Korean Journal of Radiology

  • 제19권4호
  • /
  • Pages.665-672
  • /
  • 2018
  • /
  • 1229-6929(pISSN)
  • /
  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
권호 표지
DOI QR코드

DOI QR Code

Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience

  • Yoo, Young Jin (Department of Radiology, Ajou University School of Medicine) ;
  • Ha, Eun Ju (Department of Radiology, Ajou University School of Medicine) ;
  • Cho, Yoon Joo (Department of Radiology, Ajou University School of Medicine) ;
  • Kim, Hye Lin (Department of Radiology, Ajou University School of Medicine) ;
  • Han, Miran (Department of Radiology, Ajou University School of Medicine) ;
  • Kang, So Young (Department of Biostatistics, Ajou University School of Medicine)
  • 투고 : 2017.08.23
  • 심사 : 2018.01.01
  • 발행 : 2018.08.01
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: To prospectively evaluate the diagnostic performance of computer-aided diagnosis (CAD) for detection of thyroid cancers via ultrasonography (US). Materials and Methods: This study included 50 consecutive patients with 117 thyroid nodules on US during the period between June 2016 and July 2016. A radiologist performed US examinations using real-time CAD integrated into a US scanner. We compared the diagnostic performance of radiologist, the CAD system, and the CAD-assisted radiologist for the detection of thyroid cancers. Results: The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the CAD system were 80.0, 88.1, 83.3, 85.5, and 84.6%, respectively, and were not significantly different from those of the radiologist (p > 0.05). The CAD-assisted radiologist showed improved diagnostic sensitivity compared with the radiologist alone (92.0% vs. 84.0%, p = 0.037), while the specificity and PPV were reduced (85.1% vs. 95.5%, p = 0.005 and 82.1% vs. 93.3%, p = 0.008). The radiologist assisted by the CAD system exhibited better diagnostic sensitivity and NPV than the CAD system alone (92.0% vs. 80.0%, p = 0.009 and 93.4% vs. 88.9%, p = 0.013), while the specificities and PPVs were not significantly different (88.1% vs. 85.1%, p = 0.151 and 83.3% vs. 82.1%, p = 0.613, respectively). Conclusion: The CAD system may be an adjunct to radiological intervention in the diagnosis of thyroid cancer.

키워드

과제정보

연구 과제 주관 기관 : Ajou University School of Medicine

참고문헌

  1. Brander A, Viikinkoski P, Nickels J, Kivisaari L. Thyroid gland: US screening in a random adult population. Radiology 1991;181:683-687 https://doi.org/10.1148/radiology.181.3.1947082
  2. Haugen BR. 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: what is new and what has changed? Cancer 2017;123:372-381 https://doi.org/10.1002/cncr.30360
  3. Camacho PM, Petak SM, Binkley N, Clarke BL, Harris ST, Hurley DL, et al. American Association of Clinical Endocrinologists and American College of Endocrinology clinical practice guidelines for the diagnosis and treatment of postmenopausal osteoporosis - 2016. Endocr Pract 2016;22(Suppl 4):1-42
  4. Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, et al.; Korean Society of Thyroid Radiology (KSThR) and Korean Society of Radiology. Ultrasonography diagnosis and imagingbased management of thyroid nodules: revised Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J Radiol 2016;17:370-395 https://doi.org/10.3348/kjr.2016.17.3.370
  5. Ko SY, Kim EK, Sung JM, Moon HJ, Kwak JY. Diagnostic performance of ultrasound and ultrasound elastography with respect to physician experience. Ultrasound Med Biol 2014;40:854-863 https://doi.org/10.1016/j.ultrasmedbio.2013.10.005
  6. Kim HG, Kwak JY, Kim EK, Choi SH, Moon HJ. Man to man training: can it help improve the diagnostic performances and interobserver variabilities of thyroid ultrasonography in residents? Eur J Radiol 2012;81:e352-e356 https://doi.org/10.1016/j.ejrad.2011.11.011
  7. Choi SH, Kim EK, Kwak JY, Kim MJ, Son EJ. Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules. Thyroid 2010;20:167-172 https://doi.org/10.1089/thy.2008.0354
  8. Park CS, Kim SH, Jung SL, Kang BJ, Kim JY, Choi JJ, et al. Observer variability in the sonographic evaluation of thyroid nodules. J Clin Ultrasound 2010;38:287-293
  9. Park SH, Kim SJ, Kim EK, Kim MJ, Son EJ, Kwak JY. Interobserver agreement in assessing the sonographic and elastographic features of malignant thyroid nodules. AJR Am J Roentgenol 2009;193:W416-W423 https://doi.org/10.2214/AJR.09.2541
  10. Park SJ, Park SH, Choi YJ, Kim DW, Son EJ, Lee HS, et al. Interobserver variability and diagnostic performance in US assessment of thyroid nodule according to size. Ultraschall Med 2012;33:E186-E190 https://doi.org/10.1055/s-0032-1325404
  11. Lim KJ, Choi CS, Yoon DY, Chang SK, Kim KK, Han H, et al. Computer-aided diagnosis for the differentiation of malignant from benign thyroid nodules on ultrasonography. Acad Radiol 2008;15:853-858 https://doi.org/10.1016/j.acra.2007.12.022
  12. Chang Y, Paul AK, Kim N, Baek JH, Choi YJ, Ha EJ, et al. Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: a comparison with radiologist-based assessments. Med Phys 2016;43:554 https://doi.org/10.1118/1.4939060
  13. Li LN, Ouyang JH, Chen HL, Liu DY. A computer aided diagnosis system for thyroid disease using extreme learning machine. J Med Syst 2012;36:3327-3337 https://doi.org/10.1007/s10916-012-9825-3
  14. Acharya UR, Faust O, Sree SV, Molinari F, Garberoglio R, Suri JS. Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrastenhanced ultrasound using combination of wavelets and textures: a class of $ThyroScan^{TM}$ algorithms. Technol Cancer Res Treat 2011;10:371-380 https://doi.org/10.7785/tcrt.2012.500214
  15. Acharya UR, Faust O, Sree SV, Molinari F, Suri JS. 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 2012;107:233-241 https://doi.org/10.1016/j.cmpb.2011.10.001
  16. Acharya UR, Sree SV, Krishnan MM, Molinari F, Zielez'nik W, Bardales RH, et al. Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population. J Ultrasound Med 2014;33:245-253 https://doi.org/10.7863/ultra.33.2.245
  17. Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of $ThyroScan^{TM}$ systems. Ultrasonics 2012;52:508-520 https://doi.org/10.1016/j.ultras.2011.11.003
  18. Choi YJ, Baek JH, Park HS, Shim WH, Kim TY, Shong YK, et al. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment. Thyroid 2017;27:546-552 https://doi.org/10.1089/thy.2016.0372
  19. Ha EJ, Moon WJ, Na DG, Lee YH, Choi N, Kim SJ, et al. A multicenter prospective validation study for the Korean thyroid imaging reporting and data system in patients with thyroid nodules. Korean J Radiol 2016;17:811-821 https://doi.org/10.3348/kjr.2016.17.5.811
  20. Moon WJ, Jung SL, Lee JH, Na DG, Baek JH, Lee YH, et al.; Thyroid Study Group, Korean Society of Neuro- and Head and Neck Radiology. Benign and malignant thyroid nodules: US differentiation--multicenter retrospective study. Radiology 2008;247:762-770 https://doi.org/10.1148/radiol.2473070944

피인용 문헌

  1. Effectiveness evaluation of computer-aided diagnosis system for the diagnosis of thyroid nodules on ultrasound : A systematic review and meta-analysis vol.98, pp.32, 2018, https://doi.org/10.1097/md.0000000000016379
  2. A Glimpse on Trends and Characteristics of Recent Articles Published in the Korean Journal of Radiology vol.20, pp.12, 2019, https://doi.org/10.3348/kjr.2019.0928
  3. Diagnostic Performance Evaluation of a Computer-Assisted Imaging Analysis System for Ultrasound Risk Stratification of Thyroid Nodules vol.213, pp.1, 2019, https://doi.org/10.2214/ajr.18.20740
  4. The Diagnostic Efficiency of Ultrasound Computer-Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis vol.11, pp.11, 2019, https://doi.org/10.3390/cancers11111759
  5. Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network vol.17, pp.None, 2018, https://doi.org/10.1186/s12957-019-1558-z
  6. A computer-aided diagnosing system in the evaluation of thyroid nodules-experience in a specialized thyroid center vol.17, pp.1, 2018, https://doi.org/10.1186/s12957-019-1752-z
  7. Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis vol.9, pp.4, 2018, https://doi.org/10.1159/000504390
  8. 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 Radiol vol.26, pp.None, 2018, https://doi.org/10.12659/msm.918452
  9. Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images vol.26, pp.None, 2020, https://doi.org/10.12659/msm.927007
  10. Computer-Aided Diagnosis System for the Evaluation of Thyroid Nodules on Ultrasonography: Prospective Non-Inferiority Study according to the Experience Level of Radiologists vol.21, pp.3, 2018, https://doi.org/10.3348/kjr.2019.0581
  11. Characteristics of Recent Articles Published in the Korean Journal of Radiology Based on the Citation Frequency vol.21, pp.12, 2020, https://doi.org/10.3348/kjr.2020.1322
  12. A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience vol.10, pp.None, 2018, https://doi.org/10.3389/fonc.2020.557169
  13. Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules vol.67, pp.1, 2018, https://doi.org/10.1007/s12020-019-02134-1
  14. Nodular Thyroid Disease in the Era of Precision Medicine vol.10, pp.None, 2018, https://doi.org/10.3389/fendo.2019.00907
  15. Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training vol.30, pp.6, 2018, https://doi.org/10.1007/s00330-019-06652-4
  16. False-Positive Malignant Diagnosis of Nodule Mimicking Lesions by Computer-Aided Thyroid Nodule Analysis in Clinical Ultrasonography Practice vol.10, pp.6, 2018, https://doi.org/10.3390/diagnostics10060378
  17. Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners vol.23, pp.2, 2020, https://doi.org/10.1007/s40477-020-00453-y
  18. Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images vol.47, pp.9, 2018, https://doi.org/10.1002/mp.14301
  19. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review vol.23, pp.4, 2018, https://doi.org/10.2196/25759
  20. Applications of machine learning and deep learning to thyroid imaging: where do we stand? vol.40, pp.1, 2021, https://doi.org/10.14366/usg.20068
  21. Artificial intelligence for ultrasonography: unique opportunities and challenges vol.40, pp.1, 2018, https://doi.org/10.14366/usg.20078
  22. A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules vol.11, pp.None, 2018, https://doi.org/10.3389/fonc.2021.611436
  23. Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes vol.42, pp.3, 2018, https://doi.org/10.3174/ajnr.a6922
  24. 미만성 갑상샘 질환에서 GLCM을 이용한 초음파 영상 분석 vol.15, pp.4, 2021, https://doi.org/10.7742/jksr.2021.15.4.473
  25. Artificial Intelligence in Thyroid Field-A Comprehensive Review vol.13, pp.19, 2018, https://doi.org/10.3390/cancers13194740