title: Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.
abstract: OBJECTIVE
  To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) of the prostate.
  
  
  METHODS
  A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences). The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI) that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies.
  
  
  RESULTS
  In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4-95.0), a specificity of 87.5% (with 95% CI of 69.0-95.7) and a diagnostic accuracy of 86.7% (with 95% CI of 73.8-93.8) for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature.
  
  
  CONCLUSIONS
  The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction.
authors.affiliation: Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Matthias C
authors.initials: MC
authors.lastname: Roethke
authors.affiliation: Department of Urology, University Hospital of Cologne, Cologne, Germany.
authors.forename: Timur H
authors.initials: TH
authors.lastname: Kuru
authors.affiliation: Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Maya B
authors.initials: MB
authors.lastname: Mueller-Wolf
authors.affiliation: Watson Medical bv, Den Ham, The Netherlands.
authors.forename: Erik
authors.initials: E
authors.lastname: Agterhuis
authors.affiliation: Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Christopher
authors.initials: C
authors.lastname: Edler
authors.affiliation: Department of Urology, University Hospital Heidelberg, Heidelberg, Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Hohenfellner
authors.affiliation: Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Heinz-Peter
authors.initials: HP
authors.lastname: Schlemmer
authors.affiliation: Department of Urology, University Hospital Heidelberg, Heidelberg, Germany.
authors.forename: Boris A
authors.initials: BA
authors.lastname: Hadaschik
mesh_terms: D000368:Aged; D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D061705:Image-Guided Biopsy / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012984:Software