TY  - JOUR
AU  - Kickingereder, Philipp
AU  - Bonekamp, David
AU  - Nowosielski, Martha
AU  - Kratz, Annekathrin
AU  - Sill, Martin
AU  - Burth, Sina
AU  - Wick, Antje
AU  - Eidel, Oliver
AU  - Schlemmer, Heinz-Peter
AU  - Radbruch, Alexander
AU  - Debus, Jürgen
AU  - Herold-Mende, Christel
AU  - Unterberg, Andreas
AU  - Jones, David
AU  - Pfister, Stefan
AU  - Wick, Wolfgang
AU  - von Deimling, Andreas
AU  - Bendszus, Martin
AU  - Capper, David
TI  - Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.
JO  - Radiology
VL  - 281
IS  - 3
SN  - 1527-1315
CY  - Oak Brook, Ill.
PB  - Soc.
M1  - DKFZ-2017-04904
SP  - 907 - 918
PY  - 2016
AB  - Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. Materials and Methods Retrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, 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 MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics. Results There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63
KW  - CDKN2A protein, human (NLM Chemicals)
KW  - Cyclin-Dependent Kinase Inhibitor p18 (NLM Chemicals)
KW  - Neoplasm Proteins (NLM Chemicals)
KW  - EGFR protein, human (NLM Chemicals)
KW  - Receptor, Epidermal Growth Factor (NLM Chemicals)
LB  - PUB:(DE-HGF)16
C6  - pmid:27636026
DO  - DOI:10.1148/radiol.2016161382
UR  - https://inrepo02.dkfz.de/record/128891
ER  -