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Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network

Authors
 Inyoung Youn  ;  Eunjung Lee  ;  Jung Hyun Yoon  ;  Hye Sun Lee  ;  Mi-Ri Kwon  ;  Juhee Moon  ;  Sunyoung Kang  ;  Seul Ki Kwon  ;  Kyong Yeun Jung  ;  Young Joo Park  ;  Do Joon Park  ;  Sun Wook Cho  ;  Jin Young Kwak 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 20048, 2021-10 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2021-10
Abstract
To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680-0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657-0.768 and 0.652 for AUS, 0.469-0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.
Files in This Item:
T202104487.pdf Download
DOI
10.1038/s41598-021-99622-0
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kwak, Jin Young(곽진영) ORCID logo https://orcid.org/0000-0002-6212-1495
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/185960
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