Literature DB >> 19796694

An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging.

Sylvia Drabycz1, Gloria Roldán, Paula de Robles, Daniel Adler, John B McIntyre, Anthony M Magliocco, J Gregory Cairncross, J Ross Mitchell.   

Abstract

In glioblastoma (GBM), promoter methylation of the DNA repair gene O(6)-methylguanine-DNA methyltransferase (MGMT) is associated with benefit from chemotherapy. Correlations between MGMT promoter methylation and visually assessed imaging features on magnetic resonance (MR) have been reported suggesting that noninvasive detection of MGMT methylation status might be possible. Our study assessed whether MGMT methylation status in GBM could be predicted using MR imaging. We conducted a retrospective analysis of MR images in patients with newly diagnosed GBM. Tumor texture was assessed by two methods. First, we analyzed texture by expert consensus describing the tumor borders, presence or absence of cysts, pattern of enhancement, and appearance of tumor signal in T2-weighted images. Then, we applied space-frequency texture analysis based on the S-transform. Tumor location within the brain was determined using automatized image registration and segmentation techniques. Their association with MGMT methylation was analyzed. We confirmed that ring enhancement assessed visually is significantly associated with unmethylated MGMT promoter status (P=0.006). Texture features on T2-weighted images assessed by the space-frequency analysis were significantly different between methylated and unmethylated cases (P<0.05). However, blinded classification of MGMT promoter methylation status reached an accuracy of only 71%. There were no significant differences in the locations of methylated and unmethylated GBM tumors. Our results provide further evidence that individual MR features are associated with MGMT methylation but better algorithms for predicting methylation status are needed. The relevance of this study lies on the application of novel techniques for the analysis of anatomical MR images of patients with GBM allowing the evaluation of subtleties not seen by an observer and facilitating the standardization of the methods, decreasing the potential for interobserver bias.

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Year:  2009        PMID: 19796694     DOI: 10.1016/j.neuroimage.2009.09.049

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  76 in total

Review 1.  Texture analysis: a review of neurologic MR imaging applications.

Authors:  A Kassner; R E Thornhill
Journal:  AJNR Am J Neuroradiol       Date:  2010-04-15       Impact factor: 3.825

2.  Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging.

Authors:  Won-Jin Moon; Jin Woo Choi; Hong Gee Roh; So Dug Lim; Young-Cho Koh
Journal:  Neuroradiology       Date:  2011-08-11       Impact factor: 2.804

3.  Continuing the search for MR imaging biomarkers for MGMT promoter methylation status: conventional and perfusion MRI revisited.

Authors:  Ajay Gupta; Antonio M P Omuro; Akash D Shah; Jerome J Graber; Weiji Shi; Zhigang Zhang; Robert J Young
Journal:  Neuroradiology       Date:  2011-10-18       Impact factor: 2.804

4.  Edge Contrast of the FLAIR Hyperintense Region Predicts Survival in Patients with High-Grade Gliomas following Treatment with Bevacizumab.

Authors:  N Bahrami; D Piccioni; R Karunamuni; Y-H Chang; N White; R Delfanti; T M Seibert; J A Hattangadi-Gluth; A Dale; N Farid; C R McDonald
Journal:  AJNR Am J Neuroradiol       Date:  2018-04-05       Impact factor: 3.825

5.  Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: correlation with MGMT promoter methylation status.

Authors:  Andrea Romano; L F Calabria; F Tavanti; G Minniti; M C Rossi-Espagnet; V Coppola; S Pugliese; D Guida; G Francione; C Colonnese; L M Fantozzi; A Bozzao
Journal:  Eur Radiol       Date:  2012-08-10       Impact factor: 5.315

Review 6.  Brain stem cells as the cell of origin in glioma.

Authors:  Aram S Modrek; N Sumru Bayin; Dimitris G Placantonakis
Journal:  World J Stem Cells       Date:  2014-01-26       Impact factor: 5.326

Review 7.  Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics.

Authors:  Benjamin M Ellingson
Journal:  Curr Neurol Neurosci Rep       Date:  2015-01       Impact factor: 5.081

8.  Texture analysis of images using a two-dimensional fast time-frequency transform.

Authors:  Chun Hing Cheng; Pyari Mohan Pradhan; Joseph Ross Mitchell
Journal:  J Med Imaging (Bellingham)       Date:  2015-05-21

9.  MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Authors:  Lichy Han; Maulik R Kamdar
Journal:  Pac Symp Biocomput       Date:  2018

10.  Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade.

Authors:  Karoline Skogen; Balaji Ganeshan; Catriona Good; Giles Critchley; Ken Miles
Journal:  J Neurooncol       Date:  2012-12-06       Impact factor: 4.130

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