LAUSR

PURPOSE Recurrence is the leading cause of death in hepatocellular carcinoma (HCC) patients with curative resection. In this study, we aimed to develop a preoperative predictive model based on high-throughput radiomics features and clinical factors for prediction of long- and short-term recurrence for these patients. METHODS A total of 270 patients with HCC who were followed up for at least 5 years after curative hepatectomy between June 2014 and December 2017 were enrolled in this retrospective study. Regions of interest (ROIs) were manually delineated in preoperative T2-weighted images using ITK-SNAP software on each HCC tumor slice. A total of 1197 radiomics features were extracted and the recursive feature elimination method based on logistic regression was used for radiomics signature building. 10-fold cross-validation was applied for model development. Nomogram were constructed and assessed by calibration plot, which compares nomogram-predicated probability with observed outcome. Receiver-operating characteristic was then generated to evaluate the predictive performance of the model in the development and test cohorts. RESULTS The 10 most recurrence-free survival related radiomics features were selected for the radiomics signatures. A multiparametric clinical-radiomics model combining albumin and Radiomics score (Rad-score) for recurrence prediction was further established. The integrated model demonstrated good calibration and satisfactory discrimination, with the AUC of 0.864, 95% CI 0.842-0.903, sensitivity of 0.889 and specificity of 0.644 in the test set. Calibration curve showed good agreement concerning 5-year recurrence risk predicted by the nomogram. In addition, the AUC of 1, 2, 3 and 4-year recurrence was 0.935 (95% CI, 0.836-1.000), 0.861 (95% CI, 0.723-0.999), 0.878 (95% CI, 0.762-0.994) and 0.878 (95% CI, 0.762-0.994) in the test set, respective. CONCLUSIONS The clinical-radiomics model integrating radiomic features and clinical factors can improve recurrence predictions beyond predictions made using clinical factors or radiomic features alone. Our clinical-radiomics model is a valid method to predict recurrence that should improve preoperative prognostic performance and allow more individualized treatment decisions. This article is protected by copyright. All rights reserved.