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@ARTICLE{Wei:302286,
      author       = {C. Wei and C. Eze and R. Klaar and D. Thorwarth and C.
                      Warda and J. Taugner and J. Hörner-Rieber and S. Regnery
                      and O. Jäkel$^*$ and F. Weykamp$^*$ and M. A. Palacios and
                      S. Marschner and S. Corradini and C. Belka$^*$ and C. Kurz
                      and G. Landry and M. Rabe},
      title        = {{D}eep learning-based contour propagation in magnetic
                      resonance imaging-guided radiotherapy of lung cancer
                      patients.},
      journal      = {Physics in medicine and biology},
      volume       = {70},
      issn         = {0031-9155},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2025-01305},
      pages        = {145018},
      year         = {2025},
      note         = {Med. Biol. 70 145018},
      abstract     = {Fast and accurate organ-at-risk (OAR) and gross tumor
                      volume (GTV) contour propagation methods are needed to
                      improve the efficiency of magnetic resonance (MR)
                      imaging-guided radiotherapy. We trained deformable image
                      registration networks to accurately propagate contours from
                      planning to fraction MR images. Approach: Data from 140
                      stage 1-2 lung cancer patients treated at a 0.35T MR-Linac
                      were split into 102/17/21 for training/validation/testing.
                      Additionally, 18 central lung tumor patients, treated at a
                      0.35T MR-Linac externally, and 14 stage 3 lung cancer
                      patients from a phase 1 clinical trial, treated at 0.35T or
                      1.5T MR-Linacs at three institutions, were used for external
                      testing. Planning and fraction images were paired (490
                      pairs) for training. Two hybrid transformer-convolutional
                      neural network TransMorph models with mean squared error
                      (MSE), Dice similarity coefficient (DSC), and regularization
                      losses $(TM_{MSE+Dice})$ or MSE and regularization losses
                      $(TM_{MSE})$ were trained to deformably register planning to
                      fraction images. The TransMorph models predicted
                      diffeomorphic dense displacement fields. Multi-label images
                      including seven thoracic OARs and the GTV were propagated to
                      generate fraction segmentations. Model predictions were
                      compared with contours obtained through B-spline, vendor
                      registration and the auto-segmentation method nnUNet.
                      Evaluation metrics included the DSC and Hausdorff distance
                      percentiles (50th and 95th) against clinical contours. Main
                      results: $TM_{MSE+Dice}$ and $TM_{MSE}$ achieved mean
                      OARs/GTV DSCs of 0.90/0.82 and 0.90/0.79 for the internal
                      and 0.84/0.77 and 0.85/0.76 for the central lung tumor
                      external test data. On stage 3 data, $TM_{MSE+Dice}$
                      achieved mean OARs/GTV DSCs of 0.87/0.79 and 0.83/0.78 for
                      the 0.35 T MR-Linac datasets, and 0.87/0.75 for the 1.5 T
                      MR-Linac dataset. $TM_{MSE+Dice}$ and $TM_{MSE}$ had
                      significantly higher geometric accuracy than other methods
                      on external data. No significant difference between
                      $TM_{MSE+Dice}$ and $TM_{MSE}$ was found. Significance:
                      TransMorph models achieved time-efficient segmentation of
                      fraction MRIs with high geometrical accuracy and accurately
                      segmented images obtained at different field strengths.},
      keywords     = {MR-linac (Other) / MRgRT (Other) / PUMA (Other) /
                      TransMorph (Other) / deep learning (Other) / image
                      registration (Other) / lung cancer (Other)},
      cin          = {E040 / MU01 / E050},
      ddc          = {530},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)MU01-20160331 /
                      I:(DE-He78)E050-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40570891},
      doi          = {10.1088/1361-6560/ade8d0},
      url          = {https://inrepo02.dkfz.de/record/302286},
}