Literature DB >> 28723281

Imaging Correlates of Adult Glioma Genotypes.

Marion Smits1, Martin J van den Bent1.   

Abstract

Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. © RSNA, 2017.

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Year:  2017        PMID: 28723281     DOI: 10.1148/radiol.2017151930

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  58 in total

1.  Prognostic Value of Preoperative MRI Metrics for Diffuse Lower-Grade Glioma Molecular Subtypes.

Authors:  P Darvishi; P P Batchala; J T Patrie; L M Poisson; M-B Lopes; R Jain; C E Fadul; D Schiff; S H Patel
Journal:  AJNR Am J Neuroradiol       Date:  2020-04-23       Impact factor: 3.825

2.  Glioblastoma: a prognostic value of AMT-PET?

Authors:  Rolf Bjerkvig; Frits Thorsen
Journal:  Neuro Oncol       Date:  2019-02-14       Impact factor: 12.300

Review 3.  Conventional and advanced imaging throughout the cycle of care of gliomas.

Authors:  Gilles Reuter; Martin Moïse; Wolfgang Roll; Didier Martin; Arnaud Lombard; Félix Scholtes; Walter Stummer; Eric Suero Molina
Journal:  Neurosurg Rev       Date:  2021-01-07       Impact factor: 3.042

4.  Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics.

Authors:  Zhizheng Zhuo; Liying Qu; Peng Zhang; Liwei Zhang; Yaou Liu; Yunyun Duan; Dan Cheng; Xiaolu Xu; Ting Sun; Jinli Ding; Cong Xie; Xing Liu; Sven Haller; Frederik Barkhof
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-06-16       Impact factor: 9.236

5.  Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review.

Authors:  Arian Lasocki; Mustafa Anjari; Suna Ӧrs Kokurcan; Stefanie C Thust
Journal:  Neuroradiology       Date:  2020-08-25       Impact factor: 2.804

6.  Quantitative dynamic susceptibility contrast perfusion-weighted imaging-guided customized gamma knife re-irradiation of recurrent high-grade gliomas.

Authors:  Bao Wang; Peng Zhao; Yi Zhang; Mingxu Ge; Chuanjin Lan; Chuanting Li; Qi Pang; Shangchen Xu; Yingchao Liu
Journal:  J Neurooncol       Date:  2018-04-26       Impact factor: 4.130

7.  Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas.

Authors:  P P Batchala; T J E Muttikkal; J H Donahue; J T Patrie; D Schiff; C E Fadul; E K Mrachek; M-B Lopes; R Jain; S H Patel
Journal:  AJNR Am J Neuroradiol       Date:  2019-01-31       Impact factor: 3.825

8.  Static 18F-FET PET and DSC-PWI based on hybrid PET/MR for the prediction of gliomas defined by IDH and 1p/19q status.

Authors:  Shuangshuang Song; Leiming Wang; Hongwei Yang; Yongzhi Shan; Ye Cheng; Lixin Xu; Chengyan Dong; Guoguang Zhao; Jie Lu
Journal:  Eur Radiol       Date:  2020-11-19       Impact factor: 5.315

9.  Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.

Authors:  Céline De Looze; Alan Beausang; Jane Cryan; Teresa Loftus; Patrick G Buckley; Michael Farrell; Seamus Looby; Richard Reilly; Francesca Brett; Hugh Kearney
Journal:  J Neurooncol       Date:  2018-05-16       Impact factor: 4.130

10.  MR Elastography Analysis of Glioma Stiffness and IDH1-Mutation Status.

Authors:  K M Pepin; K P McGee; A Arani; D S Lake; K J Glaser; A Manduca; I F Parney; R L Ehman; J Huston
Journal:  AJNR Am J Neuroradiol       Date:  2017-10-26       Impact factor: 3.825

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