Predicting Microvascular Invasion of Hepatocellular Carcinoma by texture analysis of multi-phase MR image

Abstract

Microvascular invasion (MVI) diagnosis is of vital importance in the curative treatment of hepatocellular carcinoma patients due to its close relationship with prognostic analysis. Currently, MVI detection often bases on surgical specimen, which is invasive for patients. This study, we extracted texture features of multi-phase MR image to predict the presence of MVI. Feature extraction employed neighboring gray level dependency emphasis (NGLDM) method, which is a common texture feature analysis method. Next, we built a SVM classifier to predict the presence of MVI using extracted features. Especially, multi-phase features were designed to enhance the precision of prediction. Enhanced MR images of pre-contrast phase and portal vein phase were used to extracting features. The method was tested by 5-fold cross- validation on the dataset. The precision of prediction was 91.31%, compared with the baseline method of 70.71%. To make the prediction more interpretable, the relationship between NGLDM texture features and the presence of MVI was discussed in the end.