Diagnostic Performance of Biomarkers for Ovarian Cancer: Protocol for an Overview, Evidence Mapping, and Adjusted Indirect Comparisons

Overview

Journal Medicine (Baltimore)

Specialty General Medicine

Date 2019 May 3

PMID 31045839

Citations 2

Authors

Jinyong Hua

Jing Liu

Mengge Hua

Runjin Cai

Muyang Li

Jing Wang

Jiancheng Wang

Affiliations

Soon will be listed here.

Abstract

Background: Ovarian cancer is one of the deadliest gynecological diseases and the annual mortality of ovarian cancer continues to rise. The prognosis of ovarian cancer is poor because it is prone to early metastasis during progression. Therefore, early diagnosis of ovarian cancer is very important. Some systematic reviews have evaluated the diagnostic value of different biomarkers for ovarian cancer. However, there is no consensus in the conclusions, and some are even contradictory. This study aims to assess the methodological and reporting quality of available systematic reviews and to find an optimal biomarker for diagnosing ovarian cancer.

Methods: The PubMed, Embase.com, the Cochrane Library of Systematic Reviews, and Web of Science were searched to identify relevant systematic reviews from inception to February 2019. We included systematic reviews that include randomized controlled trials, cross-sectional studies, case-control studies, or cohort studies as long as the systematic reviews evaluated the diagnostic performance of biomarkers for ovarian cancer. The methodological quality will be assessed using assessment of multiple systematic reviews-2 checklist, and the reporting quality will be assessed using preferred reporting items for systematic reviews and meta-analysis diagnostic test accuracy (PRISMA-DTA) checklist. The pairwise meta-analysis and indirect comparisons will be performed using STATA (13.0; Stata Corporation, College Station, TX).

Results: The results of this overview will be submitted to a peer-reviewed journal for publication.

Conclusion: This overview will provide comprehensive evidence of different biomarkers for diagnosing ovarian cancer.

Prospero Registration Number: CRD42019125880.

Citing Articles

Construction and Validation of a Novel Glycometabolism-Related Gene Signature Predicting Survival in Patients With Ovarian Cancer.

Liu L, Cai L, Liu C, Yu S, Li B, Pan L Front Genet. 2020; 11:585259.

PMID: 33281878 PMC: 7689371. DOI: 10.3389/fgene.2020.585259.

Combined Preoperative LMR and CA125 for Prognostic Assessment of Ovarian Cancer.

Tang Y, Hu H, Tang F, Lin D, Shen R, Deng L J Cancer. 2020; 11(11):3165-3171.

PMID: 32231720 PMC: 7097954. DOI: 10.7150/jca.42477.

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