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@ARTICLE{Radtke:120620,
      author       = {J. P. Radtke$^*$ and M. Wiesenfarth$^*$ and C. Kesch and M.
                      Freitag$^*$ and C. D. Alt and K. Celik and F. Distler and W.
                      Roth$^*$ and K. Wieczorek and C. Stock$^*$ and S. Duensing
                      and M. C. Roethke$^*$ and D. Teber and H.-P. Schlemmer$^*$
                      and M. Hohenfellner and D. Bonekamp$^*$ and B. A. Hadaschik},
      title        = {{C}ombined {C}linical {P}arameters and {M}ultiparametric
                      {M}agnetic {R}esonance {I}maging for {A}dvanced {R}isk
                      {M}odeling of {P}rostate {C}ancer-{P}atient-tailored {R}isk
                      {S}tratification {C}an {R}educe {U}nnecessary {B}iopsies.},
      journal      = {European urology},
      volume       = {72},
      number       = {6},
      issn         = {0302-2838},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2017-01048},
      pages        = {888-896},
      year         = {2017},
      abstract     = {Multiparametric magnetic resonance imaging (mpMRI) is
                      gaining widespread acceptance in prostate cancer (PC)
                      diagnosis and improves significant PC (sPC; Gleason
                      score≥3+4) detection. Decision making based on European
                      Randomised Study of Screening for PC (ERSPC) risk-calculator
                      (RC) parameters may overcome prostate-specific antigen (PSA)
                      limitations.We added pre-biopsy mpMRI to ERSPC-RC parameters
                      and developed risk models (RMs) to predict individual sPC
                      risk for biopsy-naïve men and men after previous biopsy.We
                      retrospectively analyzed clinical parameters of 1159 men who
                      underwent mpMRI prior to MRI/transrectal ultrasound fusion
                      biopsy between 2012 and 2015.Multivariate regression
                      analyses were used to determine significant sPC predictors
                      for RM development. The prediction performance was compared
                      with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging
                      Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus
                      PI-RADSv1.0 using receiver-operating characteristics (ROCs).
                      Discrimination and calibration of the RM, as well as net
                      decision and reduction curve analyses were evaluated based
                      on resampling methods.PSA, prostate volume, digital-rectal
                      examination, and PI-RADS were significant sPC predictors and
                      included in the RMs together with age. The ROC area under
                      the curve of the RM for biopsy-naïve men was comparable
                      with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger
                      compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and
                      PI-RADS (0.76). For postbiopsy men, the novel RM's
                      discrimination (0.81) was higher, compared with PI-RADS
                      (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4
                      plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of
                      ERSPC-RCs and PI-RADS in the decision regarding which
                      patient to receive biopsy and enabled the highest reduction
                      rate of unnecessary biopsies. Limitations include a
                      monocentric design and a lack of PI-RADSv2.0.The novel RMs,
                      incorporating clinical parameters and PI-RADS, performed
                      significantly better compared with RMs without PI-RADS and
                      provided measurable benefit in making the decision to biopsy
                      men at a suspicion of PC. For biopsy-naïve patients, both
                      our RM and ERSPC-RC3 plus PI-RADSv1.0 exceeded the
                      prediction performance compared with clinical parameters
                      alone.Combined risk models including clinical and imaging
                      parameters predict clinically relevant prostate cancer
                      significantly better than clinical risk calculators and
                      multiparametric magnetic resonance imaging alone. The risk
                      models demonstrate a benefit in making a decision about
                      which patient needs a biopsy and concurrently help avoid
                      unnecessary biopsies.},
      cin          = {E010 / C060 / G150},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)G150-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28400169},
      doi          = {10.1016/j.eururo.2017.03.039},
      url          = {https://inrepo02.dkfz.de/record/120620},
}