Development and External Validation of a Multimodal Machine Learning Model Integrating Preoperative Contrast-Enhanced CT Radiomics and Clinicopathological Features for Predicting Early Extrahepatic Recurrence After Hepatectomy for Hepatocellular Carcinoma

Background

Early extrahepatic recurrence (eEHR) after hepatectomy for hepatocellular carcinoma (HCC) significantly affects patient prognosis; however, an accurate predictive framework integrating radiomics with clinicopathological features is currently lacking. This study aims to develop and validate a multimodal machine learning model for prediction of early postoperative extrahepatic recurrence risk, thereby offering a basis for individualized postoperative intervention strategies.