Spatiotemporal Asymmetries of Longitudinal Screening Mammograms for Breast Cancer Risk Prediction.

Purpose To develop and evaluate a new deep learning-based risk model that explicitly captures bilateral and longitudinal asymmetries on sequential mammograms for predicting breast cancer risk. Materials and Methods In this institutional review board (IRB)-approved retrospective case-control study, the images of sequential mammographic examinations (at least two per patient with interexamination intervals of 12-36 months)-all of which were acquired on Hologic systems-were extracted from the CSAW-CC dataset (406 cancer patients, 6,053 controls) and an independent dataset (293 cancer patients, 297 controls). A novel model, STA-Risk, whose architecture incorporates side encoding, temporal encoding, and customized asymmetry loss, was constructed using these data. Fivefold cross-validation with the individual datasets and joint training with a mixed dataset were performed. Model performance was reported with the concordance index (C-index) and the time-dependent area under the curve (AUC) (1-5 years). Results STA-Risk achieved C-indexes of 0.72 with the CSAW-CC data and 0.73 with the independent cohort data and outperformed all the compared risk models (0.67-0.70 and 0.66-0.72, respectively), with consistently higher AUCs in the 1-to 5-year risk predictions. Ablation studies revealed that all three key components of STA-Risk contributed to this improved performance. Domain shifts were observed, but their effects were mitigated with joint training strategy; the resulting model achieved cross-cohort test C-indexes of 0.75 with the CSAW-CC dataset and 0.67 with the independent dataset. Conclusion The STA-Risk deep learning risk model constructed from spatiotemporal asymmetries detected on longitudinal mammograms improves breast cancer risk prediction over existing models. ©RSNA, 2026.