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dc.contributor.authorFunston, Garth; email: gf272@medschl.cam.ac.uk
dc.contributor.authorAbel, Gary; orcid: 0000-0003-2231-5161; email: g.a.abel@exeter.ac.uk
dc.contributor.authorCrosbie, Emma J.; orcid: 0000-0003-0284-8630; email: emma.crosbie@manchester.ac.uk
dc.contributor.authorHamilton, Willie; orcid: 0000-0003-1611-1373; email: W.Hamilton@exeter.ac.uk
dc.contributor.authorWalter, Fiona M.; orcid: 0000-0002-7191-6476; email: fmw22@medschl.cam.ac.uk
dc.date.accessioned2021-06-10T23:35:31Z
dc.date.available2021-06-10T23:35:31Z
dc.date.issued2021-06-09
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/624901/additional-files.zip?sequence=2
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/624901/cancers-13-02886.xml?sequence=3
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/624901/cancers-13-02886.pdf?sequence=4
dc.identifier.citationCancers, volume 13, issue 12, page e2886
dc.identifier.urihttp://hdl.handle.net/10034/624901
dc.descriptionFrom MDPI via Jisc Publications Router
dc.descriptionHistory: accepted 2021-06-06, pub-electronic 2021-06-09
dc.descriptionPublication status: Published
dc.descriptionFunder: Cancer Research UK; Grant(s): C8640/A23385
dc.descriptionFunder: NIHR School for Primary Care Research; Grant(s): FR17 424
dc.description.abstractCA125 is widely used as an initial investigation in women presenting with symptoms of possible ovarian cancer. We sought to develop CA125-based diagnostic prediction models and to explore potential implications of implementing model-based thresholds for further investigation in primary care. This retrospective cohort study used routinely collected primary care and cancer registry data from symptomatic, CA125-tested women in England (2011–2014). A total of 29,962 women were included, of whom 279 were diagnosed with ovarian cancer. Logistic regression was used to develop two models to estimate ovarian cancer probability: Model 1 consisted of age and CA125 level; Model 2 incorporated further risk factors. Model discrimination (AUC) was evaluated using 10-fold cross-validation. The sensitivity and specificity of various model risk thresholds (≥1% to ≥3%) were compared with that of the current CA125 cut-off (≥35 U/mL). Model 1 exhibited excellent discrimination (AUC: 0.94) on cross-validation. The inclusion of additional variables (Model 2) did not improve performance. At a risk threshold of ≥1%, Model 1 exhibited greater sensitivity (86.4% vs. 78.5%) but lower specificity (89.1% vs. 94.5%) than CA125 (≥35 U/mL). Applying the ≥1% model threshold to the cohort in place of the current CA125 cut-off, 1 in every 74 additional women identified had ovarian cancer. Following external validation, Model 1 could be used as part of a ‘risk-based triage’ system in which women at high risk of undiagnosed ovarian cancer are selected for urgent specialist investigation, while women at ‘low risk but not no risk’ are offered non-urgent investigation or interval CA125 re-testing. Such an approach has the potential to expedite ovarian cancer diagnosis, but further research is needed to evaluate the clinical impact and health–economic implications.
dc.languageen
dc.publisherMDPI
dc.rightsLicence for this article: https://creativecommons.org/licenses/by/4.0/
dc.sourceeissn: 2072-6694
dc.subjectovarian cancer
dc.subjectCA125
dc.subjectcancer antigen 125
dc.subjectprimary care
dc.subjectdiagnostic prediction model
dc.subjectearly detection
dc.subjectrisk assessment
dc.subjecttriage
dc.subjectdiagnostic pathways
dc.titleCould Ovarian Cancer Prediction Models Improve the Triage of Symptomatic Women in Primary Care? A Modelling Study Using Routinely Collected Data
dc.typearticle
dc.date.updated2021-06-10T23:35:30Z
dc.date.accepted2021-06-06


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