ArticleCancer medicine2026
An XGBoost-Based Multicenter Model for Predicting HBV-Related Hepatocellular Carcinoma: Development and Validation.
Article in Cancer medicine, 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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8 authors.
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Abstract
backgroundThe 5-year survival rate for hepatocellular carcinoma (HCC) is stage-dependent, yet existing models lack accuracy in predicting hepatitis B virus-associated HCC (HBV-HCC). We therefore aimed to develop and validate an interpretable machine learning (ML) model integrating multidimensional biomarkers for HBV-HCC risk stratification.
methodsThis retrospective multicenter study included 3568 participants (1872 HBV-infected and 1696 HBV-HCC). Patients from Mengchao Hepatobiliary Hospital were divided into training and validation sets (3:1 ratio), while those from Eastern Hepatobiliary Surgery Hospital and the First Affiliated Hospital of Xiamen University formed the external validation set. Five key predictors were identified through random forest, LASSO regression, and XGBoost methods. Seven ML models were evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, and F1-score, with the top model compared against previous models (GALAD, C-GALAD, C-GALAD II, and ASAP).
resultsKey predictors were log
conclusionsWe developed a novel diagnostic model for HBV-HCC that demonstrates higher accuracy in identifying HBV-HCC compared to existing models.
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