Development and internal validation of a CatBoost–SHAP-guided nomogram for significant liver fibrosis in children with chronic hepatitis B

BackgroundSignificant liver fibrosis can develop in children with chronic hepatitis B (CHB), yet liver biopsy is invasive and unsuitable for repeated assessment. We developed an interpretable, non-invasive prediction model for significant fibrosis in pediatric CHB and evaluated whether baseline risk stratification was associated with subsequent treatment outcomes.MethodsThis retrospective study included 232 children with CHB who underwent liver biopsy, with significant fibrosis defined as Scheuer stage S ≥ 2. Patients were randomly assigned to a development cohort (80%) and a held-out test set (20%). A CatBoost model with Bayesian hyperparameter optimization was used to identify the most informative predictors, and these variables were incorporated into a logistic regression nomogram guided by Shapley additive explanations (SHAP). Model performance was evaluated by discrimination, calibration, decision curve analysis, and DeLong testing. Associations between baseline risk groups and longitudinal outcomes were examined using generalized estimating equations adjusted for age and sex and Kaplan–Meier analyses.ResultsSignificant fibrosis was identified in 96 children (41.4%). Liver stiffness measurement, alanine aminotransferase, and platelet count were retained in the final model. The nomogram achieved an area under the receiver operating characteristic curve (AUC) of 0.828 (95% CI, 0.767–0.889) in the development cohort and 0.782 (95% CI, 0.637–0.927) in the held-out test cohort, with performance comparable to that of the CatBoost model (P = 0.240). Compared with the low-risk group, high-risk patients showed greater longitudinal reductions in alanine and aspartate aminotransferase, whereas longitudinal changes in liver stiffness measurement and hepatitis B virus DNA were similar between groups. Conversely, low-risk patients achieved virological suppression, HBeAg seroconversion, and biochemical response earlier than those classified as high risk.ConclusionsAn interpretable three-variable prediction model based on CatBoost-guided feature selection accurately identified significant fibrosis in children with CHB while maintaining performance comparable to the machine-learning model. Baseline risk stratification was also associated with subsequent treatment outcomes, although external validation is needed before clinical implementation.