Artificial intelligence predicts outcome-related molecular profiles and vascular invasion in hepatocellular carcinoma.

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 (S1/<

SUBMITTER: Seraphin TP 

PROVIDER: S-EPMC12657720 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature