摘要
目的探讨中国甲状腺影像报告和数据系统(C-TIRADS)对甲状腺结节良恶性的诊断价值。方法回顾性分析320例甲状腺结节患者共522个结节的超声图像和报告,分别根据美国放射学会甲状腺影像报告和数据系统(ACR-TIRADS)及2020年中国甲状腺影像报告和数据系统(C-TIRADS)对522个甲状腺结节进行风险分层,与病理结果对照,探讨这两种分类标准对甲状腺结节良恶性的诊断效能。结果ACR-TIRADS与C-TIRADS诊断甲状腺良恶性结节的准确率分别为74.5%、80.1%;灵敏度为90.6%、84.9%;特异度为65.2%、77.3%;阳性预测值60.2%、68.5%;阴性预测值92.3%、89.8%,总误诊率25.5%、19.9%。ACR-TIRADS与C-TIRADS诊断甲状腺良恶性结节的ROC曲线下面积(AUC)分别是0.779和0.829,差异有统计学意义(Z=1.362,P=0.049)。结论ACR-TIRADS及C-TIRADS对甲状腺良恶性结节均具有较高的诊断价值,ACR-TIRADS的灵敏度高于C-TIRADS,C-TIRADS的准确率、特异度更高,操作简便,分层更详细,临床应用价值更高。
Objective To investigate the diagnostic value of 2020 Chinese Thyroid Imaging Reporting and Data System(C-TIRADS)in determination of benignity and malignancy thyroid nodules.Methods The ultrasound images and reports of 522 thyroid nodules in 320 patients were retrospectively analyzed.Based on the American College of Radiology Thyroid Imaging Reporting and Data System(ACR-TIRADS)and C-TIRADS,the risk stratification of 522 thyroid nodules was performed and then compared with pathological results to discuss the diagnostic efficacy of these two classification criteria in the determination of benignity and malignancy of the thyroid nodules.Results The accuracy of ACR-TIRADS and C-TIRADS in the diagnosis of benign and malignant thyroid nodules was 74.5%and 80.1%,respectively,with the sensitivity of 90.6%and 84.9%and the specificity of 65.2%and 77.3%,respectively.The positive predictive value was 60.2%and 68.5%,the negative predictive value was 92.3%and 89.8%,and the total misdiagnosis rate was 25.5%and 19.9%,respectively.The area under the ROC curve(AUC)of benign and malignant thyroid nodules diagnosed by ACR-TIRADS and C-TIRADS was 0.779 and 0.829,respectively,with a statistically significant difference(Z=1.362,P=0.049).Conclusion Both ACR-TIRADS and C-TIRADS have high diagnostic value for benign and malignant thyroid nodules.ACR-TIRADS has higher sensitivity,while C-TIRADS has higher accuracy and specificity.Besides,C-TIRADS has higher clinical application value apart from its simple operation and more detailed stratification.
作者
曾艳贞
傅仲带
李雄牡
曾艳
伍学斌
ZENG Yanzhen;FU Zhongdai;LI Xiongmu;ZENG Yan;WU Xuebin(Shunde Hospital,Guangzhou University of Traditional Chinese Medicine,Foshan 528300,China)
出处
《现代医院》
2022年第11期1780-1782,1786,共4页
Modern Hospitals
基金
广东省医学科学技术研究基金(B2022348)。