Machine learning-based prediction of hepatocellular carcinoma risk in steatotic liver disease: A nationwide cohort study

Abstract

Background and aims Steatotic liver disease (SLD) has emerged as an important risk factor for hepatocellular carcinoma (HCC), often in the absence of cirrhosis. We aimed to develop explainable machine learning (ML) models to predict HCC risk in individuals with SLD using routinely collected screening data. Methods Using the Korean National Health Insurance Service database, we included adults aged 20–79 years who underwent national health screening in 2017. SLD was defined as a fatty liver index (FLI) ≥ 60. Multiple ML algorithms, including deep learning models, were trained using a 7:3 train–test split with repeated non-replacement undersampling at a 1:3 case-to-control ratio to address extreme class imbalance. Results Among 1,241,560 adults with SLD, 2,152 (0.17%) developed HCC during a 6-year follow-up period. In the internal validation cohort, the final weighted multi-head attention deep neural network ensemble achieved an area under the receiver operating characteristic curve of 0.923, with a sensitivity of 71.36% and specificity of 93.65%. SHapley Additive exPlanations consistently identified age, sex, triglycerides, total cholesterol, aminotransferases, gamma-glutamyl transferase (GGT), Charlson Comorbidity Index, and FLI as key contributors to HCC risk. In multivariable Cox models, older age, male sex, elevated GGT, higher aspartate aminotransferase and FLI, and greater comorbidity burden were positively associated with HCC risk, whereas higher triglyceride and total cholesterol levels were inversely associated. Model-based risk stratification identified four groups with distinct HCC-free survival curves; the extremely high-risk group had an approximately 74.9-fold higher hazard of HCC than the low-risk group (95% CI, 55.3–101.5). Conclusions Overall, this explainable ML model based on routine health screening variables enables robust HCC risk stratification and may help inform future targeted surveillance strategies in SLD populations after external validation.