Radiomics contributes to the extraction of undetectable features with the naked eye from high-throughput quantitative images. In this study, 2 predictive models were constructed, which allowed recognition of poorly differentiated hepatocellular carcinoma (HCC). In addition, the effectiveness of the as-constructed signature was investigated in HCC patients. A retrospective study involving 188 patients (age, 29-85 years) enrolled from November 2010 to April 2018 was carried out. All patients were divided randomly into 2 cohorts, namely, the training cohort (n = 141) and the validation cohort (n = 47). The MRI images (DICOM) were collected from PACS before ablation; in addition, the radiomics features were extracted from the 3D tumor area on T1-weighted imaging (T1WI) scans, T2-weighted imaging (T2WI) scans, arterial images, portal images and delayed phase images. In total, 200 radiomics features were extracted. t test and Mann-Whitney U test were performed to exclude some radiomics signatures. Afterwards, a radiomics signature model was built through LASSO regression by RStudio Software. We constructed 2 support vector machine (SVM)-based models: 1 with a radiomics signature only (model 1) and 1 that integrated clinical and radiomics signatures (model 2). Then, the diagnostic performance of the radiomics signature was evaluated through receiver operating characteristic (ROC) analysis. The classification accuracy in the training and validation cohorts was 80.9% and 72.3%, respectively, for model 1. In the training cohort, the area under the ROC curve (AUC) was 0.623, while it was 0.576 in the validation cohort. The classification accuracy in the training and validation cohorts were 79.4% and 74.5%, respectively, for model 2. In the training cohort, the AUC was 0.721, while it was 0.681 in the validation cohort. The MRI-based radiomics signature and clinical model can distinguish HCC patients that belong in a low differentiation group from other patients, which helps in the performance of personal medical protocols.