Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience.
10.3348/kjr.2018.19.4.665
- Author:
Young Jin YOO
1
;
Eun Ju HA
;
Yoon Joo CHO
;
Hye Lin KIM
;
Miran HAN
;
So Young KANG
Author Information
1. Department of Radiology, Ajou University School of Medicine, Suwon 16499, Korea. radhej@naver.com
- Publication Type:Original Article
- Keywords:
Artificial intelligence;
Computer-aided diagnosis;
Thyroid nodule;
Thyroid cancer;
Ultrasonography;
Ultrasound
- MeSH:
Artificial Intelligence;
Diagnosis*;
Humans;
Prospective Studies;
Sensitivity and Specificity;
Thyroid Gland*;
Thyroid Neoplasms;
Thyroid Nodule*;
Ultrasonography*
- From:Korean Journal of Radiology
2018;19(4):665-672
- CountryRepublic of Korea
- Language:English
-
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.
