Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is an independent predictor of poor outcomes subsequent to surgical resection or liver transplantation (LT); however, MVI currently cannot be reliably determined preoperatively. In this study, we investigated the association between radiomic features on preoperative ADC maps and the MVI (with or without) in resected 96 HCCs. Furthermore, we employed machine-learning methods and independently evaluated their prediction performance. Total 1029 radiomic features were extracted from cancerous VOIs on ADC maps of each patient. Finally, 7 features could differentiate HCCs with MVI versus HCCs without. The random forest classifier using the optimal feature subset achieved the best performance, with an area under the receiver operating characteristic curve 0.79, sensitivity 71.0%, specificity 85.0%, precision 73%, and recall 70%.