A Deep Learning Model to Predict Breast Cancer Recurrence Using Longitudinal Mammograms and Clinical Data.
TL;DR
The proposed multimodal deep learning framework improves both recurrence prediction and recurrence subtype discrimination over existing models.
Abstract
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 occurrence (binary classification) and recurrence subtype (local/regional/distant). The model was trained and evaluated within a single institutional cohort. Results A total of 3,923 patients and 19,684 examinations (mean age ± SD, 52.98 years ± 10.98; 3923 female) were included. In the test cohort, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.80 (95% CI: 0.74, 0.85) and an area under the precision-recall curve (AUPRC) of 0.537 (95% CI: 0.432, 0.638) for overall recurrence, outperforming unimodal approaches. For recurrence subtype prediction among recurrencepositive patients, the model achieved a balanced accuracy of 65.1% (95% CI: 60.2%, 71.1%) and an AUC 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. ©RSNA, 2026.