A Deep Learning Model to Predict Breast Cancer Recurrence Using Longitudinal Mammograms and Clinical Data

Purpose To develop a deep learning model that integrates longitudinal mammograms and static clinical data for predicting the recurrence risk and recurrence subtype of breast cancer. Materials and Methods In this retrospective study, the data of patients examined via imaging from January 2004 to December 2020 were included. A multimodal deep learning model incorporating pretreatment mammographic features and clinical data were developed. A multitask framework was implemented to jointly predict recurrence (binary classification) and recurrence subtype (local, regional, distant). The model was trained and evaluated within a single institutional cohort. Results A total of 3923 patients (mean age ± SD, 52.89 years ± 10.98; 3923 [100.0%] female) and 19 684 examinations were included. In the test cohort, the model achieved an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.74, 0.85) and an area under the precision-recall curve of 0.54 (95% CI: 0.43, 0.64) for overall recurrence, outperforming unimodal approaches. For recurrence subtype prediction among patients with recurrence, the model achieved a balanced accuracy of 65.1% (95% CI: 60.2, 71.1) and an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.68, 0.78). Conclusion The proposed multimodal deep learning framework improves both recurrence prediction and recurrence subtype discrimination over existing models. Keywords: Breast Cancer Recurrence, Multimodal, Deep Learning, Mammography, Breast, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2026.