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用于诊断甲状腺结节性质不明的基因表达分类器:一项荟萃分析

Gene expression classifier for the diagnosis of indeterminate thyroid nodules: a meta-analysis.

作者信息

Santhanam Prasanna, Khthir Rodhan, Gress Todd, Elkadry Ayman, Olajide Omolola, Yaqub Abid, Driscoll Henry

机构信息

Section of Endocrinology, Department of Internal Medicine, Byrd Clinical Center, Joan C Edwards School of Medicine, Marshall University, 1249 15th Street, Suite 3046, Huntington, WV, 25701, USA.

Department of Clinical and Translational Sciences and Department of Internal Medicine, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, 25701, USA.

出版信息

Med Oncol. 2016 Feb;33(2):14. doi: 10.1007/s12032-015-0727-3. Epub 2016 Jan 9.

Abstract

Prior studies demonstrate that a novel genomic test, the gene expression classifier (GEC), could identify a benign gene expression signature in those nodules with indeterminate cytology with a negative predictive value of greater than 95 %. Examine the performance of the AFIRMA gene expression classifier in predicting benign and malignant nodules in patients with cytologically indeterminate nodules. MEDLINE and EMBASE search for studies meeting eligibility criteria between January 1, 2005, and August 30, 2015. A total of 58 studies identified. After excluding duplicates, case reports, reviews, commentary, insufficient data, a total of seven studies selected for analysis. We combined individual patient data from seven studies that examined the GEC test for indeterminate thyroid nodules. The reference standard for determination of benign or malignant nodules was the histopathology of the thyroidectomy specimen. A QUADAS-2 report for all studies included in the final analysis was tabulated for risk of bias and applicability. The pooled sensitivity of the GEC was 95.7 % (95 % CI 92.2-97.9, I (2) value 45.4 %, p = 0.09), and the pooled specificity was 30.5 % (95 % CI 26.0-35.3, I (2) value 92.1 %, p < 0.01). Overall, the diagnostic odds ratio was 7.9 (95 % CI 4.1-15.1). Patients with benign GEC were not followed long enough to ascertain the actual false-negative rates of the index test. Our meta-analysis revealed a high pooled sensitivity and a low specificity for the AFIRMA-GEC test for indeterminate thyroid nodules. This makes it an excellent tool to rule out malignancy.

摘要

先前的研究表明,一种新型基因组检测方法——基因表达分类器(GEC),能够在细胞学检查结果不确定的结节中识别出良性基因表达特征,其阴性预测值大于95%。本研究旨在检验AFIRMA基因表达分类器在预测细胞学检查结果不确定的患者中良性和恶性结节方面的性能。检索MEDLINE和EMBASE数据库,查找2005年1月1日至2015年8月30日期间符合纳入标准的研究。共识别出58项研究。在排除重复研究、病例报告、综述、评论以及数据不足的研究后,最终选取7项研究进行分析。我们合并了7项研究中对甲状腺结节进行GEC检测的个体患者数据。确定良性或恶性结节的参考标准是甲状腺切除标本的组织病理学检查结果。对最终分析中纳入的所有研究编制QUADAS - 2报告,以评估偏倚风险和适用性。GEC的合并敏感度为95.7%(95%置信区间92.2 - 97.9,I²值45.4%,p = 0.09),合并特异度为30.5%(95%置信区间26.0 - 35.3,I²值92.1%,p < 0.01)。总体而言,诊断比值比为7.9(95%置信区间4.1 - 15.1)。GEC结果为良性的患者随访时间不足,无法确定该指标检测的实际假阴性率。我们的荟萃分析显示,对于细胞学检查结果不确定的甲状腺结节,AFIRMA - GEC检测具有较高的合并敏感度和较低的特异度。这使其成为排除恶性肿瘤的优秀工具。

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