Radiomic and genomic predictors of breast cancer risk

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ABSTRACT Over 40,000 U.S. women will die of breast cancer each year. Screening mammography saves lives but also results in potential harms. Personalized screening regimens tailored to a woman's individual risk can both improve early detection of lethal cancers through more intensive regimens for high-risk women, and reduce over-screening and over-treatment of low-risk women. However, the current clinical breast cancer risk prediction models are insufficiently accurate for discriminating high-risk and low-risk women. New radiomic deep learning algorithms, which automatically mine troves of breast tissue features from a woman's screening mammogram to predict her future cancer risk, have enormous potential to transform breast cancer screening, but have not been independently validated. New polygenic risk scores (PRS) for breast cancer also show promise for improving risk prediction, although still costly to implement on a population scale. We propose to examine whether adding radiomic and genomic risk scores can significantly improve current clinical risk prediction models in a large, diverse population-based cohort of 178K women enrolled in Kaiser Permanente's Research Program on Genes, Environment and Health (RPGEH) who were screened with 2D full-field digital mammography (FFDM). We also propose to extend the best performing radiomic deep learning algorithms to diverse screening mammography systems utilized in two large health care settings in California and New York, including a cohort of 50K women screened with 3D digital breast tomosynthesis (DBT) in the Mount Sinai Health System (MSHS). The specific aims are to: (1) Evaluate the performance of radiomic deep learning breast cancer risk prediction models, estimate their associations with 5-year and 10-year breast cancer risk, and determine the extent to which the associations are independent of known clinical risk factors; (2) Determine whether radiomic and genomic risk scores independently predict breast cancer risk, and explore potential differences by race/ethnicity and other clinical risk factors; and (3) Transfer the best radiomic deep learning algorithm(s) from 2D FFDM to 3D tomosynthesis. The proposed research will fill essential knowledge gaps needed to realize the potential of radiomics and genomics by validating new radiomic algorithms, quantifying the improvements in model performance above traditional risk factor models and new polygenic risk scores, exploring differences by race/ethnicity, and extending the best radiomic tools to diverse mammography systems utilized in two large multi-ethnic health care settings. 1