Abstract PD11-07: Ai-driven risk reclassification in HR+/HER2− breast cancer: real-world comparison with the 21-gene assay

Abstract Background Risk stratification in hormone receptor-positive breast cancer (HR+ BC) is essential for personalized therapy. While the 21-gene expression assay (Oncotype DX, ODX) guides chemotherapy (CHT) decisions, its intermediate-risk group often yields therapeutic uncertainty. The Ataraxis Breast model (ATX) is a multimodal artificial intelligence (AI) tool that integrates digital pathology with clinical variables to produce a continuous recurrence risk score. Trained on over 400 million pathology images, ATX has shown robust prognostic value in external cohorts. We evaluated ATX against ODX in real-world HR+ BC patients (pts) from Basel University Hospital (USB), focusing on the reclassification of intermediate-risk cases and overall prognostic performance. Methods We retrospectively analyzed 269 HR+/HER2- BC pts treated at USB (2010-2021) with available ODX and digitized pathology slides. The primary endpoint was disease-free interval (DFI). ATX scores stratified pts into low vs high risk. Performance metrics included hazard ratios (HR), C-index, and 10-year calibration. Risk reclassification was assessed by comparing ATX vs ODX categories. Multivariate Cox models included age, tumor grade, and ODX score. Subgroup analyses were done for node-positive and CHT-treated pts. Results ATX high-risk pts had significantly reduced DFI vs low-risk pts (HR = 2.37, p = 0.032; C-index = 0.70). In contrast, ODX intermediate-risk pts (n=167) showed poor outcome separation (HR = 1.61, p = 0.39). ATX reclassified 77% of intermediate ODX pts to low risk, and 23% to high risk. Among low ODX pts, 33% were up-classified by ATX; 44% of high ODX pts were down-classified. ATX scores aligned with clinical risk features (nodal status, tumor size) and actual outcomes. In multivariate analysis, ATX was a strong independent predictor (HR = 4.18, p 0.001) (Table 1), whereas ODX was not. In subgroups, ATX outperformed ODX: C-index was 0.75 in node-positive pts and 0.80 in those receiving CHT. Performance remained strong across tumor stages, age, and histological types, supporting ATX’s versatility. Calibration analysis showed ATX predictions aligned closely with observed 10-year recurrence risks (R2 = 0.85). Conclusions ATX showed superior prognostic accuracy and reclassification capacity vs ODX in HR+ BC pts at USB. It clarified risk for most intermediate ODX cases, supporting more precise treatment decisions. ATX may guide CHT de-escalation in low-risk pts and intensification in high-risk cases. These findings support ATX’s clinical integration and prospective validation. Citation Format: E. D. Chiru, L. Sojak, J. Witowski, K. Zeng, C. Kurzeder, S. Muenst, M. Vetter. Ai-driven risk reclassification in HR+/HER2− breast cancer: real-world comparison with the 21-gene assay abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD11-07.