Iranian Red Crescent Medical Journal (IRCMJ)

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Author Guidelines

Article Processing Charge

A Systematic Review and Meta-analysis on Ultrasound Detection of Thyroid Cancer in China

Document Type : Systematic reviews

Authors

Department of Ultrasound, First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming City, Yunnan Province, 650032, China

Abstract

Background: One major drawback of using ultrasound for diagnosing thyroid nodules is its limited ability to distinguish between benign and malignant nodules. In China, the common methods for risk stratification and guiding fine needle aspiration (FNA) in diagnosing thyroid nodules are the Chinese Thyroid Imaging Reports and Data Systems (C-TIRADS) and American College of Radiology-Thyroid Imaging Reporting and Data System (ACR-TIRADS).
Objectives: This review seeks to assess the effectiveness of C-TIRADS and ACR-TIRADS in accurately identifying the risk of malignancy in Chinese patients suspected of thyroid cancer.
Methods: A detailed search was conducted in PubMed, Google Scholar, Medline, Embase, Web of Science, Cochrane, and China National Knowledge Infrastructure (CNKI) databases from January 2018 to December 2022. The analysis only considered original articles from China reporting the use of C-TIRADS and ACR-TIRADS confirmed by histology and FNA.
Results: This review analyzed 26 studies with a total of 23,064 thyroid nodules from 19,114 patients to compare the diagnostic performance of C-TIRADS and ACR-TIRADS in predicting malignancy risk in thyroid nodules. Although the malignancy rates in each risk category were similar between the two systems, the TIRADS showed better diagnostic performance than C-TIRADS in terms of pooled specificity (95.0 % vs. 66.8 % of C-TIRADS). However, the pooled analysis showed that C-TIRADS had a better pooled sensitivity (94.6 % vs. 76.5% of ACR-TIRADS). The diagnostic odds ratio was 1.37 (95 % CI: 0.75-2.51) for ACR-TIRADS and 0.89 (95 % CI: 0.36-2.16) for C-TIRADS.
Conclusion: Based on the results, both C-TIRADS and ACR-TIRADS are effective in predicting the risk of malignancy in thyroid nodules with similar overall diagnostic accuracy. The combination of both systems can be beneficial in enhancing accuracy in suspicious or uncertain cases. The long-term experience of the trained radiologists can readily help in concluding the diagnosis. As no single system or combination of systems can provide a 100% accurate prediction of the malignancy of thyroid nodules, the ultimate diagnosis relies on the concluding assessment of experienced radiologists and the medical team.

Keywords

References
  1. Song G, Xue F, Zhang C. A Model Using Texture Features to Differentiate the Nature of Thyroid Nodules on Sonography. J Ultrasound Med. 2015;34(10):1753-1760. doi:10.7863/ultra.
  2. 14.10045.
  3. Cui Y, Mubarik S, Li R, Nawsherwan, Yu C. Trend dynamics of thyroid cancer incidence among China and the U.S. adult population from 1990 to 2017: a joinpoint and age-period-cohort analysis. BMC Public Health. 2021;21(1):624. doi: 10.1186/s12889-021-10635-w. [PubMed: 33789605].
  4. Wu J, Zhao X, Sun J, Cheng C, Yin C, Bai R. The epidemic of thyroid cancer in China: Current trends and future prediction. Front Oncol. 2022;12:932729. doi: 10.3389/fonc.2022.932729. [PubMed: 36119514].
  5. Du L, Zhao Z, Zheng R, Li H, Zhang S, Li R, Wei W, He J. Epidemiology of Thyroid Cancer: Incidence and Mortality in China, 2015. Front Oncol. 2020;10:1702. doi: 10.3389/fonc.
  6. 01702. [PubMed: 33240801].
  7. Jin Z, Zhu Y, Zhang S, Xie F, Zhang M, Zhang Y, Tian X, Zhang J, Luo Y, Cao J. Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience. Med SciMonit. 2020;26:e918452. doi: 10.12659/MSM.918452. [PubMed: 31929498].
  8. Lew JI, Solorzano CC. Use of ultrasound in the management of thyroid cancer. Oncologist. 2010;15(3):253-8. doi: 10.1634/
  9. theoncologist.2009-0324. [PubMed: 20215358].
  10. Wu J, Zhao X, Sun J, Cheng C, Yin C, Bai R. The epidemic of thyroid cancer in China: Current trends and future prediction. Front Oncol. 2022;12:932729. doi: 10.3389/fonc.2022.932729. [PubMed: 36119514].
  11. Nguyen QT, Lee EJ, Huang MG, Park YI, Khullar A, Plodkowski RA. Diagnosis and treatment of patients with thyroid cancer. Am Health Drug Benefits. 2015;8(1):30-40. [PubMed: 25964831].
  12. Sheehan MT. Biochemical Testing of the Thyroid: TSH is the Best and, Oftentimes, Only Test Needed - A Review for Primary Care. Clin Med Res. 2016;14(2):83-92. doi: 10.3121/cmr.
  13. 1309. [PubMed: 27231117].
  14. Bomeli SR, LeBeau SO, Ferris RL. Evaluation of a thyroid nodule. OtolaryngolClin North Am. 2010;43(2):229-38. doi: 10.1016/j.otc.2010.01.002. [PubMed: 20510711].
  15. Tamhane S, Gharib H. Thyroid nodule update on diagnosis and management. Clin Diabetes Endocrinol. 2016;2:17. doi: 10.1186/s40842-016-0035-7. [PubMed: 28702251].
  16. Kim JS, Kim BG, Stybayeva G, Hwang SH. Diagnostic Performance of Various Ultrasound Risk Stratification Systems for Benign and Malignant Thyroid Nodules: A Meta-Analysis. Cancers (Basel). 2023;152:424. doi: 10.3390/cancers150
  17. [PubMed: 36672373].
  18. Büchter RB, Weise A, Pieper D. Reporting of methods to prepare, pilot and perform data extraction in systematic reviews: analysis of a sample of 152 Cochrane and non-Cochrane reviews. BMC Med Res Methodol. 2021;21:240 doi: 10.1186/s12874-021-01438-z.
  19. Chen Q, Lin M, Wu S. Validating and Comparing C-TIRADS, K-TIRADS and ACR-TIRADS in Stratifying the Malignancy Risk of Thyroid Nodules. Front Endocrinol (Lausanne). 2022;13:899575. doi: 10.3389/fendo.2022.899575. [PubMed: 35784558].
  20. Fan L, Xue G, Yang F, Wang Y, Wu X. Value of ultrasound c-tirads in malignant risk stratification of thyroid nodules. Chin Remedies Clinics. 2021;21(21):3546–7.
  21. Gao L, Liu R, Jiang Y, Song W, Wang Y, Liu J, et al. Computer-aided system for diagnosing thyroid nodules on ultrasound: a comparison with radiologist-based clinical assessments. Head Neck. 2018;40(4):778–83. doi: 10.1002/hed.25049. [PubMed: 29286180]
  22. Gao L, Xi X, Jiang Y, Yang X,Wang Y, Zhu S, et al. Comparison among TIRADS (ACR TI-RADS and KWAK- TI-RADS) and 2015 ATA guidelines in the diagnostic efficiency of thyroid nodules. Endocrine. 2019;64(1):90–6. doi: 10.1007/s12020-019-018
  23. -x. [PubMed: 30659427].
  24. Gao Y, Deng D, Liu Y, Gan L, Zhang C. Application value of 2020 c-tirads in the diagnosis and follow-up management of thyroid nodules. ActaUniversitatis Med Anhui. 2022; 57(3):497–99.
  25. Li Q, Ding S, Guo W, Liu Y, Wei Y, Ding Q. Evaluation of the efficacy of c-tirads combined with artificial intelligence assisted diagnosis in thyroid nodule differential diagnosis. Chin J Ultrasonography. 2021;30(3):231–5. doi: 10.3760/cma.j.cn
  26. -20201106-00858.
  27. Li R, Li S. Value of 2020 Chinese Ti -rads in the diagnosis of benign and malignant thyroid nodules. Chin Imaging J Integrated Traditional Western Med. 2022;20(2):154–7.
  28. Lin Y, Lai S, Wang P, Li J, Chen Z, Wang L, et al. Performance of current ultrasound-based malignancy risk stratification systems for thyroid nodules in patients with follicular neoplasms. EurRadiol. 2022;23(6):3617–30. doi: 10.1007/s00330-021-08450-3. [PubMed: 34973102].
  29. Liu C, Xie L, Kong W, et al. Prediction of suspicious thyroid nodule using artificial neural network based on radiofrequency ultrasound and conventional ultrasound: A preliminary study. Ultrasonics. 2019;99:105951. doi: 10.1016/
  30. j.ultras.2019.105951. [PubMed: 31323562].
  31. Qi Q, Zhou A, Guo S, Huang X, Chen S, Li Y, Xu P. Explore the Diagnostic Efficiency of Chinese Thyroid Imaging Reporting and Data Systems by Comparing With the Other Four Systems (ACR TI-RADS, Kwak-TIRADS, KSThR-TIRADS, and EU-TIRADS): A Single-Center Study. Front Endocrinol (Lausanne). 2021;12:763897. doi: 10.3389/fendo.2021.763897. [PubMed: 34777258].
  32. Qiao M, Feng S, Shen D, Xia B. Chinese Thyroid imaging reporting and data system in the differential diagnosis of benign and malignant thyroid nodules. Chin J Med Imaging. 2021; 29(11):1070–5.
  33. Ruan JL, Yang HY, Liu RB, Liang M, Han P, Xu XL, et al. Fine needle aspiration biopsy indications for thyroid nodules: compare a point-based risk stratification system with a pattern-based risk stratification system. EurRadiol. 2019;29(9):4871–8. doi: 10.1007/s00330-018-5992-z. [PubMed: 30715590].
  34. Shen Y, LiuM, He J, Wu S, Chen M,Wan Y, et al. Comparison of different risk-stratification systems for the diagnosis of benign and malignant thyroid nodules. Front Oncol. 2019;9:378. doi: 10.3389/fonc.2019.00378. [PubMed: 31139568].
  35. Sui Y, Wang B. Two-dimensional Gray scale ultrasound combined with superb microvascular imaging in differential diagnosis of Ti-Rads 3 and 4 thyroid nodules. Acad J Guangzhou Med Coll. 2021; 49(5):78–82.
  36. Wang D, Du LY, Sun JW, Hou XJ, Wang H, Wu JQ, Zhou XL. Evaluation of thyroid nodules with coexistent Hashimoto's thyroiditis according to various ultrasound-based risk stratification systems：A retrospective research. Eur J Radiol. 2020;131:109059. doi: 10.1016/j.ejrad.2020.109059. [PubMed: 32739109].
  37. Wang L, Yang S, Yang S, Zhao C, Tian G, Gao Y, et al. Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network. World J Surg Oncol. 2019;17(1):12. doi: 10.1186/s12957-019-1558-z. [PubMed: 30621704].
  38. Wu D, Wang Y. Comparison of the diagnostic efficacy of thyroid ultrasound acr-tirads and c-tirads. Pharm Weekly. 2021; 30(15):82–4. http://www.ydzkzzs.net/2115pdf/211580.pdf
  39. Xu T, Wu Y, Wu RX, Zhang YZ, Gu JY, Ye XH, Tang W, Xu SH, Liu C, Wu XH. Validation and comparison of three newly-released Thyroid Imaging Reporting and Data Systems for cancer risk determination. Endocrine. 2019;64(2):299-307. doi: 10.1007/
  40. s12020-018-1817-8. [PubMed: 30474824].
  41. Zhang B, Tian J, Pei S, Chen Y, He X, Dong Y, et al. Machine Learning-Assisted System for Thyroid Nodule Diagnosis. Thyroid. 2019;29(6):858-867. doi: 10.1089/thy.2018.0380. [PubMed: 30929637].
  42. Zhang WB, Xu HX, Zhang YF, Guo LH, Xu SH, Zhao CK, et al. Comparisons of ACR TI-RADS, ATA guidelines, Kwak TIRADS, and KTA/KSThR guidelines in malignancy risk stratification of thyroid nodules. Clin Hemorheol Microcirc. 2020;75(2):219-232. doi: 10.3233/CH-190778. [PubMed: 31929154].
  43. Zhang G, Zhang L, Hou Y, Chen Y, Weiwei Z. Application value of ultrasound-guided fine needle aspiration biopsy in diagnosis of thyroid nodules in juveniles. Chin J Med Ultrasound. 2021; 18(9):822–7. doi: 10.3877/cma.j.issn.1672-6448.2021.09.002.
  44. Zhang W, Chen T, Liu H, He B, Mao L, Liu Y, et al. Comparisons of the five different ultrasound malignancy risk stratification system of thyroid nodules. Chin J Ultrasound Med. 2022; 38(02):132–6.
  45. Zhao H, Liu X, Lei B, et al. Diagnostic performance of thyroid imaging reporting and data system (TI-RADS) alone and in combination with contrast-enhanced ultrasonography for the characterization of thyroid nodules. Clin Hemorheol Microcirc. 2019;72(1):95-106. doi: 10.3233/CH-180457. [PubMed: 30320563].
  46. Zhou J, Song Y, Zhan W, et al. Thyroid imaging reporting and data system (TIRADS) for ultrasound features of nodules: multicentric retrospective study in China. Endocrine. 2021;
  47. (1):157-170. doi: 10.1007/s12020-020-02442-x. [PubMed: 32852733].
  48. Zhu H, Yang Y, Wu S, Chen K, Luo H, Huang J. Diagnostic performance of us-based fnab criteria of the 2020 Chinese guideline for malignant thyroid nodules: Comparison with the 2017 American college of radiology guideline, the 2015 American thyroid association guideline, and the 2016 Korean thyroid association guideline. Quant Imaging Med Surg. 2021;11(8):3604-3618. doi: 10.21037/qims-20-1365. [Pub
  49. Med: 34341735].
  50. Zhu T, Chen J, Zhou Z, Ma X, Huang Y. Differentiation of Thyroid Nodules (C-TIRADS 4) by Combining Contrast-Enhanced Ultrasound Diagnosis Model With Chinese Thyroid Imaging Reporting and Data System. Front Oncol. 2022;12:840819. doi: 10.3389/fonc.2022.840819. [PubMed: 35847847]
  51. Zhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, et al.. 2020 Chinese Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules: The C-TIRADS. Endocrine. 2020;70(2):256-279. doi: 10.1007/s12020-020-02441-y. [PubMed: 32827126].
  52. Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017;14(5):587-595. doi: 10.1016/j.jacr.2017.01.046. [PubMed: 28372962].
  53. Wang Q, Zeng Z, Nan J, Zheng Y, Liu H. Cause of Death Among Patients With Thyroid Cancer: A Population-Based Study. Front Oncol. 2022;12:852347. doi: 10.3389/fonc.2022.852347. [PubMed: 35359353].
  54. Zhou J, Song Y, Zhan W. Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of Chinese Medical Association. Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound . Thyroid Imaging Reporting and Data System (TIRADS) for Ultrasound Features of Nodules: Multicentric Retrospective Study in China. Endocrine. 2021;72:157–70.
  55. Dong W, Wu Y, Cai T, Wang X. Comparison of diagnostic performance and FNA management of the ACR-TIRADS and Chinese-TIRADS based on surgical histological evidence. Quant Imaging Med Surg. 2023;13(3):1711-1722. doi: 10.21037/qims-22-685. [PubMed: 36915304].
  56. Seminati D, Capitoli G, Leni D, Fior D, Vacirca F, Di Bella C, Galimberti S, L'Imperio V, Pagni F. Use of Diagnostic Criteria from ACR and EU-TIRADS Systems to Improve the Performance of Cytology in Thyroid Nodule Triage. Cancers (Basel). 2021;13(21):5439. doi: 10.3390/cancers13215439. [PubMed: 34771602].
  57. Hekimsoy İ, Öztürk E, Ertan Y, Orman MN, Kavukçu G, Özgen AG, Özdemir M, Özbek SS. Diagnostic performance rates of the ACR-TIRADS and EU-TIRADS based on histopathological evidence. DiagnIntervRadiol. 2021;27(4):511-518. doi: 10.5152/dir.2021.20813. [PubMed: 34313236].
Iranian Red Crescent Medical Journal (IRCMJ)
Volume 25, Issue 10 - Serial Number 10
2023
Files
History
  • Receive Date: 25 May 2023
  • Revise Date: 26 May 2024
  • Accept Date: 27 January 2024
  • First Publish Date: 27 January 2024
  • Publish Date: 01 October 2023
Share
How to cite
Statistics