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Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network

Authors
 Jieun Koh  ;  Eunjung Lee  ;  Kyunghwa Han  ;  Eun-Kyung Kim  ;  Eun Ju Son  ;  Yu-Mee Sohn  ;  Mirinae Seo  ;  Mi-Ri Kwon  ;  Jung Hyun Yoon  ;  Jin Hwa Lee  ;  Young Mi Park  ;  Sungwon Kim  ;  Jung Hee Shin  ;  Jin Young Kwak 
Citation
 SCIENTIFIC REPORTS, Vol.10(1) : 15245, 2020-09 
Journal Title
 SCIENTIFIC REPORTS 
Issue Date
2020-09
Abstract
The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n = 13,560, Severance Hospital, SH training set), the internal test (n = 634, SH test set), and the external test (n = 781, Samsung Medical Center, SMC set; n = 200, CHA Bundang Medical Center, CBMC set; n = 200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898-0.937 for the internal test set and 0.821-0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P < 0.001). AUC was not significantly different between CNNE2 and radiologists in the external test sets (P = 0.113, 0.126, and 0.690). CNN showed diagnostic performances comparable to expert radiologists for differentiating thyroid nodules on US in both the internal and external test sets.
Files in This Item:
T202003652.pdf Download
DOI
10.1038/s41598-020-72270-6
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
Kim, Sungwon(김성원) ORCID logo https://orcid.org/0000-0001-5455-6926
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Son, Eun Ju(손은주) ORCID logo https://orcid.org/0000-0002-7895-0335
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/180030
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