Radiomics Analysis on Gd-EOB-DTPA-Enhanced MRI for Prediction of Liver Function and Hepatic Cirrhosis

This retrospective study explored the value of a radiomics-based model on Gd-EOB-DTPA-Enhanced MRI for predicting liver function and cirrhosis in clinic. Multi-class radiomics feature extraction was performed on 2D-view whole liver at portal level on HBP MRI obtained 20 min after Gd-EOB-DTPA-enhanced MRI. A prediction model including 15 radiomics features using a machine learning logistic regression classifier showed the mean AUCs on train dataset and test dataset were 0.91 and 0.87 for diagnosing Child-Pugh A respectively; 0.93 and 0.93 for diagnosing liver cirrhosis, respectively. Radiomics analysis of gadoxetic acid-enhanced HBP images allows for accurate diagnosis of clinically significant liver function reservation and liver cirrhosis and may be a promising noninvasive method for assessment of liver cirrhosis.