Comparison Between Cox Proportional Hazards and Machine Learning Models for the Prognostication of Recurrence and Survival Following Liver Resection for Hepatocellular Carcinoma.

BACKGROUND: A robust prognostication model after liver resection for hepatocellular carcinoma (HCC) can guide clinical management. We aimed to develop a prognostication model for HCC recurrence and survival following liver resection, comparing between Cox proportional hazards (CPH) and supervised machine learning models. METHODS: We studied all patients who underwent liver resection for HCC between January 1, 2000 and October 31, 2022 at our institution. We aimed to predict recurrence-free survival following resection and identify risk categories for HCC recurrence. The CPH model and two supervised machine learning models (random survival forest [RSF] and extreme gradient boosting [XGB]) were used. Model performance was assessed with C-index, time-dependent area under curve (tdAUC) and Brier score. RESULTS: We studied 1290 patients, with 737 (57.1%) experiencing an event (HCC recurrence or death) over a median follow-up duration of 19.2 months. The CPH model had the overall best performance (C-index: 0.663, tdAUC at 6 months: 0.752; 1 year: 0.740; 2 years: 0.722; 5 years: 0.624). Using this model, patients stratified based on risk score could be discriminated between low, intermediate, and high-risk groups (p < 0.001). CONCLUSION: A CPH-derived prognostication model was effective for predicting and risk stratifying recurrence and survival following liver resection for HCC.

Duke Scholars

Author Chi Wei Cliburn Chan Biostatistics & Bioinformatics, Division of Integrative Geno ...