BMC Infectious Diseases | |
Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers | |
Research Article | |
Qinghui Wang1  Fengping Shan1  Changlong Lu1  Danan Wang1  Beixing Liu1  | |
[1] Institute of Immunology, China Medical University, Shenyang, Liaoning, China; | |
关键词: Artificial Neural Network; Liver Biopsy; Receive Operating Characteristic Curve; Liver Fibrosis; Artificial Neural Network Model; | |
DOI : 10.1186/1471-2334-10-251 | |
received in 2009-12-15, accepted in 2010-08-24, 发布年份 2010 | |
来源: Springer | |
【 摘 要 】
BackgroundLiver fibrosis progression is commonly found in patients with CHB. Liver biopsy is a gold standard for identifying the extent of liver fibrosis, but has many draw-backs. It is essential to construct a noninvasive model to predict the levels of risk for liver fibrosis. It would provide very useful information to help reduce the number of liver biopsies of CHB patients.Methods339 chronic hepatitis B patients with HBsAg-positive were investigated retrospectively, and divided at random into 2 subsets with twice as many patients in the training set as in the validation set; 116 additional patients were consequently enrolled in the study as the testing set. A three-layer artificial neural network was developed using a Bayesian learning algorithm. Sensitivity and ROC analysis were performed to explain the importance of input variables and the performance of the neural network.ResultsThere were 329 patients without significant fibrosis and 126 with significant fibrosis in the study. All markers except gender, HB, ALP and TP were found to be statistically significant factors associated with significant fibrosis. The sensitivity analysis showed that the most important factors in the predictive model were age, AST, platelet, and GGT, and the influence on the output variable among coal miners were 22.3-24.6%. The AUROC in 3 sets was 0.883, 0.884, and 0.920. In the testing set, for a decision threshold of 0.33, sensitivity and negative predictive values were 100% and all CHB patients with significant fibrosis would be identified.ConclusionsThe artificial neural network model based on routine and serum markers would predict the risk for liver fibrosis with a high accuracy. 47.4% of CHB patients at a decision threshold of 0.33 would be free of liver biopsy and wouldn't be missed.
【 授权许可】
Unknown
© Wang et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
【 预 览 】
Files | Size | Format | View |
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RO202311106860588ZK.pdf | 340KB | download |
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