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@ARTICLE{Schneider:300196,
      author       = {M. Schneider and S. Gutwein and D. Mönnich and C. Gani and
                      P. Fischer and C. F. Baumgartner and D. Thorwarth$^*$},
      title        = {{D}evelopment and comprehensive clinical validation of a
                      deep neural network for radiation dose modelling to enhance
                      magnetic resonance imaging guided radiotherapy.},
      journal      = {Physics $\&$ Imaging in Radiation Oncology},
      volume       = {33},
      issn         = {2405-6316},
      address      = {Amsterdam [u. a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2025-00673},
      pages        = {100723},
      year         = {2025},
      abstract     = {Online adaptive magnetic resonance imaging (MRI)-guided
                      radiotherapy requires fast dose calculation algorithms to
                      reduce intra-fraction motion uncertainties and improve
                      workflow efficiency. While Monte-Carlo simulations are
                      precise but computationally intensive, neural networks
                      promise fast and accurate dose modelling in strong magnetic
                      fields. This study aimed to train and evaluate a deep neural
                      network for dose modelling in MRI-guided radiotherapy using
                      a comprehensive clinical dataset.A dataset of 6595 clinical
                      irradiation segments from 125 1.5 T MRI-Linac radiotherapy
                      plans for various tumors sites was used. A 3D U-Net was
                      trained with 3961 segments using 3D imaging data and field
                      parameters as input, Root Mean Squared Error and a custom
                      loss function, with full Monte-Carlo simulations as ground
                      truth. For 2656 segments from 50 patients, gamma pass rates
                      (γ-PR) for 3 $mm/3\%,$ 2 $mm/2\%,$ and 1 $mm/1\%$ criteria
                      were calculated to assess dose modelling accuracy.
                      Performance was also tested in a standardized water phantom
                      to evaluate basic radiation physics properties.The neural
                      network accurately modeled dose distributions in both
                      patient and water phantom settings. Median (range) γ-PR of
                      $97.7\%$ $(87.5-100.0\%),$ $89.1\%$ $(69.7-99.4\%),$ and
                      $60.8\%$ $(38.5-82.1\%)$ were observed for treatment plans,
                      and $97.1\%$ $(55.5-100.0\%),$ $88.8\%$ $(38.8-99.7\%),$ and
                      $61.7\%$ $(17.9-94.4\%)$ for individual segments, across the
                      three criteria.High median γ-PR and accurate modelling in
                      both water phantom and clinical data demonstrate the high
                      potential of neural networks for dose modelling. However,
                      instances of lower γ-PR highlight the need for
                      comprehensive test data, improved robustness and future
                      built-in uncertainty estimation.},
      keywords     = {Artificial intelligence (Other) / Dose calculation (Other)
                      / MRI-guided radiotherapy (Other) / Online adaptive
                      radiotherapy (Other)},
      cin          = {TU01},
      ddc          = {610},
      cid          = {I:(DE-He78)TU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40093656},
      pmc          = {pmc:PMC11908596},
      doi          = {10.1016/j.phro.2025.100723},
      url          = {https://inrepo02.dkfz.de/record/300196},
}