Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving patient prognosis. AIM: To establish an intratumoral and peritumoral model for predicting ER in HCC patients following curative ablation. METHODS: This study included a total of 288 patients from three Centers. The patients were divided into a primary cohort (n = 222) and an external cohort (n = 66). Radiomics and deep learning methods were combined for feature extraction, and models were constructed following a three-step feature selection process. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), while calibration curves and decision curve analysis (DCA) were used to assess calibration and clinical utility. Finally, Kaplan-Meier (K-M) analysis was used to stratify patients according to progression-free survival (PFS) and overall survival (OS). RESULTS: The combined model, which utilizes the light gradient boosting machine learning algorithm and incorporates both intratumoral and peritumoral regions (5 mm and 10 mm), demonstrated the best predictive performance for ER following HCC ablation, achieving AUCs of 0.924 in the training set, 0.899 in the internal validation set, and 0.839 in the external validation set. Calibration and DCA curves confirmed strong calibration and clinical utility, whereas K-M curves provided risk stratification for PFS and OS in HCC patients. CONCLUSION: The most efficient model integrated the tumor region with the peritumoral 5 mm and 10 mm regions. This model provides a noninvasive, effective, and reliable method for predicting ER after curative ablation of HCC.