A Deep Learning Model Incorporating Spatiotemporal Asymmetries on Longitudinal Mammograms to Predict Breast Cancer Risk.

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-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 with Hologic systems-were extracted from the Cohort of Screen-Age Women - Case Control (CSAW-CC) dataset (406 patients with cancer and 6053 control participants) and an independent dataset (293 patients with cancer and 297 control participants). A novel model called STA-Risk (spatial and temporal asymmetry-based risk prediction), 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 receiver operating characteristic 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 (range, 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. Keywords: Breast, Mammography, Technology Assessment, Experimental Investigations, Convolutional Neural Network, Diagnosis Supplemental material is available for this article. © RSNA, 2026.