Identifying ovarian cancer in symptomatic women: a systematic review of clinical tools

Garth Funston, Victoria Hardy, Gary Abel, Emma Crosbie, Jon Emery, Willie Hamilton, Fiona M Walter

Research output: Contribution to journalArticlepeer-review

Abstract

In the absence of effective ovarian cancer screening programs, most women are diagnosed following the onset of symptoms. Symptom-based tools, including symptom checklists and risk prediction models, have been developed to aid detection. The aim of this systematic review was to identify and compare the diagnostic performance of these tools. We searched MEDLINE, EMBASE and Cochrane CENTRAL, without language restriction, for relevant studies published 1st January 2000 – 3rd March 2020. We identified 1625 unique records and included 16 studies, evaluating 21 distinct tools in a range of settings. 14 tools included only symptoms; 7 also included risk factors or blood tests. Four tools were externally validated - the Goff Symptom Index (sensitivity: 56.9-83.3%, specificity: 48.3-98.9%), a modified Goff Symptom Index (sensitivity: 71.6%, specificity: 88.5%), the Society of Gynaecologic Oncologists consensus criteria (sensitivity: 65.3-71.5%, specificity: 82.9-93.9%) and the QCancer Ovarian model (10% risk threshold - sensitivity: 64.1%, specificity: 90.1%). Study heterogeneity
precluded meta-analysis. Given the moderate accuracy of several tools on external validation, they could be of use in helping to select women for ovarian cancer investigations. However, further research is needed to assess the impact of these tools on the timely detection of ovarian cancer and on patient survival.
Original languageEnglish
JournalCancers
Publication statusAccepted/In press - 4 Dec 2020

Fingerprint

Dive into the research topics of 'Identifying ovarian cancer in symptomatic women: a systematic review of clinical tools'. Together they form a unique fingerprint.

Cite this