ArticleJHEP reports : innovation in hepatology2025
Artificial intelligence predicts outcome-related molecular profiles and vascular invasion in hepatocellular carcinoma.
Article in JHEP reports : innovation in hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation.Biomedicines · 2026Article
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- From Theory to Practice: Which Biomarkers Are Ready for Predicting Response in Advanced HCC?Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- Review
- Artificial intelligence driven exposome and multi omics integration for biomarker discovery in liver cancer: a literature review.Frontiers in immunology · 2026Review
- The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Background & Aims: Advances in digital pathology and artificial intelligence (AI) are driving progress toward personalized clinical management. In hepatocellular carcinoma (HCC), AI-based models using digitized H&E slides can be a robust tool to predict outcome-related molecular profiles and presence of microvascular invasion (mVI), with potential clinical utility. Methods: A transformer-based deep-learning (DL) model was deployed using digitized H&E slides from 431 resected HCC cases (training cohort). Five-fold cross-validation was applied, and the model was tested on two external cohorts: TCGA-LIHC (n = 363) and advanced-stage HCC cohort (n = 64). Results: The DL model effectively predicted outcome-related molecular profiles, distinguishing poor-prognosis ( Conclusions: Our H&E-based AI model enables accurate prediction of outcome-related molecular subtypes of poor prognosis and presence of mVI, offering a scalable and accessible tool to extract clinically relevant features from routine histology. Impact and implications: Outcome-related molecular profiles and the presence of microvascular invasion (mVI) are critical determinants of prognosis and treatment decisions in hepatocellular carcinoma (HCC). This study presents an artificial intelligence (AI)-based method that analyzes routine H&E-stained slides and accurately predicts: (a) biologically relevant HCC molecular subtypes associated with patient outcomes, and (b) the presence of mVI, a well-established predictor of poor outcomes and risk of recurrence, that currently requires meticulous pathological assessment of multiple H&E slides. These AI tools can offer a scalable method to support personalized treatment decisions, such as transplant eligibility, trial enrollment, or neo/adjuvant therapy planning, and may improve clinical management of HCC. Our findings lay the groundwork for incorporating AI-assisted pathology into future prospective studies aimed at improving HCC clinical management.
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Registered trials
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