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@ARTICLE{Schelb:157380,
      author       = {P. Schelb$^*$ and X. Wang$^*$ and J. P. Radtke$^*$ and M.
                      Wiesenfarth$^*$ and P. Kickingereder and A. Stenzinger$^*$
                      and M. Hohenfellner and H.-P. Schlemmer$^*$ and K. H.
                      Maier-Hein$^*$ and D. Bonekamp$^*$},
      title        = {{S}imulated clinical deployment of fully automatic deep
                      learning for clinical prostate {MRI} assessment.},
      journal      = {European radiology},
      volume       = {31},
      number       = {1},
      issn         = {1432-1084},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2020-01603},
      pages        = {302-313},
      year         = {2021},
      note         = {2021 Jan;31(1):302-313#EA:E010#LA:E010#},
      abstract     = {To simulate clinical deployment, evaluate performance, and
                      establish quality assurance of a deep learning algorithm
                      (U-Net) for detection, localization, and segmentation of
                      clinically significant prostate cancer (sPC), ISUP grade
                      group ≥ 2, using bi-parametric MRI.In 2017, 284
                      consecutive men in active surveillance, biopsy-naïve or
                      pre-biopsied, received targeted and extended systematic
                      MRI/transrectal US-fusion biopsy, after examination on a
                      single MRI scanner (3 T). A prospective adjustment scheme
                      was evaluated comparing the performance of the Prostate
                      Imaging Reporting and Data System (PI-RADS) and U-Net using
                      sensitivity, specificity, predictive values, and the Dice
                      coefficient.In the 259 eligible men (median 64 [IQR
                      61-72] years), PI-RADS had a sensitivity of $98\%$
                      $[106/108]/84\%$ [91/108] with a specificity of $17\%$
                      $[25/151]/58\%$ [88/151], for thresholds at ≥ 3/≥ 4
                      respectively. U-Net using dynamic threshold adjustment had a
                      sensitivity of $99\%$ $[107/108]/83\%$ [90/108]
                      (p > 0.99/> 0.99) with a specificity of $24\%$
                      $[36/151]/55\%$ [83/151] (p > 0.99/> 0.99) for
                      probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and
                      ≥ 4 decisions respectively, not statistically different
                      from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4
                      examination and U-Net ≥ d3 assessment significantly
                      improved the positive predictive value from 59 to $63\%$
                      (p = 0.03), on a per-patient basis.U-Net has similar
                      performance to PI-RADS in simulated continued clinical use.
                      Regular quality assurance should be implemented to ensure
                      desired performance.• U-Net maintained similar diagnostic
                      performance compared to radiological assessment of PI-RADS
                      ≥ 4 when applied in a simulated clinical deployment. •
                      Application of our proposed prospective dynamic calibration
                      method successfully adjusted U-Net performance within
                      acceptable limits of the PI-RADS reference over time, while
                      not being limited to PI-RADS as a reference. •
                      Simultaneous detection by U-Net and radiological assessment
                      significantly improved the positive predictive value on a
                      per-patient and per-lesion basis, while the negative
                      predictive value remained unchanged.},
      cin          = {E010 / E230 / C060 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)C060-20160331 / 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:32767102},
      doi          = {10.1007/s00330-020-07086-z},
      url          = {https://inrepo02.dkfz.de/record/157380},
}