Abstract PD11-02: Artificial Intelligence for Tumor-Infiltrating Lymphocytes in Early-Stage TNBC: Results of a Collaborative Prospective TIL Validation Challenge

Abstract Background: Tumor-infiltrating lymphocytes (TIL) provide key prognostic information in triple negative breast cancer (TNBC). The CATALINA challenge evaluated multiple TIL-scoring AI algorithms (“cTIL”) on whole-slide images (WSIs) from prospective clinical trial cohorts to assess analytical validity and prognostic performance of cTIL, compared to pathologist-scored stromal TIL (sTIL). Aim: To independently assess the prognostic performance of computational TIL (cTIL) models, compared to pathologist-scored sTIL in a large, prospective cohort. Methods: Two independently developed AI algorithms, producing a total of 5 cTIL scores, were applied to digitized slides blinded to sTIL score and outcomes. We compared agreement between cTIL and sTIL (Spearman’s rho) on 220 breast cancer 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: J. R. Dixon-Douglas, D. Drubay, R. Salgado, B. Acs, J. A. van de Laark, Y. Yuan, M. Amgad, L. A. Cooper, Y. B. Hagos, K. AbdulJabbar, J. Meakin, B. Van Ginneken, H. Yan, J. Lemonnier, F. Penault-Llorca, M. Lacroix-Triki, H. Jounsuu, P. Kellokumpu-Lehtinen, S. Loibl, C. Denkert, G. Viale, M. Colleoni, C. Sotiriou, M. Piccart, M. Dieci, S. Demaria, R. Kammler, A. C. Wolff, S. Adams, S. Badve, R. J. Gray, G. Curigliano, A. Vincent-Salomon, T. Nielsen, L. Pusztai, F. Ciompi, S. Michiels, S. Loi. Artificial Intelligence for Tumor-Infiltrating Lymphocytes in Early-Stage TNBC: Results of a Collaborative Prospective TIL Validation Challenge 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 PD11-02.