Diagnostic measures comparison for ovarian malignancy risk in Epithelial ovarian cancer patients: a meta-analysis.

来自 PUBMED

作者:

Suri APerumal VAmmalli PSuryan VBansal SK

展开

摘要:

Epithelial ovarian cancer has become the most frequent cause of deaths among gynecologic malignancies. Our study elucidates the diagnostic performance of Risk of Ovarian Malignancy Algorithm (ROMA), Human epididymis secretory protein 4 (HE4) and cancer antigen (CA125). To compare the diagnostic accuracy of ROMA, HE-4 and CA125 in the early diagnosis and screening of Epithelial Ovarian Cancer. Literature search in electronic databases such as Medicine: MEDLINE (through PUBMED interface), EMBASE, Google Scholar, Science Direct and Cochrane library from January 2011 to August 2020. Studies that evaluated the diagnostic measures of ROMA, HE4 and CA125 by using Chemilumincence immunoassay or electrochemiluminescence immunoassay (CLIA or ECLIA) as index tests. Using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). We included 32 studies in our meta-analysis. We calculated AUC by SROC, pooled estimated like sensitivity, specificity, likelihood ratio, diagnostic odds ratio (DOR), Tau square, Cochran Q through random effect analysis and meta-regression. Data was retrieved from 32 studies. The number of studies included for HE4, CA125 and ROMA tests was 25, 26 and 22 respectively. The patients with EOC were taken as cases, and women with benign ovarian mass were taken as control, which was 2233/5682, 2315/5875 and 2281/5068 respectively for the markers or algorithm. The pooled estimates of the markers or algorithm were sensitivity: ROMA (postmenopausal) (0.88, 95% CI 0.86-0.89) > ROMA (premenopausal) 0.80, 95% CI 0.78-0.83 > CA-125(0.84, 95% CI 0.82-0.85) > HE4 (0.73, 95% CI 0.71-0.75) specificity: HE4 (0.90, 95% CI 0.89-0.91) > ROMA (postmenopausal) (0.83, 95% CI 0.81-0.84) > ROMA (premenopausal) (0.80, 95% CI 0.79-0.82) > CA125 (0.73, 95%CI 0.72-0.74), Diagnostic odd's ratio ROMA (postmenopausal) 44.04, 95% CI 31.27-62.03, ROMA (premenopausal)-18.93, 95% CI 13.04-27.48, CA-125-13.44, 95% CI 9.97-18.13, HE4-41.03, 95% CI 27.96-60.21 AUC(SE): ROMA (postmenopausal) 0.94(0.01), ROMA (premenopausal)-0.88(0.01), HE4 0.91(0.01), CA125-0.86(0.02) through bivariate random effects model considering the heterogeneity. Our study found ROMA as the best marker to differentiate EOC from benign ovarian masses with greater diagnostic accuracy as compared to HE4 and CA125 in postmenopausal women. In premenopausal women, HE4 is a promising predictor of Epithelial ovarian cancer; however, its utilisation requires further exploration. Our study elucidates the diagnostic performance of ROMA, HE4 and CA125 in EOC. ROMA is a promising diagnostic marker of Epithelial ovarian cancers in postmenopausal women, while HE4 is the best diagnostic predictor of EOC in the premenopausal group. Our study had only EOC patients as cases and those with benign ovarian masses as controls. Further, we considered the studies estimated using the markers by the same index test: CLIA or ECLIA. The good number of studies with strict inclusion criteria reduced bias because of the pooling of studies with different analytical methods, especially for HE4. We did not consider the studies published in foreign languages. Since a few studies were available for HE4 and CA125 in the premenopausal and postmenopausal group separately, data were inadequate for sub-group analysis. Further, we did not assess these markers' diagnostic efficiency stratified by the stage and type of tumour due to insufficient studies.

收起

展开

DOI:

10.1038/s41598-021-96552-9

被引量:

15

年份:

1970

SCI-Hub (全网免费下载) 发表链接

通过 文献互助 平台发起求助,成功后即可免费获取论文全文。

查看求助

求助方法1:

知识发现用户

每天可免费求助50篇

求助

求助方法1:

关注微信公众号

每天可免费求助2篇

求助方法2:

求助需要支付5个财富值

您现在财富值不足

您可以通过 应助全文 获取财富值

求助方法2:

完成求助需要支付5财富值

您目前有 1000 财富值

求助

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证,认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献,1~5分钟即可下载全文,部分资源由于网络原因可能需要更长时间,请您耐心等待哦~

身份认证 全文购买

相似文献(546)

参考文献(52)

引证文献(15)

来源期刊

Scientific Reports

影响因子:4.991

JCR分区: 暂无

中科院分区:暂无

研究点推荐

关于我们

zlive学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们不忘初心,砥砺前行。

友情链接

联系我们

合作与服务

©2024 zlive学术声明使用前必读