ArticleDigestive diseases and sciences2026
Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches.
Article in Digestive diseases and sciences, 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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Abstract
backgroundHepatocellular carcinoma (HCC) is the most common primary liver tumor. Despite efforts to mitigate risk factors and implement surveillance programs in high-risk populations, such as screening, these strategies alone appear insufficient to significantly improve prognosis at diagnosis. The identification of novel prognostic factors remains an underdeveloped field that may play a key role in guiding optimal therapeutic decisions from the initial stages of patient management.
aimsTo develop a machine-learning prognostic model to compare the prognostic performance of different MELD-based scores at the time of HCC diagnosis and to assess their relative clinical applicability in comparison with established prognostic staging systems.
methodsA multicenter retrospective analysis including 219 patients with HCC was performed. For MELD-based score comparisons and model development, 216 patients with complete MELD, MELD-Na, and MELD 3.0 data constituted the analytic cohort. Clinical and diagnostic variables were analyzed using machine-learning approaches.
resultsIn the analytic cohort, 148 all-cause deaths occurred during follow-up. Among the MELD-derived models, MELD 3.0 showed higher discrimination than MELD and MELD-Na. EXtreme Gradient Boosting (XGB) algorithm achieved the best overall performance and calibration (AUC 0.94, Brier score 0.13, calibration slope 1.02, CITL 0.03). A parsimonious reduced-feature XGB model including TNM stage, MELD 3.0, ECOG-PS, ALP, and AFP retained most of the discriminatory performance of the full model (AUC 0.91).
conclusionsThese findings suggest that updated MELD-based scores, particularly MELD 3.0, may provide complementary prognostic information at the time of HCC diagnosis. The XGB-based model may represent a feasible tool for exploratory prognostic modeling and may support more precise risk stratification and personalized, data-driven therapeutic decisions in patients with HCC. Further validation in larger, prospective cohorts is warranted before clinical implementation.
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