Abstract 4174: Incorporating continuous mammographic density into the BOADICEA breast cancer risk prediction model
Authors: Ficorella Lorenzo , Eriksson Mikael , Czene Kamila , Leslie Goska , Yang Xin , Carver Tim J. , Easton Douglas F. , Hall Per F. , Antoniou Antonis C. 
Published: June 2023

BOADICEA, implemented in the CanRisk tool (www.canrisk.org) can be used to calculate future breast cancer risk using data on cancer family history, genetics (rare high- or moderate-risk pathogenic variants; polygenic scores (PGS)), questionnaire-based risk factors and mammographic density (MD) measured using the BI-RADS classification. The BI-RADS categorization requires manual reading which is not feasible at population level. Moreover, BI-RADS captures MD in 4 categories, whereas continuous measures are stronger risk predictors. Here, BOADICEA was extended to incorporate continuous breast density measured by the fully automated Volpara and STRATUS software, working on raw and processed images, respectively. We used data from the KARMA prospective screening cohort (60,276 participants; 1,167 incident breast cancers). The associations between Volpara or STRATUS density measurements and incident breast cancer risk were estimated in a randomly selected training subset consisting of two-thirds of the full dataset. For this, we calculated density residuals after regressing on participant’s age at mammography. Hazard ratios (HR) for the normalized residuals were calculated using a Cox proportional hazards model, adjusting for family history, BMI and all risk factors included in BOADICEA. Multiple Imputation by Chained Equations (MICE) was used to impute missing data. The remaining one-third of the KARMA cohort was used to assess the performance of BOADICEA in predicting 5-year risks, after including the estimated Volpara and STRATUS associations.Under the best fitting multivariable model including PGS, the HRs per SD of residual STRATUS density were estimated to be 1.67 (95%CI: 1.44-1.93) and 1.38 (95%CI: 1.26-1.51) for pre- and post-menopausal women, respectively. The HR estimates per SD of residual Volpara density were 1.38 (95%CI: 1.19-1.59) and 1.39 (95%CI: 1.27-1.52) for pre- and post-menopausal women, respectively. After incorporating these associations in BOADICEA, there was a marked improvement in the model discriminatory ability in the test dataset, compared to using the BI-RADS classification. The largest increase in AUC was observed for STRATUS residual density, with a 3% increase compared to using BI-RADS vs a 1.6% increase in AUC when using Volpara residual density. These increases were consistent across BOADICEA models considering different combinations of risk factors, including PGS.Including continuous breast density in BOADICEA can lead to improved breast cancer risk stratification and could allow for automated measures of MD to be more readily deployed in breast cancer risk prediction. Additional prospective studies are required to further validate the findings. The models will be implemented in the CanRisk tool.

Citation Format: Lorenzo Ficorella, Mikael Eriksson, Kamila Czene, Goska Leslie, Xin Yang, Tim J. Carver, Douglas F. Easton, Per F. Hall, Antonis C. Antoniou. Incorporating continuous mammographic density into the BOADICEA breast cancer risk prediction model. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4174.