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@ARTICLE{Bakas:1032187,
      author       = {Bakas, Spyridon and Vollmuth, Philipp and Galldiks, Norbert
                      and Booth, Thomas C and Aerts, Hugo J W L and Bi, Wenya
                      Linda and Wiestler, Benedikt and Tiwari, Pallavi and Pati,
                      Sarthak and Baid, Ujjwal and Calabrese, Evan and Lohmann,
                      Philipp and Nowosielski, Martha and Jain, Rajan and Colen,
                      Rivka and Ismail, Marwa and Rasool, Ghulam and Lupo, Janine
                      M and Akbari, Hamed and Tonn, Joerg C and Macdonald, David
                      and Vogelbaum, Michael and Chang, Susan M and Davatzikos,
                      Christos and Villanueva-Meyer, Javier E and Huang, Raymond
                      Y},
      title        = {{A}rtificial {I}ntelligence for {R}esponse {A}ssessment in
                      {N}euro {O}ncology ({AI}-{RANO}), part 2: recommendations
                      for standardisation, validation, and good clinical practice},
      journal      = {The lancet / Oncology},
      volume       = {25},
      number       = {11},
      issn         = {1470-2045},
      address      = {London},
      publisher    = {The Lancet Publ. Group},
      reportid     = {FZJ-2024-06056},
      pages        = {e589 - e601},
      year         = {2024},
      abstract     = {Technological advancements have enabled the extended
                      investigation, development, and application of computational
                      approaches in various domains, including health care. A
                      burgeoning number of diagnostic, predictive, prognostic, and
                      monitoring biomarkers are continuously being explored to
                      improve clinical decision making in neuro-oncology. These
                      advancements describe the increasing incorporation of
                      artificial intelligence (AI) algorithms, including the use
                      of radiomics. However, the broad applicability and clinical
                      translation of AI are restricted by concerns about
                      generalisability, reproducibility, scalability, and
                      validation. This Policy Review intends to serve as the
                      leading resource of recommendations for the standardisation
                      and good clinical practice of AI approaches in health care,
                      particularly in neuro-oncology. To this end, we investigate
                      the repeatability, reproducibility, and stability of AI in
                      response assessment in neuro-oncology in studies on factors
                      affecting such computational approaches, and in publicly
                      available open-source data and computational software tools
                      facilitating these goals. The pathway for standardisation
                      and validation of these approaches is discussed with the
                      view of trustworthy AI enabling the next generation of
                      clinical trials. We conclude with an outlook on the future
                      of AI-enabled neuro-oncology.},
      cin          = {INM-4 / INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-3-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525) / 5252 - Brain Dysfunction
                      and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {39481415},
      UT           = {WOS:001348280600001},
      doi          = {10.1016/S1470-2045(24)00315-2},
      url          = {https://juser.fz-juelich.de/record/1032187},
}