诊断学理论与实践 ›› 2022, Vol. 21 ›› Issue (03): 390-394.doi: 10.16150/j.1671-2870.2022.03.017
收稿日期:
2022-03-15
出版日期:
2022-06-25
发布日期:
2022-08-17
通讯作者:
侯怡卿
E-mail:shanghairuijin@126.com
ZHAO Ran, ZHAN Weiwei, HOU Yiqing()
Received:
2022-03-15
Online:
2022-06-25
Published:
2022-08-17
Contact:
HOU Yiqing
E-mail:shanghairuijin@126.com
摘要:
目的:探究计算机辅助诊断(computer-aided diagnosis,CAD)系统辅助超声医师诊断甲状腺弥漫性病变合并结节良恶性的效能。方法:收集2017年8月至2017年12月在我院就诊并行超声检查的甲状腺弥漫性病变合并结节患者342例(共533个结节),以病理检查结果为金标准,比较按常规超声诊断方法(依据成人甲状腺结节与分化型甲状腺癌指南的标准)与CAD系统辅助下超声诊断判断甲状腺弥漫性病变合并结节良恶性的灵敏度、特异度、阳性预测值、阴性预测值及受试者操作特征曲线(receiver operator characteristic curve, ROC曲线)的曲线下面积(erea under the curve, AUC)。结果:常规超声诊断甲状腺弥漫性病变合并结节良恶性的灵敏度为96.6%,特异度为72.5%,AUC为0.846;在CAD系统辅助下超声诊断的灵敏度为96.6%,特异度为80.9%,AUC为0.888。在CAD系统辅助下的诊断特异度和AUC均较高(P均<0.01)。结论:对于甲状腺弥漫性病变合并结节的患者,采用超声联合CAD系统诊断甲状腺结节良恶性时,可在保持灵敏度不变的同时提升诊断特异度,以减少不必要的穿刺活检。
中图分类号:
赵然, 詹维伟, 侯怡卿. 计算机辅助诊断系统辅助超声诊断甲状腺弥漫性病变合并结节良恶性的应用价值[J]. 诊断学理论与实践, 2022, 21(03): 390-394.
ZHAO Ran, ZHAN Weiwei, HOU Yiqing. The application value of computer-aided ultrasound diagnosis system in differentiating malignant from benign thyroid nodules in diffuse thyroid lesions[J]. Journal of Diagnostics Concepts & Practice, 2022, 21(03): 390-394.
[1] |
Haugen BR, Alexander EK, Bible KC, et al. 2015 Ame-rican Thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association Guidelines task force on thyroid nodules and differentiated thyroid cancer[J]. Thyroid, 2016, 26(1):1-133.
doi: 10.1089/thy.2015.0020 pmid: 26462967 |
[2] |
Miller KD, Siegel RL, Lin CC, et al. Cancer treatment and survivorship statistics, 2016[J]. CA Cancer J Clin, 2016, 66(4):271-289.
doi: 10.3322/caac.21349 URL |
[3] |
Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6):394-424.
doi: 10.3322/caac.21492 URL |
[4] |
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2):115-132.
doi: 10.3322/caac.21338 URL |
[5] |
Morris LG, Tuttle RM, Davies L. Changing trends in the incidence of thyroid cancer in the United States[J]. JAMA Otolaryngol Head Neck Surg, 2016, 142(7):709-711.
doi: 10.1001/jamaoto.2016.0230 URL |
[6] |
Baser H, Ozdemir D, Cuhaci N, et al. Hashimoto′s thyroiditis does not affect ultrasonographical, cytological, and histopathological features in patients with papillary thyroid carcinoma[J]. Endocr Pathol, 2015, 26(4):356-364.
doi: 10.1007/s12022-015-9401-8 URL |
[7] |
Gul K, Dirikoc A, Kiyak G, et al. The association between thyroid carcinoma and Hashimoto′s thyroiditis: the ultrasonographic and histopathologic characteristics of malignant nodules[J]. Thyroid, 2010, 20(8):873-878.
doi: 10.1089/thy.2009.0118 URL |
[8] |
Zhang Y, Wu Q, Chen Y, et al. A clinical assessment of an ultrasound computer-aided diagnosis system in diffe-rentiating thyroid nodules with radiologists of different diagnostic experience[J]. Front Oncol, 2020, 10:557169.
doi: 10.3389/fonc.2020.557169 URL |
[9] |
Choi YJ, Baek JH, Park HS, et al. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment[J]. Thyroid, 2017, 27(4):546-552.
doi: 10.1089/thy.2016.0372 URL |
[10] |
Li X, Zhang S, Zhang Q, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study[J]. Lancet Oncol, 2019, 20(2):193-201.
doi: 10.1016/S1470-2045(18)30762-9 URL |
[11] |
Hou Y, Chen C, Zhang L, et al. Using Deep Neural Network to diagnose thyroid nodules on ultrasound in patients with Hashimoto′s thyroiditis[J]. Front Oncol, 2021, 11:614172.
doi: 10.3389/fonc.2021.614172 URL |
[12] |
Lai X, Xia Y, Zhang B, et al. A meta-analysis of Hashimoto′s thyroiditis and papillary thyroid carcinoma risk[J]. Oncotarget, 2017, 8(37):62414-62424.
doi: 10.18632/oncotarget.18620 URL |
[13] |
Park M, Park SH, Kim EK, et al. Heterogeneous echogenicity of the underlying thyroid parenchyma: how does this affect the analysis of a thyroid nodule?[J]. BMC Cancer, 2013, 13:550.
doi: 10.1186/1471-2407-13-550 URL |
[14] |
Faust O, Acharya UR, Tamura T. Formal design methods for reliable computer-aided diagnosis: a review[J]. IEEE Rev Biomed Eng, 2012, 5:15-28.
doi: 10.1109/RBME.2012.2184750 URL |
[15] |
Lam J, Ying M, Cheung SY, et al. A comparison of the diagnostic accuracy and reliability of subjective grading and computer-aided assessment of intranodal vascularity in differentiating metastatic and reactive cervical lymphadenopathy[J]. Ultraschall Med, 2016, 37(1):63-67.
doi: 10.1055/s-0034-1384939 pmid: 25140495 |
[1] | 叶蕾, 李浩榕. 良恶性甲状腺结节的分子鉴别诊断进展[J]. 诊断学理论与实践, 2020, 19(04): 334-338. |
[2] | 周伟, 侯怡卿, 詹维伟. 超声造影及超声弹性成像在良恶性甲状腺结节鉴别诊断中的应用进展[J]. 诊断学理论与实践, 2020, 19(04): 344-349. |
[3] | 周建桥, 詹维伟. 2020年中国超声甲状腺影像报告和数据系统(C-TIRADS)指南解读[J]. 诊断学理论与实践, 2020, 19(04): 350-353. |
[4] | 郭艳, 葛娟娟, 陈晨, 尹吉明, 王小龙, 陈家庚, 杜燕伟, 段园园, 凡雪霖, 郑磊, 王西勇, 詹维伟, 张璐. 细针穿刺活检联合RJ-TIRADS在诊断老年甲状腺结节良恶性中的价值[J]. 诊断学理论与实践, 2020, 19(03): 286-291. |
[5] | 徐上妍, 贾晓红, 倪晓枫, 詹维伟. ACR-TIRADS与RJ-TIRADS描述词及分类在甲状腺结节评估者间的一致性研究[J]. 诊断学理论与实践, 2019, 18(2): 149-154. |
[6] | 况李君, 王怡, 陆采葑, 樊金芳, 吴敏, 周伟. 甲状腺结节鳞状上皮化生1例报道及文献复习[J]. 诊断学理论与实践, 2019, 18(04): 423-427. |
[7] | 季沁, 周一帆, 陈茉, 李杰, 丁文波, 钱涛, 褚晓秋, 王建华, 徐书杭, 刘超. 弹性成像联合ACR-TIRADS诊断甲状腺结节良恶性的临床价值研究[J]. 诊断学理论与实践, 2019, 18(03): 307-312. |
[8] | 李芹芹, 叶廷军, 毛敏静. 甲状腺细针穿刺细胞学检查与甲状腺影像报告和数据系统分级对照分析[J]. 诊断学理论与实践, 2017, 16(06): 607-611. |
[9] | 杨志芳, 詹维伟,. 超声对桥本甲状腺炎良恶性结节鉴别价值的探讨[J]. 诊断学理论与实践, 2012, 11(02): 176-181. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||