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@ARTICLE{Kickingereder:134853,
      author       = {P. Kickingereder and U. Neuberger and D. Bonekamp$^*$ and
                      P. L. Piechotta and M. Götz$^*$ and A. Wick and M. Sill$^*$
                      and A. Kratz$^*$ and R. T. Shinohara and D. Jones$^*$ and A.
                      Radbruch$^*$ and J. Muschelli and A. Unterberg and J.
                      Debus$^*$ and H.-P. Schlemmer$^*$ and C. Herold-Mende$^*$
                      and S. Pfister$^*$ and A. von Deimling$^*$ and W. Wick$^*$
                      and D. Capper$^*$ and K. Maier-Hein$^*$ and M. Bendszus},
      title        = {{R}adiomic subtyping improves disease stratification beyond
                      key molecular, clinical, and standard imaging
                      characteristics in patients with glioblastoma.},
      journal      = {Neuro-Oncology},
      volume       = {20},
      number       = {6},
      issn         = {1523-5866},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2018-00643},
      pages        = {848 - 857},
      year         = {2018},
      abstract     = {The purpose of this study was to analyze the potential of
                      radiomics for disease stratification beyond key molecular,
                      clinical, and standard imaging features in patients with
                      glioblastoma.Quantitative imaging features (n = 1043) were
                      extracted from the multiparametric MRI of 181 patients with
                      newly diagnosed glioblastoma prior to standard-of-care
                      treatment (allocated to a discovery and a validation set,
                      2:1 ratio). A subset of 386/1043 features were identified as
                      reproducible (in an independent MRI test-retest cohort) and
                      selected for analysis. A penalized Cox model with 10-fold
                      cross-validation (Coxnet) was fitted on the discovery set to
                      construct a radiomic signature for predicting
                      progression-free and overall survival (PFS and OS). The
                      incremental value of a radiomic signature beyond molecular
                      (O6-methylguanine-DNA methyltransferase [MGMT] promoter
                      methylation, DNA methylation subgroups), clinical (patient's
                      age, KPS, extent of resection, adjuvant treatment), and
                      standard imaging parameters (tumor volumes) for stratifying
                      PFS and OS was assessed with multivariate Cox models
                      (performance quantified with prediction error curves).The
                      radiomic signature (constructed from 8/386 features
                      identified through Coxnet) increased the prediction accuracy
                      for PFS and OS (in both discovery and validation sets)
                      beyond the assessed molecular, clinical, and standard
                      imaging parameters (P ≤ 0.01). Prediction errors decreased
                      by $36\%$ for PFS and $37\%$ for OS when adding the radiomic
                      signature (compared with $29\%$ and $27\%,$ respectively,
                      with molecular + clinical features alone). The radiomic
                      signature was-along with MGMT status-the only parameter with
                      independent significance on multivariate analysis (P ≤
                      0.01).Our study stresses the role of integrating radiomics
                      into a multilayer decision framework with key molecular and
                      clinical features to improve disease stratification and to
                      potentially advance personalized treatment of patients with
                      glioblastoma.},
      cin          = {E010 / E230 / E132 / C060 / G380 / B062 / L101 / G370 /
                      E050},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)E132-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)G380-20160331 / I:(DE-He78)B062-20160331 /
                      I:(DE-He78)L101-20160331 / I:(DE-He78)G370-20160331 /
                      I:(DE-He78)E050-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29036412},
      pmc          = {pmc:PMC5961168},
      doi          = {10.1093/neuonc/nox188},
      url          = {https://inrepo02.dkfz.de/record/134853},
}