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

Qiu-Feng Chen,Hua-Jun Chen,Zhe-Ting Yang,Tian-Xiu Zou

doi:10.1038/s41598-020-59433-1

Abstract

Minimal 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.

Highlights

Minimal hepatic encephalopathy (MHE) is characterized by diffuse abnormalities in cerebral structure, such as reduced cortical thickness and altered brain parenchymal volume
MHE patients performed significantly worse in all five subtests of the Psychometric Hepatic Encephalopathy Score (PHES) assessment, indicating significant cognitive deficits compared to the NHE subjects
These results suggested that when the PHES score is far from diagnostic criteria, the subject is unlikely to be misclassified

Summary

Introduction

Minimal 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. It was implied that brain structural impairments may increase susceptibility to various neurotoxic substances derived from hepatic dysfunction-associated metabolic disorders, such as ammonia and manganese[7] These structural alterations were suggested to be associated with abnormal brain electrophysiological activity and poor psychometric performance in cirrhotic patients[8]. Several studies even proposed that regional GM morphometry (such as regional volume and cortical thickness measurements) could help to predict the existence of MHE18,19 Given these findings, we used a support vector machine (SVM) learning method to test the extent to which GM volumetry can distinguish between cirrhotic patients with and without MHE. This study aimed to identify the specific GM regions that contributed the most to differentiating between the two patient groups

Objectives

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Conclusion