Radiomics‐based classification models for HBV‐related cirrhotic patients with covert hepatic encephalopathy

Introduction The significant abnormalities of precuneus (PC), which are associated with brain dysfunction, have been identified in cirrhotic patients with covert hepatic encephalopathy (CHE). The present study aimed to apply radiomics analysis to identify the significant radiomic features in PC and their subregions, combine with clinical risk factors, then build and evaluate the classification models for CHE diagnosis. Methods 106 HBV‐related cirrhotic patients (54 had current CHE and 52 had non‐CHE) underwent the three‐dimensional T1‐weighted imaging. For each participant, PC and their subregions were segmented and extracted a large number of radiomic features and then identified the features with significant discriminative power as the radiomics signature. The logistic regression analysis was employed to develop and evaluate the classification models, which are constructed using the radiomics signature and clinical risk factors. Results The classification model (R‐C model) achieved best diagnostic performance, which incorporated radiomics signature (4 radiomic features from right PC), venous blood ammonia, and the Child‐Pugh stage. And the area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, and accuracy values were 0.926, 1.000, 0.765, and 0.848, in the testing set. Application of the radiomics nomogram in the testing set still showed a good predictive accuracy. Conclusions This study presented the radiomic features of the right PC, as a potential image marker of CHE. The radiomics nomogram that incorporates the radiomics signature and clinical risk factors may facilitate the individualized prediction of CHE. Large amount radiomic features of PC and its subregions was significantly different among CHE and nCHE patients. Radiomic features from right PC were effective in distinguishing CHE from nCHE patients. Integrating the radiomics data and clinical risk factors has the ability to improve diagnostic performance of the classifier.