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Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma

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dc.contributor.author김세훈-
dc.contributor.author박예원-
dc.contributor.author안성수-
dc.contributor.author이승구-
dc.contributor.author장종희-
dc.contributor.author한경화-
dc.date.accessioned2021-09-29T02:21:50Z-
dc.date.available2021-09-29T02:21:50Z-
dc.date.issued2021-08-
dc.identifier.issn0167-594X-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/184851-
dc.description.abstractPurpose: We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations. Methods: This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes. Results: Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction. Conclusions: MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer-
dc.relation.isPartOfJOURNAL OF NEURO-ONCOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleIdentification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Pathology (병리학교실)-
dc.contributor.googleauthorSung Soo Ahn-
dc.contributor.googleauthorChansik An-
dc.contributor.googleauthorYae Won Park-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorJong Hee Chang-
dc.contributor.googleauthorSe Hoon Kim-
dc.contributor.googleauthorSeung-Koo Lee-
dc.contributor.googleauthorSoonmee Cha-
dc.identifier.doi10.1007/s11060-021-03801-y-
dc.contributor.localIdA00610-
dc.contributor.localIdA05330-
dc.contributor.localIdA02234-
dc.contributor.localIdA02912-
dc.contributor.localIdA03470-
dc.relation.journalcodeJ01629-
dc.identifier.eissn1573-7373-
dc.identifier.pmid34191225-
dc.identifier.urlhttps://link.springer.com/article/10.1007%2Fs11060-021-03801-y-
dc.subject.keywordGlioblastoma-
dc.subject.keywordMagnetic resonance imaging-
dc.subject.keywordMolecular alterations-
dc.subject.keywordMolecular profiles-
dc.contributor.alternativeNameKim, Se Hoon-
dc.contributor.affiliatedAuthor김세훈-
dc.contributor.affiliatedAuthor박예원-
dc.contributor.affiliatedAuthor안성수-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor장종희-
dc.citation.volume154-
dc.citation.number1-
dc.citation.startPage83-
dc.citation.endPage92-
dc.identifier.bibliographicCitationJOURNAL OF NEURO-ONCOLOGY, Vol.154(1) : 83-92, 2021-08-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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