Shreyamsa M, Mishra Anand, Ramakant Pooja, Parihar Anit, Singh Kul R, Rana Chanchal, Mouli Sasi
Department of Endocrine Surgery, King George's Medical University, Lucknow, Uttar Pradesh, India.
Department of Radiodiagnosis, King George's Medical University, Lucknow, Uttar Pradesh, India.
Indian J Endocrinol Metab. 2020 Nov-Dec;24(6):537-542. doi: 10.4103/ijem.IJEM_675_20. Epub 2021 Jan 12.
Ultrasonography (US) is an indispensable tool in the management of thyroid nodules, not only for assessing tumor characteristics but also to assign risk of malignancy and guide in management. Various guidelines and US-based risk stratification systems have been proposed for this purpose. This study aims to compare the diagnostic performances of multimodal US-based risk scores (French TIRADS, TMC-RSS) with conventional US-based scoring systems (Korean TIRADS, ACR-TIRADS, ATA risk stratification).
A total of 168 nodules from 139 patients were studied and categorized in each of the risk stratification systems. Sensitivity, specificity, positive and negative predictive values, and accuracy of each system were computed. ROC curves were plotted and area under curve (AUC) for each scoring system noted.
Thirty five (21%) of the 168 nodules were malignant on final histopathological examination. TMC-RSS fared the best in predicting malignant nodules with a sensitivity of 96.2% and specificity of 88.6%, while the PPV and NPV were 97% and 86.1%, respectively. The AUC for TMC-RSS was 0.924 (95% CI, 0.860-0.988; < 0.001).
Multimodal US-based risk stratification incorporating non-grayscale characteristics in addition to conventional systems like the TMC-RSS improves the diagnostic performance of ultrasound imaging of thyroid nodules.
超声检查(US)是甲状腺结节管理中不可或缺的工具,不仅用于评估肿瘤特征,还用于评估恶性风险并指导管理。为此已提出了各种指南和基于超声的风险分层系统。本研究旨在比较基于多模态超声的风险评分(法国甲状腺影像报告和数据系统、TMC-RSS)与传统基于超声的评分系统(韩国甲状腺影像报告和数据系统、美国放射学会甲状腺影像报告和数据系统、美国甲状腺协会风险分层)的诊断性能。
对139例患者的168个结节进行研究,并在每个风险分层系统中进行分类。计算每个系统的敏感性、特异性、阳性和阴性预测值以及准确性。绘制ROC曲线并记录每个评分系统的曲线下面积(AUC)。
168个结节中有35个(21%)在最终组织病理学检查中为恶性。TMC-RSS在预测恶性结节方面表现最佳,敏感性为96.2%,特异性为88.6%,阳性预测值和阴性预测值分别为97%和86.1%。TMC-RSS的AUC为0.924(95%CI,0.860-0.988;P<0.001)。
除了像TMC-RSS这样的传统系统外,纳入非灰度特征的基于多模态超声的风险分层可提高甲状腺结节超声成像的诊断性能。