Machine Learning-based Personalized Prediction of Hepatocellular Carcinoma Recurrence after Radiofrequency Ablation

Abstract Background and Aims Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods We included a total of 1778 treatment-naive HCC patients who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model, and six ML models – including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index, and was validated externally using the split-sample method. Results The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into two, three, and four different risk groups (p Conclusions We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.