We investigated whether artificial intelligence (AI)-based tumor microenvironment profiling correlates with treatment efficacy in unresectable hepatocellular carcinoma (HCC) patients treated with immune checkpoint inhibitor (ICI) therapies. Spatial distribution of immune/non-immune cells from pretreatment H p = 0.037) but not in the anti-PD-1 monotherapy cohort (HR 1.02 0.59-1.77; p = 0.935). Conversely, inflamed immune phenotype, characterized by high intratumoral TIL densities, predicted longer PFS after anti-PD-1 monotherapy (HR 0.50 0.25-0.99; p = 0.042) but not after atezo-bev (HR 0.92 0.50-1.69; p = 0.762). Our exploratory analysis using AI/deep-learning model demonstrated high TEC density predicted superior outcomes with atezo-bev, while TIL presence correlated with improved anti-PD-1 monotherapy efficacy in HCC patients, suggesting potential clinical applicability in treatment selection.
Kim et al. (2025) studied this question.