Identification of patients with and without minimal hepatic encephalopathy based on gray matter volumetry using a support vector machine learning algorithm

Abstract

AbstractMinimal hepatic encephalopathy (MHE) is characterized by diffuse abnormalities in cerebral structure, such as reduced cortical thickness and altered brain parenchymal volume. This study tested the potential of gray matter (GM) volumetry to differentiate between cirrhotic patients with and without MHE using a support vector machine (SVM) learning method. High-resolution, T1-weighted magnetic resonance images were acquired from 24 cirrhotic patients with MHE and 29 cirrhotic patients without MHE (NHE). Voxel-based morphometry was conducted to evaluate the GM volume (GMV) for each subject. An SVM classifier was employed to explore the ability of the GMV measurement to diagnose MHE, and the leave-one-out cross-validation method was used to assess classification accuracy. The SVM algorithm based on GM volumetry achieved a classification accuracy of 83.02%, with a sensitivity of 83.33% and a specificity of 82.76%. The majority of the most discriminative GMVs were located in the bilateral frontal lobe, bilateral lentiform nucleus, bilateral thalamus, bilateral sensorimotor areas, bilateral visual regions, bilateral temporal lobe, bilateral cerebellum, left inferior parietal lobe, and right precuneus/posterior cingulate gyrus. Our results suggest that SVM analysis based on GM volumetry has the potential to help diagnose MHE in cirrhotic patients.