Radiomics in evaluation of small-hepatocellular carcinoma and benign cirrhotic nodule based on magnetic resonance plain images

Background There are adequate evidences showing that early diagnosis significantly improves the prognosis of patients. Currently, radiology diagnosis to s-HCC is still difficult. Radiomics, a new type of quantitative imaging diagnosis method, has been widely used in the study of multi-system diseases. The purpose of this study is to evaluate the value of radiomics based on MRI plain images in differentiating s-HCC and benign cirrhotic nodule.Methods A total of 78 patients with 114 lesions (89 s-HCCs and 25 benign cirrhotic nodules) were retrospectively enrolled during September 2017 to January 2020. MRI plain images (T1WI, T2WI) of each nodule was segmented to form VOI, and 1223 quantitative radiomics features were extracted from each sequence. 10 optimal features were respectively selected from the T1WI, T2WI, and T1WI+T2WI based on SelectKBest. Logistic Regression (LR) was applied in establishing 2 radiomics models based on single sequence images (T1WI and T2WI) respectively and 1 radiomics model based on T1WI+T2WI. Clinical information (including age, gender, AFP level and the longest diameter of lesion) was collected and analyzed using binary logic analysis to obtain statistically significant (pResults 78 patients with 114 lesions were included in this study. There were 89 s-HCCs, 11 of nodules were e-HCCs, and the remaining 25 were benign cirrhotic nodules, including 3 DNs and 22 RNs. There was no significant difference in diagnostic performance among the radiomics model based on T2WI, T1WI+T2WI and the fusion model. And their diagnostic performance were higher than that of the radiomics model based on T1WI. Conclusions The radiomics model based on MRI plain images can be used to differentiate s-HCC and benign cirrhotic nodule. Radiomics features obtained from T2WI played a key role in the differentiation of s-HCC and benign cirrhotic nodule.