Identifying Ovarian Cancer in Symptomatic Women: A Systematic Review of Clinical Tools

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 between 1 January 2000 and 3 March 2020. We identified 1625 unique records and included 16 studies, evaluating 21 distinct tools in a range of settings. Fourteen tools included only symptoms; seven 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.

Keywords: diagnostic prediction model; early detection; ovarian cancer; ovarian cancer symptoms; risk assessment; symptoms; triage tool.

Figure 1

Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram illustrating the study selection process.

Figure 2

QUADAS-2 Risk of Bias Assessment. Green = “low”, orange = “high”, blue = “unclear” risk of bias.