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@ARTICLE{Lohmann:877539,
      author       = {Lohmann, Philipp and Galldiks, Norbert and Kocher, Martin
                      and Heinzel, Alexander and Filss, Christian P. and Stegmayr,
                      Carina and Mottaghy, Felix M. and Fink, Gereon R. and Shah,
                      N. J. and Langen, Karl-Josef},
      title        = {{R}adiomics in neuro-oncology: {B}asics, workflow, and
                      applications},
      journal      = {Methods},
      volume       = {188},
      issn         = {1046-2023},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2020-02275},
      pages        = {112-121},
      year         = {2021},
      abstract     = {Over the last years, the amount, variety, and complexity of
                      neuroimaging data acquired in patients with brain tumors for
                      routine clinical purposes and the resulting number of
                      imaging parameters have substantially increased.
                      Consequently, a timely and cost-effective evaluation of
                      imaging data is hardly feasible without the support of
                      methods from the field of artificial intelligence (AI). AI
                      can facilitate and shorten various time-consuming steps in
                      the image processing workflow, e.g., tumor segmentation,
                      thereby optimizing productivity. Besides, the automated and
                      computer-based analysis of imaging data may help to increase
                      data comparability as it is independent of the experience
                      level of the evaluating clinician. Importantly, AI offers
                      the potential to extract new features from the routinely
                      acquired neuroimages of brain tumor patients. In combination
                      with patient data such as survival, molecular markers, or
                      genomics, mathematical models can be generated that allow,
                      for example, the prediction of treatment response or
                      prognosis, as well as the noninvasive assessment of
                      molecular markers. The subdiscipline of AI dealing with the
                      computation, identification, and extraction of image
                      features, as well as the generation of prognostic or
                      predictive mathematical models, is termed radiomics. This
                      review article summarizes the basics, the current workflow,
                      and methods used in radiomics with a focus on feature-based
                      radiomics in neuro-oncology and provides selected examples
                      of its clinical application.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN / INM-3},
      ddc          = {540},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      $I:(DE-82)080010_20140620$ / I:(DE-Juel1)INM-3-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525) / 5252 - Brain Dysfunction
                      and Plasticity (POF4-525) / DFG project 428090865 -
                      Radiomics basierend auf MRT und Aminosäure PET in der
                      Neuroonkologie},
      pid          = {G:(DE-HGF)POF4-5253 / G:(DE-HGF)POF4-5252 /
                      G:(GEPRIS)428090865},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {32522530},
      UT           = {WOS:000631883800011},
      doi          = {10.1016/j.ymeth.2020.06.003},
      url          = {https://juser.fz-juelich.de/record/877539},
}