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@ARTICLE{Bonekamp:141988,
      author       = {D. Bonekamp$^*$ and S. Kohl$^*$ and M. Wiesenfarth$^*$ and
                      P. Schelb$^*$ and J. P. Radtke$^*$ and M. Götz$^*$ and P.
                      Kickingereder$^*$ and K. Yaqubi$^*$ and B. Hitthaler and N.
                      Gählert$^*$ and T. A. Kuder$^*$ and F. Deister$^*$ and M.
                      Freitag$^*$ and M. Hohenfellner and B. A. Hadaschik and
                      H.-P. Schlemmer$^*$ and K. Maier-Hein$^*$},
      title        = {{R}adiomic {M}achine {L}earning for {C}haracterization of
                      {P}rostate {L}esions with {MRI}: {C}omparison to {ADC}
                      {V}alues.},
      journal      = {Radiology},
      volume       = {289},
      number       = {1},
      issn         = {1527-1315},
      address      = {Oak Brook, Ill.},
      publisher    = {Soc.},
      reportid     = {DKFZ-2018-02218},
      pages        = {128 - 137},
      year         = {2018},
      abstract     = {Purpose To compare biparametric contrast-free radiomic
                      machine learning (RML), mean apparent diffusion coefficient
                      (ADC), and radiologist assessment for characterization of
                      prostate lesions detected during prospective MRI
                      interpretation. Materials and Methods This
                      single-institution study included 316 men (mean age ±
                      standard deviation, 64.0 years ± 7.8) with an indication
                      for MRI-transrectal US fusion biopsy between May 2015 and
                      September 2016 (training cohort, 183 patients; test cohort,
                      133 patients). Lesions identified by prospective clinical
                      readings were manually segmented for mean ADC and radiomics
                      analysis. Global and zone-specific random forest RML and
                      mean ADC models for classification of clinically significant
                      prostate cancer (Gleason grade group ≥ 2) were developed
                      on the training set and the fixed models tested on an
                      independent test set. Clinical readings, mean ADC, and
                      radiomics were compared by using the McNemar test and
                      receiver operating characteristic (ROC) analysis. Results In
                      the test set, radiologist interpretation had a per-lesion
                      sensitivity of $88\%$ (53 of 60) and specificity of $50\%$
                      (79 of 159). Quantitative measurement of the mean ADC
                      (cut-off 732 mm2/sec) significantly reduced false-positive
                      (FP) lesions from 80 to 60 (specificity $62\%$ [99 of 159])
                      and false-negative (FN) lesions from seven to six
                      (sensitivity $90\%$ [54 of 60]) (P = .048). Radiologist
                      interpretation had a per-patient sensitivity of $89\%$ (40
                      of 45) and specificity of $43\%$ (38 of 88). Quantitative
                      measurement of the mean ADC reduced the number of patients
                      with FP lesions from 50 to 43 (specificity $51\%$ [45 of
                      88]) and the number of patients with FN lesions from five to
                      three (sensitivity $93\%$ [42 of 45]) (P = .496). Comparison
                      of the area under the ROC curve (AUC) for the mean ADC
                      (AUCglobal = 0.84; AUCzone-specific ≤ 0.87) vs the RML
                      (AUCglobal = 0.88, P = .176; AUCzone-specific ≤ 0.89, P
                      ≥ .493) showed no significantly different performance.
                      Conclusion Quantitative measurement of the mean apparent
                      diffusion coefficient (ADC) improved differentiation of
                      benign versus malignant prostate lesions, compared with
                      clinical assessment. Radiomic machine learning had
                      comparable but not better performance than mean ADC
                      assessment. © RSNA, 2018 Online supplemental material is
                      available for this article.},
      cin          = {E010 / E230 / E020 / C060 / L101},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)E020-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)L101-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30063191},
      doi          = {10.1148/radiol.2018173064},
      url          = {https://inrepo02.dkfz.de/record/141988},
}