PURPOSE: To appraise the ability of the computed tomography (CT) radiomics signature for prediction of early recurrence (ER) in patients with hepatocellular carcinoma (HCC). METHODS: 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. RESULTS: The radiomics signature was built by 14 selected radiomics features and was significantly associated with ER (P < 0.001); the AUCs of the "train set" and the "test set" were 0.818 (95% CI 0.760-0.865) and 0.719 (95% CI 0.621-0.805), respectively. The tumor size, tumor capsule, and γ-glutamyl transferase (GGT) were significantly associated with ER in the clinical model (P < 0.05). The combined model showed incremental prognostic value, with the AUCs of "training dataset" and "test dataset" were 0.846 (95% CI 0.792-0.890) and 0.737 (95% CI 0.640-0.820), respectively. The radiomics signature, tumor size, and the level of GGT were independent predictors of ER (P < 0.05). CONCLUSIONS: The CT radiomics signature can be conveniently used to predict the ER in patient with HCC. The combined model performed better for prediction of ER than radiomics signature or clinical model.