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@ARTICLE{Schrader:291445,
      author       = {A. Schrader$^*$ and N. Netzer$^*$ and T. Hielscher$^*$ and
                      M. Görtz$^*$ and K. S. Zhang$^*$ and V. Schütz and A.
                      Stenzinger and M. Hohenfellner and H.-P. Schlemmer$^*$ and
                      D. Bonekamp$^*$},
      title        = {{P}rostate cancer risk assessment and avoidance of prostate
                      biopsies using fully automatic deep learning in prostate
                      {MRI}: comparison to {PI}-{RADS} and integration with
                      clinical data in nomograms.},
      journal      = {European radiology},
      volume       = {34},
      issn         = {0938-7994},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2024-01408},
      pages        = {7909–7920},
      year         = {2024},
      note         = {#EA:E010#LA:E010# / Volume 34, pages 7909–7920, (2024)},
      abstract     = {Risk calculators (RCs) improve patient selection for
                      prostate biopsy with clinical/demographic information,
                      recently with prostate MRI using the prostate imaging
                      reporting and data system (PI-RADS). Fully-automated deep
                      learning (DL) analyzes MRI data independently, and has been
                      shown to be on par with clinical radiologists, but has yet
                      to be incorporated into RCs. The goal of this study is to
                      re-assess the diagnostic quality of RCs, the impact of
                      replacing PI-RADS with DL predictions, and potential
                      performance gains by adding DL besides PI-RADS.One thousand
                      six hundred twenty-seven consecutive examinations from 2014
                      to 2021 were included in this retrospective single-center
                      study, including 517 exams withheld for RC testing.
                      Board-certified radiologists assessed PI-RADS during
                      clinical routine, then systematic and MRI/Ultrasound-fusion
                      biopsies provided histopathological ground truth for
                      significant prostate cancer (sPC). nnUNet-based DL ensembles
                      were trained on biparametric MRI predicting the presence of
                      sPC lesions (UNet-probability) and a PI-RADS-analogous
                      five-point scale (UNet-Likert). Previously published RCs
                      were validated as is; with PI-RADS substituted by
                      UNet-Likert (UNet-Likert-substituted RC); and with both
                      UNet-probability and PI-RADS (UNet-probability-extended RC).
                      Together with a newly fitted RC using clinical data, PI-RADS
                      and UNet-probability, existing RCs were compared by
                      receiver-operating characteristics, calibration, and
                      decision-curve analysis.Diagnostic performance remained
                      stable for UNet-Likert-substituted RCs. DL contained
                      complementary diagnostic information to PI-RADS. The
                      newly-fitted RC spared $49\%$ [252/517] of biopsies while
                      maintaining the negative predictive value $(94\%),$ compared
                      to PI-RADS ≥ 4 cut-off which spared $37\%$ [190/517] (p <
                      0.001).Incorporating DL as an independent diagnostic marker
                      for RCs can improve patient stratification before biopsy, as
                      there is complementary information in DL features and
                      clinical PI-RADS assessment.For patients with positive
                      prostate screening results, a comprehensive diagnostic
                      workup, including prostate MRI, DL analysis, and individual
                      classification using nomograms can identify patients with
                      minimal prostate cancer risk, as they benefit less from the
                      more invasive biopsy procedure.The current MRI-based
                      nomograms result in many negative prostate biopsies. The
                      addition of DL to nomograms with clinical data and PI-RADS
                      improves patient stratification before biopsy. Fully
                      automatic DL can be substituted for PI-RADS without
                      sacrificing the quality of nomogram predictions. Prostate
                      nomograms show cancer detection ability comparable to
                      previous validation studies while being suitable for the
                      addition of DL analysis.},
      keywords     = {Deep learning (Other) / Nomograms (Other) / Prostatic
                      neoplasms, Multiparametric magnetic resonance imaging
                      (Other) / Risk assessment (Other)},
      cin          = {E010 / C060 / E250},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)E250-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38955845},
      doi          = {10.1007/s00330-024-10818-0},
      url          = {https://inrepo02.dkfz.de/record/291445},
}