BACKGROUND: In China, liver resection has been proven to be one of the most important strategies for hepatocellular carcinoma patients, but the recurrence rate is high. This study sought to investigate the prognostic value of pretreatment tumor and peritumor contrast-enhanced CT radiomics features for early and late recurrence of BCLC stage 0-B hepatocellular carcinoma after liver resection. METHODS: This study involved 329 hepatocellular carcinoma patients after liver resection. A radiomics model was built by using Lasso-Cox regression model. Association between radiomics model and recurrence-free survival was explored by using Harrell's concordance index (C-Index) and receiver operating characteristic (ROC) curves. Then, we combined the radiomics model and clinical factors to establish a nomogram whose calibration and discriminatory ability were revealed. RESULTS: Ten significant tumor and peritumor features were screened to build the radiomics model whose C-indices were 0.743 [95% CI, 0.707 to 0.778] and 0.69 [95% CI, 0.629 to 0.751] in the training and validation cohorts. Moreover, the discriminative accuracy of the radiomics model improved with peritumor features entry. The C-indices of the combined model were 0.773 [95% CI, 0.739 to 0.806] and 0.727 [95% CI, 0.667 to 0.787] in the training and validation cohorts, outperforming the radiomics model. CONCLUSIONS: The tumor and peritumor contrast-enhanced CT radiomic signature is a quantitative imaging biomarker that could improve the prediction of early and late recurrence after liver resection for hepatocellular carcinoma patients when used in addition to clinical predictors.