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Diagnosis of thyroid micronodules on ultrasound using a deep convolutional neural network
DC Field | Value | Language |
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dc.contributor.author | 곽진영 | - |
dc.contributor.author | 노미리비 | - |
dc.contributor.author | 박영진 | - |
dc.contributor.author | 윤정현 | - |
dc.contributor.author | 이혜선 | - |
dc.contributor.author | 한경화 | - |
dc.date.accessioned | 2023-05-31T05:35:10Z | - |
dc.date.available | 2023-05-31T05:35:10Z | - |
dc.date.issued | 2023-05 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/194214 | - |
dc.description.abstract | To assess the performance of deep convolutional neural network (CNN) to discriminate malignant and benign thyroid nodules < 10 mm in size and compare the diagnostic performance of CNN with those of radiologists. Computer-aided diagnosis was implemented with CNN and trained using ultrasound (US) images of 13,560 nodules ≥ 10 mm in size. Between March 2016 and February 2018, US images of nodules < 10 mm were retrospectively collected at the same institution. All nodules were confirmed as malignant or benign from aspirate cytology or surgical histology. Diagnostic performances of CNN and radiologists were assessed and compared for area under curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Subgroup analyses were performed based on nodule size with a cut-off value of 5 mm. Categorization performances of CNN and radiologists were also compared. A total of 370 nodules from 362 consecutive patients were assessed. CNN showed higher negative predictive value (35.3% vs. 22.6%, P = 0.048) and AUC (0.66 vs. 0.57, P = 0.04) than radiologists. CNN also showed better categorization performance than radiologists. In the subgroup of nodules ≤ 5 mm, CNN showed higher AUC (0.63 vs. 0.51, P = 0.08) and specificity (68.2% vs. 9.1%, P < 0.001) than radiologists. Convolutional neural network trained with thyroid nodules ≥ 10 mm in size showed overall better diagnostic performance than radiologists in the diagnosis and categorization of thyroid nodules < 10 mm, especially in nodules ≤ 5 mm. | - |
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.subject.MESH | Humans | - |
dc.subject.MESH | Neural Networks, Computer | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Thyroid Nodule* / diagnostic imaging | - |
dc.subject.MESH | Thyroid Nodule* / pathology | - |
dc.subject.MESH | Ultrasonography / methods | - |
dc.title | Diagnosis of thyroid micronodules on ultrasound using a deep convolutional neural network | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Miribi Rho | - |
dc.contributor.googleauthor | Sei Hyun Chun | - |
dc.contributor.googleauthor | Eunjung Lee | - |
dc.contributor.googleauthor | Hye Sun Lee | - |
dc.contributor.googleauthor | Jung Hyun Yoon | - |
dc.contributor.googleauthor | Vivian Youngjean Park | - |
dc.contributor.googleauthor | Kyunghwa Han | - |
dc.contributor.googleauthor | Jin Young Kwak | - |
dc.identifier.doi | 10.1038/s41598-023-34459-3 | - |
dc.contributor.localId | A00182 | - |
dc.contributor.localId | A05327 | - |
dc.contributor.localId | A01572 | - |
dc.contributor.localId | A02595 | - |
dc.contributor.localId | A03312 | - |
dc.contributor.localId | A04267 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 37142760 | - |
dc.contributor.alternativeName | Kwak, Jin Young | - |
dc.contributor.affiliatedAuthor | 곽진영 | - |
dc.contributor.affiliatedAuthor | 노미리비 | - |
dc.contributor.affiliatedAuthor | 박영진 | - |
dc.contributor.affiliatedAuthor | 윤정현 | - |
dc.contributor.affiliatedAuthor | 이혜선 | - |
dc.contributor.affiliatedAuthor | 한경화 | - |
dc.citation.volume | 13 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 7231 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.13(1) : 7231, 2023-05 | - |
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