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@ARTICLE{Vollmuth:909633,
      author       = {Vollmuth, Philipp and Foltyn, Martha and Huang, Raymond Y
                      and Galldiks, Norbert and Petersen, Jens and Isensee, Fabian
                      and van den Bent, Martin J and Barkhof, Frederik and Park,
                      Ji Eun and Park, Yae Won and Ahn, Sung Soo and Brugnara,
                      Gianluca and Meredig, Hagen and Jain, Rajan and Smits,
                      Marion and Pope, Whitney B and Maier-Hein, Klaus and Weller,
                      Michael and Wen, Patrick Y and Wick, Wolfgang and Bendszus,
                      Martin},
      title        = {{A}rtificial intelligence ({AI})-based decision support
                      improves reproducibility of tumor response assessment in
                      neuro-oncology: {A}n international multi-reader study},
      journal      = {Neuro-Oncology},
      volume       = {25},
      number       = {3},
      issn         = {1522-8517},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2022-03304},
      pages        = {533–543},
      year         = {2023},
      abstract     = {Background: To assess whether AI-based decision support
                      allows more reproducible and standardized assessment of
                      treatment response on MRI in neuro-oncology as compared to
                      manual 2-dimensional measurements of tumor burden using the
                      RANO criteria.Methods: A series of 30 patients (15
                      lower-grade gliomas, 15 glioblastoma) with availability of
                      consecutive MRI scans was selected. The time to progression
                      (TTP) on MRI was separately evaluated for each patient by 15
                      investigators over two rounds. In the 1 st round the TTP was
                      evaluated based on the RANO-criteria, whereas in the 2 nd
                      round the TTP was evaluated by incorporating additional
                      information from AI-enhanced MRI-sequences depicting the
                      longitudinal changes in tumor volumes. The agreement of the
                      TTP-measurements between investigators was evaluated using
                      concordance correlation coefficients (CCC) with confidence
                      intervals (CI) and p-values obtained using bootstrap
                      resampling.Results: The CCC of TTP-measurements between
                      investigators was 0.77 $(95\%CI=0.69,0.88)$ with RANO alone
                      and increased to 0.91 $(95\%CI=0.82,0.95)$ with AI-based
                      decision support (p=0.005). This effect was significantly
                      greater (p=0.008) for patients with lower-grade gliomas
                      (CCC=0.70 $[95\%CI=0.56,0.85]$ without vs. 0.90
                      $[95\%CI=0.76,0.95]$ with AI-based decision support) as
                      compared to glioblastoma (CCC=0.83 $[95\%CI=0.75,0.92]$
                      without vs. 0.86 $[95\%CI=0.78,0.93]$ with AI-based decision
                      support). Investigators with less years of experience judged
                      the AI-based decision as more helpful (p=0.02).Conclusions:
                      AI-based decision support has the potential to yield more
                      reproducible and standardized assessment of treatment
                      response in neuro-oncology as compared to manual
                      2-dimensional measurements of tumor burden, particularly in
                      patients with lower-grade gliomas. A fully-functional
                      version of this AI-based processing pipeline is provided as
                      open-source
                      (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).Keywords:
                      AI-based decision support; RANO; tumor response assessment;
                      tumor volumetry.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35917833},
      UT           = {WOS:000843014300001},
      doi          = {10.1093/neuonc/noac189},
      url          = {https://juser.fz-juelich.de/record/909633},
}