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Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication

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dc.date.accessioned2023-07-06T00:31:39Z-
dc.date.available2023-07-06T00:31:39Z-
dc.date.issued2022-05-
dc.identifier.issn0938-7994-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/194850-
dc.description.abstractObjectives: To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified). Methods: Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data. Results: Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812). Conclusion: Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis. Key points: • Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer International-
dc.relation.isPartOfEUROPEAN RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHBrain Neoplasms* / diagnostic imaging-
dc.subject.MESHBrain Neoplasms* / genetics-
dc.subject.MESHGlioma* / diagnostic imaging-
dc.subject.MESHGlioma* / genetics-
dc.subject.MESHHumans-
dc.subject.MESHIsocitrate Dehydrogenase / genetics-
dc.subject.MESHMutation-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHRisk Assessment-
dc.titleDiffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorEun Bee Jang-
dc.contributor.googleauthorHo Sung Kim-
dc.contributor.googleauthorJi Eun Park-
dc.contributor.googleauthorSeo Young Park-
dc.contributor.googleauthorYeo Kyung Nam-
dc.contributor.googleauthorSoo Jung Nam-
dc.contributor.googleauthorYoung-Hoon Kim-
dc.contributor.googleauthorJeong Hoon Kim-
dc.identifier.doi10.1007/s00330-022-08850-z-
dc.relation.journalcodeJ00851-
dc.identifier.eissn1432-1084-
dc.identifier.pmid35587830-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00330-022-08850-z-
dc.subject.keywordDiagnostic molecular pathology-
dc.subject.keywordGlioma-
dc.subject.keywordImaging genomics-
dc.subject.keywordPrognosis-
dc.citation.volume32-
dc.citation.number11-
dc.citation.startPage7780-
dc.citation.endPage7788-
dc.identifier.bibliographicCitationEUROPEAN RADIOLOGY, Vol.32(11) : 7780-7788, 2022-05-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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