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.

Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review

“…Wei et al conducted a prospective study in which an ANN was constructed in order to isolate and predict biomarkers for fibrosis reversal in 141 treatment-naïve HBV patients with fibrosis S2/S3 staging between two treatment groups [ 42 ]. One consisted of 2 years of Entecavir therapy, and the other was Entecavir alternating with Entecavir combined with pegylated interferon (Peg-IFN).…”

Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review

“…Liver fibrosis is characterized by highly increased accumulation of collagen and thus scarred tissue of the liver. Several models have been presented to describe the development of fibrotic tissue due to various causes, e.g., treatment with carbon tetrachloride ( CCl 4 ) (Dutta-Moscato et al, 2014 ), chronic Hepatitis B (Wei et al, 2019 ), and Hepatitis C (Lara et al, 2014 ) or fibrosis in general (Friedman and Hao, 2017 ). Cirrhosis is an aggravated and non-reversible form of fibrosis.…”

Recent Advances in Understanding, Diagnosing, and Treating Hepatitis B Virus Infection

“…In consequence, the ML system outperformed FIB-4 scoring in predicting advanced hepatic fibrosis [ 96 ]. Additionally, an ANN model was demonstrated as effective in diagnosing liver fibrosis reversion for chronic HBV-induced liver fibrosis patients [ 97 ]. Machine learning algorithms have been recently applied for the accurate identification of individuals at high risk of HBV infection and the developed model was proposed as an improvement for the detection rate of positive HBsAg [ 98 ].…”