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Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients
Joint Authors
Yang, Xu
Wei, Wei
Wu, Xiaoning
Zhou, Jialing
Sun, Yameng
Kong, Yuanyuan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-21
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established.
Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic HBV patients after regular antiviral therapy.
In our study, 141 consecutive patients requiring liver biopsy at baseline and 1.5 years were enrolled.
Several serum biomarkers and liver stiffness were measured during antiviral therapy in both reverse and nonreverse groups.
Statistically significant variables between two groups were selected to form an input layer of the ANN model.
The ROC (receiver-operating characteristic) curve and AUC (area under the curve) were calculated for comparison of effectiveness of the ANN model and logistic regression model in predicting HBV-induced liver fibrosis reverse.
The prevalence of fibrosis reverse of HBV patients was about 39% (55/141) after 78-week antiviral therapy.
The Ishak scoring system was used to assess fibrosis reverse.
Our study manifested that AST (aspartate aminotransferase; importance coefficient = 0.296), PLT (platelet count; IC = 0.159), WBC (white blood cell; IC = 0.142), CHE (cholinesterase; IC = 0.128), LSM (liver stiffness measurement; IC = 0.125), ALT (alanine aminotransferase; IC = 0.110), and gender (IC = 0.041) were the most crucial predictors of reverse.
The AUC of the ANN model and logistic model was 0.809 ± 0.062 and 0.756 ± 0.059, respectively.
In our study, we concluded that the ANN model with variables consisting of AST, PLT, WBC, CHE, LSM, ALT, and gender may be useful in diagnosing liver fibrosis reverse for chronic HBV-induced liver fibrosis patients.
American Psychological Association (APA)
Wei, Wei& Wu, Xiaoning& Zhou, Jialing& Sun, Yameng& Kong, Yuanyuan& Yang, Xu. 2019. Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1130681
Modern Language Association (MLA)
Wei, Wei…[et al.]. Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1130681
American Medical Association (AMA)
Wei, Wei& Wu, Xiaoning& Zhou, Jialing& Sun, Yameng& Kong, Yuanyuan& Yang, Xu. Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1130681
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130681