Radiomics-based distinction of small (≤2 cm) hepatocellular carcinoma and precancerous lesions based on unenhanced MRI

To assess the feasibility of a radiomics model based on unenhanced magnetic resonance imaging (MRI) to differentiate small hepatocellular carcinoma (S-HCC) (≤2 cm) and pre-hepatocellular carcinoma (Pre-HCC). One hundred and fourteen histopathologically confirmed 114 hepatic nodules were analysed retrospectively. All patients had undergone MRI before surgery using a 3 T MRI system. Each nodule was segmented on unenhanced MRI sequences (T1-weighted imaging [T1] and T2WI with fat-suppression [FS-T2]). Radiomics features were extracted and the optimal features were selected using the least absolute shrinkage and selection operator (LASSO). The support vector machine (SVM) was used to establish the radiomics model. One abdominal radiologist performed the conventional qualitative analysis for classification of S-HCC and Pre-HCC. The diagnostic performances of the radiomics and radiologist models were evaluated using receiver operating characteristic (ROC) analysis. Radiomics features (n=1,223) were extracted from each sequence and the optimal features were selected from T1, FS-T2, and T1+FS-T2 to construct the radiomics models. The radiomics model based on T1+FS-T2 showed the best performance among the three models, with areas under the ROC curves (AUCs) of 0.95 (95 % confidence interval [CI], 0.875–0.986) and 0.942 (95 % CI, 0.775–0.985), accuracies of 86 % and 88.5 %, sensitivities of 94.12 % and 100 %, and specificities of 85.48 % and 85.19 %, respectively. The radiomics model on FS-T2 showed better performance on a single sequence than that of the T1-based model. The diagnostic performance for the radiomic model was significantly higher than that for the radiologist (AUC = 0.518, p