Using convolutional neural network predicts microvascular invasion in hepatocellular carcinoma based on Gd-EOB-DTPA-enhanced MRI

An accurate preoperative assessment of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance in choosing appropriate surgical interventions. We aimed to investigate diagnostic performance of Gd-EOB-DTPA-enhanced MRI for prediction of MVI in HCC using convolutional neural network (CNN). The CNN model based on hepatobiliary phase (HBP) images had great diagnostic efficiency for the prediction of MVI with the AUC of 0.858 (range, 0.854, 0.893). Deep learning with CNN based on Gd-EOB-DTPA-enhanced MRI can be conducive to preoperative prediction of MVI in HCC.