Radiomics-based distinction of small (≤2cm) HCC and precancerous lesion based on precontrast MRI

Background: The pre-HCC describe the precancerous condition of HCC, which include regenerative nodule (RN), low-grade dysplastic nodule (LGDN) and high-grade dysplastic nodule (HGDN) [1]. Purpose: To assess the feasibility of radiomics based on precontrast MRI for the distinguish of small hepatocellular carcinoma (S-HCC) (≤2cm) and pre-hepatocellular carcinoma (pre-HCC). Method: We retrospectively analyzed 146 nodules with pathological confirmed from 78 patients. Each nodule was segment on precontrast MRI sequence (T1WI and fat-suppression T2WI (FS-T2WI) ), retrospectively. 1223 radiomics features were extracted and the optimal features from T1WI, FS-T2WI and T1WI+FS-T2WI were selected by the least absolute shrinkage and selection operator (LASSO). We used the logistic regression classifier to establish the radiomics model. Result: We applied logistic regression classifier to identify S-HCC and pre-HCC on the basis of valuable radiomics features extracted from T1WI, FS-T2WI, T1WI+FS-T2WI, respectively. The AUC, sensitivity and specificity of the training group and test group based on T1WI were 0.879 (95% CI 0.797 -0.962), 78.9% and 83.1% and 0.796 (95% CI 0.619-0.973), 66.7% and 83.3%, respectively. The AUC, sensitivity and specificity of the training group and test group based on FS-T2WI were 0.911 (95% CI 0.839-0.983), 94.7% , 83.1% and 0.907 (95% CI 0.779-1), 83.3%, 88.9%, respectively. The AUC, sensitivity and specificity of the training group and test group based on T1WI+FS-T2WI were 0.918 (95% CI 0.844-0.991), 84.2% , 87.3% and 0.870 (95% CI 0.728-1), 84.2%, 87.3%, respectively. Conclusion: This study suggested that radiomics model may serve as an adjunct and noninvasive tool to classify S-HCC and pre-HCC based on precontrast MRI.