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    Could ovarian cancer prediction models improve the triage of symptomatic women in primary care? a modelling study using routinely collected data.

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    Authors
    Funston, G.
    Abel, G.
    Crosbie, Emma J
    Hamilton, W
    Walter, F. M.
    Affiliation
    The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.ondon E1 2AB, UK.
    Issue Date
    2021
    
    Metadata
    Show full item record
    Abstract
    CA125 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.
    Citation
    Funston G, Abel G, Crosbie EJ, Hamilton W, Walter FM. Could Ovarian Cancer Prediction Models Improve the Triage of Symptomatic Women in Primary Care? A Modelling Study Using Routinely Collected Data. Cancers. 2021 Jun 9;13(12):2886.
    Journal
    Cancers
    URI
    http://hdl.handle.net/10541/624492
    DOI
    10.3390/cancers13122886
    PubMed ID
    34207611
    Additional Links
    https://dx.doi.org/10.3390/cancers13122886
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.3390/cancers13122886
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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