; round 2 or above difference, -0.06 [95% CI: -0.18, 0.11] [P = .43]). Conclusion DBT had lower recall rates and could help detect more cancers than DM across three screening rounds, with no difference in interval or advanced cancer rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Skaane in this issue.

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure.

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors.

Deep learning techniques, including convolutional neural networks (CNN), have the potential to improve breast cancer risk prediction compared to traditional risk models. We assessed whether combining a CNN-based mammographic evaluation with clinical factors in the Breast Cancer Surveillance Consortium (BCSC) model improved risk prediction. We conducted a retrospective cohort study among 23,467 women, age 35-74, undergoing screening mammography (2014-2018). We extracted electronic health record (EHR) data on risk factors. We identified 121 women who subsequently developed invasive breast cancer at least 1year after the baseline mammogram. Mammograms were analyzed with a pixel-wise mammographic evaluation using CNN architecture. We used logistic regression models with breast cancer incidence as the outcome and predictors including clinical factors only (BCSC model) or combined with CNN risk score (hybrid model). We compared model prediction performance via area under the receiver operating characteristics curves (AUCs). Mean age was 55.9years (SD, 9.5) with 9.3% non-Hispanic Black and 36% Hispanic. Our hybrid model did not significantly improve risk prediction compared to the BCSC model (AUC of 0.654 vs 0.624, respectively, p = 0.063). In subgroup analyses, the hybrid model outperformed the BCSC model among non-Hispanic Blacks (AUC 0.845 vs. 0.589; p = 0.026) and Hispanics (AUC 0.650 vs 0.595; p = 0.049). We aimed to develop an efficient breast cancer risk assessment method using CNN risk score and clinical factors from the EHR. With future validation in a larger cohort, our CNN model combined with clinical factors may help predict breast cancer risk in a cohort of racially/ethnically diverse women undergoing screening.

National Performance Benchmarks for Screening Digital Breast Tomosynthesis: Update from the Breast Cancer Surveillance Consortium.

Background It is important to establish screening mammography performance benchmarks for quality improvement efforts. Purpose To establish performance benchmarks for digital breast tomosynthesis (DBT) screening and evaluate performance trends over time in U.S. community practice. Materials and Methods In this retrospective study, DBT screening examinations were collected from five Breast Cancer Surveillance Consortium (BCSC) registries between 2011 and 2018. Performance measures included abnormal interpretation rate (AIR), cancer detection rate (CDR), sensitivity, specificity, and false-negative rate (FNR) and were calculated based on the American College of Radiology Breast Imaging Reporting and Data System, fifth edition, and compared with concurrent BCSC DM screening examinations, previously published BCSC and National Mammography Database benchmarks, and expert opinion acceptable performance ranges. Benchmarks were derived from the distribution of performance measures across radiologists (n = 84 or n = 73 depending on metric) and were presented as percentiles. Results A total of 896 101 women undergoing 2 301 766 screening examinations (458 175 DBT examinations [median age, 58 years; age range, 18-111 years] and 1 843 591 DM examinations [median age, 58 years; age range, 18-109 years]) were included in this study. DBT screening performance measures were as follows: AIR, 8.3% (95% CI: 7.5, 9.3); CDR per 1000 screens, 5.8 (95% CI: 5.4, 6.1); sensitivity, 87.4% (95% CI: 85.2, 89.4); specificity, 92.2% (95% CI: 91.3, 93.0); and FNR per 1000 screens, 0.8 (95% CI: 0.7, 1.0). When compared with BCSC DM screening examinations from the same time period and previously published BCSC and National Mammography Database performance benchmarks, all performance measures were higher for DBT except sensitivity and FNR, which were similar to concurrent and prior DM performance measures. The following proportions of radiologists achieved acceptable performance ranges with DBT: 97.6% for CDR, 91.8% for sensitivity, 75.0% for AIR, and 74.0% for specificity. Conclusion In U.S. community practice, large proportions of radiologists met acceptable performance ranges for screening performance metrics with DBT. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Abstract PD14-07: PD14-07 Associations of Breast Cancer Risk Level and Prediction of Tumor Aggressiveness in the Athena Breast Health Network

Abstract Background: The Athena Breast Health Network (Athena) is a University of California (UC) initiative integrating clinical care and research to drive improvements in breast cancer screening. Through standardized, self-reported clinical intake forms, a patient’s breast health status and information regarding their breast cancer risk is captured before each mammography appointment. Breast cancer risk models provide a level of risk to develop breast cancer but do not take into account aggressiveness of breast cancer. Here, we evaluated outcome data of a large screening cohort for association between risk level and aggressiveness at diagnosis. Methods: We calculated Breast Cancer Surveillance Consortium (BCSC) risk scores for a cohort of 8,923 consented UCSF Athena participants from the years 2012-2018 with a median follow-up of 5-years. To identify those who developed invasive breast cancer on or after completing an Athena intake form, we performed a cancer linkage with the San Francisco Mammography Registry (SFMR), a local registry that regularly collects cancer data from the California Cancer Registry. We classified tumors as aggressive if they met one or more of the following criteria: hormone receptor (HR)-negative, HER2-positive, grade 3. All other tumors were classified as non-aggressive. We used student’s t-tests to examine associations between BCSC 5-year risk score, the development of invasive breast cancer, and tumor aggressiveness among cases. To account for the association between older age and higher BCSC risk score (as well as HR-positive subtypes), we stratified by percentiles of BCSC risk by age (top 2.5% vs. bottom 97.5%). The top 2.5% by age threshold consistently identifies women with lifetime risk of 23–28% and was chosen as high-risk threshold to trigger annual screening in the WISDOM study (Dreher: PMID34843026). Results: Of 8,923 participants, 170 (2%) developed breast cancer during the follow-up period. The average 5-year BCSC risk score for women with breast cancer was 1.81% and 1.47% for those without (p&amp;lt; 0.001). Among women with breast cancer, 123 (72%) developed non-aggressive cancers and 47 (28%) developed aggressive cancers. The average 5-year BCSC risk score for women with non-aggressive and aggressive cancers was 1.89% and 1.60%, respectively (p=0.13). In analyses stratified by percentile of BCSC risk by age, 521 (6%) participants had a BCSC 5-year risk score in the top 2.5% by age and 8,402 (94%) participants had a BCSC 5-year risk score in the bottom 97.5%. A higher percentage of women with non-aggressive cancers vs healthy women (controls) were in the top 2.5% by age (p = 0.001), but the percentage of aggressive cancers vs healthy women in the top 2.5% by age was similar (p = 0.61). Conclusion: Through this study we confirmed that higher 5-year BCSC risk scores are associated with higher overall breast cancer development. Interestingly, participants with the highest 5-year BCSC risk scores (top 2.5% by age), are more likely to develop cancers with non-aggressive features (low grade, hormone positive). This suggests that the BCSC model may preferentially predict less aggressive tumors, and those with the highest 5-year BCSC risk may be more likely to benefit from endocrine risk reduction therapy. There remains a gap in our ability to identity those at risk for aggressive cancers. Our findings highlight the need for screening programs to better understand who is at risk for what type of breast cancer. Current work is focused on developing models tailored to risk prediction of aggressive cancers. Citation Format: Katherine Leggat-Barr, Tomiyuri Lewis, Rosalyn Sayaman, Paige Warner, Kathy Malvin, Leah Sabacan, Elene Tsopurashvili, WISDOM Study and Athena Breast Health Network Investigators and Advocate Partners, Allison Stover Fiscalini, Jeffrey Tice, Karla Kerlikowske, Yiwey Shieh, Laura J. Esserman, Laura Van ’t Veer. PD14-07 Associations of Breast Cancer Risk Level and Prediction of Tumor Aggressiveness in the Athena Breast Health Network [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD14-07.

Abstract PD16-02: Factors Associated with Receipt of Digital Breast Tomosynthesis Among Racially/Ethnically Diverse Women Undergoing Screening Mammography

Abstract Introduction: Digital breast tomosynthesis (DBT) is increasingly utilized in breast cancer screening, including among women at high risk for breast cancer. While there is a lack of rigorous data from randomized controlled trials demonstrating superior efficacy compared to two-dimensional (2D) digital mammography, observational studies suggest that DBT might have lower rates of false-positive results and increased detection of invasive cancer than 2D mammography. However, uptake of DBT might be lower among racial/ethnic minorities, which could contribute to breast cancer disparities. We evaluated whether sociodemographic and breast cancer risk factors were associated with receipt of DBT vs. 2D mammography among a racially/ethnically diverse population of women undergoing screening mammography. Methods: We conducted a respective cohort study among women, age 40-74 years, who underwent screening mammography at Columbia University Irving Medical Center (CUIMC) in New York, NY, from February 2020 to January 2022. We extracted data from the electronic health record (EHR) on age, race/ethnicity, first-degree family history of breast cancer (yes/no), prior breast biopsies (yes/no), and mammographic breast density (high vs. low), and calculated individual 5-year risks of invasive breast cancer according to the Breast Cancer Surveillance Consortium (BCSC) model. High risk was defined as a 5-year invasive breast cancer risk 1.67%. Our primary outcome was receipt of at least one DBT screening examination from 2020-2022 (yes/no). We conducted multivariable logistic regression analyses to assess the association between demographic/clinical factors and receipt of DBT. Results: Among 5617 evaluable women, mean age was 55.4 years (SD, 9.5 years) and 56% identified as non-Hispanic White, 10% as non-Hispanic Black, 17% as Hispanic, 8% as Asian, and 9% other/unknown. Over 60% of women had high breast density, and 34% met high-risk criteria. Seventy percent of women had at least one DBT from 2020-2022. In multivariable analyses (Table 1), women with high vs. low breast density were 2.5 times more likely to receive DBT (odds ratio [OR]=2.51, 95% confidence interval [CI]=2.19-2.88), while first-degree family history of breast cancer, prior breast biopsy, and age were inversely associated with DBT. Racial/ethnic minorities were less than half as likely to undergo DBT compared to non-Hispanic Whites; for example, Hispanic women were over 85% less likely to receive DBT (OR=0.14, 95% CI=0.11-0.16). Overall, there was no association between breast cancer risk status (high vs. low/average) and receipt of DBT (OR=1.00, 95% CI 0.92-1.08). Conclusion: We observed that the majority of women undergoing screening mammography at CUIMC from 2020-2022 received DBT for breast cancer screening. However, racial/ethnic minorities, including non-Hispanic Blacks and Hispanics, were significantly less likely than non-Hispanic Whites to have received DBT. Breast cancer risk according the BCSC model was also not associated with receipt of DBT. Future studies should determine which subsets of women are more likely to benefit from DBT. Citation Format: Julia E. McGuinness, Gargi Patel, Jacquelyn N. Amenta, Rita Kukafka, Katherine D. Crew. Factors Associated with Receipt of Digital Breast Tomosynthesis Among Racially/Ethnically Diverse Women Undergoing Screening Mammography [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD16-02.

Abstract P3-03-14: Comparing risk of breast cancer and cardiovascular disease and uptake of chemoprevention and statins among racially/ethnically diverse women with atypical hyperplasia or lobular carcinoma in situ

Abstract Background: Chemoprevention with selective estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs) has been shown in randomized controlled trials to decrease breast cancer incidence by 50-65% among women at high risk for breast cancer. However, chemoprevention uptake remains low among high-risk women. Women with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS) derive the greatest benefit from SERMs and AIs for breast cancer risk reduction. A potential barrier to chemoprevention uptake is competing comorbidities, including atherosclerotic cardiovascular disease (ASCVD). We calculated risk of breast cancer and ASCVD among women with AH or LCIS and assessed uptake of chemoprevention and statins among women who met high-risk criteria for both breast cancer and ASCVD. Methods: We conducted a retrospective cohort study among women, age 40-74 years, with AH or LCIS diagnosed in 2007-2015 at Columbia University Irving Medical Center (CUIMC) in New York City. Eligible women had sufficient data in the electronic health record (EHR) to calculate 5 and 10-year invasive breast cancer risk according to the Breast Cancer Surveillance Consortium (BCSC) risk calculator, including age, race/ethnicity, first-degree family history of breast cancer, breast biopsy results, and mammographic density. We calculated 10-year ASCVD risk using the 2013 American College of Cardiology (ACC)/American Heart Association (AHA) ASCVD risk calculator using additional EHR data, including systolic blood pressure, total and HDL cholesterol, history of diabetes, treatment for hypertension, and current smoking status. High-risk criteria to determine eligibility for SERMs/AIs and statins was defined as a 5-year invasive breast cancer risk 1.67% and 10-year ASCVD risk 7.5%, respectively. We compared mean 10-year risk of breast cancer vs. ASCVD using a paired t-test and uptake of SERMs/AIs vs. statins among women at high risk for breast cancer and ASCVD, respectively, using McNemar’s test. Results: Among 298 evaluable women, mean age was 58.2 years (standard deviation [SD], 8.34), with 33% non-Hispanic White, 41% Hispanic, 9% non-Hispanic Black, 6% Asian, and 11% other/unknown race/ethnicity. About 98% of women met high-risk criteria for breast cancer and 30% were high risk for ASCVD. Mean 10-year risk of breast cancer was higher than mean 10-year risk of ASCVD (9.14% vs. 6.69%, p&amp;lt; 0.001). Among women who met high-risk criteria for both breast cancer and ASCVD, uptake of statins was higher compared to SERMs/AIs (58% vs. 21%, p&amp;lt; 0.001). Comparing non-Hispanic Whites vs. racial/ethnic minorities, mean 10-year breast cancer risk was higher (12.12% vs. 7.71%, p&amp;lt; 0.001), but there were no statistically significant differences in ASCVD risk or uptake of chemoprevention or statins. Conclusions: Among women with AH or LCIS, mean absolute risk of breast cancer was higher compared to risk of ASCVD, however, uptake of statins was higher compared to chemoprevention with SERMs or AIs. To address under-utilization of chemoprevention among high-risk women, use of SERMs or AIs should be placed in the context of medications used for other chronic diseases, such as statins for ASCVD. Citation Format: Luisa Nilan, Jacquelyn N. Amenta, Julia E. McGuinness, Rita Kukafka, Katherine D. Crew, Kehinde Lawal. Comparing risk of breast cancer and cardiovascular disease and uptake of chemoprevention and statins among racially/ethnically diverse women with atypical hyperplasia or lobular carcinoma in situ [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-14.