Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study
Introduction Cytology‐based triaging is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but it suffers from subjectivity and a lack of sensitivity and reproducibility. The diagnostic performance of an artificial intelligence‐enabled liquid‐based cytology (...
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| Veröffentlicht in: | Acta obstetricia et gynecologica Scandinavica 2023-08, Vol.102 (8), p.1026-1033 |
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| creator | Xue, Peng Xu, Hai‐Miao Tang, Hong‐Ping Wu, Wen‐Qing Seery, Samuel Han, Xiao Ye, Hu Jiang, Yu Qiao, You‐Lin |
| description | Introduction
Cytology‐based triaging is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but it suffers from subjectivity and a lack of sensitivity and reproducibility. The diagnostic performance of an artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) triage approach remains unclear. Here, we compared the clinical performance of AI‐LBC, human cytologists and HPV16/18 genotyping at triaging HPV‐positive women.
Material and methods
HPV‐positive women were triaged using AI‐LBC, human cytologists and HPV16/18 genotyping. Histologically confirmed cervical intraepithelial neoplasia grade 2/3 or higher (CIN2+/CIN3+) were accepted as thresholds for clinical performance assessments.
Results
Of the 3514 women included, 13.9% (n = 489) were HPV‐positive. The sensitivity of AI‐LBC was comparable to that of cytologists (86.49% vs 83.78%, P = 0.744) but substantially higher than HPV16/18 typing at detecting CIN2+ (86.49% vs 54.05%, P = 0.002). While the specificity of AI‐LBC was significantly lower than HPV16/18 typing (51.33% vs 87.17%, P |
| doi_str_mv | 10.1111/aogs.14611 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10377999</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2826220099</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4491-e495f043534aa09e219b548652cc3a9f0dcc759dc92d5e6e411a296d634006cf3</originalsourceid><addsrcrecordid>eNp9kctu1DAUhi0EokPLhgdAltggpBTf4ozZVKMKWqRKRWpha3mck-DKE6d20io7HgFekSfBmSnlsqg31rE_fT4-P0IvKDmkeb01oU2HVEhKH6EFlYQURFD2GC0IIbSQXKg99Cylq1yxSiyfoj1ecbokXC7Qj1VKkJLrWmzi4BpnnfHYdQN471roLGDozNpDjb27Hl3989v3tUm5tNMQfGgn3ISIh-hMO0tOP33JRB-SG9wN4Nuwge4dNrgP_ejN4EL3RxBDSrlKYOfz_Gwaxno6QE8a4xM8v9v30ecP7y-PT4uz85OPx6uzwgqhaAFClQ0RvOTCGKKAUbUuxVKWzFpuVENqa6tS1VaxugQJglLDlKzzOAiRtuH76Gjn7cf1BmoL3RCN1310GxMnHYzT_9507qtuw42mhFeVUiobXt8ZYrgeIQ1645LNgzMdhDFptmSSMUK26Kv_0KswxvznmRKcCKaUzNSbHbUdTYTmvhtK9By1nqPW26gz_PLv_u_R39lmgO6AW-dhekClV-cnFzvpL8ESu4M</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2843042996</pqid></control><display><type>article</type><title>Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study</title><source>MEDLINE</source><source>Wiley Online Library Open Access</source><source>Wiley Online Library Journals Frontfile Complete</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><creator>Xue, Peng ; Xu, Hai‐Miao ; Tang, Hong‐Ping ; Wu, Wen‐Qing ; Seery, Samuel ; Han, Xiao ; Ye, Hu ; Jiang, Yu ; Qiao, You‐Lin</creator><creatorcontrib>Xue, Peng ; Xu, Hai‐Miao ; Tang, Hong‐Ping ; Wu, Wen‐Qing ; Seery, Samuel ; Han, Xiao ; Ye, Hu ; Jiang, Yu ; Qiao, You‐Lin</creatorcontrib><description>Introduction
Cytology‐based triaging is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but it suffers from subjectivity and a lack of sensitivity and reproducibility. The diagnostic performance of an artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) triage approach remains unclear. Here, we compared the clinical performance of AI‐LBC, human cytologists and HPV16/18 genotyping at triaging HPV‐positive women.
Material and methods
HPV‐positive women were triaged using AI‐LBC, human cytologists and HPV16/18 genotyping. Histologically confirmed cervical intraepithelial neoplasia grade 2/3 or higher (CIN2+/CIN3+) were accepted as thresholds for clinical performance assessments.
Results
Of the 3514 women included, 13.9% (n = 489) were HPV‐positive. The sensitivity of AI‐LBC was comparable to that of cytologists (86.49% vs 83.78%, P = 0.744) but substantially higher than HPV16/18 typing at detecting CIN2+ (86.49% vs 54.05%, P = 0.002). While the specificity of AI‐LBC was significantly lower than HPV16/18 typing (51.33% vs 87.17%, P < 0.001), it was significantly higher than cytologists at detecting CIN2+ (51.33% vs 40.93%, P < 0.001). AI‐LBC reduced referrals to colposcopy by approximately 10%, compared with cytologists (51.53% vs 60.94%, P = 0.003). Similar patterns were also observed for CIN3+.
Conclusions
AI‐LBC has equivalent sensitivity and higher specificity compared with cytologists, with more efficient colposcopy referrals for HPV‐positive women. AI‐LBC could be particularly useful in regions where experienced cytologists are few in number. Further investigations are needed to determine triaging performance through prospective designs.
Cytology triage is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but suffers from subjectivity, lack of reproducibility and sensitivity. In this study, authors investigated the performance of Artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) for triage of HPV‐positive women. Compared with cytologists, AI‐LBC showed equivalent sensitivity and higher specificity with more efficient colposcopy referrals for triage of HPV‐positive women.</description><identifier>ISSN: 0001-6349</identifier><identifier>EISSN: 1600-0412</identifier><identifier>DOI: 10.1111/aogs.14611</identifier><identifier>PMID: 37318036</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Artificial Intelligence ; Cellular biology ; Cervical cancer ; cervical cancer screening ; Colposcopy ; Cross-Sectional Studies ; cytology ; Early Detection of Cancer - methods ; Female ; Gynecology ; HPV triage ; Human papillomavirus ; Human papillomavirus 16 - genetics ; Human papillomavirus 18 - genetics ; Humans ; Original ; Pap smear ; Papillomavirus Infections - diagnosis ; Population-based studies ; Pregnancy ; Reproducibility of Results ; Triage - methods ; Uterine Cervical Dysplasia - pathology ; Uterine Cervical Neoplasms - pathology ; Womens health</subject><ispartof>Acta obstetricia et gynecologica Scandinavica, 2023-08, Vol.102 (8), p.1026-1033</ispartof><rights>2023 The Authors. published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).</rights><rights>2023 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4491-e495f043534aa09e219b548652cc3a9f0dcc759dc92d5e6e411a296d634006cf3</citedby><cites>FETCH-LOGICAL-c4491-e495f043534aa09e219b548652cc3a9f0dcc759dc92d5e6e411a296d634006cf3</cites><orcidid>0000-0003-3002-8146</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377999/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377999/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,1411,11541,27901,27902,45550,45551,46027,46451,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37318036$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xue, Peng</creatorcontrib><creatorcontrib>Xu, Hai‐Miao</creatorcontrib><creatorcontrib>Tang, Hong‐Ping</creatorcontrib><creatorcontrib>Wu, Wen‐Qing</creatorcontrib><creatorcontrib>Seery, Samuel</creatorcontrib><creatorcontrib>Han, Xiao</creatorcontrib><creatorcontrib>Ye, Hu</creatorcontrib><creatorcontrib>Jiang, Yu</creatorcontrib><creatorcontrib>Qiao, You‐Lin</creatorcontrib><title>Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study</title><title>Acta obstetricia et gynecologica Scandinavica</title><addtitle>Acta Obstet Gynecol Scand</addtitle><description>Introduction
Cytology‐based triaging is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but it suffers from subjectivity and a lack of sensitivity and reproducibility. The diagnostic performance of an artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) triage approach remains unclear. Here, we compared the clinical performance of AI‐LBC, human cytologists and HPV16/18 genotyping at triaging HPV‐positive women.
Material and methods
HPV‐positive women were triaged using AI‐LBC, human cytologists and HPV16/18 genotyping. Histologically confirmed cervical intraepithelial neoplasia grade 2/3 or higher (CIN2+/CIN3+) were accepted as thresholds for clinical performance assessments.
Results
Of the 3514 women included, 13.9% (n = 489) were HPV‐positive. The sensitivity of AI‐LBC was comparable to that of cytologists (86.49% vs 83.78%, P = 0.744) but substantially higher than HPV16/18 typing at detecting CIN2+ (86.49% vs 54.05%, P = 0.002). While the specificity of AI‐LBC was significantly lower than HPV16/18 typing (51.33% vs 87.17%, P < 0.001), it was significantly higher than cytologists at detecting CIN2+ (51.33% vs 40.93%, P < 0.001). AI‐LBC reduced referrals to colposcopy by approximately 10%, compared with cytologists (51.53% vs 60.94%, P = 0.003). Similar patterns were also observed for CIN3+.
Conclusions
AI‐LBC has equivalent sensitivity and higher specificity compared with cytologists, with more efficient colposcopy referrals for HPV‐positive women. AI‐LBC could be particularly useful in regions where experienced cytologists are few in number. Further investigations are needed to determine triaging performance through prospective designs.
Cytology triage is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but suffers from subjectivity, lack of reproducibility and sensitivity. In this study, authors investigated the performance of Artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) for triage of HPV‐positive women. Compared with cytologists, AI‐LBC showed equivalent sensitivity and higher specificity with more efficient colposcopy referrals for triage of HPV‐positive women.</description><subject>Artificial Intelligence</subject><subject>Cellular biology</subject><subject>Cervical cancer</subject><subject>cervical cancer screening</subject><subject>Colposcopy</subject><subject>Cross-Sectional Studies</subject><subject>cytology</subject><subject>Early Detection of Cancer - methods</subject><subject>Female</subject><subject>Gynecology</subject><subject>HPV triage</subject><subject>Human papillomavirus</subject><subject>Human papillomavirus 16 - genetics</subject><subject>Human papillomavirus 18 - genetics</subject><subject>Humans</subject><subject>Original</subject><subject>Pap smear</subject><subject>Papillomavirus Infections - diagnosis</subject><subject>Population-based studies</subject><subject>Pregnancy</subject><subject>Reproducibility of Results</subject><subject>Triage - methods</subject><subject>Uterine Cervical Dysplasia - pathology</subject><subject>Uterine Cervical Neoplasms - pathology</subject><subject>Womens health</subject><issn>0001-6349</issn><issn>1600-0412</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNp9kctu1DAUhi0EokPLhgdAltggpBTf4ozZVKMKWqRKRWpha3mck-DKE6d20io7HgFekSfBmSnlsqg31rE_fT4-P0IvKDmkeb01oU2HVEhKH6EFlYQURFD2GC0IIbSQXKg99Cylq1yxSiyfoj1ecbokXC7Qj1VKkJLrWmzi4BpnnfHYdQN471roLGDozNpDjb27Hl3989v3tUm5tNMQfGgn3ISIh-hMO0tOP33JRB-SG9wN4Nuwge4dNrgP_ejN4EL3RxBDSrlKYOfz_Gwaxno6QE8a4xM8v9v30ecP7y-PT4uz85OPx6uzwgqhaAFClQ0RvOTCGKKAUbUuxVKWzFpuVENqa6tS1VaxugQJglLDlKzzOAiRtuH76Gjn7cf1BmoL3RCN1310GxMnHYzT_9507qtuw42mhFeVUiobXt8ZYrgeIQ1645LNgzMdhDFptmSSMUK26Kv_0KswxvznmRKcCKaUzNSbHbUdTYTmvhtK9By1nqPW26gz_PLv_u_R39lmgO6AW-dhekClV-cnFzvpL8ESu4M</recordid><startdate>202308</startdate><enddate>202308</enddate><creator>Xue, Peng</creator><creator>Xu, Hai‐Miao</creator><creator>Tang, Hong‐Ping</creator><creator>Wu, Wen‐Qing</creator><creator>Seery, Samuel</creator><creator>Han, Xiao</creator><creator>Ye, Hu</creator><creator>Jiang, Yu</creator><creator>Qiao, You‐Lin</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-3002-8146</orcidid></search><sort><creationdate>202308</creationdate><title>Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study</title><author>Xue, Peng ; Xu, Hai‐Miao ; Tang, Hong‐Ping ; Wu, Wen‐Qing ; Seery, Samuel ; Han, Xiao ; Ye, Hu ; Jiang, Yu ; Qiao, You‐Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4491-e495f043534aa09e219b548652cc3a9f0dcc759dc92d5e6e411a296d634006cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial Intelligence</topic><topic>Cellular biology</topic><topic>Cervical cancer</topic><topic>cervical cancer screening</topic><topic>Colposcopy</topic><topic>Cross-Sectional Studies</topic><topic>cytology</topic><topic>Early Detection of Cancer - methods</topic><topic>Female</topic><topic>Gynecology</topic><topic>HPV triage</topic><topic>Human papillomavirus</topic><topic>Human papillomavirus 16 - genetics</topic><topic>Human papillomavirus 18 - genetics</topic><topic>Humans</topic><topic>Original</topic><topic>Pap smear</topic><topic>Papillomavirus Infections - diagnosis</topic><topic>Population-based studies</topic><topic>Pregnancy</topic><topic>Reproducibility of Results</topic><topic>Triage - methods</topic><topic>Uterine Cervical Dysplasia - pathology</topic><topic>Uterine Cervical Neoplasms - pathology</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xue, Peng</creatorcontrib><creatorcontrib>Xu, Hai‐Miao</creatorcontrib><creatorcontrib>Tang, Hong‐Ping</creatorcontrib><creatorcontrib>Wu, Wen‐Qing</creatorcontrib><creatorcontrib>Seery, Samuel</creatorcontrib><creatorcontrib>Han, Xiao</creatorcontrib><creatorcontrib>Ye, Hu</creatorcontrib><creatorcontrib>Jiang, Yu</creatorcontrib><creatorcontrib>Qiao, You‐Lin</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Acta obstetricia et gynecologica Scandinavica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xue, Peng</au><au>Xu, Hai‐Miao</au><au>Tang, Hong‐Ping</au><au>Wu, Wen‐Qing</au><au>Seery, Samuel</au><au>Han, Xiao</au><au>Ye, Hu</au><au>Jiang, Yu</au><au>Qiao, You‐Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study</atitle><jtitle>Acta obstetricia et gynecologica Scandinavica</jtitle><addtitle>Acta Obstet Gynecol Scand</addtitle><date>2023-08</date><risdate>2023</risdate><volume>102</volume><issue>8</issue><spage>1026</spage><epage>1033</epage><pages>1026-1033</pages><issn>0001-6349</issn><eissn>1600-0412</eissn><abstract>Introduction
Cytology‐based triaging is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but it suffers from subjectivity and a lack of sensitivity and reproducibility. The diagnostic performance of an artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) triage approach remains unclear. Here, we compared the clinical performance of AI‐LBC, human cytologists and HPV16/18 genotyping at triaging HPV‐positive women.
Material and methods
HPV‐positive women were triaged using AI‐LBC, human cytologists and HPV16/18 genotyping. Histologically confirmed cervical intraepithelial neoplasia grade 2/3 or higher (CIN2+/CIN3+) were accepted as thresholds for clinical performance assessments.
Results
Of the 3514 women included, 13.9% (n = 489) were HPV‐positive. The sensitivity of AI‐LBC was comparable to that of cytologists (86.49% vs 83.78%, P = 0.744) but substantially higher than HPV16/18 typing at detecting CIN2+ (86.49% vs 54.05%, P = 0.002). While the specificity of AI‐LBC was significantly lower than HPV16/18 typing (51.33% vs 87.17%, P < 0.001), it was significantly higher than cytologists at detecting CIN2+ (51.33% vs 40.93%, P < 0.001). AI‐LBC reduced referrals to colposcopy by approximately 10%, compared with cytologists (51.53% vs 60.94%, P = 0.003). Similar patterns were also observed for CIN3+.
Conclusions
AI‐LBC has equivalent sensitivity and higher specificity compared with cytologists, with more efficient colposcopy referrals for HPV‐positive women. AI‐LBC could be particularly useful in regions where experienced cytologists are few in number. Further investigations are needed to determine triaging performance through prospective designs.
Cytology triage is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but suffers from subjectivity, lack of reproducibility and sensitivity. In this study, authors investigated the performance of Artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) for triage of HPV‐positive women. Compared with cytologists, AI‐LBC showed equivalent sensitivity and higher specificity with more efficient colposcopy referrals for triage of HPV‐positive women.</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>37318036</pmid><doi>10.1111/aogs.14611</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-3002-8146</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Wiley Online Library Open Access; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
| subjects | Artificial Intelligence Cellular biology Cervical cancer cervical cancer screening Colposcopy Cross-Sectional Studies cytology Early Detection of Cancer - methods Female Gynecology HPV triage Human papillomavirus Human papillomavirus 16 - genetics Human papillomavirus 18 - genetics Humans Original Pap smear Papillomavirus Infections - diagnosis Population-based studies Pregnancy Reproducibility of Results Triage - methods Uterine Cervical Dysplasia - pathology Uterine Cervical Neoplasms - pathology Womens health |
| title | Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study |
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