Advertisement

La radiologia medica

, Volume 124, Issue 2, pp 118–125 | Cite as

A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound

  • Salvatore GittoEmail author
  • Giorgia Grassi
  • Chiara De Angelis
  • Cristian Giuseppe Monaco
  • Silvana Sdao
  • Francesco Sardanelli
  • Luca Maria Sconfienza
  • Giovanni Mauri
HEAD, NECK AND DENTAL RADIOLOGY
  • 96 Downloads

Abstract

Aim of the study

To compare the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system for thyroid ultrasound (US) with that of a non-computer-aided radiologist in the characterization of low-to-high suspicion thyroid nodules.

Methods

This retrospective study included a consecutive series of adult patients referred for US-guided fine-needle aspiration biopsy (FNAB) of a thyroid nodule. All patients were eligible for thyroid nodule FNAB according to the current international guidelines. An interventional radiologist experienced in thyroid imaging acquired the US images subsequently used for post-processing, performed FNAB and provided the US features of each nodule. A radiology resident and an endocrinology resident in consensus performed post-processing using the CAD system to assess the same nodule characteristics. The diagnostic performance and agreement of US features between the CAD system and the radiologist were compared.

Results

Sixty-two patients (50 F; age 60 ± 12 years) were enrolled: 77.4% (48/62) of thyroid nodules were benign, 22.6% (14/62) were undetermined to malignant and required follow-up or surgery. Interobserver agreement between the CAD system and the radiologist was substantial for orientation (K = 0.69), fair for composition (K = 0.36), echogenicity (K = 0.36), K-TIRADS (K = 0.29), and slight for margins (K = 0.03). The radiologist demonstrated a significantly higher sensitivity than the CAD system (78.6% vs. 21.4%; P = 0.008), while there was no statistical difference in specificity (66.7% vs. 81.3%; P = 0.065).

Conclusion

This CAD system is less sensitive than an experienced radiologist and showed slight-to-substantial agreement with the radiologist for the characterization of thyroid nodules. Although it is an innovative tool with good potential, additional efforts are needed to improve its diagnostic performance.

Keywords

Computer-aided diagnosis Nodule Thyroid Ultrasound 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that the have no conflict of interest.

Ethical standards

All human and animal studies have been approved by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

References

  1. 1.
    Guth S, Theune U, Aberle J, Galach A, Bamberger CM (2009) Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination. Eur J Clin Invest 39:699–706CrossRefGoogle Scholar
  2. 2.
    Wolinski K, Stangierski A, Ruchala M (2017) Comparison of diagnostic yield of core-needle and fine-needle aspiration biopsies of thyroid lesions: systematic review and meta-analysis. Eur Radiol 27:431–436CrossRefGoogle Scholar
  3. 3.
    Mittendorf EA, Tamarkin SW, McHenry CR (2002) The results of ultrasound-guided fine-needle aspiration biopsy for evaluation of nodular thyroid disease. Surgery 132:648–653 discussion 653–644 CrossRefGoogle Scholar
  4. 4.
    Mainini AP, Monaco C, Pescatori LC et al (2017) Image-guided thermal ablation of benign thyroid nodules. J Ultrasound 20:11–22CrossRefGoogle Scholar
  5. 5.
    Mauri G, Cova L, Monaco CG et al (2016) Benign thyroid nodules treatment using percutaneous laser ablation (PLA) and radiofrequency ablation (RFA). Int J Hyperthermia.  https://doi.org/10.1080/02656736.2016.1244707:1-5 Google Scholar
  6. 6.
    Mauri G, Sconfienza LM (2016) Percutaneous ablation holds the potential to substitute for surgery as first choice treatment for symptomatic benign thyroid nodules. Int J Hyperthermia.  https://doi.org/10.1080/02656736.2016.1257827:1-2 Google Scholar
  7. 7.
    Pacella CM, Mauri G, Achille G et al (2015) Outcomes and risk factors for complications of laser ablation for thyroid nodules: a multicenter study on 1531 patients. J Clin Endocrinol Metab 100:3903–3910CrossRefGoogle Scholar
  8. 8.
    Pacella CM, Mauri G, Cesareo R et al (2017) A comparison of laser with radiofrequency ablation for the treatment of benign thyroid nodules: a propensity score matching analysis. Int J Hyperthermia.  https://doi.org/10.1080/02656736.2017.1332395:1-9 Google Scholar
  9. 9.
    Gitto S, Grassi G, De Angelis C et al (2018) A computer-aided diagnosis system for the assessment and characterisation of low-to-high suspicion thyroid nodules on ultrasound. Eur Congr Radiol.  https://doi.org/10.1594/ecr2018/C-0070
  10. 10.
    Gharib H, Papini E, Garber JR et al (2016) 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 22:622–639CrossRefGoogle Scholar
  11. 11.
    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 26:1–133CrossRefGoogle Scholar
  12. 12.
    Russ G, Bonnema SJ, Erdogan MF, Durante C, Ngu R, Leenhardt L (2017) European thyroid association guidelines for ultrasound malignancy risk stratification of thyroid nodules in adults: the EU-TIRADS. Eur Thyroid J 6:225–237CrossRefGoogle Scholar
  13. 13.
    Shin JH, Baek JH, Chung J et al (2016) Ultrasonography diagnosis and imaging-based management of thyroid nodules: revised Korean Society of Thyroid Radiology Consensus Statement and Recommendations. Kor J Radiol 17:370–395CrossRefGoogle Scholar
  14. 14.
    Tessler FN, Middleton WD, Grant EG et al (2017) ACR thyroid imaging, reporting and data system (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol 14:587–595CrossRefGoogle Scholar
  15. 15.
    Cibas ES, Ali SZ, Conference NCITFSotS (2009) The Bethesda system for reporting thyroid cytopathology. Am J Clin Pathol 132:658–665CrossRefGoogle Scholar
  16. 16.
    Horvath E, Majlis S, Rossi R et al (2009) An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J Clin Endocrinol Metab 94:1748–1751CrossRefGoogle Scholar
  17. 17.
    Na DG, Baek JH, Sung JY et al (2016) Thyroid imaging reporting and data system risk stratification of thyroid nodules: categorization based on solidity and echogenicity. Thyroid 26:562–572CrossRefGoogle Scholar
  18. 18.
    Russ G, Royer B, Bigorgne C, Rouxel A, Bienvenu-Perrard M, Leenhardt L (2013) Prospective evaluation of thyroid imaging reporting and data system on 4550 nodules with and without elastography. Eur J Endocrinol 168:649–655CrossRefGoogle Scholar
  19. 19.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefGoogle Scholar
  20. 20.
    Davies L, Welch HG (2014) Current thyroid cancer trends in the United States. JAMA Otolaryngol Head Neck Surg 140:317–322CrossRefGoogle Scholar
  21. 21.
    Cheng SP, Lee JJ, Lin JL, Chuang SM, Chien MN, Liu CL (2013) Characterization of thyroid nodules using the proposed thyroid imaging reporting and data system (TI-RADS). Head Neck 35:541–547CrossRefGoogle Scholar
  22. 22.
    Choi SH, Kim EK, Kwak JY, Kim MJ, Son EJ (2010) Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules. Thyroid 20:167–172CrossRefGoogle Scholar
  23. 23.
    Acharya UR, Faust O, Sree SV, Molinari F, Garberoglio R, Suri JS (2011) Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: a class of ThyroScan algorithms. Technol Cancer Res Treat 10:371–380CrossRefGoogle Scholar
  24. 24.
    Acharya UR, Faust O, Sree SV, Molinari F, Suri JS (2012) ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Comput Methods Programs Biomed 107:233–241CrossRefGoogle Scholar
  25. 25.
    Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS (2012) Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan systems. Ultrasonics 52:508–520CrossRefGoogle Scholar
  26. 26.
    Chang Y, Paul AK, Kim N et al (2016) Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: a comparison with radiologist-based assessments. Med Phys 43:554CrossRefGoogle Scholar
  27. 27.
    Choi YJ, Baek JH, Park HS et al (2017) A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: initial Clinical Assessment. Thyroid 27:546–552CrossRefGoogle Scholar
  28. 28.
    Gao L, Liu R, Jiang Y et al (2017) Computer-aided system for diagnosing thyroid nodules on ultrasound: a comparison with radiologist-based clinical assessments. Head Neck.  https://doi.org/10.1002/hed.25049 Google Scholar

Copyright information

© Italian Society of Medical Radiology 2018

Authors and Affiliations

  • Salvatore Gitto
    • 1
    Email author
  • Giorgia Grassi
    • 2
  • Chiara De Angelis
    • 1
  • Cristian Giuseppe Monaco
    • 1
  • Silvana Sdao
    • 3
  • Francesco Sardanelli
    • 4
    • 5
  • Luca Maria Sconfienza
    • 5
    • 6
  • Giovanni Mauri
    • 7
  1. 1.Scuola di Specializzazione in RadiodiagnosticaUniversità degli Studi di MilanoMilanItaly
  2. 2.Scuola di Specializzazione in Endocrinologia e Malattie del MetabolismoUniversità degli Studi di MilanoMilanItaly
  3. 3.Fondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
  4. 4.Servizio di Radiologia, IRCCS Policlinico San DonatoSan Donato MilaneseItaly
  5. 5.Dipartimento di Scienze Biomediche per la SaluteUniversità degli Studi di MilanoMilanItaly
  6. 6.Unità Operativa di Radiologia Diagnostica ed InterventisticaIRCCS Istituto Ortopedico GaleazziMilanItaly
  7. 7.Divisione di Radiologia InterventisticaIEO, Istituto Europeo di Oncologia IRCCSMilanoItaly

Personalised recommendations