摘要
目的应用超声引导下甲状腺细针穿刺技术比较4种临床指南的诊断效果。资料与方法回顾性分析经甲状腺细针穿刺的725例患者共740个甲状腺结节,根据国际上确定的4种指南准则对甲状腺结节进行分类,以甲状腺细针穿刺结果作为标准,比较4种临床指南的诊断效能。结果美国放射学会(ACR)甲状腺超声诊断指南的敏感度、特异度、阳性预测值、阴性预测值、准确度分别为75.1%、64.7%、58.2%、79.8%、68.7%;美国甲状腺学会(ATA)指南分别为86.0%、49.9%、52.9%、84.5%、64.2%;美国临床内分泌协会(AACE)/美国内分泌学院(ACE)/意大利内分泌协会(AME)指南分别为81.9%、56.4%、55.2%、82.6%、66.5%;美国国家综合癌症网络(NCCN)指南分别为93.2%、41.8%、51.2%、90.3%、62.2%。结论不同临床指南对于甲状腺癌的诊断具有各自的优缺点,ATA及NCCN指南的敏感度更高,而ACR及AACE/ACE/AME指南的特异度更高。
Purpose To compare the diagnostic efficacy of four clinical guidelines by ultrasound-guided fine-needle aspiration biopsy of thyroid.Materials and Methods A retrospective analysis of thyroid fine needle aspiration biopsy of 740 thyroid nodules in 725 patients was conducted.The thyroid nodules of patients were classified according to four international clinical guidelines.The diagnostic efficacy of these four clinical guidelines was compared based on the results of fine-needle aspiration.Results The sensitivity,specificity,positive predictive value,negative predictive value and accuracy were 75.1%,64.7%,58.2%,79.8%and 68.7%according to thyroid ultrasound diagnostic guidelines of American College of Radiology(ACR),those were 86.0%,49.9%,52.9%,84.5%and 64.2%according to the American Thyroid Association(ATA)guidelines,those were 81.9%,56.4%,55.2%,82.6%and 66.5%according to American Association of Clinical Endocrinologists(AACE)/American College of Endocrinology(ACE)/Associazione Medici Endocrinology(AME)guidelines,those were 93.2%,41.8%,51.2%,90.3%and 62.2%according to the National Comprehensive Cancer Network(NCCN)guidelines,respectively.Conclusion Different clinical guidelines have their own advantages and disadvantages for the diagnosis of thyroid cancer.Guidelines of ATA and NCCN has more higher sensitivity,while ACR and AACE/ACE/AME guidelines have a greater specificity.
作者
顾诗瑶
黄瑛
GU Shiyao;HUANG Ying(Department of Ultrasound,Shengjing Hospital of China Medical University,Shenyang 110000,China)
出处
《中国医学影像学杂志》
CSCD
北大核心
2019年第11期853-856,861,共5页
Chinese Journal of Medical Imaging
基金
辽宁省自然科学基金(20180550215)
中国医科大学首批健康医疗大数据研究课题(HMB201902103)