AI assessment of tumor-infiltrating lymphocytes on routine H&E-slides as a predictor of response to neoadjuvant therapy in breast cancer-a real-world study.

Abstract

Tumor-infiltrating lymphocytes (TILs) are a predictive and prognostic biomarker in triple-negative (TNBC) and HER2 + breast cancer (BC). This study applies artificial intelligence (AI) to evaluate their value in a multi-institutional cohort of TNBC and HER2 + BC patients treated with neoadjuvant chemotherapy (NACT). A supervised deep learning pipeline was developed to analyze hematoxylin and eosin-stained whole-slide images from a discovery cohort of 273 patients and a validation cohort of 245 BC patients. AI quantified stromal TILs percentage, stromal TILs density, and intraepithelial TILs density. Associations between AI-derived TILs metrics, clinicopathological characteristics, and patient outcomes were assessed. AI-based scores were highly correlated with pathologists' scores (Spearman R = 0.61-0.77, p-val < .001). Higher AI-assessed TILs levels were significantly associated with better NACT response, and both stromal and intraepithelial TILs were strong and independent predictors of pathological complete response in TNBC and HER2 + subtypes. Furthermore, patients with higher TILs had longer disease-free survival and overall survival in the discovery cohort and TNBC subtype, but not in HER2 + BC. This study supports AI-driven TILs quantification as a predictive and prognostic tool in BC patients receiving NACT. AI-derived stromal and intraepithelial TILs densities are independent predictors of response, highlighting their potential for integration into digital pathology workflows for risk stratification.