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评估更新后的微小RNA分类器在不确定和RAS突变型甲状腺结节管理中的临床性能:一项多机构研究。

Evaluating the clinical performance of an updated microRNA classifier in indeterminate and RAS-mutated thyroid nodule management: A multi-institutional study.

作者信息

Tumati Abhinay, Marshall Teagan E, Greenspun Benjamin, Chen Zhengming, Azar Sara Abou, Keutgen Xavier M, Laird Amanda M, Beninato Toni, Zarnegar Rasa, Fahey Thomas J, Finnerty Brendan M

机构信息

Department of Surgery, Weill Cornell Medicine, New York, NY.

Department of Surgery, Weill Cornell Medicine, New York, NY. Electronic address: https://twitter.com/TeaganEMarshall.

出版信息

Surgery. 2025 Jan;177:108833. doi: 10.1016/j.surg.2024.07.076. Epub 2024 Oct 11.

Abstract

BACKGROUND

Integrating microRNA markers with next-generation sequencing panels may enhance risk assessment of cytologically indeterminate thyroid nodules. The ThyGeNEXT-ThyraMIRv1 multiplatform test version 1 demonstrated limited utility in risk-stratifying RAS-mutated indeterminate thyroid nodules. We sought to validate the updated ThyraMIRv2 platform in clinical practice.

METHODS

ThyGeNEXT/ThyraMIRv2, a 3-tiered microRNA classifier, were evaluated using a previously studied multi-institutional cohort of Bethesda III/IV nodules, with positive results having risk of malignancy ≥10%. In addition, ThyraMIRv2's clinical utility in RAS-mutated indeterminate thyroid nodules was assessed.

RESULTS

In 366 indeterminate thyroid nodules, ThyraMIRv2 platform yielded a 30.3% positive-call rate. ThyraMIRv2 platform + nodules had greater operative rates (63.9% vs 36.1%, P < .0001) and cancer/noninvasive follicular thyroid neoplasm with papillary-like nuclear features diagnosis (65.9% vs 25.0%, P < .0001) than ThyraMIRv2 platform nodules. Compared with multiplatform test version 1, ThyraMIRv2 platform's diagnostic testing parameters did not improve significantly. Among 68 RAS-mutated nodules, ThyraMIRv2 classified 36.8%, 55.9%, and 7.4% as positive, moderate, and negative, respectively. All moderate nodules had risk of malignancy ≥10% and were combined with the positive cohort. No significant differences existed in operative rate (81.0% vs 60.0%, P = .272) or cancer/noninvasive follicular thyroid neoplasm with papillary-like nuclear features diagnosis (47.6% vs 40.0%, P > .999) between RAS-mutated positive/moderate and negative groups. For RAS-mutated nodules, ThyraMIRv2 demonstrated improved sensitivity (93.8% vs 64.7, P = .003) and decreased specificity (4.5% vs 34.8%, P = .008) compared with ThyGeNEXT-ThyraMIRv1 multiplatform test version 1, with comparable negative predictive value (33.3% vs 40.0%, P = .731) and positive predictive value (58.8% vs 59.5%, P = .864).

CONCLUSION

ThyraMIRv2 platform does not improve indeterminate thyroid nodule malignancy stratification compared to ThyGeNEXT-ThyraMIRv1 multiplatform test version 1. ThyraMIRv2 improves malignant RAS-mutated nodule detection but increases false positives. Future studies encompassing a larger cohort of RAS-mutated with surgical pathology results are warranted to better characterize the performance parameters of this classifier.

摘要

背景

将微小RNA标志物与下一代测序面板相结合,可能会提高甲状腺细胞学检查结果不确定的结节的风险评估。ThyGeNEXT-ThyraMIRv1多平台测试版本1在对RAS突变的甲状腺细胞学检查结果不确定的结节进行风险分层时效用有限。我们试图在临床实践中验证更新后的ThyraMIRv2平台。

方法

使用先前研究的多机构队列中的贝塞斯达III/IV级结节对ThyGeNEXT/ThyraMIRv2(一种三层微小RNA分类器)进行评估,阳性结果的恶性风险≥10%。此外,评估了ThyraMIRv2在RAS突变的甲状腺细胞学检查结果不确定的结节中的临床效用。

结果

在366个甲状腺细胞学检查结果不确定的结节中,ThyraMIRv2平台的阳性检出率为30.3%。与ThyraMIRv2平台结节相比,ThyraMIRv2平台+结节的手术率更高(63.9%对36.1%,P <.0001),癌症/具有乳头状核特征的非侵袭性滤泡性甲状腺肿瘤诊断率更高(65.9%对25.0%,P <.0001)。与多平台测试版本1相比,ThyraMIRv2平台的诊断测试参数没有显著改善。在68个RAS突变的结节中,ThyraMIRv2分别将36.8%、55.9%和7.4%分类为阳性、中度和阴性。所有中度结节的恶性风险≥10%,并与阳性队列合并。RAS突变的阳性/中度和阴性组之间的手术率(81.0%对60.0%,P =.272)或癌症/具有乳头状核特征的非侵袭性滤泡性甲状腺肿瘤诊断率(47.6%对40.0%,P >.999)没有显著差异。对于RAS突变的结节,与ThyGeNEXT-ThyraMIRv1多平台测试版本1相比,ThyraMIRv2显示出更高的敏感性(93.8%对64.7,P =.003)和更低的特异性(4.5%对34.8%,P =.008),阴性预测值(33.3%对40.0%,P =.731)和阳性预测值(58.8%对59.5%,P =.864)相当。

结论

与ThyGeNEXT-ThyraMIRv1多平台测试版本1相比,ThyraMIRv2平台并没有改善甲状腺细胞学检查结果不确定的结节的恶性分层。ThyraMIRv2提高了RAS突变的恶性结节的检测率,但增加了假阳性。有必要进行未来的研究,纳入更多有手术病理结果的RAS突变队列,以更好地描述该分类器的性能参数。

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