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000128891 1001_ $$0P:(DE-He78)3da06896bf2a50a84d40c33c3b7a9b3e$$aKickingereder, Philipp$$b0$$eFirst author$$udkfz
000128891 245__ $$aRadiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.
000128891 260__ $$aOak Brook, Ill.$$bSoc.$$c2016
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000128891 520__ $$aPurpose 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%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR [P < .01], 61% for RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters was not significant. Conclusion The authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma. (©) RSNA, 2016 Online supplemental material is available for this article.
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000128891 650_7 $$2NLM Chemicals$$aCDKN2A protein, human
000128891 650_7 $$2NLM Chemicals$$aCyclin-Dependent Kinase Inhibitor p18
000128891 650_7 $$2NLM Chemicals$$aNeoplasm Proteins
000128891 650_7 $$0EC 2.7.10.1$$2NLM Chemicals$$aEGFR protein, human
000128891 650_7 $$0EC 2.7.10.1$$2NLM Chemicals$$aReceptor, Epidermal Growth Factor
000128891 7001_ $$0P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93$$aBonekamp, David$$b1$$udkfz
000128891 7001_ $$aNowosielski, Martha$$b2
000128891 7001_ $$0P:(DE-HGF)0$$aKratz, Annekathrin$$b3
000128891 7001_ $$0P:(DE-He78)45440b44791309bd4b7dbb4f73333f9b$$aSill, Martin$$b4$$udkfz
000128891 7001_ $$aBurth, Sina$$b5
000128891 7001_ $$aWick, Antje$$b6
000128891 7001_ $$aEidel, Oliver$$b7
000128891 7001_ $$0P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aSchlemmer, Heinz-Peter$$b8$$udkfz
000128891 7001_ $$0P:(DE-He78)77588f5b9413339755a66e739d316c7d$$aRadbruch, Alexander$$b9$$udkfz
000128891 7001_ $$aDebus, Jürgen$$b10
000128891 7001_ $$0P:(DE-He78)c146c0b611b8fb654444ec078766f5ea$$aHerold-Mende, Christel$$b11$$udkfz
000128891 7001_ $$aUnterberg, Andreas$$b12
000128891 7001_ $$0P:(DE-He78)551bb92841f634070997aa168d818492$$aJones, David$$b13$$udkfz
000128891 7001_ $$0P:(DE-He78)f746aa965c4e1af518b016de3aaff5d9$$aPfister, Stefan$$b14$$udkfz
000128891 7001_ $$0P:(DE-He78)92e9783ca7025f36ce14e12cd348d2ee$$aWick, Wolfgang$$b15$$udkfz
000128891 7001_ $$0P:(DE-He78)a8a10626a848d31e70cfd96a133cc144$$avon Deimling, Andreas$$b16$$udkfz
000128891 7001_ $$aBendszus, Martin$$b17
000128891 7001_ $$0P:(DE-He78)51bf9ae9cb5771b30c483e5597ef606c$$aCapper, David$$b18$$eLast author$$udkfz
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