Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients

Abstract

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.

Figure 1

Flowchart of predicting liver fibrosis reverse factors and analytic method. (a) Summary of enrolled patients and relevant variables of reverse in our chronic HBV-induced fibrosis antiviral treatment cohort. (b) At the end of treatment, fifty-five patients reversed according to the Ishak scoring system. (c) Assessing predictors in the ANN model. Seven statistically different variables were pointed as the input layer, and the outcome was liver fibrosis reverse. (d) Evaluating diagnosis efficacy by AUC, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio.

Figure 2

Predictive importance of the input variables. As demonstrated in the ANN model, AST, PLT, WBC, CHE, LSM, ALT, and gender were the most important predictors of liver cirrhosis reverse by sensitive analysis. AST was the most crucial node with the weight coefficient of 0.296 in our model.

Figure 3

A three-layer neural network for the prediction of liver cirrhosis reverse. A feedforward backpropagation ANN model consisting of AST, PLT, WBC, CHE, LSM, ALT, and gender was constructed in 141 liver cirrhosis patients.