Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China.
Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, China.
Front Endocrinol (Lausanne). 2023 Aug 31;14:1227339. doi: 10.3389/fendo.2023.1227339. eCollection 2023.
The performance in evaluating thyroid nodules on ultrasound varies across different risk stratification systems, leading to inconsistency and uncertainty regarding diagnostic sensitivity, specificity, and accuracy.
Comparing diagnostic performance of detecting thyroid cancer among distinct ultrasound risk stratification systems proposed in the last five years.
Systematic search was conducted on PubMed, EMBASE, and Web of Science databases to find relevant research up to December 8, 2022, whose study contents contained elucidation of diagnostic performance of any one of the above ultrasound risk stratification systems (European Thyroid Imaging Reporting and Data System[Eu-TIRADS]; American College of Radiology TIRADS [ACR TIRADS]; Chinese version of TIRADS [C-TIRADS]; Computer-aided diagnosis system based on deep learning [S-Detect]). Based on golden diagnostic standard in histopathology and cytology, single meta-analysis was performed to obtain the optimal cut-off value for each system, and then network meta-analysis was conducted on the best risk stratification category in each system.
This network meta-analysis included 88 studies with a total of 59,304 nodules. The most accurate risk category thresholds were TR5 for Eu-TIRADS, TR5 for ACR TIRADS, TR4b and above for C-TIRADS, and possible malignancy for S-Detect. At the best thresholds, sensitivity of these systems ranged from 68% to 82% and specificity ranged from 71% to 81%. It identified the highest sensitivity for C-TIRADS TR4b and the highest specificity for ACR TIRADS TR5. However, sensitivity for ACR TIRADS TR5 was the lowest. The diagnostic odds ratio (DOR) and area under curve (AUC) were ranked first in C-TIRADS.
Among four ultrasound risk stratification options, this systemic review preliminarily proved that C-TIRADS possessed favorable diagnostic performance for thyroid nodules.
https://www.crd.york.ac.uk/prospero, CRD42022382818.
不同的风险分层系统在评估甲状腺结节的超声表现方面存在差异，导致诊断敏感性、特异性和准确性存在不一致和不确定性。
比较过去五年提出的不同超声风险分层系统在检测甲状腺癌方面的诊断性能。
系统检索了 PubMed、EMBASE 和 Web of Science 数据库，以查找截至 2022 年 12 月 8 日的相关研究，其研究内容包括阐述上述任何一种超声风险分层系统（欧洲甲状腺影像报告和数据系统[Eu-TIRADS]；美国放射学院 TIRADS[ACR TIRADS]；中文 TIRADS [C-TIRADS]；基于深度学习的计算机辅助诊断系统[S-Detect]）的诊断性能。基于组织病理学和细胞学的金标准诊断，对每个系统进行了单因素荟萃分析，以获得每个系统的最佳截止值，然后对每个系统中最佳风险分层类别进行了网络荟萃分析。
本网络荟萃分析共纳入 88 项研究，共计 59304 个结节。最准确的风险类别阈值为 Eu-TIRADS 的 TR5、ACR TIRADS 的 TR5、C-TIRADS 的 TR4b 及以上和 S-Detect 的可能恶性肿瘤。在最佳阈值下，这些系统的敏感性范围为 68%至 82%，特异性范围为 71%至 81%。结果发现 C-TIRADS TR4b 的敏感性最高，ACR TIRADS TR5 的特异性最高。然而，ACR TIRADS TR5 的敏感性最低。C-TIRADS 的诊断比值比（DOR）和曲线下面积（AUC）排名第一。
在四种超声风险分层选择中，本系统评价初步证明 C-TIRADS 对甲状腺结节具有良好的诊断性能。
