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@ARTICLE{Zhang:169296,
      author       = {K. S. Zhang$^*$ and P. Schelb$^*$ and S. Kohl$^*$ and J. P.
                      Radtke$^*$ and M. Wiesenfarth$^*$ and L. Schimmöller and T.
                      A. Kuder$^*$ and A. Stenzinger and M. Hohenfellner and H.-P.
                      Schlemmer$^*$ and K. Maier-Hein$^*$ and D. Bonekamp$^*$},
      title        = {{I}mprovement of {PI}-{RADS}-dependent prostate cancer
                      classification by quantitative image assessment using
                      radiomics or mean {ADC}.},
      journal      = {Magnetic resonance imaging},
      volume       = {82},
      issn         = {0730-725X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2021-01393},
      pages        = {9-17},
      year         = {2021},
      note         = {#EA:E010#LA:E010# / 2021 Jun 18;82:9-17},
      abstract     = {Background Currently, interpretation of prostate MRI is
                      performed qualitatively. Quantitative assessment of the mean
                      apparent diffusion coefficient (mADC) is promising to
                      improve diagnostic accuracy while radiomic machine learning
                      (RML) allows to probe complex parameter spaces to identify
                      the most promising multi-parametric models. We have
                      previously developed quantitative RML and ADC classifiers
                      for prediction of clinically significant prostate cancer
                      (sPC) from prostate MRI, however these have not been
                      combined with radiologist PI-RADS assessment. Purpose To
                      propose and evaluate diagnostic algorithms combining
                      quantitative ADC or RML and qualitative PI-RADS assessment
                      for prediction of sPC. Methods and population The previously
                      published quantitative models (RML and mADC) were utilized
                      to construct four algorithms: 1) Down(ADC) and 2) Down(RML):
                      clinically detected PI-RADS positive prostate lesions
                      (defined as either PI-RADS≥3 or ≥4) were downgraded to
                      MRI negative upon negative quantitative assessment; and 3)
                      Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded
                      to MRI-positive upon positive assessment of quantitative
                      parameters. Analyses were performed at the individual lesion
                      level and the patient level in 133 consecutive patients with
                      suspicion for clinically significant prostate cancer (sPC,
                      International Society of Urological Pathology (ISUP) grade
                      group≥2), the test set subcohort of a previously published
                      patient population. McNemar test was used to compare
                      differences in sensitivity, specificity and accuracy.
                      Differences between lesions of different prostate zones were
                      assessed using ANOVA. Reduction in false positive
                      assessments was assessed as ratios. Results Compared to
                      clinical assessment at the PI-RADS≥4 cut-off alone,
                      algorithms Down(ADC/RML) improved specificity from $43\%$ to
                      $65\%$ (p = $0.001)/62\%$ (p = 0.003), while sensitivity did
                      not change significantly at $89\%$ compared to $87\%$ (p =
                      $1.0)/89\%$ (unchanged) on the patient level. Reduction of
                      false positive lesions was $50\%$ [26/52] in the PZ and
                      $53\%$ [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a
                      patient basis, to an unfavorable loss of specificity from
                      $43\%$ to $30\%$ (p = $0.039)/32\%$ (p = 0.106), with
                      insignificant increase of sensitivity from $89\%$ to
                      $96\%/96\%$ (both p = 1.0). Compared to clinical assessment
                      at the PI-RADS≥3 cut-off alone, similar results were
                      observed for Down(ADC) with significantly increased
                      specificity from $2\%$ to $23\%$ (p < 0.001) and unchanged
                      sensitivity on the lesion level; patient level specificity
                      increased only non-significantly. Conclusion Downgrading
                      PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC
                      measurements or RML classifiers can increase diagnostic
                      accuracy by enhancing specificity and preserving sensitivity
                      for detection of sPC and reduce false positives.},
      keywords     = {ADC (Other) / MRI (Other) / PI-RADS (Other) / Prostate
                      cancer (Other) / Radiomics (Other)},
      cin          = {E010 / E230 / C060 / E020 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)C060-20160331 / I:(DE-He78)E020-20160331 /
                      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:34147597},
      doi          = {10.1016/j.mri.2021.06.013},
      url          = {https://inrepo02.dkfz.de/record/169296},
}