Academic Journal
Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches.
| Τίτλος: | Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches. |
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| Συγγραφείς: | Martínez-Blanco P; Gastroenterology Department, Cuenca University Hospital, 16002, Cuenca, Spain.; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain., Suárez M; Gastroenterology Department, Cuenca University Hospital, 16002, Cuenca, Spain.; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain.; Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain., Mateo J; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain. Jorge.Mateo@uclm.es.; Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain. Jorge.Mateo@uclm.es., Gil-Rojas S; Gastroenterology Department, Cuenca University Hospital, 16002, Cuenca, Spain.; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain., Martínez-García N; Internal Medicine Unit, Guadalajara University Hospital, 19002, Guadalajara, Spain., Blasco P; Department of Pharmacy, General University Hospital, 46014, Valencia, Spain., Torralba M; Internal Medicine Unit, Guadalajara University Hospital, 19002, Guadalajara, Spain.; Faculty of Medicine, Universidad de Alcalá de Henares, 28801, Alcalá de Henares, Spain.; Translational Research Group in Cellular Immunology (GITIC), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain., Torres AM; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain.; Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain. |
| Πηγή: | Digestive diseases and sciences [Dig Dis Sci] 2026 Oct; Vol. 71 (10), pp. 4743-4755. Date of Electronic Publication: 2026 May 30. |
| Τύπος έκδοσης: | Journal Article; Comparative Study; Multicenter Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 7902782 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2568 (Electronic) Linking ISSN: 01632116 NLM ISO Abbreviation: Dig Dis Sci Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2005- : New York, NY : Springer Science + Business Media Original Publication: New York, Plenum Pub. Corp. |
| Ιατρικοί όροι (MeSH): | Carcinoma, Hepatocellular*/diagnosis , Carcinoma, Hepatocellular*/mortality , Carcinoma, Hepatocellular*/pathology , Liver Neoplasms*/diagnosis , Liver Neoplasms*/mortality , Liver Neoplasms*/pathology , Boosting Machine Learning Algorithms*, Aged ; Female ; Humans ; Male ; Middle Aged ; Neoplasm Staging ; Predictive Learning Models ; Prognosis ; Retrospective Studies ; Severity of Illness Index |
| Περίληψη: | 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. (© 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.) |
| Competing Interests: | Declarations. Conflicts of interest: The authors declare no competing interests. Informed consent: Patient consent was waived due to the number of patients, the study design (retrospective), the absence of a medical prescription, and the number of deceased patients. Institutional review board: The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University Hospital of Guadalajara (Ref. CEIm: 2023.16. EO). |
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| Contributed Indexing: | Keywords: Hepatocellular carcinoma; MELD; Machine learning; Prognosis; XGBoost |
| Entry Date(s): | Date Created: 20260530 Date Completed: 20260911 Latest Revision: 20260915 |
| Update Code: | 20260916 |
| DOI: | 10.1007/s10620-026-10032-6 |
| PMID: | 42218308 |
| Βάση Δεδομένων: | MEDLINE |
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