Korean J Radiol.  2018 Aug;19(4):665-672. 10.3348/kjr.2018.19.4.665.

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

• Yoo YJ
• Ha EJ
• Cho YJ
• Kim HL
• Han M
• Kang SY

• KMID: 2413695
• DOI: http://doi.org/10.3348/kjr.2018.19.4.665

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.

Keyword

Artificial intelligence; Computer-aided diagnosis; Thyroid nodule; Thyroid cancer; Ultrasonography; Ultrasound

MeSH Terms

Artificial Intelligence
Diagnosis*
Humans
Prospective Studies
Sensitivity and Specificity
Thyroid Gland*
Thyroid Neoplasms
Thyroid Nodule*
Ultrasonography*

Figure

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  Fig. 1 US image of thyroid nodule acquired via CAD system.A. Solid hypoechoic nodule with suspicious US features is evident in left thyroid gland. Region of interest is manually drawn around lesion. B. CAD software automatically calculates mass contours and presents US features on right of screen, and possible diagnosis as malignant nodule at bottom. CAD = computer-aided diagnosis, US = ultrasonography

• 

  Fig. 2 53-year-old woman with bilateral thyroid nodules.A. US images show solid isoechoic nodule without suspicious US features in right thyroid gland. Radiological diagnosis suggested benign nodule. B. CAD system presented possible diagnosis of benign nodule. Histology confirmed adenomatous hyperplasia.

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  Fig. 3 47-year-old woman with right thyroid nodule.A. US images show solid hypoechoic nodule with suspicious US features in right thyroid gland. Radiological diagnosis suggested malignant nodule. B. CAD system presented possible diagnosis as malignant nodule. Histology confirmed diagnosis of papillary thyroid carcinoma.

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  Fig. 4 36-year-old woman with left thyroid nodule.A. US images show solid isoechoic nodule with thick peripheral halo in left thyroid gland. Radiologist diagnosed it as benign nodule. B. CAD system suggested possible diagnosis of malignant nodule following US misdiagnosis. Histology confirmed diagnosis of adenomatous hyperplasia.

• 

  Fig. 5 Comparison of receiver operating characteristic curves for CAD, radiologist, and CAD-assisted radiologist in thyroid cancer diagnosis.

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