《用于甲状腺结节的不确定病例的 Afirma 基因表达分类器的诊断性能：一项荟萃分析》。

The Diagnostic Performance of Afirma Gene Expression Classifier for the Indeterminate Thyroid Nodules: A Meta-Analysis.

机构信息

Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.

Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.

出版信息

Biomed Res Int. 2019 Aug 20;2019:7150527. doi: 10.1155/2019/7150527. eCollection 2019.

BACKGROUND

Approximately 15 to 30% of thyroid nodules evaluated by fine-needle aspiration (FNA) were classified as indeterminate; the accurate diagnostic molecular tests of these nodules remain a challenge. We aimed to evaluate the diagnostic performance of Afirma gene expression classifier (GEC) for the indeterminate thyroid nodules (ITNs).

METHODS

Studies published from January 2005 to December 2018 were systematically reviewed. The gold reference standard relied on the histopathologic results diagnosis from thyroidectomy surgical specimens. MetaDisc software was used to investigate the pooled sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves.

RESULTS

A total of 18 studies involving 5290 patients with 3290 cases of ITNs were included. Collected data revealed that the pooled sensitivity of GEC was 95.5% (95% CI 93.3%-97.0%, p < 0.001), the specificity was 22.1% (95% CI 19.4%-24.9%, p < 0.001), the NPV was 88.2% (95% CI 0.833-0.921, p < 0.001), the PPV was 44.3% (95% CI 0.416-0.471, p < 0.001), and the DOR was 5.25 (95% CI 3.42-8.04, p= 0.855).

CONCLUSION

The GEC has quite high sensitivity of 95.5% but low specificity of 22.1%. The high sensitivity makes it probable to rule out malignant nodules. Thus, over half of nodules with GEC-suspicious results still require further validation like molecular markers, diagnostic surgery, or long follow-up, which limits its use in future clinical practice.

背景

通过细针抽吸（FNA）评估的甲状腺结节中，约有 15%至 30%被归类为不确定；这些结节的准确诊断分子检测仍然是一个挑战。我们旨在评估 Afirma 基因表达分类器（GEC）对不确定甲状腺结节（ITN）的诊断性能。

方法

系统回顾了 2005 年 1 月至 2018 年 12 月发表的研究。金参考标准依赖于甲状腺切除术手术标本的组织病理学结果诊断。MetaDisc 软件用于研究汇总敏感性、特异性、阴性预测值（NPV）、阳性预测值（PPV）、诊断比值比（DOR）和汇总受试者工作特征（SROC）曲线。

结果

共纳入 18 项涉及 5290 例患者和 3290 例 ITN 的研究。收集的数据显示，GEC 的汇总敏感性为 95.5%（95%置信区间 93.3%-97.0%，p<0.001），特异性为 22.1%（95%置信区间 19.4%-24.9%，p<0.001），NPV 为 88.2%（95%置信区间 0.833-0.921，p<0.001），PPV 为 44.3%（95%置信区间 0.416-0.471，p<0.001），DOR 为 5.25（95%置信区间 3.42-8.04，p=0.855）。