Wang Mingyan, Yang Siyuan, Yang Linxin, Lin Ning
Ultrasound Department of Shengli Clinical Medical College of Fujian Medical University, Fuzhou City, Fujian Province, 350001, People's Republic of China.
Ultrasound Department of Fujian Provincial Hospital, Fuzhou City, Fujian Province, 350001, People's Republic of China.
Ther Clin Risk Manag. 2024 Aug 23;20:515-528. doi: 10.2147/TCRM.S458576. eCollection 2024.
The study aimed to compare the diagnostic performance of AI-SONICTM Thyroid System (AI-SONICTM) with six thyroid nodule ultrasound risk stratification systems, as well as the interobserver agreement among different-year ultrasound examiners using the same diagnostic approach.
This retrospective study included patients who underwent thyroid ultrasound examination and surgery between 2010 and 2022. Three ultrasound examiners with 2, 5, and 10 years of experience, respectively, used AI-SONICTM and six guidelines to risk-stratify the nodules. The diagnostic performance and interobserver agreement were assessed.
A total of 370 thyroid nodules were included, including 195 papillary thyroid carcinomas (PTC) and 175 benign nodules. For physicians of varying seniority from low to high, AI-SONICTM had a moderate sensitivities of 82.56%, 83.08%, 84.62%, respectively, while AACE/ACE/AME had the highest diagnostic sensitivities (96.41%, 95.38%, 96.41%, respectively); And relatively higher specificities were 85.14%, 85.71%, 85.71% for KSThR, while moderate specificities with values of 84.0%, 85.14%, and 85.71%, respectively were found for AI-SONICTM; The accuracy was highest for ATA (excluding non-classifiable nodules), with values of 87.26%, 87.93%, and 88.82%, respectively, while the accuracy for AI-SONICTM were 83.24%, 84.05%, and 85.14%, respectively. The Kendall's tau coefficient indicated strong or moderate interobserver agreement among all examiners using different diagnostic methods (Kendall's tau coefficient >0.6, P<0.001). AI-SONICTM showed the highest interobserver agreement (Kendall's tau coefficient=0.995, P<0.001). A binary probit regression analysis showed that nodules with cystic components had a significantly higher regression coefficient value of 0.983 (P=0.002), indicating that AI-SONICTM may have higher accuracy for nodules with cystic components.
AI-SONICTM and the six thyroid nodule ultrasound risk stratification systems showed high diagnostic performance for papillary thyroid carcinoma. All examiners showed strong or moderate interobserver agreement when using different diagnostic methods. AI-SONICTM may have higher accuracy for nodules with cystic components.
本研究旨在比较AI-SONIC™甲状腺系统（AI-SONIC™）与六种甲状腺结节超声风险分层系统的诊断性能，以及不同年份的超声检查人员使用相同诊断方法时的观察者间一致性。
这项回顾性研究纳入了2010年至2022年间接受甲状腺超声检查和手术的患者。三位分别具有2年、5年和10年经验的超声检查人员使用AI-SONIC™和六种指南对结节进行风险分层。评估了诊断性能和观察者间一致性。
共纳入370个甲状腺结节，其中包括195个甲状腺乳头状癌（PTC）和175个良性结节。对于资历从低到高的医生，AI-SONIC™的敏感性分别为中等的82.56%、83.08%、84.62%，而美国临床内分泌医师协会/美国内分泌学会/美国医学内分泌学会（AACE/ACE/AME）具有最高的诊断敏感性（分别为96.41%、95.38%、96.41%）；KSThR的特异性相对较高，分别为85.14%、85.71%、85.71%，而AI-SONIC™的特异性为中等，分别为84.0%、85.14%和85.71%；美国甲状腺协会（ATA）（不包括不可分类的结节）的准确性最高，分别为87.26%、87.93%和88.82%，而AI-SONIC™的准确性分别为83.24%、84.05%和85.14%。肯德尔tau系数表明，使用不同诊断方法的所有检查人员之间存在强或中等的观察者间一致性（肯德尔tau系数>0.6，P<0.001）。AI-SONIC™表现出最高的观察者间一致性（肯德尔tau系数=0.995，P<0.001）。二元概率回归分析表明，具有囊性成分的结节的回归系数值显著更高，为0.983（P=0.002），表明AI-SONIC™对于具有囊性成分的结节可能具有更高的准确性。
AI-SONIC™和六种甲状腺结节超声风险分层系统对甲状腺乳头状癌显示出较高的诊断性能。所有检查人员在使用不同诊断方法时均表现出强或中等的观察者间一致性。AI-SONIC™对于具有囊性成分的结节可能具有更高的准确性。
