title: Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance.
abstract: OBJECTIVE
  To determine the effect of computer-aided diagnosis (CAD) on less-experienced and experienced observer performance in differentiation of benign from malignant prostate lesions at 3-T multiparametric magnetic resonance (MR) imaging.
  
  
  METHODS
  The institutional review board waived the need for informed consent. Retrospectively, 34 patients were included who had prostate cancer and had undergone multiparametric MR imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced MR imaging prior to radical prostatectomy. Six radiologists less experienced in prostate imaging and four radiologists experienced in prostate imaging were asked to characterize different regions suspicious for cancer as benign or malignant on multiparametric MR images first without and subsequently with CAD software. The effect of CAD was analyzed by using a multiple-reader, multicase, receiver operating characteristic analysis and a linear mixed-model analysis.
  
  
  RESULTS
  In 34 patients, 206 preannotated regions, including 67 malignant and 64 benign regions in the peripheral zone (PZ) and 19 malignant and 56 benign regions in the transition zone (TZ), were evaluated. Stand-alone CAD had an overall area under the receiver operating characteristic curve (AUC) of 0.90. For PZ and TZ lesions, the AUCs were 0.92 and 0.87, respectively. Without CAD, less-experienced observers had an overall AUC of 0.81, which significantly increased to 0.91 (P = .001) with CAD. For experienced observers, the AUC without CAD was 0.88, which increased to 0.91 (P = .17) with CAD. For PZ lesions, less-experienced observers increased their AUC from 0.86 to 0.95 (P < .001) with CAD. Experienced observers showed an increase from 0.91 to 0.93 (P = .13). For TZ lesions, less-experienced observers significantly increased their performance from 0.72 to 0.79 (P = .01) with CAD and experienced observers increased their performance from 0.81 to 0.82 (P = .42).
  
  
  CONCLUSIONS
  Addition of CAD significantly improved the performance of less-experienced observers in distinguishing benign from malignant lesions; when less-experienced observers used CAD, they reached similar performance as experienced observers. The stand-alone performance of CAD was similar to performance of experienced observers.
authors.affiliation: Department of Radiology, Radboud University Medical Centre Nijmegen, Geert Grootepleinzuid 10, 6525 GA Nijmegen, The Netherlands. t.hambrock@rad.umcn.nl
authors.forename: Thomas
authors.initials: T
authors.lastname: Hambrock
authors.forename: Pieter C
authors.initials: PC
authors.lastname: Vos
authors.forename: Christina A
authors.initials: CA
authors.lastname: Hulsbergen-van de Kaa
authors.forename: Jelle O
authors.initials: JO
authors.lastname: Barentsz
authors.forename: Henkjan J
authors.initials: HJ
authors.lastname: Huisman
mesh_terms: D000368:Aged; D003287:Contrast Media / Q000493:pharmacokinetics; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000601:surgery; D012372:ROC Curve; D012189:Retrospective Studies
chemical_list: D003287:Contrast Media