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@ARTICLE{Kickingereder:128891,
      author       = {P. Kickingereder$^*$ and D. Bonekamp$^*$ and M. Nowosielski
                      and A. Kratz$^*$ and M. Sill$^*$ and S. Burth and A. Wick
                      and O. Eidel and H.-P. Schlemmer$^*$ and A. Radbruch$^*$ and
                      J. Debus and C. Herold-Mende$^*$ and A. Unterberg and D.
                      Jones$^*$ and S. Pfister$^*$ and W. Wick$^*$ and A. von
                      Deimling$^*$ and M. Bendszus and D. Capper$^*$},
      title        = {{R}adiogenomics of {G}lioblastoma: {M}achine
                      {L}earning-based {C}lassification of {M}olecular
                      {C}haracteristics by {U}sing {M}ultiparametric and
                      {M}ultiregional {MR} {I}maging {F}eatures.},
      journal      = {Radiology},
      volume       = {281},
      number       = {3},
      issn         = {1527-1315},
      address      = {Oak Brook, Ill.},
      publisher    = {Soc.},
      reportid     = {DKFZ-2017-04904},
      pages        = {907 - 918},
      year         = {2016},
      abstract     = {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: $63\%-69\%,$ 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.},
      keywords     = {CDKN2A protein, human (NLM Chemicals) / Cyclin-Dependent
                      Kinase Inhibitor p18 (NLM Chemicals) / Neoplasm Proteins
                      (NLM Chemicals) / EGFR protein, human (NLM Chemicals) /
                      Receptor, Epidermal Growth Factor (NLM Chemicals)},
      cin          = {E012 / E010 / C060 / L101 / B062 / G370 / G380},
      ddc          = {610},
      cid          = {I:(DE-He78)E012-20160331 / I:(DE-He78)E010-20160331 /
                      I:(DE-He78)C060-20160331 / I:(DE-He78)L101-20160331 /
                      I:(DE-He78)B062-20160331 / I:(DE-He78)G370-20160331 /
                      I:(DE-He78)G380-20160331},
      pnm          = {317 - Translational cancer research (POF3-317)},
      pid          = {G:(DE-HGF)POF3-317},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27636026},
      doi          = {10.1148/radiol.2016161382},
      url          = {https://inrepo02.dkfz.de/record/128891},
}