Preoperative Noninvasive Prediction of Recurrence-Free Survival in Hepatocellular Carcinoma Using CT-Based Radiomics Model

Purpose: This study aims to explore the value of radiomics combined with clinical parameters in predicting recurrence-free survival (RFS) after the resection of hepatocellular carcinoma (HCC). Patients and Methods: In this retrospective study, a total of 322 patients with HCC who underwent contrast-enhanced computed tomography (CT) and radical surgical resection were enrolled and randomly divided into a training group (n = 223) and a validation group (n = 97). In the training group, Univariate and multivariate Cox regression analyses were employed to obtain clinical variables related to RFS for constructing the clinical model. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were employed to construct the radiomics model, and the clinical-radiomics model was further constructed. Model prediction performance was subsequently assessed by the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. Additionally, Kaplan-Meier analysis was used to evaluate the model's value in predicting RFS. Correlations between radiomics features and pathological parameters were analyzed. Results: The clinical-radiomics model predicted RFS at 1, 2, and 3 years more accurately than the clinical or radiomics model alone (training group, AUC = 0.834, 0.765 and 0.831, respectively; validation group, AUC = 0.715, 0.710 and 0.793, respectively). The predicted high-risk subgroup based on the clinical-radiomics nomogram had shorter RFS than predicted low-risk subgroup in data sets, enabling risk stratification of various clinical subgroups. Correlation analysis revealed that the rad-score was positively related to microvascular invasion (MVI) and Edmondson-Steiner grade. Conclusion: The clinical-radiomics model effectively predicts RFS in HCC patients and identifies high-risk individuals for recurrence.