title: Virtual dissection CT colonography: evaluation of learning curves and reading times with and without computer-aided detection.
abstract: OBJECTIVE
  To prospectively evaluate the learning curves and reading times of inexperienced readers who used the virtual dissection reading method for retrospective computed tomographic (CT) colonography data sets, with and without concurrent computer-aided detection (CAD).
  
  
  METHODS
  An Institutional Review Board approved this study; informed consent was waived. Four radiologists without experience in CT colonography evaluated 100 optical colonoscopy-proved data sets of 100 patients (49 men, 51 women; mean age, 59 years +/- 13 [standard deviation]; range, 21-85 years) by using the virtual dissection reading method. Two readers used concurrent CAD. Data sets were read during five consecutive 1-day sessions (20 data sets per session). Polyp detection and false-positive rates, receiver operating characteristics (ROCs), and reading times were calculated for individual, CAD group, and non-CAD group readings. Diagnostic values were compared by calculating the 95% confidence intervals (CIs) around the relative risk. Areas under ROC curves (AUCs) (Hanley and McNeil for paired analysis and z statistics for unpaired analysis) and reading times (Wilcoxon signed rank test) were compared across the sessions, within each session and for the whole study.
  
  
  RESULTS
  The range of detection rates was 79 of 111 (.71 [95% CI: .61, .79]) to 91 of 111 (.82 [95% CI: .73, .88]). The range of false-positive rates was 17 of 111 (.15 [95% CI: .09, .23]) to 22 of 111 (.20 [95% CI: .12, .28]). All readers' AUCs rose from session 1 to session 4; this rise was significant (P < .05) for the non-CAD group. Only during session 1 was the CAD group AUC (.83) higher than the non-CAD group AUC (.54) (P < .05). Comparison of CAD and non-CAD reading times showed no significant difference for the whole study or during each session (P > .05).
  
  
  CONCLUSIONS
  The virtual dissection reading technique allows short learning curves, which may be improved by the concurrent use of CAD, without significant effect on average reading time.
authors: Department of Medical Imaging, Clinique Saint-Joseph, Rue de Hesbaye, 75, 4000 Liège, Belgium. Danielle.hock@chc.be
authors: Danielle
authors: D
authors: Hock
authors: Roxanne
authors: R
authors: Ouhadi
authors: Roland
authors: R
authors: Materne
authors: Amir-Samy
authors: AS
authors: Aouchria
authors: Isabelle
authors: I
authors: Mancini
authors: Thomas
authors: T
authors: Broussaud
authors: Paul
authors: P
authors: Magotteaux
authors: Alain
authors: A
authors: Nchimi
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011361:Professional Competence; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis; D013997:Time Factors; D014584:User-Computer Interface