ArticleJournal of hepatocellular carcinoma2026
Development and Validation of Predictive Machine Learning Models for Postoperative Recurrence and Microvascular Invasion in Hepatocellular Carcinoma Using Nuclear Magnetic Resonance Metabolomics.
Article in Journal of hepatocellular carcinoma, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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12 authors.
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Abstract
Background: Hepatocellular carcinoma (HCC) has a poor prognosis, necessitating better diagnostic tools. Nuclear magnetic resonance (NMR)-based metabolomics has emerged as a powerful tool for cancer biomarker discovery, yet its application in HCC prognosis remains underexplored. This study aimed to identify plasma metabolic biomarkers for the diagnosis and prognosis of HCC, and to develop predictive models for postoperative recurrence and microvascular invasion (MVI) to enhance clinical management. Methods: We performed untargeted NMR metabolomic profiling of plasma from 92 HCC patients and 92 matched healthy controls. Differential metabolites were identified, and their diagnostic performance was assessed using receiver operating characteristic curves. Predictive models for postoperative recurrence and MVI were developed and validated using multiple machine learning algorithms, such as random forest, support vector machine, and gradient boosting machine models. Results: Significant metabolic differences were identified, with 67 metabolites and blood-lipid indicators showing marked alterations. Acetic acid, dimethylsulfone, glycerol, glycine, and low-density lipoprotein (LDL)-3 cholesterol exhibited the highest discriminatory power (area under the curve [AUC] ≥0.954). Regarding HCC recurrence prediction, the StepCox[forward] + random survival forest model achieved an AUC of 0.811 and was an independent prognostic indicator (multivariate Cox HR = 1.20, 95% CI: 1.11-1.30, P < 0.001). Regarding MVI prediction, the support vector machine model demonstrated superior performance (AUC = 0.957). Calibration curve, decision curve, and SHapley Additive exPlanations (SHAP) analyses confirmed model robustness and clinical utility. Two online platforms were developed for clinical implementation. Conclusion: This study developed and validated NMR-based prognostic and MVI prediction models for HCC, offering valuable tools for precision management. Their clinical value warrants further validation in larger prospective cohorts.
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