% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Kocher:875334,
      author       = {Kocher, Martin and Ruge, Maximilian I. and Galldiks,
                      Norbert and Lohmann, Philipp},
      title        = {{A}pplications of radiomics and machine learning for
                      radiotherapy of malignant brain tumors},
      journal      = {Strahlentherapie und Onkologie},
      volume       = {196},
      issn         = {1439-099X},
      address      = {Heidelberg},
      publisher    = {Springer Medizin},
      reportid     = {FZJ-2020-01957},
      pages        = {856–867},
      year         = {2020},
      abstract     = {BackgroundMagnetic resonance imaging (MRI) and amino acid
                      positron-emission tomography (PET) of the brain contain a
                      vast amount of structural and functional information that
                      can be analyzed by machine learning algorithms and radiomics
                      for the use of radiotherapy in patients with malignant brain
                      tumors.MethodsThis study is based on comprehensive
                      literature research on machine learning and radiomics
                      analyses in neuroimaging and their potential application for
                      radiotherapy in patients with malignant glioma or brain
                      metastases.ResultsFeature-based radiomics and deep
                      learning-based machine learning methods can be used to
                      improve brain tumor diagnostics and automate various steps
                      of radiotherapy planning. In glioma patients, important
                      applications are the determination of WHO grade and
                      molecular markers for integrated diagnosis in patients not
                      eligible for biopsy or resection, automatic image
                      segmentation for target volume planning, prediction of the
                      location of tumor recurrence, and differentiation of
                      pseudoprogression from actual tumor progression. In patients
                      with brain metastases, radiomics is applied for additional
                      detection of smaller brain metastases, accurate segmentation
                      of multiple larger metastases, prediction of local response
                      after radiosurgery, and differentiation of radiation injury
                      from local brain metastasis relapse. Importantly, high
                      diagnostic accuracies of $80–90\%$ can be achieved by most
                      approaches, despite a large variety in terms of applied
                      imaging techniques and computational
                      methods.ConclusionClinical application of automated image
                      analyses based on radiomics and artificial intelligence has
                      a great potential for improving radiotherapy in patients
                      with malignant brain tumors. However, a common problem
                      associated with these techniques is the large variability
                      and the lack of standardization of the methods applied.},
      cin          = {INM-3 / INM-4},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-4-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572) / DFG
                      project 428090865 - Radiomics basierend auf MRT und
                      Aminosäure PET in der Neuroonkologie},
      pid          = {G:(DE-HGF)POF3-572 / G:(GEPRIS)428090865},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32394100},
      UT           = {WOS:000531760500002},
      doi          = {10.1007/s00066-020-01626-8},
      url          = {https://juser.fz-juelich.de/record/875334},
}