Radiomic and genomic predictors of breast cancer risk

摘要 每年有超过40，000名美国女性死于乳腺癌。筛查乳房X光检查可以挽救生命，但也 导致潜在危害。针对女性个体风险量身定制的个性化筛查方案既可以 通过为高危妇女提供更强化的治疗方案，改善致命癌症的早期发现， 过度筛查和过度治疗低风险女性。然而，目前临床乳腺癌的风险 预测模型在区分高风险和低风险妇女方面不够准确。新辐射 深度学习算法，它可以从女性的筛查中自动挖掘乳腺组织特征 乳房X光检查来预测她未来的癌症风险，有巨大的潜力来改变乳腺癌筛查， 但尚未得到独立验证。新的乳腺癌多基因风险评分（PRS）也显示， 这是改善风险预测的承诺，尽管在人口规模上实施仍然昂贵。我们建议 检查是否增加放射组学和基因组风险评分可以显着改善当前的临床风险 在一个大型的，多样化的人口为基础的队列的178 K妇女参加了凯撒永久的预测模型， 基因、环境与健康研究计划（RPGEH）的2D全视野数字筛查 乳房X线摄影（FFDM）。我们还建议将性能最好的放射组学深度学习算法扩展到 在加州和纽约的两个大型卫生保健机构中使用的不同的筛查乳房X线摄影系统， 包括在西奈山接受3D数字乳腺断层合成摄影（DBT）筛查的5万名女性 卫生系统（MSHS）。具体目标是：（1）评估放射组学深度学习的性能 乳腺癌风险预测模型，估计它们与5年和10年乳腺癌风险的关联， 并确定关联独立于已知临床风险因素的程度;（2） 确定放射组学和基因组风险评分是否独立预测乳腺癌风险，并探索 种族/民族和其他临床风险因素的潜在差异;以及（3）将最佳放射组学深度 从2D FFDM到3D断层合成的学习算法。拟议的研究将填补必要的知识 通过验证新的放射组学算法来实现放射组学和基因组学的潜力所需的差距， 量化模型性能优于传统风险因素模型和新的多基因模型 风险评分，探索种族/民族差异，并将最佳放射组学工具扩展到不同的 在两个大型多族裔医疗保健机构中使用乳房X线摄影系统。 1

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