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Cited 3 times in

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

DC FieldValueLanguage
dc.contributor.author김세훈-
dc.contributor.author안성수-
dc.contributor.author이승구-
dc.contributor.author장종희-
dc.contributor.author최윤성-
dc.contributor.author박예원-
dc.date.accessioned2019-10-28T01:56:42Z-
dc.date.available2019-10-28T01:56:42Z-
dc.date.issued2019-
dc.identifier.issn1229-6929-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/171416-
dc.description.abstractOBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. MATERIALS AND METHODS: Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade. RESULTS: The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68). CONCLUSION: Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherKorean Society of Radiology-
dc.relation.isPartOfKorean Journal of Radiology-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleRadiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Pathology (병리학교실)-
dc.contributor.googleauthorYae Won Park-
dc.contributor.googleauthorYoon Seong Choi-
dc.contributor.googleauthorSung Soo Ahn-
dc.contributor.googleauthorJong Hee Chang-
dc.contributor.googleauthorSe Hoon Kim-
dc.contributor.googleauthorSeung-Koo Lee-
dc.identifier.doi10.3348/kjr.2018.0814-
dc.contributor.localIdA00610-
dc.contributor.localIdA02234-
dc.contributor.localIdA02234-
dc.contributor.localIdA02912-
dc.contributor.localIdA02912-
dc.contributor.localIdA03470-
dc.contributor.localIdA03470-
dc.contributor.localIdA04137-
dc.contributor.localIdA04137-
dc.relation.journalcodeJ02884-
dc.identifier.eissn2005-8330-
dc.identifier.pmid31464116-
dc.subject.keywordGrade-
dc.subject.keywordLower-grade glioma-
dc.subject.keywordMagnetic resonance imaging-
dc.subject.keywordRadiomics-
dc.subject.keywordThe Cancer Genome Atlas-
dc.contributor.alternativeNameKim, Se Hoon-
dc.contributor.affiliatedAuthor김세훈-
dc.contributor.affiliatedAuthor안성수-
dc.contributor.affiliatedAuthor안성수-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor장종희-
dc.contributor.affiliatedAuthor장종희-
dc.contributor.affiliatedAuthor최윤성-
dc.contributor.affiliatedAuthor최윤성-
dc.citation.volume20-
dc.citation.number9-
dc.citation.startPage1381-
dc.citation.endPage1389-
dc.identifier.bibliographicCitationKorean Journal of Radiology, Vol.20(9) : 1381-1389, 2019-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers

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