Skip to main content

Advertisement

Log in

Thyroid imaging reporting and data system (TIRADS) for ultrasound features of nodules: multicentric retrospective study in China

  • Original Article
  • Published:
Endocrine Aims and scope Submit manuscript

Abstract

Purpose

To establish a practical and simplified Chinese thyroid imaging reporting and data system (C-TIRADS) based on the Chinese patient database.

Methods

A total of 2141 thyroid nodules that were neither cystic nor spongy were used in the current study. These specimens were derived from 2141 patients in 131 alliance hospitals of the Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The ultrasound features, including location, orientation, margin, halo, composition, echogenicity, echotexture, echogenic foci and posterior features were assessed. Univariate and multivariate analyses were performed to investigate the association between ultrasound features and malignancy. The regression equation, the weighting, and the counting methods were used to determine the malignant risk of the thyroid nodules. The areas under the receiver operating characteristic curve (Az values) were calculated.

Results

Of the 2141 thyroid nodules, 1572 were benign, 565 were malignant, and 4 were borderline. Vertical orientation, ill-defined, or irregular margin (including extrathyroidal extension), microcalcifications, solid, and markedly hypoechoic were positively associated with malignancy, while comet-tail artifacts were negatively associated with malignancy. The logistic regression equation yielded the highest Az value of 0.913, which was significantly higher than that obtained using the weighting method (0.893) and the counting method (0.890); however, no significant difference was found between the latter two. The C-TIRADS, based on the counting method, was designed following the principle of balancing the diagnostic performance and sensitivity of the risk stratification with the ease of use.

Conclusions

A relatively simple C-TIRADS was established using the counting value of positive and negative ultrasound features.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. B.R. Haugen, E.K. Alexander, K.C. Bible, G.M. Doherty, S.J. Mandel, Y.E. Nikiforov, F. Pacini, G.W. Randolph, A.M. Sawka, M. Schlumberger, K.G. Schuff, S.I. Sherman, J.A. Sosa, D.L. Steward, R.M. Tuttle, L. Wartofsky, 2015 American 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. Thyroid 26(1), 1–133 (2016). https://doi.org/10.1089/thy.2015.0020

    Article  PubMed  PubMed Central  Google Scholar 

  2. R.P. Tufano, S.I. Noureldine, P. Angelos, Incidental thyroid nodules and thyroid cancer: considerations before determining management. JAMA Otolaryngol. Head. Neck Surg. 141(6), 566–572 (2015). https://doi.org/10.1001/jamaoto.2015.0647

    Article  PubMed  Google Scholar 

  3. L.R. Remonti, C.K. Kramer, C.B. Leitao, L.C. Pinto, J.L. Gross, Thyroid ultrasound features and risk of carcinoma: a systematic review and meta-analysis of observational studies. Thyroid 25(5), 538–550 (2015). https://doi.org/10.1089/thy.2014.0353

    Article  PubMed  PubMed Central  Google Scholar 

  4. H. Gharib, E. Papini, J.R. Garber, D.S. Duick, R.M. Harrell, L. Hegedus, R. Paschke, R. Valcavi, P. Vitti, A.A.A.T.F.O.T. Nodules, American Association of Clinical Endocrinologists, American College of Endocrinology, and Associazione Medici Endocrinologi Medical Guidelines for Clinical Practice for the Diagnosis and Management of Thyroid Nodules–2016 Update. Endocr. Pract. 22(5), 622–639 (2016). https://doi.org/10.4158/EP161208.GL

    Article  PubMed  Google Scholar 

  5. M.C. Frates, C.B. Benson, J.W. Charboneau, E.S. Cibas, O.H. Clark, B.G. Coleman, J.J. Cronan, P.M. Doubilet, D.B. Evans, J.R. Goellner, I.D. Hay, B.S. Hertzberg, C.M. Intenzo, R.B. Jeffrey, J.E. Langer, P.R. Larsen, S.J. Mandel, W.D. Middleton, C.C. Reading, S.I. Sherman, F.N. Tessler, Management of thyroid nodules detected at US: society of radiologists in ultrasound consensus conference statement. Radiology 237(3), 794–800 (2005). https://doi.org/10.1148/radiol.2373050220

    Article  PubMed  Google Scholar 

  6. E. Horvath, S. Majlis, R. Rossi, C. Franco, J.P. Niedmann, A. Castro, M. Dominguez, An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J. Clin. Endocrinol. Metab. 94(5), 1748–1751 (2009). https://doi.org/10.1210/jc.2008-1724

    Article  CAS  PubMed  Google Scholar 

  7. J.Y. Park, H.J. Lee, H.W. Jang, H.K. Kim, J.H. Yi, W. Lee, S.H. Kim, A proposal for a thyroid imaging reporting and data system for ultrasound features of thyroid carcinoma. Thyroid 19(11), 1257–1264 (2009). https://doi.org/10.1089/thy.2008.0021

    Article  PubMed  Google Scholar 

  8. J.Y. Kwak, K.H. Han, J.H. Yoon, H.J. Moon, E.J. Son, S.H. Park, H.K. Jung, J.S. Choi, B.M. Kim, E.K. Kim, Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology 260(3), 892–899 (2011). https://doi.org/10.1148/radiol.11110206

    Article  PubMed  Google Scholar 

  9. G. Russ, Risk stratification of thyroid nodules on ultrasonography with the French TI-RADS: description and reflections. Ultrasonography 35(1), 25–38 (2016). https://doi.org/10.14366/usg.15027

    Article  PubMed  Google Scholar 

  10. J.F. Sánchez, TI-RADS classifcation of thyroid nodules based on a score modifed according to ultrasound criteria for malignancy. Revista Argentina de Radiología 78(3), 138–148 (2014)

    Article  Google Scholar 

  11. D. Songsaeng, S. Soodchuen, P. Korpraphong, A. Suwanbundit, Siriraj thyroid imaging reporting and data system and its effcacy. Siriraj Med. J. 69(5), 262–267 (2017)

    Google Scholar 

  12. S.Y. Xu, W.W. Zhan, W.H. Wang, Evaluation of thyroid nodules by a scoring and categorizing method based on sonographic features. J. Ultrasound Med. 34(12), 2179–2185 (2015). https://doi.org/10.7863/ultra.14.11041

    Article  PubMed  Google Scholar 

  13. J.H. Shin, J.H. Baek, J. Chung, E.J. Ha, J.H. Kim, Y.H. Lee, H.K. Lim, W.J. Moon, D.G. Na, J.S. Park, Y.J. Choi, S.Y. Hahn, S.J. Jeon, S.L. Jung, D.W. Kim, E.K. Kim, J.Y. Kwak, C.Y. Lee, H.J. Lee, J.H. Lee, J.H. Lee, K.H. Lee, S.W. Park, J.Y. Sung,Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology, Ultrasonography diagnosis and imaging-based management of thyroid nodules: Revised Korean society of thyroid radiology consensus statement and recommendations. Korean J. Radiol. 17(3), 370–395 (2016). https://doi.org/10.3348/kjr.2016.17.3.370

    Article  PubMed  PubMed Central  Google Scholar 

  14. G. Russ, S.J. Bonnema, M.F. Erdogan, C. Durante, R. Ngu, L. Leenhardt, European thyroid association guidelines for ultrasound malignancy risk stratification of thyroid nodules in adults: the EU-TIRADS. Eur. Thyroid J. 6(5), 225–237 (2017). https://doi.org/10.1159/000478927

    Article  PubMed  PubMed Central  Google Scholar 

  15. F.N. Tessler, W.D. Middleton, E.G. Grant, J.K. Hoang, L.L. Berland, S.A. Teefey, J.J. Cronan, M.D. Beland, T.S. Desser, M.C. Frates, L.W. Hammers, U.M. Hamper, J.E. Langer, C.C. Reading, L.M. Scoutt, A.T. Stavros, Re: “ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee”. J. Am. Coll. Radiol. 14(5), 587–595 (2017). https://doi.org/10.1016/j.jacr.2017.01.046

    Article  PubMed  Google Scholar 

  16. Y.J. Choi, J.H. Baek, S.H. Baek, W.H. Shim, K.D. Lee, H.S. Lee, Y.K. Shong, E.J. Ha, J.H. Lee, Web-based malignancy risk estimation for thyroid nodules using ultrasonography characteristics: development and validation of a predictive model. Thyroid 25(12), 1306–1312 (2015). https://doi.org/10.1089/thy.2015.0188

    Article  PubMed  Google Scholar 

  17. E.G. Grant, F.N. Tessler, J.K. Hoang, J.E. Langer, M.D. Beland, L.L. Berland, J.J. Cronan, T.S. Desser, M.C. Frates, U.M. Hamper, W.D. Middleton, C.C. Reading, L.M. Scoutt, A.T. Stavros, S.A. Teefey, Thyroid ultrasound reporting lexicon: white paper of the ACR thyroid imaging, reporting and data system (TIRADS) committee. J. Am. Coll. Radiol. 12(12 Pt A), 1272–1279 (2015). https://doi.org/10.1016/j.jacr.2015.07.011

    Article  PubMed  Google Scholar 

  18. E.J. Ha, D.G. Na, J.H. Baek, J.Y. Sung, J.H. Kim, S.Y. Kang, U.S. Fine-Needle Aspiration, Biopsy for thyroid malignancy: diagnostic performance of seven society guidelines applied to 2000 thyroid nodules. Radiology 287(3), 893–900 (2018). https://doi.org/10.1148/radiol.2018171074

    Article  PubMed  Google Scholar 

  19. J.H. Yoon, K. Han, E.K. Kim, H.J. Moon, J.Y. Kwak, Diagnosis and management of small thyroid nodules: a comparative study with six guidelines for thyroid nodules. Radiology 283(2), 560–569 (2017). https://doi.org/10.1148/radiol.2016160641

    Article  PubMed  Google Scholar 

  20. J.H. Yoon, H.S. Lee, E.K. Kim, H.J. Moon, J.Y. Kwak, Malignancy risk stratification of thyroid nodules: comparison between the thyroid imaging reporting and data system and the 2014 American Thyroid Association Management Guidelines. Radiology 278(3), 917–924 (2016). https://doi.org/10.1148/radiol.2015150056

    Article  PubMed  Google Scholar 

  21. S.M. Ha, H.S. Ahn, J.H. Baek, H.Y. Ahn, Y.J. Chung, B.Y. Cho, S.B. Park, Validation of three scoring risk-stratification models for thyroid nodules. Thyroid 27(12), 1550–1557 (2017). https://doi.org/10.1089/thy.2017.0363

    Article  PubMed  Google Scholar 

  22. W.D. Middleton, S.A. Teefey, C.C. Reading, J.E. Langer, M.D. Beland, M.M. Szabunio, T.S. Desser, Comparison of performance characteristics of American college of radiology TI-RADS, Korean Society of Thyroid Radiology TIRADS, and American Thyroid Association Guidelines. AJR Am. J. Roentgenol. 210(5), 1148–1154 (2018). https://doi.org/10.2214/AJR.17.18822

    Article  PubMed  Google Scholar 

  23. Z. Chen, W. Xu, Y. Huang, X. Jin, J. Deng, S. Zhu, H. Liu, S. Zhang, Y. Yu, Associations of noniodized salt and thyroid nodule among the Chinese population: a large cross-sectional study. Am. J. Clin. Nutr. 98(3), 684–692 (2013). https://doi.org/10.3945/ajcn.112.054353

    Article  CAS  PubMed  Google Scholar 

  24. J. Yin, C. Wang, Q. Shao, D. Qu, Z. Song, P. Shan, T. Zhang, J. Xu, Q. Liang, S. Zhang, J. Huang, Relationship between the prevalence of thyroid nodules and metabolic syndrome in the iodine-adequate area of Hangzhou, China: a cross-sectional and cohort study. Int. J. Endocrinol. 2014, 675796 (2014). https://doi.org/10.1155/2014/675796

    Article  PubMed  PubMed Central  Google Scholar 

  25. W. Xu, Z. Chen, N. Li, H. Liu, L. Huo, Y. Huang, X. Jin, J. Deng, S. Zhu, S. Zhang, Y. Yu, Relationship of anthropometric measurements to thyroid nodules in a Chinese population. BMJ Open 5(12), e008452 (2015). https://doi.org/10.1136/bmjopen-2015-008452

    Article  PubMed  PubMed Central  Google Scholar 

  26. E.R. DeLong, D.M. DeLong, D.L. Clarke-Pearson, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3), 837–845 (1988)

    Article  CAS  PubMed  Google Scholar 

  27. D.G. Na, J.H. Baek, J.Y. Sung, J.H. Kim, J.K. Kim, Y.J. Choi, H. Seo, Thyroid imaging reporting and data system risk stratification of thyroid nodules: categorization based on solidity and echogenicity. Thyroid 26(4), 562–572 (2016). https://doi.org/10.1089/thy.2015.0460

    Article  PubMed  Google Scholar 

  28. S. Jasim, T.J. Baranski, S.A. Teefey, W.D. Middleton, Investigating the effect of thyroid nodule location on the risk of thyroid cancer. Thyroid 30(3), 401–407 (2020). https://doi.org/10.1089/thy.2019.0478

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. V. Ramundo, L. Lamartina, R. Falcone, L. Ciotti, C. Lomonaco, M. Biffoni, L. Giacomelli, M. Maranghi, C. Durante, G. Grani, Is thyroid nodule location associated with malignancy risk? Ultrasonography 38(3), 231–235 (2019). https://doi.org/10.14366/usg.18050

    Article  PubMed  Google Scholar 

  30. F. Zhang, O. Oluwo, F.B. Castillo, P. Gangula, M. Castillo, F. Farag, S. Zakaria, T. Zahedi, Thyroid nodule location on ultrasonography as a predictor of malignancy. Endocr. Pract. 25(2), 131–137 (2019). https://doi.org/10.4158/EP-2018-0361

    Article  CAS  PubMed  Google Scholar 

  31. J. Li, X. Ma, K. Cui, Re: “ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee”. J. Am. Coll. Radiol. 15(3 Pt A), 380–381 (2018). https://doi.org/10.1016/j.jacr.2017.12.009

    Article  PubMed  Google Scholar 

  32. J.Y. Kwak, I. Jung, J.H. Baek, S.M. Baek, N. Choi, Y.J. Choi, S.L. Jung, E.K. Kim, J.A. Kim, J.H. Kim, K.S. Kim, J.H. Lee, J.H. Lee, H.J. Moon, W.J. Moon, J.S. Park, J.H. Ryu, J.H. Shin, E.J. Son, J.Y. Sung, D.G. Na, Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology, Image reporting and characterization system for ultrasound features of thyroid nodules: multicentric Korean retrospective study. Korean J. Radiol. 14(1), 110–117 (2013). https://doi.org/10.3348/kjr.2013.14.1.110

    Article  PubMed  Google Scholar 

  33. R.L.C. Delfim, L. Veiga, A.P.A. Vidal, F. Lopes, M. Vaisman, P. Teixeira, Likelihood of malignancy in thyroid nodules according to a proposed Thyroid Imaging Reporting and Data System (TI-RADS) classification merging suspicious and benign ultrasound features. Arch. Endocrinol. Metab. 61(3), 211–221 (2017). https://doi.org/10.1590/2359-3997000000262

    Article  PubMed  Google Scholar 

  34. S.Y. Ko, H.S. Lee, E.K. Kim, J.Y. Kwak, Application of the thyroid imaging reporting and data system in thyroid ultrasonography interpretation by less experienced physicians. Ultrasonography 33(1), 49–57 (2014). https://doi.org/10.14366/usg.13016

    Article  PubMed  Google Scholar 

  35. Zhang, W.B., Xu, H.X., Zhang, Y.F., Guo, L.H., Xu, S.H., Zhao, C.K., Liu, B.J.: Comparisons of ACR TI-RADS, ATA guidelines, Kwak TI-RADS, and KTA/KSThR guidelines in malignancy risk stratification of thyroid nodules. Clin. Hemorheol. Microcirc. (2020). https://doi.org/10.3233/CH-190778

  36. G. Ardito, L. Revelli, E. Giustozzi, M. Salvatori, G. Fadda, F. Ardito, N. Avenia, A. Ferretti, L. Rampin, S. Chondrogiannis, P.M. Colletti, D. Rubello, Aggressive papillary thyroid microcarcinoma: prognostic factors and therapeutic strategy. Clin. Nucl. Med. 38(1), 25–28 (2013). https://doi.org/10.1097/RLU.0b013e318279bc65

    Article  PubMed  Google Scholar 

  37. S. Noguchi, H. Yamashita, S. Uchino, S. Watanabe, Papillary microcarcinoma. World J. Surg. 32(5), 747–753 (2008). https://doi.org/10.1007/s00268-007-9453-0

    Article  PubMed  PubMed Central  Google Scholar 

  38. H.J. Moon, H.S. Lee, E.K. Kim, S.Y. Ko, J.Y. Seo, W.J. Park, H.Y. Park, J.Y. Kwak, Thyroid nodules </= 5 mm on ultrasonography: are they “leave me alone” lesions? Endocrine 49(3), 735–744 (2015). https://doi.org/10.1007/s12020-015-0526-9

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the project of Shanghai municipal health and family planning commission (Grant 20174Y0069). We would like to express our sincere gratitude to all doctors from the Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound (CAAU) member hospitals who provided ultrasound data on thyroid nodules for this study.

The Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of Chinese Medical Association

Bao Ming Luo13, Bei Jian Huang14, ChaoYang Wen15, ChengRong Mi16, DaoZhong Huang17, EnSheng Xue18, Gang Wu19, GuoQing Du20, HaiTao Ran20, HuiJuan Xiang21, JiaAn Zhu22, Jian Wang23, JianChu Li24, Jie Tang25, Jing Li26, JingChun Yang27, Lei Zhang28, LiGang Cui28, LingYun Bao29, LiXue Yin30, Man Lu31, Mei Zhu32, Min Chen33, Nima Yuzhen34, PengFei Zhang35, Rong Wu36, RuiJun Guo37, ShaoYun Hao38, ShiBao Fang39, Tao Chen40, WeiWei Zhan41, Ying Zhu42, YingJia Li43, YongPing Lu44, YouBin Deng17, YuanYi Zheng45, Yue Chen46, YuKun Luo47, YuLan Peng48

The Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound

Bai BaoYan49, Cai YuanJin50, Chang Xin51, Che Guihua52, Chen Fu53, Chen hongTian54, Chen hongYan55, Chen hongYan56, Chen huiPin57, Chen Jiehuan58, Chen nianQiao59, Chen Wu24, Chen Xinguang60, Chen XiuPing61, Cui Guanghe62, Dai LiPing63, Deng XueDong64, Dong LiLi65, Du Gang66, Fang Chao67, Fang FengKai68, Fei ZhengDong69, Feng LiLi70, Fu Jian71, Guan Ling72, Guo JianQin73, Han Wen74, He Nianan75, He ShaoZheng76, He XueMei77, Hou AiQin78, Hu Jie79, Hu LiYan80, Huang DingWei81, Huang JianYuan82, Huang Li83, Huang PeiPei84, Huang WeiWei85, Jia LiQiong86, Jiang Xinhui87, Kang huiLi88, Kong XiangChong89, Lei XiaoQing90, Li AnYang91, Li Chen92, Li Cheng93, Li ChuanYin94, Li Dong95, Li HaiYan96, Li hongMei97, Li huiWen98, Li JianXin99, Li Ning100, Li QiaoYing101, Li QinYing102, Li Tao103, Li WenDong104, Li XingYun105, Li Zhao106, Liang GuoSheng107, Lin Jie108, Liu Aihua109, Liu HongMin110, Liu Jia111, Liu Kun112, Liu YanChao113, Lou KeXin114, Lu YeJun115, Mao Feng116, Miao Juan117, Ni XueJun118, Pan XiaoJie119, Pang Yun120, Peng Mei121, Peng ZhenYi62, Pi YanMin122, Qi TingYue123, Qin QianMiao124, Qing Shunhua125, Qu JianFeng126, Ren Jinhe127, Renagu Li.aiSha128, Ru RongRong129, Shen Tao130, Shi HongWei131, Shi Jie132, Shi LiYing133, Shou JinDuo134, Song LinLin135, Su DeMin136, Sun AnYi137, Sun Zhuogui138, Tang Binhui139, Tang Li Na140, Wan Qing141, Wang Fang142, Wang Fang143, Wang Jing144, Wang JinPing145, Wang Li146, Wang Wei147, Wang XinFang148, Wang YaLi149, Wang YanBin150, Wang YanQing151, Wang YanZhen152, Wang YingChun153, Wang YuanSheng154, Wang ZhaoRui155, Wu ChangJun156, Wu HaiYan157, Wu Jing158, Wu JinYu159, Wu Liang160, Wu LinSong161, Wu Qing162, Wu Tao163, Wu Ting164, Wu Ting165, Wu WenJing166, Wu ZhiLing167, Wu ZhongQiu168, Xiao LiFang169, Xie ChuanWen170, Xie Xiaohong171, Xu Quan172, Xue Dan173, Yan JingBin174, Yan JiPing175, Yang JianQing176, Yang Jie177, Yang QingYa178, Yang XiaoQing179, Yang XueWen180, Yang Yan181, Yang YingMei182, Yang Yinguang183, Ye Xinhua184, Ye YuQuan185, You Tao186, Yu Liang187, Yu XiaoQin188, Yuan Hui189, Yuan Zhihong190, Ze Liang191, Zeng Shue192, Zhang Hui193, Zhang Jian194, Zhang JianLei195, Zhang LiJuan196, Zhang LiLi197, Zhang Na198, Zhang PanPan199, Zhang QunXia200, Zhang Tong201, Zhang WenJun202, Zhang XiaoDong203, Zhang Yan204, Zhang Yan205, Zhang Yuhong206, Zhang Yuhua207, Zhang YunFei208, Zhang ZiZhen209, Zhao Feng210, Zhao Li211, Zhao Yu212, Zhou Hong213, Zhou JianQiao93, Zhou Na214, Zhou Peng215, Zhou Ruhai216, Zhou XianLi217, Zhou YiBo218, Zhu Bin219, Zhu LiSha220, Zhu Zheng221, Zou Bao222

Author information

Authors and Affiliations

Authors

Consortia

Corresponding authors

Correspondence to JianQiao Zhou, YanYan Song or WeiWei Zhan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Members of the Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of Chinese Medical Association and The Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound are listed below Acknowledgements.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 72, 157–170 (2021). https://doi.org/10.1007/s12020-020-02442-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12020-020-02442-x

Keywords

Navigation