AI-assisted image analysis of tumor-infiltrating lymphocytes as a prognostic marker in chemotherapy-naïve luminal breast cancer.

Abstract

In triple-negative and HER2-positive breast cancer, tumor-infiltrating lymphocytes (TILs) are established predictive and prognostic biomarkers, but their role in luminal subtypes remains unclear. We investigated the prognostic significance of TILs using AI-based image analysis in 2298 luminal breast cancers from the DBCG99c cohort, classified by PAM50. Stromal (sTIL) and intraepithelial (iTIL) densities were quantified automatically using a commercial platform. Higher stromal TIL infiltration was associated with improved overall survival, particularly within the first 5 years (heterogeneity p = 0.01). In multivariable models, higher sTILs independently predicted lower risk of distant or any recurrence (sHR = 0.95, 95% CI 0.91-0.99) and improved survival (HR = 0.91, 95% CI 0.85-0.97 early; HR = 0.99, 95% CI 0.97-1.00 late). Intraepithelial TILs were not prognostic. No significant interactions were observed by molecular subtype, nodal status, or grade, although a nonsignificant trend toward stronger iTIL effects appeared in luminal B tumors. AI-based TIL quantification thus provides independent prognostic information in high-risk luminal breast cancer.