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SCC-Ag在宫颈鳞癌病例中诊断淋巴结转移效能的Meta分析

Diagnostic Value of Serum Squamous Cell Carcinoma Antigen on Lymphatic Metastasis in Cervical Cancer:a Meta analysis

  • 摘要: 目的 系统评价鳞状细胞癌相关抗原(Squamous Cell Carcinoma antigen,SCC-Ag)在宫颈鳞癌患者中诊断淋巴结转移的诊断效能。方法 研究者检索PubMed、Embase、Cochrane Library及中国期刊网、万方数据库、中国生物医学网,收集当前有关宫颈癌SCC-Ag的所有中英文文献,并根据QUADAS(Quality Assessment of Diagnostic Accuracy Studies)质量评价标准评价纳入文献的质量。用Meta-Disc1.4、Stata11.0软件对纳入研究进行异质性检验,合并敏感度、特异性等指标,绘制SROC曲线、漏斗图。结果共纳入10篇符合纳入标准的文献。经随机效应模型分析,SCC-Ag对宫颈癌淋巴结转移的诊断指标合并值分别为:合并敏感度60%[95%CI(0.54~0.65)],合并特异性76%[95%CI(0.73~0.78)],合并诊断比值比5.38[95%CI(3.27~8.87)],SROC曲线下面积(AUC)0.7126(欠满意)。诊断界值的选择可能是纳入文献异质性的主要原因。结论纳入文献质量较好,SCC-Ag对宫颈鳞癌淋巴结转移虽然有一定的诊断价值,但仍不是一个理想的诊断指标。

     

    Abstract: Objective To establish the overall diagnostic accuracy of the measurements of serum squamous cell carcinoma antigen (SCC-Ag) for lymph node metastasis(LNM) in squamous cell cervical cancer. Methods After a systematic review of current studies,we pooled the sensitivity,specificity of serum SCC-Ag in diagnosis LNM cases from cervical cancer patient using random effects models.Qualified studies were retrieved from The Cochrane Library,Cochrane Central Register of Controlled Trials,Pubmed,Embase,CNKI,WanFang,and CBM database.Two reviewers independently assessed the methodological quality of each study with the tool of QUADAS.We drew the Summary Receiver Operating Characteristic (SROC) curve、funnel figure of the included studies and made Meta-analyses using Meta-Disc 1.4 and Stata 11.0 software. Results Ten studies met the inclusion criteria for the analysis.After testing the heterogeneity of the included studies,a random effect model was selected to calculate the pool weighted sensitivity and specificity with 95% confidence interval:the sensitivity was 60%[95%CI(0.54~0.65)],the specificity was76%[95%CI(0.73~0.78)],the DOR was 5.38[95%CI(3.27~8.87)],and the AUC of SROC was 0.7126(unsatisfied).The sources of heterogeneity maybe according to the cutoff. Conclusion SCC-Ag has certain diagnosis value in LNM cases,but it is not a perfect index to identify LNM cases from cervical cancer patients.

     

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