Skip to main content

Advertisement

Log in

Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

The purpose of this study was to evaluate the role of the radiomics score using US images to predict malignancy in AUS/FLUS and FN/SFN nodules.

Methods

One hundred fifty-five indeterminate thyroid nodules in 154 patients who received initial US-guided FNA for diagnostic purposes were included in this retrospective study. A representative US image of each tumor was acquired, and square ROIs covering the whole nodule were drawn using the Paint program of Windows 7. Texture features were extracted by in-house texture analysis algorithms implemented in MATLAB 2019b. The LASSO logistic regression model was used to choose the most useful predictive features, and ten-fold cross-validation was performed. Two prediction models were constructed using multivariable logistic regression analysis: one based on clinical variables, and the other based on clinical variables with the radiomics score. Predictability of the two models was assessed with the AUC of the ROC curves.

Results

Clinical characteristics did not significantly differ between malignant and benign nodules, except for mean nodule size. Among 730 candidate texture features generated from a single US image, 15 features were selected. Radiomics signatures were constructed with a radiomics score, using selected features. In multivariable logistic regression analysis, higher radiomics score was associated with malignancy (OR = 10.923; p < 0.001). The AUC of the malignancy prediction model composed of clinical variables with the radiomics score was significantly higher than the model composed of clinical variables alone (0.839 vs 0.583).

Conclusions

Quantitative US radiomics features can help predict malignancy in thyroid nodules with indeterminate cytology.

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.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

AUC:

Area under the curve

AUS/FLUS:

Atypia of undetermined significance/follicular lesions of undetermined significance

CI:

Confidence interval

FN/SFN:

Follicular neoplasm or suspicious for a follicular neoplasm

GEC:

The Afirma gene expression classifier

GMP:

The ThyroSeq gene mutation panel

LASSO:

Least absolute shrinkage and selection operator

NPV:

Negative predictive value

OR:

Odds ratio

ROC:

Receiver operating characteristic

ROIs:

Regions of interest

US-FNA:

US-guided fine-needle aspiration

References

  1. Vander JB, Gaston EA, Dawber TR (1968) The significance of nontoxic thyroid nodules. Final report of a 15-year study of the incidence of thyroid malignancy. Ann Intern Med 69:537–540

    Article  CAS  PubMed  Google Scholar 

  2. Tunbridge WM, Evered DC, Hall R et al (1977) The spectrum of thyroid disease in a community: the Whickham survey. Clin Endocrinol (Oxf) 7:481–493

    Article  CAS  Google Scholar 

  3. Tan GH, Gharib H (1997) Thyroid incidentalomas: management approaches to nonpalpable nodules discovered incidentally on thyroid imaging. Ann Intern Med 126:226–231

    Article  CAS  PubMed  Google Scholar 

  4. Haugen BR, Alexander EK, Bible KC et al (2016) 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 26:1–133

    Article  PubMed  PubMed Central  Google Scholar 

  5. Cibas ES, Ali SZ (2017) The 2017 Bethesda System for Reporting Thyroid Cytopathology. Thyroid 27:1341–1346

    Article  PubMed  Google Scholar 

  6. Yoon JH, Moon HJ, Kim EK, Kwak JY (2011) Inadequate cytology in thyroid nodules: should we repeat aspiration or follow-up? Ann Surg Oncol 18:1282–1289

    Article  PubMed  Google Scholar 

  7. Yoon JH, Kwak JY, Kim EK et al (2010) How to approach thyroid nodules with indeterminate cytology. Ann Surg Oncol 17:2147–2155

    Article  PubMed  Google Scholar 

  8. Kim DW, Lee EJ, Jung SJ, Ryu JH, Kim YM (2011) Role of sonographic diagnosis in managing Bethesda class III nodules. AJNR Am J Neuroradiol 32:2136–2141

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Yoon JH, Lee HS, Kim EK, Moon HJ, Kwak JY (2014) A nomogram for predicting malignancy in thyroid nodules diagnosed as atypia of undetermined significance/follicular lesions of undetermined significance on fine needle aspiration. Surgery 155:1006–1013

    Article  PubMed  Google Scholar 

  10. Grani G, Lamartina L, Ascoli V et al (2017) Ultrasonography scoring systems can rule out malignancy in cytologically indeterminate thyroid nodules. Endocrine 57:256–261

    Article  CAS  PubMed  Google Scholar 

  11. Xing M, Tufano RP, Tufaro AP et al (2004) Detection of BRAF mutation on fine needle aspiration biopsy specimens: a new diagnostic tool for papillary thyroid cancer. J Clin Endocrinol Metab 89:2867–2872

    Article  CAS  PubMed  Google Scholar 

  12. Yoon JH, Kwon HJ, Lee HS, Kim EK, Moon HJ, Kwak JY (2015) RAS mutations in AUS/FLUS cytology: does it have an additional role in BRAFV600E mutation-negative nodules? Medicine (Baltimore) 94:e1084

    Article  CAS  Google Scholar 

  13. Mendez W, Rodgers SE, Lew JI, Montano R, Solorzano CC (2008) Role of surgeon-performed ultrasound in predicting malignancy in patients with indeterminate thyroid nodules. Ann Surg Oncol 15:2487–2492

    Article  PubMed  Google Scholar 

  14. Choi SH, Kim EK, Kwak JY, Kim MJ, Son EJ (2010) Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules. Thyroid 20:167–172

    Article  PubMed  Google Scholar 

  15. Park CS, Kim SH, Jung SL et al (2010) Observer variability in the sonographic evaluation of thyroid nodules. J Clin Ultrasound 38:287–293

    PubMed  Google Scholar 

  16. Park SJ, Park SH, Choi YJ et al (2012) Interobserver variability and diagnostic performance in US assessment of thyroid nodule according to size. Ultraschall Med 33:E186–E190

    Article  CAS  PubMed  Google Scholar 

  17. Koh J, Choi JR, Han KH et al (2013) Proper indication of BRAF(V600E) mutation testing in fine-needle aspirates of thyroid nodules. PLoS One 8:e64505

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sciacchitano S, Lavra L, Ulivieri A et al (2017) Comparative analysis of diagnostic performance, feasibility and cost of different test-methods for thyroid nodules with indeterminate cytology. Oncotarget 8:49421–49442

    Article  PubMed  PubMed Central  Google Scholar 

  19. Piccardo A, Puntoni M, Dezzana M et al (2020) Indeterminate thyroid nodules. The role of (18)F-FDG PET/CT in the “era” of ultrasonography risk stratification systems and new thyroid cytology classifications. Endocrine 69:553–561

    Article  CAS  PubMed  Google Scholar 

  20. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  21. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  PubMed  PubMed Central  Google Scholar 

  22. Nie K, Al-Hallaq H, Li XA et al (2019) NCTN assessment on current applications of radiomics in oncology. Int J Radiat Oncol Biol Phys 104:302–315

    Article  PubMed  PubMed Central  Google Scholar 

  23. Liu T, Zhou S, Yu J et al (2019) Prediction of lymph node metastasis in patients with papillary thyroid carcinoma: a radiomics method based on preoperative ultrasound images. Technol Cancer Res Treat 18:1533033819831713

    Article  PubMed  PubMed Central  Google Scholar 

  24. Liu T, Ge X, Yu J et al (2018) Comparison of the application of B-mode and strain elastography ultrasound in the estimation of lymph node metastasis of papillary thyroid carcinoma based on a radiomics approach. Int J Comput Assist Radiol Surg 13:1617–1627

    Article  PubMed  Google Scholar 

  25. Park VY, Han K, Lee E et al (2019) Association between radiomics signature and disease-free survival in conventional papillary thyroid carcinoma. Sci Rep 9:4501

    Article  PubMed  PubMed Central  Google Scholar 

  26. Liang J, Huang X, Hu H et al (2018) Predicting malignancy in thyroid nodules: radiomics score versus 2017 American College of Radiology thyroid imaging, reporting and data system. Thyroid 28:1024–1033

    Article  CAS  PubMed  Google Scholar 

  27. Zhao CK, Ren TT, Yin YF et al (2020) A comparative analysis of two machine learning-based diagnostic patterns with thyroid imaging reporting and data system for thyroid nodules: diagnostic performance and unnecessary biopsy rate. Thyroid. https://doi.org/10.1089/thy.2020.0305

  28. Kim EK, Park CS, Chung WY et al (2002) New sonographic criteria for recommending fine-needle aspiration biopsy of nonpalpable solid nodules of the thyroid. AJR Am J Roentgenol 178:687–691

    Article  PubMed  Google Scholar 

  29. Cibas ES, Ali SZ (2009) The Bethesda System For Reporting Thyroid Cytopathology. Am J Clin Pathol 132:658–66530

    Article  PubMed  Google Scholar 

  30. Alexander EK, Kennedy GC, Baloch ZW et al (2012) Preoperative diagnosis of benign thyroid nodules with indeterminate cytology. N Engl J Med 367:705–715

    Article  CAS  PubMed  Google Scholar 

  31. Nikiforova MN, Wald AI, Roy S, Durso MB, Nikiforov YE (2013) Targeted next-generation sequencing panel (ThyroSeq) for detection of mutations in thyroid cancer. J Clin Endocrinol Metab 98:E1852–E1860

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Barbosa TLM, Junior COM, Graf H et al (2019) ACR TI-RADS and ATA US scores are helpful for the management of thyroid nodules with indeterminate cytology. BMC Endocr Disord 19:112

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ahmadi S, Herbst R, Oyekunle T et al (2019) Using the ATA and ACR TI-RADS sonographic classifications as adjunctive predictors of malignancy for indeterminate thyroid nodules. Endocr Pract 25:908–917

    Article  PubMed  Google Scholar 

  34. Kloos RT (2017) Molecular profiling of thyroid nodules: current role for the Afirma gene expression classifier on clinical decision making. Mol Imaging Radionucl Ther 26:36–49

    Article  PubMed  PubMed Central  Google Scholar 

  35. Deaver KE, Haugen BR, Pozdeyev N, Marshall CB (2018) Outcomes of Bethesda categories III and IV thyroid nodules over 5 years and performance of the Afirma gene expression classifier: a single-institution study. Clin Endocrinol (Oxf) 89:226–232

    Article  Google Scholar 

  36. Lastra RR, Pramick MR, Crammer CJ, LiVolsi VA, Baloch ZW (2014) Implications of a suspicious Afirma test result in thyroid fine-needle aspiration cytology: an institutional experience. Cancer Cytopathol 122:737–744

    Article  PubMed  Google Scholar 

  37. Wu JX, Young S, Hung ML et al (2016) Clinical factors influencing the performance of gene expression classifier testing in indeterminate thyroid nodules. Thyroid 26:916–922

    Article  PubMed  Google Scholar 

  38. Brauner E, Holmes BJ, Krane JF et al (2015) Performance of the Afirma gene expression classifier in Hürthle cell thyroid nodules differs from other indeterminate thyroid nodules. Thyroid 25:789–796

    Article  PubMed  Google Scholar 

  39. Nikiforov YE, Ohori NP, Hodak SP et al (2011) Impact of mutational testing on the diagnosis and management of patients with cytologically indeterminate thyroid nodules: a prospective analysis of 1056 FNA samples. J Clin Endocrinol Metab 96:3390–3397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Nikiforov YE, Carty SE, Chiosea SI et al (2014) Highly accurate diagnosis of cancer in thyroid nodules with follicular neoplasm/suspicious for a follicular neoplasm cytology by ThyroSeq v2 next-generation sequencing assay. Cancer 120:3627–3634

    Article  CAS  PubMed  Google Scholar 

  41. Nikiforova MN, Mercurio S, Wald AI et al (2018) Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules. Cancer 124:1682–1690

    Article  CAS  PubMed  Google Scholar 

  42. Steward DL, Carty SE, Sippel RS et al (2019) Performance of a multigene genomic classifier in thyroid nodules with indeterminate cytology: a prospective blinded multicenter study. JAMA Oncol 5:204–212

    Article  PubMed  Google Scholar 

  43. Jug R, Foo WC, Jones C, Ahmadi S, Jiang XS (2020) High-risk and intermediate-high-risk results from the ThyroSeq v2 and v3 thyroid genomic classifier are associated with neoplasia: Independent performance assessment at an academic institution. Cancer Cytopathol. https://doi.org/10.1002/cncy.22283

  44. Gao LY, Wang Y, Jiang YX et al (2017) Ultrasound is helpful to differentiate Bethesda class III thyroid nodules: a PRISMA-compliant systematic review and meta-analysis. Medicine (Baltimore) 96:e6564

    Article  Google Scholar 

  45. He YP, Xu HX, Zhao CK et al (2017) Cytologically indeterminate thyroid nodules: increased diagnostic performance with combination of US TI-RADS and a new scoring system. Sci Rep 7:6906

    Article  PubMed  PubMed Central  Google Scholar 

  46. Trimboli P, Fulciniti F, Zilioli V, Ceriani L, Giovanella L (2017) Accuracy of international ultrasound risk stratification systems in thyroid lesions cytologically classified as indeterminate. Diagn Cytopathol 45:113–117

    Article  PubMed  Google Scholar 

  47. Kim SH, Park CS, Jung SL et al (2010) Observer variability and the performance between faculties and residents: US criteria for benign and malignant thyroid nodules. Korean J Radiol 11:149–155

    Article  PubMed  PubMed Central  Google Scholar 

  48. Yoon JH, Han K, Lee E et al (2020) Radiomics in predicting mutation status for thyroid cancer: a preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma. PLoS One 15:e0228968

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Park VY, Han K, Kim HJ et al (2020) Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma. PLoS One 15:e0227315

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kwon MR, Shin JH, Park H, Cho H, Hahn SY, Park KW (2020) Radiomics study of thyroid ultrasound for predicting BRAF mutation in papillary thyroid carcinoma: preliminary results. AJNR Am J Neuroradiol 41:700–705

    Article  PubMed  PubMed Central  Google Scholar 

  51. Lu W, Zhong L, Dong D et al (2019) Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma. Eur J Radiol 118:231–238

    Article  PubMed  Google Scholar 

  52. Cho BY, Choi HS, Park YJ et al (2013) Changes in the clinicopathological characteristics and outcomes of thyroid cancer in Korea over the past four decades. Thyroid 23:797–804

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1002375).

The authors declare that they have no competing interests.

Funding

This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1002375). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jin Young Kwak.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jin Young Kwak.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

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

Supplementary Information

ESM 1

(DOCX 160 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yoon, J., Lee, E., Kang, SW. et al. Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology. Eur Radiol 31, 5059–5067 (2021). https://doi.org/10.1007/s00330-020-07670-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07670-3

Keywords

Navigation