Automated quantification of tumor-infiltrating lymphocytes by machine learning reveals prognostic and immunogenomic features in lung cancer

Tumor-infiltrating lymphocytes (TILs) are key components of the tumor microenvironment (TME) and are recognized as prognostic and predictive biomarkers in non-small cell lung cancer (NSCLC). However, manual TIL assessment on hematoxylin and eosin (H P = 0.004). The predicted IC50 values did not differ for standard chemotherapies but varied for the selected compounds. The Haralick-based classification model achieved an AUC of 0.87 (95% CI 0.835–0.901) in internal cross-validation, which improved to 0.892 (95% CI 0.848–0.913) when tumour stage was incorporated. This study demonstrated that automated TIL quantification is feasible and prognostically relevant in lung cancer and may provide a hypothesis-generating marker of immune activation for future immunotherapy studies; however, direct validation in immunotherapy-treated cohorts is required before clinical implementation.