Differentiation of Small (≤3 Cm) Hepatocellular Carcinomas From Benign Nodules in Cirrhotic Liver: Additive Value of MRI-Based Radiomics Analysis to LI-RADS Version 2018 Algorithm

Background: Accurate characterization of small nodules in a cirrhotic liver is challenging. We aimed to determine the additive value of MRI-based radiomics analysis to Liver Imaging Reporting and Data System version 2018 (LI-RADS v 2018) algorithm in differentiating small (≤ 3 cm) hepatocellular carcinomas (HCCs) from benign nodules in cirrhotic liver. Methods Cirrhosis patients (150) with histopathologically confirmed small liver nodules (HCC, 112; benign nodules, 44) were included. Based on the LI-RADS algorithm, a LI-RADS category was assigned for each lesion. A radiomics signature was generated based on texture features extracted from T1-weighted, T2W, and apparent diffusion coefficient (ADC) images by using the least absolute shrinkage and selection operator regression model. A nomogram model was developed for the combined diagnosis. Diagnostic performance was assessed using receiver operating characteristic curve (ROC) analysis. Results A radiomics signature consisting of eight features were significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [A z ] = 0.898) and the MRI-Based radiomics signature (A z = 0.917) demonstrated good discrimination; and the nomogram model showed a superior classification performance (A z = 0.975). Compared with LI-RADS alone, the combined approach significantly improved the specificity (97.7% vs 81.8%, p  = 0.030) and positive predictive value (99.1% vs 92.9%, p  = 0.031) and afforded comparable sensitivity (97.3% vs 93.8%, p  = 0.215) and negative predictive value (93.5% vs 83.7%, p  = 0.188). Conclusions MRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in cirrhotic liver.