Abstract PS3-04-08: Independent Validation of a Pathology-Based Multimodal Artificial Intelligence Biomarker for Predicting Risk of Distant Metastasis in Postmenopausal, Estrogen Receptor-Positive, Early-Stage Breast Cancer Patients: Analysis of the ABCSG Trial 8

Abstract Background: Genomic assays are currently used to determine prognosis and predict treatment outcome in patients with estrogen receptor-positive (ER+), HER2-negative early breast cancer (EBC). However, their use is expensive, time-consuming and not available in many clinical facilities. We previously developed a multimodal artificial intelligence (MMAI) model using clinical and histopathological data from more than 12,000 patients from six phase III trials, offering a faster, non-genomic alternative for personalized risk stratification. We present the first external independent validation of this MMAI model using the ABCSG trial 8 - a large, prospective phase III trial of a low- to moderate-risk cohort of ER+ postmenopausal breast cancer patients receiving endocrine therapy (ET) only - to assess its prognostic performance for distant metastasis (DM). Methods: ER+ HER2- patients from the prospective-randomized trial ABCSG 8 with digitized baseline H0.001) and MMAI risk group (intermediate vs. low risk HR 95% CI = 2.19 1.34 - 3.57, p=0.002; high vs. low risk HR 95% CI = 4.45 3.19 - 6.19, p0.001). In multivariable analysis (MVA) adjusting for age, tumor size, nodal status, both continuous MMAI raw score (p0.001) and MMAI risk group (p0.001) remained independently associated with DM. The MMAI raw score remained significantly associated with the risk of DM across clinically meaningful subgroups, including lymph node status (N0 and N1), tumor grade (Grade 1 and 2), histology (invasive ductal carcinoma and invasive lobular carcinoma), and trial-defined Ki67 levels (≤5%, 6-29%, ≥30%). Additionally, the image-only component of the MMAI was independently associated with risk of DM (p0.001 in both UVA and MVA). Conclusion: This study validates the MMAI model, originally developed for HR+ HER2- EBC, as a strong prognostic tool for DM in lower-risk postmenopausal women with ER+ HER2- EBC. This MMAI technology promises to improve personalized risk stratification and adjuvant treatment decisions in ER+ HER2- EBC as a cost-effective, non-tissue consuming, and faster alternative to genomic assays. Citation Format: M. Filipits, D. Hlauschek, J. Zhang, M. Balic, R. Kates, R. Greil, F. Fitzal, N. Toro-Bauer, G. Rinnerthaler, H. Pinckaers, K. Sotlar, Z. Bago-Horvath, W. Hulla, P. Regitnig, A. Piehler, C. E. Geyer, H. Kreipe, J. Griffin, N. Harbeck, N. Wolmark, M. Gnant. Independent Validation of a Pathology-Based Multimodal Artificial Intelligence Biomarker for Predicting Risk of Distant Metastasis in Postmenopausal, Estrogen Receptor-Positive, Early-Stage Breast Cancer Patients: Analysis of the ABCSG Trial 8 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 PS3-04-08.