Rapid and label-free analysis of biological characteristics of hepatocelluar carcinoma tissues based on histological images

INTRODUCTION: Hepatocellular carcinoma (HCC) exhibits substantial molecular heterogeneity, complicating treatment selection and clinical management. OBJECTIVES: We developed an artificial intelligence-based histopathological framework to predict HCC biological characteristics from routine hematoxylin and eosin (H&E)-stained slides. METHODS: Using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, transcriptomic phenotypes associated with angiogenesis, stemness, and autophagy were identified through gene set analysis, differential expression analysis, and consensus clustering, and evaluated for overall survival and drug sensitivity. Whole-slide images were processed into 224 × 224-pixel tumor tiles for phenotype prediction using feature extraction (UNI, ResNet50, CTrans, GigaPath, and Virchow2) combined with classic multiple instance learning and clustering-constrained attention-based multiple instance learning.. Models were externally validated at Renmin Hospital of Wuhan University and Tongji Hospital. Interpretability was assessed using attention heatmaps, and predictions were evaluated against molecular markers and tumor differentiation grades. RESULTS: Unsupervised clustering identified two subgroups per phenotype, with significant difference in survival (all p < 0.004) and sensitivities to sorafenib or transcatheter arterial chemoembolization (all p < 0.0001). The tumor detection model achieved an area under the receiver operating characteristic curve (AUC) of 97.53%. The phenotype prediction models achieved internal optimal AUCs of 85.51%-91.05% and accuracies of 79.63%-88.89%. External optimal AUCs were 70.94%, 88.64%, and 91.17% for angiogenesis, stemness, and autophagy, with accuracies of 62.00%, 78.00%, and 84.00%, respectively. Attention heatmaps revealed distinct morphological features among subgroups, including neovascularization, increased nuclear-to-cytoplasmic ratios, and cytoplasmic vacuolization. Model predictions showed concordance with CD31/CD105 (κ = 0.398-0.432), CD133/LGR5 (κ = 0.428-0.629), Beclin-1/p62-LC3B (κ = 0.592-0.762) (all p < 0.05), and tumor differentiation grade (κ = - 0.167 to - 0.175, p < 0.05). CONCLUSION: This proof-of-concept AI framework predicted transcriptome-defined HCC phenotypes from routine histology, with predictions associated with prognosis and therapeutic response. It may provide a cost-effective approach for assessing HCC heterogeneity and complement molecular profiling.