File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Artificial neural network accurately predicts hepatitis B surface antigen seroclearance

TitleArtificial neural network accurately predicts hepatitis B surface antigen seroclearance
Authors
Issue Date2014
Citation
PLoS One, 2014, v. 9 n. 6, p. e99422 How to Cite?
AbstractBACKGROUND & 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.
Persistent Identifierhttp://hdl.handle.net/10722/198050
ISSN
2015 Impact Factor: 3.057
2015 SCImago Journal Rankings: 1.395

 

DC FieldValueLanguage
dc.contributor.authorZheng, MHen_US
dc.contributor.authorSeto, WKWen_US
dc.contributor.authorShi, KQen_US
dc.contributor.authorWong, DKHen_US
dc.contributor.authorFung, JYYen_US
dc.contributor.authorHung, IFNen_US
dc.contributor.authorFong, DYTen_US
dc.contributor.authorYuen, JCHen_US
dc.contributor.authorTong, Ten_US
dc.contributor.authorLai, CLen_US
dc.contributor.authorYuen, RMFen_US
dc.date.accessioned2014-06-25T02:42:11Z-
dc.date.available2014-06-25T02:42:11Z-
dc.date.issued2014en_US
dc.identifier.citationPLoS One, 2014, v. 9 n. 6, p. e99422en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://hdl.handle.net/10722/198050-
dc.description.abstractBACKGROUND & 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.en_US
dc.languageengen_US
dc.relation.ispartofPLoS Oneen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleArtificial neural network accurately predicts hepatitis B surface antigen seroclearanceen_US
dc.typeArticleen_US
dc.identifier.emailSeto, WKW: wkseto2@hku.hken_US
dc.identifier.emailWong, DKH: danywong@hku.hken_US
dc.identifier.emailFung, JYY: jfung@hkucc.hku.hken_US
dc.identifier.emailHung, IFN: ivanhung@hkucc.hku.hken_US
dc.identifier.emailFong, DYT: dytfong@hku.hken_US
dc.identifier.emailYuen, JCH: jchyuen@hkucc.hku.hken_US
dc.identifier.emailLai, CL: hrmelcl@hku.hken_US
dc.identifier.emailYuen, RMF: mfyuen@hku.hken_US
dc.identifier.authoritySeto, WKW=rp01659en_US
dc.identifier.authorityWong, DKH=rp00492en_US
dc.identifier.authorityFung, JYY=rp00518en_US
dc.identifier.authorityHung, IFN=rp00508en_US
dc.identifier.authorityFong, DYT=rp00253en_US
dc.identifier.authorityYuen, RMF=rp00479en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0099422en_US
dc.identifier.hkuros229438en_US
dc.identifier.volume9en_US
dc.identifier.issue6en_US
dc.identifier.spagee99422en_US
dc.identifier.epagee99422en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats