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Image Reporting and Characterization System for Ultrasound Features of Thyroid Nodules: Multicentric Korean Retrospective Study

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dc.contributor.author정인경-
dc.contributor.author곽진영-
dc.contributor.author김은경-
dc.contributor.author김정아-
dc.contributor.author문희정-
dc.contributor.author손은주-
dc.date.accessioned2014-12-18T08:23:08Z-
dc.date.available2014-12-18T08:23:08Z-
dc.date.issued2013-
dc.identifier.issn1229-6929-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/86207-
dc.description.abstractOBJECTIVE: The objective of this retrospective study was to develop and validate a simple diagnostic prediction model by using ultrasound (US) features of thyroid nodules obtained from multicenter retrospective data. MATERIALS AND METHODS: Patient data were collected from 20 different institutions and the data included 2000 thyroid nodules from 1796 patients. For developing a diagnostic prediction model to estimate the malignant risk of thyroid nodules using suspicious malignant US features, we developed a training model in a subset of 1402 nodules from 1260 patients. Several suspicious malignant US features were evaluated to create the prediction model using a scoring tool. The scores for such US features were estimated by calculating odds ratios, and the risk score of malignancy for each thyroid nodule was defined as the sum of these individual scores. Later, we verified the usefulness of developed scoring system by applying into the remaining 598 nodules from 536 patients. RESULTS: Among 2000 tumors, 1268 were benign and 732 were malignant. In our multiple regression analysis models, the following US features were statistically significant for malignant nodules when using the training data set: hypoechogenicity, marked hypoechogenicity, non-parallel orientation, microlobulated or spiculated margin, ill-defined margins, and microcalcifications. The malignancy rate was 7.3% in thyroid nodules that did not have suspicious-malignant features on US. Area under the receiver operating characteristic (ROC) curve was 0.867, which shows that the US risk score help predict thyroid malignancy well. In the test data set, the malignancy rates were 6.2% in thyroid nodules without malignant features on US. Area under the ROC curve of the test set was 0.872 when using the prediction model. CONCLUSION: The predictor model using suspicious malignant US features may be helpful in risk stratification of thyroid nodules.-
dc.description.statementOfResponsibilityopen-
dc.relation.isPartOfKOREAN JOURNAL OF RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHImage Interpretation, Computer-Assisted*-
dc.subject.MESHKorea-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHPredictive Value of Tests-
dc.subject.MESHROC Curve-
dc.subject.MESHRegression Analysis-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHRisk-
dc.subject.MESHThyroid Nodule/diagnostic imaging*-
dc.subject.MESHUltrasonography-
dc.titleImage Reporting and Characterization System for Ultrasound Features of Thyroid Nodules: Multicentric Korean Retrospective Study-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학)-
dc.contributor.googleauthorJin Young Kwak-
dc.contributor.googleauthorInkyung Jung-
dc.contributor.googleauthorJung Hwan Baek-
dc.contributor.googleauthorSeon Mi Baek-
dc.contributor.googleauthorNami Choi-
dc.contributor.googleauthorYoon Jung Choi-
dc.contributor.googleauthorSo Lyung Jung-
dc.contributor.googleauthorEun-Kyung Kim-
dc.contributor.googleauthorJeong-Ah Kim-
dc.contributor.googleauthorJi-hoon Kim-
dc.contributor.googleauthorKyu Sun Kim-
dc.contributor.googleauthorJeong Hyun Lee-
dc.contributor.googleauthorJoon Hyung Lee-
dc.contributor.googleauthorHee Jung Moon-
dc.contributor.googleauthorWon-Jin Moon-
dc.contributor.googleauthorJeong Seon Park-
dc.contributor.googleauthorJi Hwa Ryu-
dc.contributor.googleauthorJung Hee Shin-
dc.contributor.googleauthorEun Ju Son-
dc.contributor.googleauthorJin Yong Sung-
dc.contributor.googleauthorDong Gyu Na-
dc.identifier.doi10.3348/kjr.2013.14.1.110-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA03693-
dc.contributor.localIdA00182-
dc.contributor.localIdA00801-
dc.contributor.localIdA00888-
dc.contributor.localIdA01397-
dc.contributor.localIdA01988-
dc.relation.journalcodeJ02884-
dc.identifier.eissn2005-8330-
dc.identifier.pmid23323040-
dc.subject.keywordThyroid-
dc.subject.keywordThyroid cancer-
dc.subject.keywordUltrasound-
dc.contributor.alternativeNameJung, In Kyung-
dc.contributor.alternativeNameKwak, Jin Young-
dc.contributor.alternativeNameKim, Eun Kyung-
dc.contributor.alternativeNameKim, Jeong Ah-
dc.contributor.alternativeNameMoon, Heui Jeong-
dc.contributor.alternativeNameSon, Eun Ju-
dc.contributor.affiliatedAuthorJung, In Kyung-
dc.contributor.affiliatedAuthorKwak, Jin Young-
dc.contributor.affiliatedAuthorKim, Eun-Kyung-
dc.contributor.affiliatedAuthorKim, Jeong Ah-
dc.contributor.affiliatedAuthorMoon, Heui Jeong-
dc.contributor.affiliatedAuthorSon, Eun Ju-
dc.rights.accessRightsfree-
dc.citation.volume14-
dc.citation.number1-
dc.citation.startPage110-
dc.citation.endPage117-
dc.identifier.bibliographicCitationKOREAN JOURNAL OF RADIOLOGY, Vol.14(1) : 110-117, 2013-
dc.identifier.rimsid28850-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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