Cited 8 times in
Clinical and diffusion parameters may noninvasively predict TERT promoter mutation status in grade II meningiomas
DC Field | Value | Language |
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dc.contributor.author | 강석구 | - |
dc.contributor.author | 김세훈 | - |
dc.contributor.author | 박예원 | - |
dc.contributor.author | 신일아 | - |
dc.contributor.author | 안성수 | - |
dc.contributor.author | 이승구 | - |
dc.contributor.author | 장종희 | - |
dc.date.accessioned | 2022-07-08T03:23:00Z | - |
dc.date.available | 2022-07-08T03:23:00Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 0150-9861 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/188820 | - |
dc.description.abstract | Background and purpose: Increasing evidence suggests that genomic and molecular markers need to be integrated in grading of meningioma. Telomerase reverse transcriptase promoter (TERTp) mutation is receiving attention due to its clinical relevance in the treatment of meningiomas. The predictive ability of conventional and diffusion MRI parameters for determining the TERTp mutation status in grade II meningiomas has yet been identified. Material and methods: In this study, 63 patients with surgically confirmed grade II meningiomas (56 TERTp wildtype, 7 TERTp mutant) were included. Conventional imaging features were qualitatively assessed. The maximum diameter, volume of the tumors and histogram parameters from the apparent diffusion coefficient (ADC) were assessed. Independent clinical and imaging risk factors for TERTp mutation were investigated using multivariable logistic regression. The discriminative value of the prediction models with and without imaging features was evaluated. Results: In the univariable regression, older age (odds ratio [OR] = 1.13, P = 0.005), larger maximum diameter (OR = 1.09, P = 0.023), larger volume (OR = 1.04, P = 0.014), lower mean ADC (OR = 0.02, P = 0.025), and lower ADC 10th percentile (OR = 0.01, P = 0.014) were predictors of TERTp mutation. In multivariable regression, age (OR = 1.13, P = 0.009) and ADC 10th percentile (OR = 0.01, P = 0.038) were independent predictors of variables for predicting the TERTp mutation status. The performance of the prediction model increased upon inclusion of imaging parameters (area under the curves of 0.86 and 0.91, respectively, without and with imaging parameters). Conclusion: Older age and lower ADC 10th percentile may be useful parameters to predict TERTp mutation in grade II meningiomas. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English, French | - |
dc.publisher | Masson. | - |
dc.relation.isPartOf | JOURNAL OF NEURORADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Child | - |
dc.subject.MESH | Diffusion Magnetic Resonance Imaging | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Meningeal Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Meningeal Neoplasms* / genetics | - |
dc.subject.MESH | Meningioma* / diagnostic imaging | - |
dc.subject.MESH | Meningioma* / genetics | - |
dc.subject.MESH | Mutation | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Telomerase* / genetics | - |
dc.title | Clinical and diffusion parameters may noninvasively predict TERT promoter mutation status in grade II meningiomas | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Neurosurgery (신경외과학교실) | - |
dc.contributor.googleauthor | Ilah Shin | - |
dc.contributor.googleauthor | Yae Won Park | - |
dc.contributor.googleauthor | Sung Soo Ahn | - |
dc.contributor.googleauthor | Seok-Gu Kang | - |
dc.contributor.googleauthor | Jong Hee Chang | - |
dc.contributor.googleauthor | Se Hoon Kim | - |
dc.contributor.googleauthor | Seung-Koo Lee | - |
dc.identifier.doi | 10.1016/j.neurad.2021.02.007 | - |
dc.contributor.localId | A00036 | - |
dc.contributor.localId | A00610 | - |
dc.contributor.localId | A05330 | - |
dc.contributor.localId | A05848 | - |
dc.contributor.localId | A02234 | - |
dc.contributor.localId | A02912 | - |
dc.contributor.localId | A03470 | - |
dc.relation.journalcode | J03937 | - |
dc.identifier.pmid | 33716047 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0150986121000560?via%3Dihub | - |
dc.subject.keyword | Diffusion weighted imaging | - |
dc.subject.keyword | Magnetic resonance imaging | - |
dc.subject.keyword | Meningioma | - |
dc.subject.keyword | Radiogenomics | - |
dc.subject.keyword | Telomerase reverse transcriptase promoter gene | - |
dc.contributor.alternativeName | Kang, Seok Gu | - |
dc.contributor.affiliatedAuthor | 강석구 | - |
dc.contributor.affiliatedAuthor | 김세훈 | - |
dc.contributor.affiliatedAuthor | 박예원 | - |
dc.contributor.affiliatedAuthor | 신일아 | - |
dc.contributor.affiliatedAuthor | 안성수 | - |
dc.contributor.affiliatedAuthor | 이승구 | - |
dc.contributor.affiliatedAuthor | 장종희 | - |
dc.citation.volume | 49 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 59 | - |
dc.citation.endPage | 65 | - |
dc.identifier.bibliographicCitation | JOURNAL OF NEURORADIOLOGY, Vol.49(1) : 59-65, 2022-01 | - |
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