Abstract PD11-04: Comparative performance of an AI-based digital pathology tool and genomic signatures in early ER+/HER2- breast cancer

Abstract Background Prognostic tools are important for guiding adjuvant therapy in early breast cancer (EBC), particularly for patients with estrogen receptor-positive, HER2-negative (ER+/HER2-) tumors, where optimizing the treatment strategy, like making a de-escalation decision, remains a clinical challenge (1). Recent advances in artificial intelligence (AI) applied to whole-slide images have opened new possibilities for capturing prognostic information directly from routinely available H35(24):2838-2847. doi:10.1200/JCO.2017.74.04722. Garberis I, Gaury V, Saillard C, et al. Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides. Nat Commun. 2025;16:5876.3. Dubsky P, Filipits M, Jakesz R, et al. EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Ann Oncol. 2012;24(3):640-647. doi:10.1093/annonc/mds3584. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817-2826. Citation Format: V. Aubert, V. Gaury, I. Garberis, Z. Vaquette, E. Hocquet, D. Almaraz-Klippel, F. Daidj, D. Drubay, D. Jacobs, N. Arfaoui, L. Guillou, D. Lin, J. Guillon, C. Barcenas, F. Andre, S. Krishnamurthy, M. Lacroix-Triki. Comparative performance of an AI-based digital pathology tool and genomic signatures in early ER+/HER2- breast cancer 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 PD11-04.