Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.

ABSTRACT: Breast cancer (BC) risk assessment aims to enhance individualized screening and prevention strategies. While recent deep learning (DL) models based on mammography have shown promise in short-term risk prediction, they primarily rely on single-time-point (STP) exams, ignoring temporal changes in breast tissue from sequence exams. We present the Multi-Time Point Breast Cancer Risk Model (MTP-BCR), a novel DL approach that integrates traditional risk factors and longitudinal mammography data to capture subtle tissue changes indicative of future malignancy. Using a large in-house dataset with 171,168 mammograms from 9133 women, MTP-BCR achieved superior performance in 10-year risk prediction, with an AUC of 0.80 (95% CI, 0.78-0.82) at the patient level, outperforming STP-based and traditional

SUBMITTER: Wang X 

PROVIDER: S-EPMC12572377 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature