Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.

Abstract

Hepatocellular carcinoma (HCC), a leading cause of cancer mortality, faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity. Pathomics, an emerging discipline that integrates artificial intelligence (AI) with quantitative pathology image analysis, aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens. This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images. Pathomics integrates deep learning with histopathological features to enable precise tumour classification (e.g., HCC vs cholangiocarcinoma), microvascular invasion (MVI) detection, recurrence risk stratification, and survival prediction. Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers, whereas multiomics integration links morphometric patterns to molecular signatures (e.g., EZH2 expression and immune infiltration). Despite these breakthroughs, critical bottlenecks persist, including limited multicentre validation studies, "black box" model interpretability, and clinical workflow integration. Future studies should emphasize AI-enhanced multimodal fusion (radiogenomics and liquid biopsy) and standardized platforms to bridge computational pathology and precision oncology, ultimately improving personalized therapeutic strategies for liver malignancies. This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.