% 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{Lohmann:874447,
      author       = {Lohmann, Philipp and Kocher, Martin and Ruge, Maximillian
                      I. and Visser-Vandewalle, Veerle and Shah, N. Jon and Fink,
                      Gereon R. and Langen, Karl-Josef and Galldiks, Norbert},
      title        = {{PET}/{MRI} {R}adiomics in {P}atients {W}ith {B}rain
                      {M}etastases},
      journal      = {Frontiers in neurology},
      volume       = {11},
      issn         = {1664-2295},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2020-01448},
      pages        = {1},
      year         = {2020},
      abstract     = {Although a variety of imaging modalities are used or
                      currently being investigated for patients with brain tumors
                      including brain metastases, clinical image interpretation to
                      date uses only a fraction of the underlying complex,
                      high-dimensional digital information from routinely acquired
                      imaging data. The growing availability of high-performance
                      computing allows the extraction of quantitative imaging
                      features from medical images that are usually beyond human
                      perception. Using machine learning techniques and advanced
                      statistical methods, subsets of such imaging features are
                      used to generate mathematical models that represent
                      characteristic signatures related to the underlying tumor
                      biology and might be helpful for the assessment of prognosis
                      or treatment response, or the identification of molecular
                      markers. The identification of appropriate, characteristic
                      image features as well as the generation of predictive or
                      prognostic mathematical models is summarized under the term
                      radiomics. This review summarizes the current status of
                      radiomics in patients with brain metastases.},
      cin          = {INM-3 / INM-4 / INM-11},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-4-20090406 /
                      I:(DE-Juel1)INM-11-20170113},
      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:32116995},
      UT           = {WOS:000517298900001},
      doi          = {10.3389/fneur.2020.00001},
      url          = {https://juser.fz-juelich.de/record/874447},
}