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@ARTICLE{Lohmann:877685,
      author       = {Lohmann, Philipp and Bousabarah, Khaled and Hoevels,
                      Mauritius and Treuer, Harald},
      title        = {{R}adiomics in radiation oncology—basics, methods, and
                      limitations},
      journal      = {Strahlentherapie und Onkologie},
      volume       = {196},
      issn         = {0039-2073},
      address      = {Heidelberg},
      publisher    = {Springer Medizin},
      reportid     = {FZJ-2020-02395},
      pages        = {848–855},
      year         = {2020},
      abstract     = {Over the past years, the quantity and complexity of imaging
                      data available for the clinical management of patients with
                      solid tumors has increased substantially. Without the
                      support of methods from the field of artificial intelligence
                      (AI) and machine learning, a complete evaluation of the
                      available image information is hardly feasible in clinical
                      routine. Especially in radiotherapy planning, manual
                      detection and segmentation of lesions is laborious, time
                      consuming, and shows significant variability among
                      observers. Here, AI already offers techniques to support
                      radiation oncologists, whereby ultimately, the productivity
                      and the quality are increased, potentially leading to an
                      improved patient outcome. Besides detection and segmentation
                      of lesions, AI allows the extraction of a vast number of
                      quantitative imaging features from structural or functional
                      imaging data that are typically not accessible by means of
                      human perception. These features can be used alone or in
                      combination with other clinical parameters to generate
                      mathematical models that allow, for example, prediction of
                      the response to radiotherapy. Within the large field of AI,
                      radiomics is the subdiscipline that deals with the
                      extraction of quantitative image features as well as the
                      generation of predictive or prognostic mathematical models.
                      This review gives an overview of the basics, methods, and
                      limitations of radiomics, with a focus on patients with
                      brain tumors treated by radiation therapy.},
      cin          = {INM-4},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406},
      pnm          = {573 - Neuroimaging (POF3-573) / DFG project 428090865 -
                      Radiomics basierend auf MRT und Aminosäure PET in der
                      Neuroonkologie},
      pid          = {G:(DE-HGF)POF3-573 / G:(GEPRIS)428090865},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32647917},
      UT           = {WOS:000546839700001},
      doi          = {10.1007/s00066-020-01663-3},
      url          = {https://juser.fz-juelich.de/record/877685},
}