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Background <p>Hepatocellular 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.</p> Aims <p>To 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.</p> Methods <p>A 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.</p> Results <p>In 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).</p> Conclusions <p>These 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.</p>

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Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches

  • 期刊论文

  • 发表日期：

    2026年5月30日

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  • Pablo Martínez-Blanco,
  • Miguel Suárez,
  • Jorge Mateo,
  • Sergio Gil-Rojas,
  • Natalia Martínez-García,
  • Pilar Blasco,
  • Miguel Torralba,
  • Ana María Torres

摘要

Background

Hepatocellular 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.

Aims

To 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.

Methods

A 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.

Results

In 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).

Conclusions

These 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.