Kim Ji-Sun, Kim Byung Guk, Stybayeva Gulnaz, Hwang Se Hwan
Department of Otolaryngology-Head and Neck Surgery, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55902, USA.
Cancers (Basel). 2023 Jan 9;15(2):424. doi: 10.3390/cancers15020424.
To evaluate the diagnostic performance of ultrasound risk-stratification systems for the discrimination of benign and malignant thyroid nodules and to determine the optimal cutoff values of individual risk-stratification systems.
PubMed, Embase, SCOPUS, Web of Science, and Cochrane library databases were searched up to August 2022. Sensitivity and specificity data were collected along with the characteristics of each study related to ultrasound risk stratification systems.
Sixty-seven studies involving 76,512 thyroid nodules were included in this research. The sensitivity, specificity, diagnostic odds ratios, and area under the curves by K-TIRADS (4), ACR-TIRADS (TR5), ATA (high suspicion), EU-TIRADS (5), and Kwak-TIRADS (4b) for malignancy risk stratification of thyroid nodules were 92.5%, 63.5%, 69.8%, 70.6%, and 95.8%, respectively; 62.8%, 89.6%, 87.2%, 83.9%, and 63.8%, respectively; 20.7111, 16.8442, 15.7398, 12.2986, and 38.0578, respectively; and 0.792, 0.882, 0.859, 0.843, and 0.929, respectively.
All ultrasound-based risk-stratification systems had good diagnostic performance. Although this study determined the best cutoff values in individual risk-stratification systems based on statistical assessment, clinicians could adjust or alter cutoff values based on the clinical purpose of the ultrasound and the reciprocal changes in sensitivity and specificity.
评估超声风险分层系统对甲状腺良恶性结节鉴别的诊断性能,并确定各风险分层系统的最佳临界值。
检索截至2022年8月的PubMed、Embase、SCOPUS、Web of Science和Cochrane图书馆数据库。收集敏感性和特异性数据以及每项研究中与超声风险分层系统相关的特征。
本研究纳入了67项涉及76512个甲状腺结节的研究。K-TIRADS(4类)、ACR-TIRADS(TR5类)、ATA(高度可疑)、EU-TIRADS(5类)和Kwak-TIRADS(4b类)对甲状腺结节恶性风险分层的敏感性、特异性、诊断比值比和曲线下面积分别为92.5%、63.5%、69.8%、70.6%和95.8%;分别为62.8%、89.6%、87.2%、83.9%和63.8%;分别为20.7111、16.8442、15.7398、12.2986和38.0578;分别为0.792、0.882、0.859、0.843和0.929。
所有基于超声的风险分层系统均具有良好的诊断性能。尽管本研究基于统计评估确定了各风险分层系统中的最佳临界值,但临床医生可根据超声的临床目的以及敏感性和特异性的相互变化来调整或改变临界值。