Radiology Department, Taiping Hospital, Jalan Taming Sari, 34000 Taiping, Perak, Malaysia.
Radiology Department, Taiping Hospital, Jalan Taming Sari, 34000 Taiping, Perak, Malaysia.
Clin Imaging. 2020 Sep;65:133-137. doi: 10.1016/j.clinimag.2020.04.029. Epub 2020 Apr 25.
Thyroid Imaging Reporting Data System (TI-RADS) is used to characterize thyroid nodules while reducing unnecessary FNAC. Over the years, several versions of TI-RADS have been developed but there is no consensus on which TI-RADS is the best system. This study aimed to compare the diagnostic accuracy and ability of ACR TI-RADS, EU TI-RADS, K TI-RADS, AI TI-RADS to eliminate unnecessary FNAC.
In this prospective study, thyroid nodules were characterized by using the four TI-RADS systems and US-guided FNAC was done for nodule with the highest ACR TI-RADS score. Correlation between TI-RADS and FNAC results were analyzed.
Out of 244 thyroid nodules, 100 nodules with either size <1 cm (43 nodules) non-diagnostic or inconclusive FNAC results (57 nodules) were excluded. Seven nodules (4.9%) were confirmed to be malignant on FNAC. K TI-RADS showed 100% sensitivity and NPV but the lowest specificity (40.2%). EU TI-RADS had the highest specificity (83.2%) but the lowest sensitivity (57.1%) and NPV (97.4%). ACR TI-RADS had an average sensitivity (85.7%) and NPV (98.6%). The specificity of ACR TI-RADS (51.1%) was lower than EU TI-RADS but higher than K TI-RADS. AI TI-RADS showed higher specificity (61.8% vs 51.1%, p < 0.05) but comparable NPV and sensitivity to ACR TI-RADS. AI TI-RADS was able to avoid the highest number of unnecessary FNAC (62.5%) followed by ACR TI-RADS(54.2%), EU TI-RADS(37.5%) and K TI-RADS(11.8%).
AI TI-RADS is a more simple scoring system with better overall diagnostic performance and ability to exclude unnecessary FNAC with high NPV.
Highest number of unnecessary FNAC thyroid could be prevented by applying AI TI-RADS.
甲状腺影像报告数据系统（TI-RADS）用于对甲状腺结节进行分类，以减少不必要的细针穿刺抽吸活检（FNAC）。多年来，已经开发出了多个版本的 TI-RADS，但哪种 TI-RADS 系统是最好的尚未达成共识。本研究旨在比较 ACR TI-RADS、EU TI-RADS、K TI-RADS、AI TI-RADS 消除不必要 FNAC 的诊断准确性和能力。
在这项前瞻性研究中，使用四个 TI-RADS 系统对甲状腺结节进行分类，对 ACR TI-RADS 评分最高的结节进行 US 引导下 FNAC。分析 TI-RADS 与 FNAC 结果之间的相关性。
在 244 个甲状腺结节中，排除了 100 个直径<1cm（43 个）的结节或非诊断性或不确定的 FNAC 结果（57 个）。FNAC 证实 7 个结节（4.9%）为恶性。K TI-RADS 的敏感性和阴性预测值（NPV）均为 100%，但特异性最低（40.2%）。EU TI-RADS 的特异性最高（83.2%），但敏感性（57.1%）和 NPV（97.4%）最低。ACR TI-RADS 的平均敏感性（85.7%）和 NPV（98.6%）。ACR TI-RADS 的特异性（51.1%）低于 EU TI-RADS，但高于 K TI-RADS。AI TI-RADS 的特异性（61.8%比 51.1%，p<0.05）更高，NPV 和敏感性与 ACR TI-RADS 相当。AI TI-RADS 能够避免最高数量的不必要 FNAC（62.5%），其次是 ACR TI-RADS（54.2%）、EU TI-RADS（37.5%）和 K TI-RADS（11.8%）。
AI TI-RADS 是一种更简单的评分系统，具有更好的整体诊断性能和排除 FNAC 的能力，NPV 较高。
应用 AI TI-RADS 可防止最多数量的不必要 FNAC 甲状腺。
