ArticleBrain and behavior2021
Radiomics-based classification models for HBV-related cirrhotic patients with covert hepatic encephalopathy.
Article in Brain and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 5 citations in OpenAlex.
- Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment.Computational and structural biotechnology journal · 2024Review
- Establishment and validation of a nomogram model for riskprediction of hepatic encephalopathy: a retrospective analysis.Scientific reports · 2023Article
- A nationwide evaluation of the prevalence of and risk factors associated with anxiety, depression and insomnia symptoms during the return-to-work period of coronavirus disease 2019 in China.Social psychiatry and psychiatric epidemiology · 2021Article
- Radiomics-based classification models for HBV-related cirrhotic patients with covert hepatic encephalopathy.Brain and behavior · 2021Article
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Authors and funding
13 authors at 2 institutions in 2 countries.
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Abstract
introductionThe 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.
methods106 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.
resultsThe 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.
conclusionsThis 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.
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