Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma

To appraise the ability of the computed tomography (CT) radiomics signature for prediction of early recurrence (ER) in patients with hepatocellular carcinoma (HCC). A set of 325 HCC patients were enrolled in this retrospective study and the whole dataset was divided into 2 cohorts, including “training set” (225 patients) and “test set” (100 patients). All patients who underwent partial hepatectomy were followed up at least within 1 year. 656 Radiomics features were extracted from arterial-phase and portal venous-phase CT images. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Univariate analysis was used to identify clinical and radiomics significant features. Models (radiomics signature, clinical model, and combined model) were evaluated by area under the curve (AUC) of receiver operating characteristic curve. The models’ performances for prediction of ER were assessed. The radiomics signature was built by 14 selected radiomics features and was significantly associated with ER (P