Abstract PD11-01: Development of a Multi-Modal Artificial Intelligence (MMAI) Model for Predicting Distant Metastasis in HR+ Early-Stage Invasive Breast Cancer

Abstract Background: Accurately predicting the risk of distant metastasis (DM) in hormone receptor–positive (HR+) early-stage breast cancer (EBC) remains a key clinical challenge. Current prognostic tools often rely on limited clinical or genomic features. We developed and evaluated a pathology-based multimodal artificial intelligence (MMAI) prognostic biomarker that integrates clinical and histopathological data from six Phase III randomized trials to predict the risk of DM in HR+ EBC. Methods: Digitized pre-treatment biopsy and surgical slides from six Phase III trials conducted by three cooperative groups (WSG, NSABP, and ABCSG) were used in this study. The MMAI model was developed on 8616 patients from four trials: ADAPT, PlanB, B34, and ABCSG 6, incorporating AI-derived histopathological features alongside clinical variables including age, tumor size, nodal status to predict risk of DM. The continuous MMAI raw score was locked prior to validating in two cohorts: NSABP B14 Tamoxifen Set and NSABP B39 with HR-positive invasive breast cancer. The primary endpoint was time to DM, and model performance was evaluated using area under the time-dependent receiver operating characteristic curve (tdAUC), Cox Proportional Hazards regression and Kaplan-Meier curves. 10-year tdAUC, hazard ratios (HR), and the 10-year estimated DM rates and 95% confidence intervals (CI) were reported. A clinical comparator model including age, tumor size, and pathological N stage was used as a performance benchmark. Performance in pre-specified subgroups including nodal and menopausal status was also assessed. Cut points were empirically determined by maximizing the difference in restricted mean survival time of 10-year DM between risk groups with clinically guided constraints, using data of 4,332 pN0-1 patients pooled from the six trials (ABCSG6, ADAPT, B14, B34, B39, and PlanB) that were held-out for model testing and evaluation. Results: MMAI raw scores were generated for 2,188 patients in B14, and 1,198 patients in B39 with HR+ Stage I-II, EBC. The median follow-up time for B14 was 17.6 years, and 9.4 years for B39. MMAI demonstrated strong prognostic performance: in B14, the locked MMAI showed a 10-year tdAUC of 0.71 [0.67-0.74] compared to the clinical comparator model (0.65 [0.62-0.69]); in B39, the 10-year tdAUC for MMAI was 0.72 [0.59-0.82] versus 0.69 [0.60-0.79] for the clinical comparator model. The MMAI raw score was significantly associated with risk of DM in both B14 (HR [95%CI] = 2.06 [1.81-2.35]) and B39 (HR [95%CI] = 2.31 [1.63-3.28]). The score remained significant after adjusting for age, tumor size, and pathological N stage in both B14 (HR [95%CI] = 1.94 [1.68-2.25]) and B39 (HR [95%CI] = 2.06 [1.36-3.11]). Subgroup analyses showed consistent prognostic performance across nodal status and menopausal status. Using additional data as described, cut points were chosen to ensure sufficient sample sizes within risk categories, balancing statistical power for future validation and clinical interpretability. This resulted in 65.5% of patients being classified as low risk, 10.5% as intermediate risk, and 24.0% as high risk, with the corresponding 10-year DM-free rates of 95.5% (95%CI: 94.6% - 96.3%), 89.5% (95%CI: 86.2% - 92.4%), and 83.6% (95%CI: 81.1% - 86.0%), respectively. Conclusion: We have successfully developed and evaluated an MMAI model across six Phase III randomized breast cancer trials, demonstrating its utility as a prognostic biomarker for predicting risk of DM in HR+ EBC patients. This non-tissue destructive, faster turnaround technology is practical for real-world use and holds significant promise for personalizing patient care in breast cancer. Citation Format: C. E. Geyer Jr., D. A. Kates-Harbeck, P. Rastogi, R. Kates, M. Filipits, D. Hlauschek, C. Fesl, M. Christgen, U. Nitz, S. Kuemmel, M. Graeser, H. Christgen, O. Gluz, T. Freeman, S. Anderson, H. Pinckaers, A. Piehler, W. Zwerink, J. Zhang, S. Joun, J. Ross, C. Chao, J. Griffin, H. Kreipe, M. Gnant, N. Wolmark, N. Harbeck. Development of a Multi-Modal Artificial Intelligence (MMAI) Model for Predicting Distant Metastasis in HR+ Early-Stage Invasive 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 PD11-01.