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Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network
DC Field | Value | Language |
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dc.contributor.author | 곽진영 | - |
dc.contributor.author | 윤정현 | - |
dc.contributor.author | 이혜선 | - |
dc.date.accessioned | 2021-11-19T01:38:43Z | - |
dc.date.available | 2021-11-19T01:38:43Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/185960 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Inyoung Youn | - |
dc.contributor.googleauthor | Eunjung Lee | - |
dc.contributor.googleauthor | Jung Hyun Yoon | - |
dc.contributor.googleauthor | Hye Sun Lee | - |
dc.contributor.googleauthor | Mi-Ri Kwon | - |
dc.contributor.googleauthor | Juhee Moon | - |
dc.contributor.googleauthor | Sunyoung Kang | - |
dc.contributor.googleauthor | Seul Ki Kwon | - |
dc.contributor.googleauthor | Kyong Yeun Jung | - |
dc.contributor.googleauthor | Young Joo Park | - |
dc.contributor.googleauthor | Do Joon Park | - |
dc.contributor.googleauthor | Sun Wook Cho | - |
dc.contributor.googleauthor | Jin Young Kwak | - |
dc.identifier.doi | 10.1038/s41598-021-99622-0 | - |
dc.contributor.localId | A00182 | - |
dc.contributor.localId | A02595 | - |
dc.contributor.localId | A03312 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 34625636 | - |
dc.contributor.alternativeName | Kwak, Jin Young | - |
dc.contributor.affiliatedAuthor | 곽진영 | - |
dc.contributor.affiliatedAuthor | 윤정현 | - |
dc.contributor.affiliatedAuthor | 이혜선 | - |
dc.citation.volume | 11 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 20048 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.11(1) : 20048, 2021-10 | - |
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