Journal of gastroenterology and hepatology · 2026
Comparable Predictive Performance of Non-VCTE-Based Machine Learning Model for HBV-Infected Hepatocellular Carcinoma Risk.
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Yi Hahn, Lee Hye Won, Shin Jae Il, Lee Hyung Woong, Lim Tae Seop, Kim Beom Kyung, Kim Namkug
Аннотация
BACKGROUND: Accurate risk stratification for hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients remains challenging. Vibration-controlled transient elastography (VCTE) is useful for HCC prediction; however, its nationwide use might be limited in CHB-endemic or resource-limited regions. We developed and validated non-VCTE-based machine learning (ML) models for HCC prediction, integrating routine clinical and laboratory parameters.
METHODS: We analyzed a multicenter cohort of CHB patients receiving entecavir or tenofovir. ML-based survival models were developed and validated, with their performance compared both with and without VCTE-derived parameters (liver stiffness measurement/controlled attenuation parameter) to analyze VCTE's role amidst routine variables. Non-VCTE-based ML models were then compared against the modified PAGE-B (mPAGE-B) score. Model performance was assessed by Uno's C-index, area under the ROC curve (AUC), and Brier score at 5-, 6-, and 7-year follow-up using cross-validation and external validation.
RESULTS: Among 2736 patients (training n = 1954; validation n = 782), no statistically significant difference in predictive performance was observed between ML-based models with and without VCTE-derived parameters. Non-VCTE-based ML models consistently demonstrated superior overall performance and a statistically significant difference compared to the mPAGE-B score. The non-VCTE-based RSF model demonstrated the highest discrimination (5-year C-index: 0.821; Brier score: 0.0592) in external validation. Statistically significant improvements over the mPAGE-B score were observed across all metrics except for rpCox's AUC up to 5 years.
CONCLUSIONS: ML-based models provide superior long-term HCC risk prediction in CHB patients compared to the mPAGE-B score. Importantly, their performance remains robust even without VCTE-derived parameters, suggesting that effective risk stratification is achievable across diverse clinical settings, especially CHB-endemic or resource-limited regions.
METHODS: We analyzed a multicenter cohort of CHB patients receiving entecavir or tenofovir. ML-based survival models were developed and validated, with their performance compared both with and without VCTE-derived parameters (liver stiffness measurement/controlled attenuation parameter) to analyze VCTE's role amidst routine variables. Non-VCTE-based ML models were then compared against the modified PAGE-B (mPAGE-B) score. Model performance was assessed by Uno's C-index, area under the ROC curve (AUC), and Brier score at 5-, 6-, and 7-year follow-up using cross-validation and external validation.
RESULTS: Among 2736 patients (training n = 1954; validation n = 782), no statistically significant difference in predictive performance was observed between ML-based models with and without VCTE-derived parameters. Non-VCTE-based ML models consistently demonstrated superior overall performance and a statistically significant difference compared to the mPAGE-B score. The non-VCTE-based RSF model demonstrated the highest discrimination (5-year C-index: 0.821; Brier score: 0.0592) in external validation. Statistically significant improvements over the mPAGE-B score were observed across all metrics except for rpCox's AUC up to 5 years.
CONCLUSIONS: ML-based models provide superior long-term HCC risk prediction in CHB patients compared to the mPAGE-B score. Importantly, their performance remains robust even without VCTE-derived parameters, suggesting that effective risk stratification is achievable across diverse clinical settings, especially CHB-endemic or resource-limited regions.