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@ARTICLE{Holzschuh:277318,
      author       = {J. C. Holzschuh$^*$ and M. Mix and J. Ruf and T. Hölscher
                      and J. Kotzerke and A. Vrachimis and P. Doolan and H. Ilhan
                      and I. M. Marinescu$^*$ and S. K. B. Spohn$^*$ and T.
                      Fechter$^*$ and D. Kuhn$^*$ and P. Bronsert and C. Gratzke
                      and R. Grosu and S. C. Kamran and P. Heidari and T. S. C. Ng
                      and A. Könik and A.-L. Grosu$^*$ and C. Zamboglou},
      title        = {{D}eep {L}earning based {A}utomated {D}elineation of the
                      {I}ntraprostatic {G}ross {T}umour {V}olume in {PSMA}-{PET}
                      for {P}atients with {P}rimary {P}rostate {C}ancer.},
      journal      = {Radiotherapy and oncology},
      volume       = {188},
      issn         = {0167-8140},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2023-01336},
      pages        = {109774},
      year         = {2023},
      note         = {2023 Nov:188:109774},
      abstract     = {With the increased use of focal radiation dose escalation
                      for primary prostate cancer (PCa), accurate delineation of
                      gross tumor volume (GTV) in prostate-specific membrane
                      antigen PET (PSMA-PET) becomes crucial. Manual approaches
                      are time-consuming and observer dependent. The purpose of
                      this study was to create a deep learning model for the
                      accurate delineation of the intraprostatic GTV in PSMA-PET.A
                      3D U-Net was trained on 128 different 18F-PSMA-1007 PET
                      images from three different institutions. Testing was done
                      on 52 patients including one independent internal cohort
                      (Freiburg: n=19) and three independent external cohorts
                      (Dresden: n=14 18F-PSMA-1007, Boston: Massachusetts General
                      Hospital (MGH): n=9 18F-DCFPyL-PSMA and Dana-Farber Cancer
                      Institute (DFCI): n=10 68Ga-PSMA-11). Expert contours were
                      generated in consensus using a validated technique. CNN
                      predictions were compared to expert contours using Dice
                      similarity coefficient (DSC). Co-registered whole-mount
                      histology was used for the internal testing cohort to assess
                      sensitivity/specificity.Median DSCs were Freiburg: 0.82
                      (IQR: 0.73-0.88), Dresden: 0.71 (IQR: 0.53-0.75), MGH: 0.80
                      (IQR: 0.64-0.83) and DFCI: 0.80 (IQR: 0.67-0.84),
                      respectively. Median sensitivity for CNN and expert contours
                      were 0.88 (IQR: 0.68-0.97) and 0.85 (IQR: 0.75-0.88)
                      (p=0.40), respectively. GTV volumes did not differ
                      significantly (p>0.1 for all comparisons). Median
                      specificity of 0.83 (IQR: 0.57-0.97) and 0.88 (IQR:
                      0.69-0.98) were observed for CNN and expert contours
                      (p=0.014), respectively. CNN prediction took 3.81 seconds on
                      average per patient.The CNN was trained and tested on
                      internal and external datasets as well as histopathology
                      reference, achieving a fast GTV segmentation for three
                      PSMA-PET tracers with high diagnostic accuracy comparable to
                      manual experts.},
      cin          = {FR01},
      ddc          = {610},
      cid          = {I:(DE-He78)FR01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37394103},
      doi          = {10.1016/j.radonc.2023.109774},
      url          = {https://inrepo02.dkfz.de/record/277318},
}