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@ARTICLE{Kickingereder:128894,
      author       = {P. Kickingereder$^*$ and M. Götz$^*$ and J. Muschelli and
                      A. Wick and U. Neuberger and R. T. Shinohara and M. Sill$^*$
                      and M. Nowosielski and H.-P. Schlemmer$^*$ and A.
                      Radbruch$^*$ and W. Wick$^*$ and M. Bendszus and K.
                      Maier-Hein$^*$ and D. Bonekamp$^*$},
      title        = {{L}arge-scale {R}adiomic {P}rofiling of {R}ecurrent
                      {G}lioblastoma {I}dentifies an {I}maging {P}redictor for
                      {S}tratifying {A}nti-{A}ngiogenic {T}reatment {R}esponse.},
      journal      = {Clinical cancer research},
      volume       = {22},
      number       = {23},
      issn         = {1557-3265},
      address      = {Philadelphia, Pa. [u.a.]},
      publisher    = {AACR},
      reportid     = {DKFZ-2017-04907},
      pages        = {5765 - 5771},
      year         = {2016},
      abstract     = {Antiangiogenic treatment with bevacizumab, a mAb to the
                      VEGF, is the single most widely used therapeutic agent for
                      patients with recurrent glioblastoma. A major challenge is
                      that there are currently no validated biomarkers that can
                      predict treatment outcome. Here we analyze the potential of
                      radiomics, an emerging field of research that aims to
                      utilize the full potential of medical imaging.A total of
                      4,842 quantitative MRI features were automatically extracted
                      and analyzed from the multiparametric tumor of 172 patients
                      (allocated to a discovery and validation set with a 2:1
                      ratio) with recurrent glioblastoma prior to bevacizumab
                      treatment. Leveraging a high-throughput approach, radiomic
                      features of patients in the discovery set were subjected to
                      a supervised principal component (superpc) analysis to
                      generate a prediction model for stratifying treatment
                      outcome to antiangiogenic therapy by means of both
                      progression-free and overall survival (PFS and OS).The
                      superpc predictor stratified patients in the discovery set
                      into a low or high risk group for PFS (HR = 1.60; P = 0.017)
                      and OS (HR = 2.14; P < 0.001) and was successfully validated
                      for patients in the validation set (HR = 1.85, P = 0.030 for
                      PFS; HR = 2.60, P = 0.001 for OS).Our radiomic-based superpc
                      signature emerges as a putative imaging biomarker for the
                      identification of patients who may derive the most benefit
                      from antiangiogenic therapy, advances the knowledge in the
                      noninvasive characterization of brain tumors, and stresses
                      the role of radiomics as a novel tool for improving decision
                      support in cancer treatment at low cost. Clin Cancer Res;
                      22(23); 5765-71. ©2016 AACR.},
      cin          = {E012 / E130 / E010 / G370 / E132 / C060 / L101},
      ddc          = {610},
      cid          = {I:(DE-He78)E012-20160331 / I:(DE-He78)E130-20160331 /
                      I:(DE-He78)E010-20160331 / I:(DE-He78)G370-20160331 /
                      I:(DE-He78)E132-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)L101-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27803067},
      pmc          = {pmc:PMC5503450},
      doi          = {10.1158/1078-0432.CCR-16-0702},
      url          = {https://inrepo02.dkfz.de/record/128894},
}