Large language model and Gd-EOB-DTPA-enhanced MRI-based risk stratification system for postoperative hepatocellular carcinoma: a multicenter study.

OBJECTIVE: To develop and validate a Fully Automated Stratification System (FASS) integrating serum biomarkers, automated radiomic features, and large language model (LLM)-derived semantic features for prognostic prediction in patients with solitary hepatocellular carcinoma (HCC) after hepatic resection. MATERIALS AND METHODS: A total of 448 patients with solitary HCC from three centers were retrospectively enrolled. Automated tumor segmentation was performed using a modified MedNeXt-loss framework, and radiomic features were extracted from Gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI. Five LLMs were compared for feature-level accuracy and completeness, and the best-performing model was incorporated into the FASS. Prognostic models based on serum, radiomic, and LLM-semantic features were integrated and evaluated using concordance index, time-dependent ROC, and decision curve analyses. Biological relevance was explored through RNA sequencing and pathway enrichment analyses. RESULTS: The MedNeXt-loss framework achieved robust segmentation (Dice = 0.77). ChatGPT-4o demonstrated the best balance between predictive accuracy and completeness and was used for subsequent modeling. In multivariate analysis, AFP, AST, and the ChatGPT-4o-derived irregular margin were independent predictors of overall survival. The integrated FASS achieved high prognostic performance (C-index 0.78 and 0.76 in test and external validation cohorts) and effectively stratified patients into distinct risk groups (log-rank p < 0.05). Transcriptomic analyses revealed inflammatory and cytokine signaling activation in the high-risk group. CONCLUSION: FASS enables fully automated, interpretable, and biologically informed prognostic assessment in solitary HCC, supporting precision decision-making in hepatobiliary oncology. KEY POINTS: QuestionCan large language models improve preoperative hepatocellular carcinoma risk stratification by integrating advanced image interpretation and semantic analysis? FindingsThe system enabled fully automated analysis, identified AFP, AST and LLM-derived irregular margin as independent predictors, and effectively stratified postoperative risk across cohorts. Clinical relevanceThis fully automated, interpretable platform enables reliable postoperative risk stratification, helping identify high-risk patients early and potentially improving outcomes after resection of solitary hepatocellular carcinoma.