ArticleAlimentary pharmacology & therapeutics2026
Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.
Article in Alimentary pharmacology & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.Alimentary pharmacology & therapeutics · 2026Article
- Editorial: Prediction of Survival in Hepatocellular Cancer-Rise and Fall of the Machines.Alimentary pharmacology & therapeutics · 2026Article
- Editorial: Prediction of Survival in Hepatocellular Cancer-Rise and Fall of the Machines. Authors' Reply.Alimentary pharmacology & therapeutics · 2026Article
- Building the next generation of gastroenterology and hepatology research leaders in Asia: from disease burden to scientific leadership.Nature reviews. Gastroenterology & hepatology · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrognostic models for hepatocellular carcinoma (HCC) may have limited accuracy. We aimed to construct and validate a novel prognostic model for HCC that incorporates biomarkers for liver function and tumour characteristics.
methodsConsecutive participants (n = 1102) with HCC from five international tertiary institutions in Asia and the U.S. comprised the derivation (n = 627), internal validation (n = 270) and external validation (n = 205) cohorts. The Liver Cancer Risk predictioN (LCRN) Index was constructed using a gradient-boosted decision tree model based on the Cox proportional hazards framework. The discriminative performance of the LCRN Index was evaluated using Harrell's concordance index (C-index) and compared to the albumin bilirubin grade (ALBI), Barcelona Clinic for Liver Cancer (BCLC) staging and Cox regression model. Model calibration was assessed using the integrated Brier score and calibration plots.
resultsThe median (IQR) age was 66.0 (58.0-73.0) years, median (IQR) body mass index was 23.9 (22.1-26.1) kg/m
conclusionThe LCRN index is a promising tool for prognosticating HCC. If further validated, these data may have potential clinical implications for the management of HCC.
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Registered trials
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