Abstract:Background and Aims Recurrence remains common in patients with intermediate to advanced liver cancer after sequential transarterial chemoembolization (TACE) and radiofrequency ablation (RFA). Reliable preoperative risk stratification tools are lacking. This study aimed to develop and validate a multimodal ultrasound-based machine learning model integrating superb microvascular imaging (SMI) and shear wave elastography (SWE) to predict postoperative recurrence after sequential TACE-RFA therapy in patients with intermediate to advanced liver cancer, with model interpretability assessed using SHAP analysis.Methods A total of 215 patients with BCLC stage B/C liver cancer were retrospectively enrolled and randomly divided into a training set (n=150) and a testing set (n=65) at a 7∶3 ratio. Clinical characteristics, laboratory parameters, and SMI/SWE-derived features were collected. Feature importance was ranked using random forest, support vector machine, and extreme gradient boosting (XGB), followed by intersection selection and bidirectional stepwise logistic regression to identify independent predictors. Seven machine learning algorithms were trained with 10-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, Brier score, and decision curve analysis (DCA). Tree SHAP was applied to interpret feature contributions.Results Preoperative tumor diameter, AFP level, maximum elastic modulus (Emax), elastic ratio (Eratio), and vascular morphology were identified as independent predictors of recurrence. The XGB model achieved the best performance, with AUCs of 0.989 in the training set and 0.959 in the testing set. Brier scores were 0.034 and 0.056, respectively. DCA demonstrated superior net benefit across a wide range of threshold probabilities. SHAP analysis indicated that higher Eratio, elevated AFP, irregular vascular morphology, and larger tumor diameter were associated with increased recurrence risk, whereas lower Emax was associated with reduced risk.Conclusion The proposed SMI-SWE-based XGB model provides accurate and clinically useful prediction of recurrence after sequential TACE-RFA therapy in intermediate to advanced liver cancer. SHAP-based interpretation enhances model transparency and supports individualized preoperative risk assessment.