Weitz, Michelle

Abstract Current genomic assays provide prognostic information and guide chemotherapy decisions in early-stage hormone receptor–positive/human epidermal growth factor receptor 2–negative (HR+/HER2−) breast cancer, but limited tissue sampling may incompletely capture tumor heterogeneity. We developed and validated an image-based artificial intelligence (AI) model integrating dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinicopathologic features to predict recurrence risk in early-stage HR+/HER2 − breast cancer. In this retrospective multi-institutional study, the model was trained using pre-treatment imaging and clinicopathologic data from 522 patients and independently validated in 550 additional patients treated between 2010 and 2023. By analyzing spatial tumor characteristics, the model generated individualized recurrence risk scores that outperformed clinicopathologic features alone. In the validation cohort, median age was 53 years (range, 24–90), 15.6% of patients self-identified as African American, 89.7% had clinical T1–T2 disease, and 34.4% had 1–3 positive lymph nodes. Five-year recurrence-free survival was 93.8% (95% CI, 90.0–96.2%) among patients classified as low risk compared with 82.6% (95% CI, 74.8–88.1%) among those classified as high risk, corresponding to an adjusted hazard ratio of 2.28. Prognostic performance was maintained across clinically relevant subgroups, including age and nodal status. These findings support the potential of a noninvasive image-based AI approach for real-time prognostication and treatment personalization in early-stage HR+/HER2 − breast cancer.