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Article: Machine learning interpretation of extended human papillomavirus genotyping by Onclarity in an Asian cervical cancer screening population

TitleMachine learning interpretation of extended human papillomavirus genotyping by Onclarity in an Asian cervical cancer screening population
Authors
Keywordscervical cancer
primary screening
HPV test
Onclarity
cobas
Issue Date2019
PublisherAmerican Society for Microbiology. The Journal's web site is located at http://jcm.asm.org/
Citation
Journal of Clinical Microbiology, 2019, Epub How to Cite?
AbstractThis study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical intraepithelial neoplasia 2/3 or above (CIN2/3+) in an Asian screening population and (ii) to explore improving the cervical cancer detection specificity of Onclarity by machine learning. We tested 605 stratified random archived samples of cervical liquid-based cytology samples with both assays. All samples had biopsy diagnosis or repeated negative cytology follow-up. Association rule mining (ARM) was employed to discover coinfection likely to give rise to CIN2/3+. Outcome classifiers interpreting the extended genotyping results of Onclarity were built with different underlying models. The sensitivities (Onclarity, 96.32%; Cobas, 95.71%) and specificities (Onclarity, 46.38%; Cobas, 45.25%) of the high-risk HPV (hrHPV) components of the two tests were not significantly different. When HPV16 and HPV18 were used to further interpret hrHPV-positive cases, Onclarity displayed significantly higher specificity (Onclarity, 87.10%; Cobas, 80.77%). Both hrHPV tests achieved the same sensitivities (Onclarity, 90.91%; Cobas, 90.91%) and similar specificities (Onclarity, 48.46%; Cobas, 51.98%) when used for triaging atypical squamous cells of undetermined significance. Positivity in both HPV16 and HPV33/58 of the Onclarity channels entails the highest probability of developing CIN2/3+. Incorporating other hrHPVs into the outcome classifiers improved the specificity of identifying CIN2/3 to up to 94.32%. The extended genotyping of Onclarity therefore can help to highlight patients having the highest risk of developing CIN2/3+, with the potential to reduce unnecessary colposcopy and negative psychosocial impact on women receiving the reports.
Persistent Identifierhttp://hdl.handle.net/10722/277761
ISSN
2017 Impact Factor: 4.054
2015 SCImago Journal Rankings: 2.151

 

DC FieldValueLanguage
dc.contributor.authorWong, OGW-
dc.contributor.authorNg, IFY-
dc.contributor.authorTsun, OKL-
dc.contributor.authorPang, HH-
dc.contributor.authorIp, PPC-
dc.contributor.authorCheung, ANY-
dc.date.accessioned2019-10-04T08:00:49Z-
dc.date.available2019-10-04T08:00:49Z-
dc.date.issued2019-
dc.identifier.citationJournal of Clinical Microbiology, 2019, Epub-
dc.identifier.issn0095-1137-
dc.identifier.urihttp://hdl.handle.net/10722/277761-
dc.description.abstractThis study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical intraepithelial neoplasia 2/3 or above (CIN2/3+) in an Asian screening population and (ii) to explore improving the cervical cancer detection specificity of Onclarity by machine learning. We tested 605 stratified random archived samples of cervical liquid-based cytology samples with both assays. All samples had biopsy diagnosis or repeated negative cytology follow-up. Association rule mining (ARM) was employed to discover coinfection likely to give rise to CIN2/3+. Outcome classifiers interpreting the extended genotyping results of Onclarity were built with different underlying models. The sensitivities (Onclarity, 96.32%; Cobas, 95.71%) and specificities (Onclarity, 46.38%; Cobas, 45.25%) of the high-risk HPV (hrHPV) components of the two tests were not significantly different. When HPV16 and HPV18 were used to further interpret hrHPV-positive cases, Onclarity displayed significantly higher specificity (Onclarity, 87.10%; Cobas, 80.77%). Both hrHPV tests achieved the same sensitivities (Onclarity, 90.91%; Cobas, 90.91%) and similar specificities (Onclarity, 48.46%; Cobas, 51.98%) when used for triaging atypical squamous cells of undetermined significance. Positivity in both HPV16 and HPV33/58 of the Onclarity channels entails the highest probability of developing CIN2/3+. Incorporating other hrHPVs into the outcome classifiers improved the specificity of identifying CIN2/3 to up to 94.32%. The extended genotyping of Onclarity therefore can help to highlight patients having the highest risk of developing CIN2/3+, with the potential to reduce unnecessary colposcopy and negative psychosocial impact on women receiving the reports.-
dc.languageeng-
dc.publisherAmerican Society for Microbiology. The Journal's web site is located at http://jcm.asm.org/-
dc.relation.ispartofJournal of Clinical Microbiology-
dc.rightsJournal of Clinical Microbiology. Copyright © American Society for Microbiology.-
dc.subjectcervical cancer-
dc.subjectprimary screening-
dc.subjectHPV test-
dc.subjectOnclarity-
dc.subjectcobas-
dc.titleMachine learning interpretation of extended human papillomavirus genotyping by Onclarity in an Asian cervical cancer screening population-
dc.typeArticle-
dc.identifier.emailWong, OGW: wonggw@hkucc.hku.hk-
dc.identifier.emailNg, IFY: idy999@hku.hk-
dc.identifier.emailTsun, OKL: okltsun@hku.hk-
dc.identifier.emailPang, HH: herbpang@hku.hk-
dc.identifier.emailIp, PPC: philipip@hku.hk-
dc.identifier.emailCheung, ANY: anycheun@hkucc.hku.hk-
dc.identifier.authorityPang, HH=rp01857-
dc.identifier.authorityIp, PPC=rp01890-
dc.identifier.authorityCheung, ANY=rp00542-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1128/JCM.00997-19-
dc.identifier.hkuros306285-
dc.publisher.placeUnited States-

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