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Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer

DC FieldValueLanguage
dc.contributor.author곽진영-
dc.contributor.author구자승-
dc.contributor.author남기현-
dc.contributor.author문희정-
dc.contributor.author박영진-
dc.contributor.author윤정현-
dc.contributor.author윤지영-
dc.contributor.author이잔디-
dc.contributor.author조영석-
dc.date.accessioned2021-01-19T07:42:35Z-
dc.date.available2021-01-19T07:42:35Z-
dc.date.issued2020-11-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/181302-
dc.description.abstractPurpose: To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer. Methods: 469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0-100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAFV600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAFV600E mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves. Results: In this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAFV600E mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAFV600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAFV600E mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004). Conclusion: Deep learning-based CAD for thyroid US can help us predict the BRAFV600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherPublic Library of Science-
dc.relation.isPartOfPLOS ONE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAdult-
dc.subject.MESHAged-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHCarcinoma, Papillary / diagnosis-
dc.subject.MESHCarcinoma, Papillary / epidemiology-
dc.subject.MESHCarcinoma, Papillary / genetics*-
dc.subject.MESHCarcinoma, Papillary / pathology-
dc.subject.MESHDiagnosis, Computer-Assisted-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHMutation / genetics-
dc.subject.MESHProto-Oncogene Proteins B-raf / genetics*-
dc.subject.MESHThyroid Gland / diagnostic imaging-
dc.subject.MESHThyroid Gland / pathology-
dc.subject.MESHThyroid Neoplasms / diagnostic imaging-
dc.subject.MESHThyroid Neoplasms / epidemiology-
dc.subject.MESHThyroid Neoplasms / genetics*-
dc.subject.MESHThyroid Neoplasms / pathology-
dc.subject.MESHThyroid Nodule-
dc.subject.MESHTomography, X-Ray Computed-
dc.titleArtificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorJiyoung Yoon-
dc.contributor.googleauthorEunjung Lee-
dc.contributor.googleauthorJa Seung Koo-
dc.contributor.googleauthorJung Hyun Yoon-
dc.contributor.googleauthorKee-Hyun Nam-
dc.contributor.googleauthorJandee Lee-
dc.contributor.googleauthorYoung Suk Jo-
dc.contributor.googleauthorHee Jung Moon-
dc.contributor.googleauthorVivian Youngjean Park-
dc.contributor.googleauthorJin Young Kwak-
dc.identifier.doi10.1371/journal.pone.0242806-
dc.contributor.localIdA00182-
dc.contributor.localIdA00198-
dc.contributor.localIdA00198-
dc.contributor.localIdA01245-
dc.contributor.localIdA01245-
dc.contributor.localIdA01397-
dc.contributor.localIdA01397-
dc.contributor.localIdA01572-
dc.contributor.localIdA01572-
dc.contributor.localIdA02595-
dc.contributor.localIdA02595-
dc.contributor.localIdA05730-
dc.contributor.localIdA05730-
dc.contributor.localIdA03066-
dc.contributor.localIdA03066-
dc.contributor.localIdA03853-
dc.contributor.localIdA03853-
dc.relation.journalcodeJ02540-
dc.identifier.eissn1932-6203-
dc.identifier.pmid33237975-
dc.contributor.alternativeNameKwak, Jin Young-
dc.contributor.affiliatedAuthor곽진영-
dc.contributor.affiliatedAuthor구자승-
dc.contributor.affiliatedAuthor구자승-
dc.contributor.affiliatedAuthor남기현-
dc.contributor.affiliatedAuthor남기현-
dc.contributor.affiliatedAuthor문희정-
dc.contributor.affiliatedAuthor문희정-
dc.contributor.affiliatedAuthor박영진-
dc.contributor.affiliatedAuthor박영진-
dc.contributor.affiliatedAuthor윤정현-
dc.contributor.affiliatedAuthor윤정현-
dc.contributor.affiliatedAuthor윤지영-
dc.contributor.affiliatedAuthor윤지영-
dc.contributor.affiliatedAuthor이잔디-
dc.contributor.affiliatedAuthor이잔디-
dc.contributor.affiliatedAuthor조영석-
dc.contributor.affiliatedAuthor조영석-
dc.citation.volume15-
dc.citation.number11-
dc.citation.startPagee0242806-
dc.identifier.bibliographicCitationPLOS ONE, Vol.15(11) : e0242806, 2020-11-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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