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Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

DC Field Value Language
dc.contributor.author김의현-
dc.contributor.author강석구-
dc.contributor.author장종희-
dc.contributor.author김세훈-
dc.contributor.author이승구-
dc.contributor.author최윤성-
dc.contributor.author안성수-
dc.date.accessioned2020-09-29T06:24:57Z-
dc.date.available2020-09-29T06:24:57Z-
dc.date.issued2020-07-
dc.identifier.issn0938-7994-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/179555-
dc.description.abstractBackground and purpose: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status. Materials and methods: Radiomic features (n = 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (n = 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (n = 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and IDH status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics. Results: The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501-0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003-0.209). Conclusion: Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas. Key points: • Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer International-
dc.relation.isPartOfEUROPEAN RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleMachine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Neurosurgery (신경외과학교실)-
dc.contributor.googleauthorYoon Seong Choi-
dc.contributor.googleauthorSung Soo Ahn-
dc.contributor.googleauthorJong Hee Chang-
dc.contributor.googleauthorSeok-Gu Kang-
dc.contributor.googleauthorEui Hyun Kim-
dc.contributor.googleauthorSe Hoon Kim-
dc.contributor.googleauthorRajan Jain-
dc.contributor.googleauthorSeung-Koo Lee-
dc.identifier.doi10.1007/s00330-020-06737-5-
dc.contributor.localIdA00837-
dc.contributor.localIdA00036-
dc.contributor.localIdA03470-
dc.contributor.localIdA00610-
dc.contributor.localIdA02912-
dc.contributor.localIdA04137-
dc.contributor.localIdA02234-
dc.relation.journalcodeJ00851-
dc.identifier.eissn1432-1084-
dc.identifier.pmid32162004-
dc.identifier.urlhttps://link.springer.com/article/10.1007%2Fs00330-020-06737-5-
dc.subject.keywordGlioma-
dc.subject.keywordMachine learning-
dc.subject.keywordPrognosis-
dc.subject.keywordSurvival-
dc.contributor.alternativeNameKim, Eui Hyun-
dc.contributor.affiliatedAuthor김의현-
dc.contributor.affiliatedAuthor강석구-
dc.contributor.affiliatedAuthor장종희-
dc.contributor.affiliatedAuthor김세훈-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor최윤성-
dc.contributor.affiliatedAuthor안성수-
dc.citation.volume30-
dc.citation.number7-
dc.citation.startPage3834-
dc.citation.endPage3842-
dc.identifier.bibliographicCitationEUROPEAN RADIOLOGY, Vol.30(7) : 3834-3842, 2020-07-
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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