An interpretable machine learning model using routine clinical data for early recurrence prediction in hepatocellular carcinoma

Accurate prediction of early postoperative recurrence in hepatocellular carcinoma (HCC) remains challenging. We developed and validated an interpretable machine learning model to predict early recurrence after curative hepatectomy using routine clinical data. A cohort of 1,120 HCC patients from two centers (2014–2024) was split into training, hold-out test, and external validation sets. Nine predictors were selected via univariate Cox regression. The model integrated three machine learning algorithms to predict recurrence-free survival. In hold-out testing, it outperformed conventional staging in time-dependent area under the curve (0.772 vs. 0.637 at 4–24 months) and stratified patients into distinct low-, moderate-, and high-risk groups. Compared to high-risk patients, moderate-risk patients had significantly lower recurrence hazard (HR = 0.39; 95% CI: 0.24–0.64), and low-risk patients exhibited markedly reduced risk (HR = 0.10; 95% CI: 0.03–0.27). External validation confirmed robust risk stratification (log-rank test, p < 0.001). SHapley Additive exPlanations analysis identified tumor diameter as the top predictor, enabling transparent and personalized risk profiling. This model provides a non-invasive, cost-effective, and generalizable tool for predicting early HCC recurrence with enhanced interpretability.