Abstract PS3-06-22: Artificial Intelligence Predicts OncotypeDX Recurrence Scores Directly from H&E-Stained Whole Slide Images of ER+/HER2- Node-Negative Breast Cancer Surgical Sections

Abstract Background: Oncotype DX (ODX) is a widely used genomic assay for risk stratification and guiding adjuvant treatment decision making in early stage ER+/HER2- node-negative breast cancer. However, its cost and turnaround time can significantly limit its use, particularly in low-resource settings. We developed a deep learning model to predict the ODX recurrence score directly from H25, Low: ODX≤25 when age is greater than 50). In addition, Kaplan-Meier analysis was used to compare patient outcomes based on treatment decisions guided by either ODX or our model. Results: The median age at diagnosis of the 300 patients in our external validation cohort was 53 years (range: 26–82). The ODX recurrence scores ranged from 0 to 71, with 150 patients having an ODX score below 26. Ninety-five patients (32%) were premenopausal. Our AI model predicted ODX scores directly from H 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-06-22.