Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance

PLOS ONE, Dec 2019

Background & Aims Hepatitis B surface antigen (HBsAg) seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB). This study aimed to develop artificial neural networks (ANNs) that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. Methods Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion), and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs) were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC). Results Serum quantitative HBsAg (qHBsAg) and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001) and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (Pā€Š=ā€Š0.197) and LRM-HBsAg seroconversion was solely based on qHBsAg (Pā€Š=ā€Š0.01). For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05). Although the performance of ANN-HBsAg seroconversion (AUROC 0.757) was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. Conclusions ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.

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Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance

et al. (2014) Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance. PLoS ONE 9(6): e99422. doi:10.1371/journal.pone.0099422 Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance Ming-Hua Zheng 0 Wai-Kay Seto 0 Ke-Qing Shi 0 Danny Ka-Ho Wong 0 James Fung 0 Ivan Fan-Ngai Hung 0 Daniel Yee-Tak Fong 0 John Chi-Hang Yuen 0 Teresa Tong 0 Ching-Lung Lai 0 Man-Fung Yuen 0 Isabelle A. Chemin, CRCL-INSERM, France 0 1 Department of Infection and Liver Diseases, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University , Wenzhou, Zhejiang , China , 2 Department of Medicine, the University of Hong Kong, Queen Mary Hospital , Hong Kong , China , 3 Department of Nursing Studies, the University of Hong Kong, Queen Mary Hospital , Hong Kong , China , 4 State Key Laboratory for Liver Research, the University of Hong Kong, Queen Mary Hospital , Hong Kong , China Background & Aims: Hepatitis B surface antigen (HBsAg) seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB). This study aimed to develop artificial neural networks (ANNs) that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. Methods: Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion), and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs) were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC). Results: Serum quantitative HBsAg (qHBsAg) and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P,0.001) and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P = 0.197) and LRM-HBsAg seroconversion was solely based on qHBsAg (P = 0.01). For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P,0.05). Although the performance of ANN-HBsAg seroconversion (AUROC 0.757) was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. Conclusions: ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future. - . These authors contributed equally to this work. In clinical practice, hepatitis B surface antigen (HBsAg) seroclearance and seroconversion have been recommended as the ideal outcomes in both the natural history of HBV infection and as endpoint for the treatment of CHB [1]. Earlier HBsAg seroclearance or seroconversion is likely resulted in a better prognosis because of lower HBV replication as well as less liver damage [1,2]. A few studies have explored the incidence of spontaneous HBsAg seroclearance in CHB patients of both Asian and European populations using long-term follow-up cohorts and the annual incidence ranges from 0.62% to 2.26% [3,4,5,6,7,8]. Because of the more rarity of spontaneous HBsAg seroconversion, compared to HBsAg seroclearance, the incidence and long-term outcomes of CHB patients experiencing this event remain disputed. Existing evidences indicate that HBsAg seroclearance or seroconversion confers favorable long-term outcomes in patients without hepatocellular carcinoma (HCC) or decompensated liver cirrhosis [9,10,11,12]. Predictive factors for spontaneous HBsAg seroclearance or seroconversion using various parameters have attracted much attention recently. Previous studies had demonstrated that lowering HBV DNA level was an important predictor for spontaneous HBsAg seroclearance [5,6,8,13]. Furthermore, with the technological advances of quantitative HBsAg (qHBsAg), it has been suggested as a promising new marker in monitoring immunological response in both treated and untreated CHB patients, as well as a potential predictor of liver disease progression [14]. Our previous study showed that low qHBsAg levels and an increased reduction rate in qHBsAg levels were the most significant predictors of spontaneous HBsAg seroclearance with 3 years of follow-up [15]. These findings have been further validated by other studies [4,5,13,16,17]. However, our previous study had several limitations. No specific time point was identified where qHBsAg kinetics could have the highest predictive value. Also the accuracy of qHBsAg levels in predicting HBsAg seroclearance [area under receiver operating characteristic curve (AUROC) 0.833] still warrants improvement [15]. In all currently available studies [4,5,13,15,16,17], the predictability of qHBsAg levels for (...truncated)


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Ming-Hua Zheng, Wai-Kay Seto, Ke-Qing Shi, Danny Ka-Ho Wong, James Fung, Ivan Fan-Ngai Hung, Daniel Yee-Tak Fong, John Chi-Hang Yuen, Teresa Tong, Ching-Lung Lai, Man-Fung Yuen. Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance, PLOS ONE, 2014, Volume 9, Issue 6, DOI: 10.1371/journal.pone.0099422