Browse > Article
http://dx.doi.org/10.3348/kjr.2018.19.4.665

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)
Publication Information
Korean Journal of Radiology / v.19, no.4, 2018 , pp. 665-672 More about this Journal
Abstract
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.
Keywords
Artificial intelligence; Computer-aided diagnosis; Thyroid nodule; Thyroid cancer; Ultrasonography; Ultrasound;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 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   DOI
2 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   DOI
3 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   DOI
4 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   DOI
5 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   DOI
6 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   DOI
7 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   DOI
8 Brander A, Viikinkoski P, Nickels J, Kivisaari L. Thyroid gland: US screening in a random adult population. Radiology 1991;181:683-687   DOI
9 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   DOI
10 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
11 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   DOI
12 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   DOI
13 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   DOI
14 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   DOI
15 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   DOI
16 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   DOI
17 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
18 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   DOI
19 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   DOI
20 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   DOI