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Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland

Authors
 Ilah Shin  ;  Young Jae Kim  ;  Kyunghwa Han  ;  Eunjung Lee  ;  Hye Jung Kim  ;  Jung Hee Shin  ;  Hee Jung Moon  ;  Ji Hyun Youk  ;  Kwang Gi Kim  ;  Jin Young Kwak 
Citation
 ULTRASONOGRAPHY, Vol.39(3) : 257-265, 2020-07 
Journal Title
ULTRASONOGRAPHY
ISSN
 2288-5919 
Issue Date
2020-07
Keywords
Artificial neural network ; Follicular neoplasm ; Machine learning ; Support vector machine ; Ultrasonography
Abstract
Purpose: This study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US).

Methods: In this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared.

Results: In total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVM-based models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement.

Conclusion: Machine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.
Files in This Item:
T202002396.pdf Download
DOI
10.14366/usg.19069
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kwak, Jin Young(곽진영) ORCID logo https://orcid.org/0000-0002-6212-1495
Moon, Hee Jung(문희정) ORCID logo https://orcid.org/0000-0002-5643-5885
Shin, Ilah(신일아)
Youk, Ji Hyun(육지현) ORCID logo https://orcid.org/0000-0002-7787-780X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179498
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