单位：[1]Clinical Epidemiology and Evidence-Based Medical Center, National Clinical Research Center for Digestive Disease, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China医技科室北京市临床医学研究所国家消化中心（方法学平台）首都医科大学附属北京友谊医院[2]Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis, National Clinical Research Center for Digestive Disease, Beijing 100050, China临床科室国家中心肝病分中心首都医科大学附属北京友谊医院[3]School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
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 1.5years 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.
基金：
National Science and Technology Major Special Project for Infectious Diseases [2018ZX10302204]; Digestive Medical Coordinated Development Center of Beijing Municipal Administration of Hospitals [XXX0104]; Beijing Municipal Administration of Hospitals Incubating Program [PX2018071]; Research Foundation of Beijing Friendship Hospital, Capital Medical University [yyqdkt2015-15]
第一作者单位：[1]Clinical Epidemiology and Evidence-Based Medical Center, National Clinical Research Center for Digestive Disease, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
通讯作者：
推荐引用方式(GB/T 7714)：
Wei Wei,Wu Xiaoning,Zhou Jialing,et al.Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients[J].COMPUTATIONAL and MATHEMATICAL METHODS in MEDICINE.2019,2019:doi:10.1155/2019/7239780.
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,2019,
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 2019.(2019)
