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Authors
Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Cheshier SH, Napel S, Zaharchuk G, Plevritis SK.
Citation
Radiology. 2014 Oct;273(1):168-74. doi: 10.1148/radiol.14131731. Epub 2014 May 12. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. PMID: 24827998
Description
Pubmed Abstract
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Description
PURPOSE:
To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to
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create radiogenomic
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maps associating
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these features
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with various molecular data.
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MATERIALS AND METHODS:
Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from
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Glioblastoma Multiforme Collection (TCGA-GBM) collection after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn
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and saved in AIM format. Quantitative image features were derived from these ROIs.
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Robust quantitative image features
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were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-
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retest analysis. The
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robust features
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were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these
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robust image features
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were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with
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robust image features
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by using the Spearman correlation test to
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create radiogenomic
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maps and to link
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robust image features
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with molecular pathways.
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RESULTS:
Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of
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54 image features. Three
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enhancement features
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were significantly correlated with survival, 77 significant correlations were found between
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robust quantitative features
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and the VASARI feature set, and
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seven image features
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were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to
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link image features
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with gene expression modules and allowed linkage of 56% (30 of 54) of
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the image features
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with biologic processes.
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CONCLUSION:
Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively.
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Collection Summary
More information about the data set can be found on the TCGA-GBM collection page.
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