Prediction of Pathological Complete Response in Hepatocellular Carcinoma Using Machine Learning Models

Background Pathological complete response (PCR) in hepatocellular carcinoma (HCC) following conversion therapy is associated with improved prognosis and influences treatment decisions. This study aims to develop and validate machine learning-based predictive models for assessing PCR in HCC patients. Methods This retrospective single-center study included 110 HCC patients after propensity score matching. Four machine learning models—LASSO, RF, XGBoost, and Decision Tree—were developed to predict PCR. After training models, the performance was assessed in the test set. Feature importance was analyzed, and a public visualization tool was developed. Results The RF model demonstrated the highest predictive accuracy (AUC: 0.962), followed by XGBoost (AUC: 0.929), Decision Tree (AUC: 0.874), and LASSO (AUC: 0.799). Key predictive factors included tumor invasion, AFP levels, and tumor diameter. The RF model effectively distinguished PCR and NPCR groups, providing robust prediction capabilities. Conclusion Machine learning models, particularly RF, significantly enhance the accuracy of PCR prediction in HCC patients. This approach highlights the potential of integrating demographic, laboratory, and radiographic data for personalized treatment planning.