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@ARTICLE{Nachbar:276390,
      author       = {M. Nachbar and M. Lo Russo and C. Gani and S. Boeke and D.
                      Wegener and F. Paulsen and D. Zips$^*$ and T. Roque and N.
                      Paragios and D. Thorwarth$^*$},
      title        = {{A}utomatic {AI}-based contouring of prostate {MRI} for
                      online adaptive radiotherapy.},
      journal      = {Zeitschrift für medizinische Physik},
      volume       = {34},
      number       = {2},
      issn         = {0939-3889},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2023-01087},
      pages        = {197-207},
      year         = {2024},
      note         = {2024 May;34(2):197-207},
      abstract     = {MR-guided radiotherapy (MRgRT) online plan adaptation
                      accounts for tumor volume changes, interfraction motion and
                      thus allows daily sparing of relevant organs at risk. Due to
                      the high interfraction variability of bladder and rectum,
                      patients with tumors in the pelvic region may strongly
                      benefit from adaptive MRgRT. Currently, fast automatic
                      annotation of anatomical structures is not available within
                      the online MRgRT workflow. Therefore, the aim of this study
                      was to train and validate a fast, accurate deep learning
                      model for automatic MRI segmentation at the MR-Linac for
                      future implementation in a clinical MRgRT workflow.For a
                      total of 47 patients, T2w MRI data were acquired on a 1.5 T
                      MR-Linac (Unity, Elekta) on five different days. Prostate,
                      seminal vesicles, rectum, anal canal, bladder, penile bulb,
                      body and bony structures were manually annotated. These
                      training data consisting of 232 data sets in total was used
                      for the generation of a deep learning based autocontouring
                      model and validated on 20 unseen T2w-MRIs. For quantitative
                      evaluation the validation set was contoured by a radiation
                      oncologist as gold standard contours (GSC) and compared in
                      MATLAB to the automatic contours (AIC). For the evaluation,
                      dice similarity coefficients (DSC), and $95\%$ Hausdorff
                      distances $(95\%$ HD), added path length (APL) and surface
                      DSC (sDSC) were calculated in a caudal-cranial window of ±
                      4 cm with respect to the prostate ends. For qualitative
                      evaluation, five radiation oncologists scored the AIC on the
                      possible usage within an online adaptive workflow as
                      follows: (1) no modifications needed, (2) minor adjustments
                      needed, (3) major adjustments/ multiple minor adjustments
                      needed, (4) not usable.The quantitative evaluation revealed
                      a maximum median $95\%$ HD of 6.9 mm for the rectum and
                      minimum median $95\%$ HD of 2.7 mm for the bladder. Maximal
                      and minimal median DSC were detected for bladder with 0.97
                      and for penile bulb with 0.73, respectively. Using a
                      tolerance level of 3 mm, the highest and lowest sDSC were
                      determined for rectum (0.94) and anal canal (0.68),
                      respectively. Qualitative evaluation resulted in a mean
                      score of 1.2 for AICs over all organs and patients across
                      all expert ratings. For the different autocontoured
                      structures, the highest mean score of 1.0 was observed for
                      anal canal, sacrum, femur left and right, and pelvis left,
                      whereas for prostate the lowest mean score of 2.0 was
                      detected. In total, $80\%$ of the contours were rated be
                      clinically acceptable, $16\%$ to require minor and $4\%$
                      major adjustments for online adaptive MRgRT.In this study,
                      an AI-based autocontouring was successfully trained for
                      online adaptive MR-guided radiotherapy on the 1.5 T MR-Linac
                      system. The developed model can automatically generate
                      contours accepted by physicians $(80\%)$ or only with the
                      need of minor corrections $(16\%)$ for the irradiation of
                      primary prostate on the clinically employed sequences.},
      keywords     = {Adaptive radiotherapy (Other) / Automatic annotations
                      (Other) / Deep learning (Other) / MR-Linac (Other) / MR-only
                      (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:37263911},
      doi          = {10.1016/j.zemedi.2023.05.001},
      url          = {https://inrepo02.dkfz.de/record/276390},
}