Abstract Background. Pathologic complete response (pCR) is the absence of residual invasive cancer in the breast and axillary lymph nodes after neoadjuvant therapy. In breast cancer treatment, pCR is a proven surrogate for long-term outcomes. However, accurately predicting pCR at diagnosis remains a clinical challenge. Current tools primarily rely on clinical, genomic, or transcriptomic data. Advances in computational pathology and deep learning enable the extraction of meaningful features from H 0.5). Highest model performance was in HER2+ cohorts: (1) Paclitaxel + Trastuzumab and (2) Paclitaxel + Pertuzumab + Trastuzumab (AUROC = 0.893, 0.785) (Table 1). Of the 6 arms, MIL outperformed the elastic net trained on pathologist-assessed histology features in 5 arms. After including FTV and RPS in the elastic net, MIL still outperformed in 3 arms. Across subtypes, the model predicted better in HR+ subgroups (HR+/HER2- AUROC = 0.706, HR+/HER2+ AUROC = 0.677) than in HR- subgroups (HR-/HER2+ AUROC = 0.533, HR-/HER2- AUROC = 0.548). Conclusion. These findings demonstrate the feasibility of applying MIL vision models to predict treatment-specific response in breast cancer, even with frozen section WSIs and limited data. MIL detects important histology patterns not captured by conventional pathology. Even with added MRI and transcriptomic data, the model provides complementary predictive value. This approach enables early, accurate predictions from routine histology and supports personalized, less toxic treatment—particularly in under-resourced settings. Citation Format: A. Sun, S. Venters, C. Yau, D. Wolf, G. Hirst, M. Campbell, A. Asare, W. Symmans, L. Brown-Swigart, N. Hylton, J. Perlmutter, A. DeMichele, D. Yee, H. Rugo, A. Borowsky, F. Howard, L. Esserman, L. van't Veer, A. Basu. Predicting treatment outcomes in breast cancer from H 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD11-03.
Sun et al. (2026) studied this question.