Abstract P022: Quantitative measures of breast density and breast cancer risk prediction among black women in a screening population
Authors: Mahmoud Mattia A. , McCarthy Anne Marie , Kontos Despina , Conant Emily , Chen Jinbo , Ehsan Sarah , Pantalone Lauren , Mankowski Walter 
Published: January 2023

Abstract
Background: Although mammographic density (MD) is a strong predictor of invasive breast cancer, it has been shown to increase the discriminatory ability of existing risk prediction models only slightly. Breast density is assessed visually by radiologists according to the Breast Imaging Reporting and Data System (BI-RADS) criteria, which has been shown to lack reproducibility. Additionally, the racial diversity of women included in the previous studies was limited. Quantitative measures of breast density have been developed that automatically measure density directly from images. Our prior work found that Black women had lower BI-RADS breast density, despite having a greater quantity of dense breast tissue on average compared with white women when quantitative measures were used. The study purpose was to determine if adding quantitative breast density measures improved breast cancer risk prediction for both white and Black women compared to the Breast Cancer Risk Assessment Tool (BCRAT). Methods: A total of 16,942 women (N=6881 white, N=10061 Black) screened with full-field digital mammography (FFDM) or with a combination of FFDM and digital breast tomosynthesis (DBT) at the Hospital of the University of Pennsylvania (HUP) between September 1, 2010 to December 31, 2014 were included. Area breast density measurements including dense area and area percent density were obtained using a fully automated, validated LIBRA software. All patients were followed from the date of first screening mammogram visit until breast cancer diagnosis or end of follow up on December 31, 2019. We used the BCRA R package (v2.1) for the BCRAT (https://dceg.cancer.gov/tools/risk-assessment/bcra) to estimate the expected 5 year absolute risk for breast cancer. We evaluated the area under the curve (AUC) and calibration (observed to expected ratio, O/E) of the following models: BCRAT alone, BCRAT + BI-RADS density, BCRAT + quantitative density measures, and BCRAT + BI-RADS density + quantitative density measures. Results: There were 123 breast cancers among white and 123 breast cancers among Black women. Adding dense area and area percent density to the BCRAT alone or BCRAT plus BI-RADS density did not improve predictive accuracy for white or Black women. AUC remained close to 0.59 for white women and 0.61 for Black women in all models, with no statistically significant differences in AUCs (DeLong Test p value = 0.09). Underprediction was worse in white women than in Black women. Under-prediction of the BCRAT was reduced when adding percent density from [O/E 1.24 vs. O/E 1.17] in white women. Calibration stayed relatively the same (O/E=1.10) for Black women even when adding both quantitative MD measures. Conclusion: Our results suggest that adding quantitative area mammographic density measurements to the BCRAT does not improves breast cancer risk prediction among Black or white women. Given the increasing use of digital breast tomosynthesis (DBT), future studies should examine whether volumetric breast density measures have superior predictive value among Black women.
Citation Format: Mattia A. Mahmoud, Anne Marie McCarthy, Despina Kontos, Emily Conant, Jinbo Chen, Sarah Ehsan, Lauren Pantalone, Walter Mankowski. Quantitative measures of breast density and breast cancer risk prediction among black women in a screening population. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P022.