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Title: Radiogenomics of glioblastoma: Machine-learning based classification of molecular characteristics using multiparametric and multiregional MRI features      
keywords:
Epigenomics
ID:
PRJNA338764
description:
Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma. Overall design: The Illumina Infinium HumanMethylation450 array (Illumina, San Diego, USA) data from n=152 patients were used to obtain genome-wide assessment of DNA methylation, according to the manufacturer’s instructions at the Genomics and Proteomics Core Facility of the German Cancer Research Center (DKFZ) as described previously (Sturm et al. Cancer Cell. 2012 Oct 16;22(4):425-37)
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landingpage: http://www.ncbi.nlm.nih.gov/bioproject/PRJNA338764
authentication:
none
authorization:
none
ID:
pmid:27636026
name:
Homo sapiens
ncbiID:
ncbitax:9606
abbreviation:
NCBI
homePage: http://www.ncbi.nlm.nih.gov
ID:
SCR:006472
name:
National Center for Biotechnology Information
homePage: http://www.ncbi.nlm.nih.gov/bioproject
ID:
SCR:004801
name:
NCBI BioProject