Diagnostic Accuracy of the ADNEX Model for Ovarian Cancer at the 15% Cut-Off Value: A Systematic Review and Meta-Analysis

ObjectivesTo evaluate the diagnostic accuracy of the ADNEX model for ovarian cancer at the 15% cut-off value.MethodsStudies on the identified diagnosis of the ADNEX model for ovarian cancer published in PubMed, Embase, the Cochrane Library and Web of Science databases from January 1st, 2014 to February 20th, 2021 were searched. Two researchers independently screened the retrieved studies and extracted the basic features and parameter data. The quality of the eligible studies was evaluated by Quality Assessment of Diagnostic Accuracy Studies-2, and the result was summarized by Review Manager 5.3. Meta-Disc 1.4 and STATA 16.0 were used in statistical analysis. Heterogeneity of this meta-analysis was calculated. Meta-regression was performed to investigate the potential sources of heterogeneity. Sensitivity analysis and Deek’s funnel plot analysis were conducted to evaluate the stability and publication bias, respectively.Results280 studies were initially retrieved through the search strategy, and 10 eligible studies were ultimately included. The random-effects model was selected for data synthesis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the summary receiver operating characteristic curve were 0.92 (95% CI: 0.89–0.94), 0.82 (95% CI: 0.78–0.86), 5.2 (95% CI: 4.1–6.4), 0.10 (95% CI: 0.07–0.13), 54.0 (95% CI: 37.0–77.0) and 0.95 (95% CI: 0.91–0.95). Meta-regression based on study design, country, enrollment and blind method was not statistically significant. This meta-analysis was stable with no obvious publication bias.ConclusionsThe ADNEX model at the 15% cut-off had high diagnostic accuracy in identifying ovarian cancer.

Abstract licence: CC BY

1. 18.

   Early Detection of Ovarian Cancer · 2007

2. 22.

   Meta-DiSc: a software for meta-analysis of test accuracy data · 2006

38 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.