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Development and Validation of MRI-Based Radiomics Models for Diagnosing Juvenile Myoclonic Epilepsy

DC Field Value Language
dc.contributor.author김경민-
dc.contributor.author손범석-
dc.contributor.author안성수-
dc.contributor.author이승구-
dc.contributor.author이원우-
dc.contributor.author주민경-
dc.contributor.author한경화-
dc.contributor.author허경-
dc.date.accessioned2023-03-03T02:16:38Z-
dc.date.available2023-03-03T02:16:38Z-
dc.date.issued2022-12-
dc.identifier.issn1229-6929-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/192792-
dc.description.abstractObjective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherKorean Society of Radiology-
dc.relation.isPartOfKOREAN JOURNAL OF RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAdult-
dc.subject.MESHArea Under Curve-
dc.subject.MESHBrain / diagnostic imaging-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMagnetic Resonance Imaging-
dc.subject.MESHMale-
dc.subject.MESHMyoclonic Epilepsy, Juvenile* / diagnostic imaging-
dc.titleDevelopment and Validation of MRI-Based Radiomics Models for Diagnosing Juvenile Myoclonic Epilepsy-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Neurology (신경과학교실)-
dc.contributor.googleauthorKyung Min Kim-
dc.contributor.googleauthorHeewon Hwang-
dc.contributor.googleauthorBeomseok Sohn-
dc.contributor.googleauthorKisung Park-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorSung Soo Ahn-
dc.contributor.googleauthorWonwoo Lee-
dc.contributor.googleauthorMin Kyung Chu-
dc.contributor.googleauthorKyoung Heo-
dc.contributor.googleauthorSeung-Koo Lee-
dc.identifier.doi10.3348/kjr.2022.0539-
dc.contributor.localIdA05748-
dc.contributor.localIdA04960-
dc.contributor.localIdA02234-
dc.contributor.localIdA02912-
dc.contributor.localIdA06019-
dc.contributor.localIdA03950-
dc.contributor.localIdA04267-
dc.contributor.localIdA04341-
dc.relation.journalcodeJ02884-
dc.identifier.eissn2005-8330-
dc.identifier.pmid36447416-
dc.subject.keywordIdiopathic generalized epilepsy-
dc.subject.keywordJuvenile myoclonic epilepsy-
dc.subject.keywordMagnetic resonance imaging-
dc.subject.keywordRadiomics-
dc.subject.keywordTexture analysis-
dc.contributor.alternativeNameKim, Kyung Min-
dc.contributor.affiliatedAuthor김경민-
dc.contributor.affiliatedAuthor손범석-
dc.contributor.affiliatedAuthor안성수-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor이원우-
dc.contributor.affiliatedAuthor주민경-
dc.contributor.affiliatedAuthor한경화-
dc.contributor.affiliatedAuthor허경-
dc.citation.volume23-
dc.citation.number12-
dc.citation.startPage1281-
dc.citation.endPage1289-
dc.identifier.bibliographicCitationKOREAN JOURNAL OF RADIOLOGY, Vol.23(12) : 1281-1289, 2022-12-
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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