Clinical and Imaging-Based Prognostic Models for Recurrence and Local Tumor Progression Following Thermal Ablation of Hepatocellular Carcinoma: A Systematic Review

<b>Background:</b> Early detection of patients at high risk for recurrence or local tumor progression (LTP) following thermal ablation of hepatocellular carcinoma (HCC) is essential for treatment selection and individualized follow-up. This systematic review aims to assess and compare the performance of prognostic models predicting recurrence or LTP in patients with HCC treated with thermal ablation. <b>Methods:</b> PubMed, Web of Science, Cochrane, and Embase were searched for studies developing models to predict recurrence after thermal ablation in treatment-naïve HCC patients, using imaging and clinical data with reported test set performance. Risk of bias and applicability were assessed by the Prediction model Risk of Bias Assessment Tool. Data on model performance, feature extraction and modeling technique was collected. <b>Results:</b> In total, 16 studies comprising 39 prognostic models were included, all developed using retrospective data from China or Korea. Outcomes included recurrence-free survival, (intrahepatic) early recurrence, LTP, late recurrence and aggressive intrasegmental recurrence. Predictive parameters varied across models addressing identical outcomes. Outcome definitions also differed. Nine models were externally validated. Most studies had a high risk of bias due to methodological limitations. <b>Conclusions:</b> Variability in model development methodology and type of predictors was found. Models that integrated multiple types of predictors consistently outperformed those relying on one type. To advance predictive tools toward clinical implementation, future research should prioritize standardized outcome definitions, external testing, and transparent reporting. Until these challenges are addressed, current evaluated models should be regarded as promising but preliminary tools.