Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit

Abstract

Genomic testing has transformed treatment decisions for hormone receptor-positive, HER2-negative (HR+/HER2-) early breast cancer; however, it remains inaccessible to many patients worldwide due to high costs and logistical barriers. Here, we developed an artificial intelligence (AI) model using a multimodal deep learning approach that estimates Oncotype DX 21-gene recurrence scores (RS) from routine histopathology images and clinicopathologic variables, including age at diagnosis, tumor size, and receptor status. Using a foundation model pre-trained on 171,189 histopathological slides, we fine-tuned and validated our AI model on the TAILORx randomized trial (n=8,284). Among 2,407 patients in the TAILORx validation, the model classifies 45.6% of patients as low-risk, 42.4% as intermediate risk, and 12.0% as high-risk. For predicting high genomic risk disease (RS≥26), occurring in 15.9% in the TAILORx validation set, the model achieves AUC=0.898. Patient stratification by our model shows strong prognostic value across multiple clinical endpoints, including recurrence-free interval, distant recurrence-free interval, and disease-free survival. Importantly, chemotherapy benefit is demonstrated for premenopausal patients classified by our model as high AI risk and chemotherapy benefit is ruled out for postmenopausal patients classified as low AI risk. External validation across six independent cohorts (n=5,497 patients) demonstrates robust generalization of the AI model for prognostication and prediction of RS. Notably, in postmenopausal patients, the AI model reclassifies approximately 30% of clinically high-risk cases, defined by the MINDACT criteria, as low-risk. These findings demonstrate that artificial intelligence applied to standard histopathology can be a valuable tool for chemotherapy decision-making in HR+/HER2- early breast cancer. This approach can help reduce unnecessary chemotherapy and extend precision medicine, particularly in resource-limited settings, where genomic testing is not widely accessible.