Afirma 基因表达分类器、Afirma 基因测序分类器、ThyroSeq v2 和 ThyroSeq v3 对不确定甲状腺结节(Bethesda III 和 IV)的诊断准确性:一项荟萃分析。

IF 2.6 3区 医学 Q3 ENDOCRINOLOGY & METABOLISM
Endocrine Connections Pub Date : 2024-06-13 Print Date: 2024-07-01 DOI:10.1530/EC-24-0170
Irfan Vardarli, Susanne Tan, Rainer Görges, Bernhard K Krämer, Ken Herrmann, Christoph Brochhausen
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引用次数: 0

摘要

目的:对细胞学(ITN)不确定的甲状腺结节的处理仍是一项挑战。为了评估商用分子检验在 ITN 方面的性能,我们进行了这项综合荟萃分析:我们使用 PubMed/Medline、Embase 和 Cochrane 图书馆进行了电子检索。筛选出评估 Afirma 基因表达分类器 (GEC)、Afirma 基因测序分类器 (GSC)、ThyroSeq v2 (TSv2) 或 ThyroSeq v3 (TSv3) 对 ITN 患者(仅 Bethesda III 或 IV 类)诊断准确性的研究;使用 Stata 进行统计分析:53项研究中的71个样本(GEC,n=38;GSC,n=16;TSv2,n=9;TSv3,n=8)被纳入研究,涉及6490例ITN细胞学分子诊断(GEC、GSC、TSv2或TSv3)的细针穿刺(FNA)。Meta 分析显示了以下汇总估计值:灵敏度 0.95(95% CI,0.94-0.97),特异性 0.35(0.28-0.43),阳性似然比 (LR+) 1.5(1.3-1.6),阴性似然比 (LR-) 0.13(0.09-0.19),其中 TSv3 的表现最佳(ROC 曲线下面积 0.95(0.93-0.96),其次是 TSv2。96),其次是 TSv2(0.90(0.87-0.92))、GSC(0.86(0.82-0.88))和 GEC(0.82(0.78-0.85));排除性能最好的是 GSC(LR-,0.07(0.02-0.19)),其次是 TSv3(0.11(0.05-0.24))和 GEC(0.16(0.10-0.28));TSv2 的规则入属性最好(LR+,2.9(1.4-4.6)),其次是 GSC(1.9(1.6-2.4))。元回归分析显示,研究设计、贝塞斯达类别和分子检测类型是独立因素:我们发现,在 ITN 患者中,TSv3 的分子诊断效果最好,其次是 TSv2、GSC 和 GEC。在排除恶性肿瘤方面,GSC和TSV2优于其他检验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnostic accuracy of Afirma gene expression classifier, Afirma gene sequencing classifier, ThyroSeq v2 and ThyroSeq v3 for indeterminate (Bethesda III and IV) thyroid nodules: a meta-analysis.

Objective: The management of thyroid nodules with indeterminate cytology (ITN) is still a challenge. To evaluate the performance of commercial molecular tests for ITN, we performed this comprehensive meta-analysis.

Methods: We performed an electronic search using PubMed/Medline, Embase, and the Cochrane Library. Studies assessing the diagnostic accuracy of Afirma gene expression classifier (GEC), Afirma gene sequencing classifier (GSC), ThyroSeq v2 (TSv2), or ThyroSeq v3 (TSv3) in patients with ITN (only Bethesda category III or IV) were selected; Statistical analyses were performed by using Stata.

Results: Seventy-one samples (GEC, n = 38; GSC, n = 16; TSv2, n = 9; TSv3, n = 8) in 53 studies, involving 6490 fine needle aspirations (FNAs) with ITN cytology with molecular diagnostics (GEC, GSC, TSv2, or TSv3), were included in the study. The meta-analysis showed the following pooled estimates: sensitivity 0.95 (95% CI: 0.94-0.97), specificity 0.35 (0.28-0.43), positive likelihood ratio (LR+) 1.5 (1.3-1.6), and negative likelihood ratio (LR-) 0.13 (0.09-0.19), with the best performance for TSv3 (area under the ROC curve 0.95 (0.93-0.96), followed by TSv2 (0.90 (0.87-0.92)), GSC (0.86 (0.82-0.88)), and GEC (0.82 (0.78-0.85)); the best rule-out property was observed for GSC (LR-, 0.07 (0.02-0.19)), followed by TSv3 (0.11 (0.05-0.24)) and GEC (0.16 (0.10-0.28), and the best rule-in was observed for TSv2 (LR+, 2,9 (1.4-4.6)), followed by GSC (1.9 (1.6-2.4)). A meta-regression analysis revealed that study design, Bethesda category, and type of molecular test were independent factors.

Conclusion: We showed that in patients with ITN, TSv3 has the best molecular diagnostic performance, followed by TSv2, GSC, and GEC. As regards rule-out malignancy, GSC, and rule-in, TSV2 is superior to other tests.

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来源期刊
Endocrine Connections
Endocrine Connections Medicine-Internal Medicine
CiteScore
5.00
自引率
3.40%
发文量
361
审稿时长
6 weeks
期刊介绍: Endocrine Connections publishes original quality research and reviews in all areas of endocrinology, including papers that deal with non-classical tissues as source or targets of hormones and endocrine papers that have relevance to endocrine-related and intersecting disciplines and the wider biomedical community.
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