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@ARTICLE{Netzer:169017,
      author       = {N. Netzer$^*$ and C. Weißer$^*$ and P. Schelb$^*$ and X.
                      Wang$^*$ and X. Qin$^*$ and M. Görtz and V. Schütz and J.
                      P. Radtke$^*$ and T. Hielscher$^*$ and C. Schwab and A.
                      Stenzinger and T. A. Kuder$^*$ and R. Gnirs$^*$ and M.
                      Hohenfellner and H.-P. Schlemmer$^*$ and K. H.
                      Maier-Hein$^*$ and D. Bonekamp$^*$},
      title        = {{F}ully {A}utomatic {D}eep {L}earning in {B}i-institutional
                      {P}rostate {M}agnetic {R}esonance {I}maging: {E}ffects of
                      {C}ohort {S}ize and {H}eterogeneity.},
      journal      = {Investigative radiology},
      volume       = {56},
      number       = {12},
      issn         = {0020-9996},
      address      = {[s.l.]},
      publisher    = {Ovid},
      reportid     = {DKFZ-2021-01185},
      pages        = {799-808},
      year         = {2021},
      note         = {#EA:E010#LA:E010# /December 2021 - Volume 56 - Issue 12 - p
                      799-808},
      abstract     = {The potential of deep learning to support radiologist
                      prostate magnetic resonance imaging (MRI) interpretation has
                      been demonstrated.The aim of this study was to evaluate the
                      effects of increased and diversified training data (TD) on
                      deep learning performance for detection and segmentation of
                      clinically significant prostate cancer-suspicious lesions.In
                      this retrospective study, biparametric (T2-weighted and
                      diffusion-weighted) prostate MRI acquired with multiple
                      1.5-T and 3.0-T MRI scanners in consecutive men was used for
                      training and testing of prostate segmentation and lesion
                      detection networks. Ground truth was the combination of
                      targeted and extended systematic MRI-transrectal ultrasound
                      fusion biopsies, with significant prostate cancer defined as
                      International Society of Urological Pathology grade group
                      greater than or equal to 2. U-Nets were internally validated
                      on full, reduced, and PROSTATEx-enhanced training sets and
                      subsequently externally validated on the institutional test
                      set and the PROSTATEx test set. U-Net segmentation was
                      calibrated to clinically desired levels in cross-validation,
                      and test performance was subsequently compared using
                      sensitivities, specificities, predictive values, and Dice
                      coefficient.One thousand four hundred eighty-eight
                      institutional examinations (median age, 64 years;
                      interquartile range, 58-70 years) were temporally split into
                      training (2014-2017, 806 examinations, supplemented by 204
                      PROSTATEx examinations) and test (2018-2020, 682
                      examinations) sets. In the test set, Prostate
                      Imaging-Reporting and Data System (PI-RADS) cutoffs greater
                      than or equal to 3 and greater than or equal to 4 on a
                      per-patient basis had sensitivity of $97\%$ (241/249) and
                      $90\%$ (223/249) at specificity of $19\%$ (82/433) and
                      $56\%$ (242/433), respectively. The full U-Net had
                      corresponding sensitivity of $97\%$ (241/249) and $88\%$
                      (219/249) with specificity of $20\%$ (86/433) and $59\%$
                      (254/433), not statistically different from PI-RADS (P > 0.3
                      for all comparisons). U-Net trained using a reduced set of
                      171 consecutive examinations achieved inferior performance
                      (P < 0.001). PROSTATEx training enhancement did not improve
                      performance. Dice coefficients were 0.90 for prostate and
                      0.42/0.53 for MRI lesion segmentation at PI-RADS category
                      3/4 equivalents.In a large institutional test set, U-Net
                      confirms similar performance to clinical PI-RADS assessment
                      and benefits from more TD, with neither institutional nor
                      PROSTATEx performance improved by adding multiscanner or
                      bi-institutional TD.},
      cin          = {E010 / C060 / E020 / E230 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)E020-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34049336},
      doi          = {10.1097/RLI.0000000000000791},
      url          = {https://inrepo02.dkfz.de/record/169017},
}