title: Learning curve of virtual chromoendoscopy for the prediction of hyperplastic and adenomatous colorectal lesions: a prospective 2-center study.
abstract: BACKGROUND
  Computed virtual chromoendoscopy (CVC) enables high-definition imaging of mucosal lesions with improved tissue contrast. Previous studies have shown that CVC yields an improved detection rate of colorectal lesions. However, the learning curve for interpretation of CVC images is unknown.
  
  
  OBJECTIVE
  To examine the learning curve of correctly identifying hyperplastic and adenomatous colorectal lesions by using CVC.
  
  
  METHODS
  Prospective, 2-center study.
  
  
  METHODS
  Consecutive patients undergoing screening colonoscopy were included. CVC images were analyzed by using corresponding polypectomies as the reference standard followed by a prospective, double-blind review of i-scan images.
  
  
  METHODS
  A training set containing 20 images with known histology was reviewed to standardize image interpretation, followed by a blind review of 110 unknown images. Overall, 4 endoscopists from 2 different endoscopy centers evaluated the images, which were obtained by 1 endoscopist using high-definition endoscopy with CVC.
  
  
  RESULTS
  Patients were included in a prospective fashion. Seventy-seven of 110 colorectal lesions were adenomas and 33 were hyperplastic lesions. Mean diameter of colonic polyps was 4 mm (range, 2-20 mm). Overall accuracy for the group was 73.9% for lesions 1 to 22, 79.6% for lesions 23 to 44, 84.1% for lesions 45 to 66, 87.5% for lesions 67 to 88, and 94.3% for lesions 89 to 110. Accuracy of i-scan for prediction of polyp histology was not dependent on polyp size (≤5 mm, 6-10 mm, or > 10 mm). The ability to obtain high-quality images was stable over time, and high-quality images were constantly produced.
  
  
  CONCLUSIONS
  Post-hoc assessment.
  
  
  CONCLUSIONS
  Accurate interpretation of CVC images for prediction of hyperplastic and adenomatous colorectal lesions follows a learning curve but can be learned rapidly.
authors.affiliation: Department of Medicine I, University of Erlangen-Nuremberg, Erlangen, Germany.
authors.forename: Helmut
authors.initials: H
authors.lastname: Neumann
authors.forename: Michael
authors.initials: M
authors.lastname: Vieth
authors.forename: Lucía C
authors.initials: LC
authors.lastname: Fry
authors.forename: Claudia
authors.initials: C
authors.lastname: Günther
authors.forename: Raja
authors.initials: R
authors.lastname: Atreya
authors.forename: Markus F
authors.initials: MF
authors.lastname: Neurath
authors.forename: Klaus
authors.initials: K
authors.lastname: Mönkemüller
mesh_terms: D000236:Adenoma / Q000175:diagnosis / Q000473:pathology*; D000368:Aged; D053704:Capsule Endoscopy / Q000379:methods*; D003111:Colonic Polyps / Q000175:diagnosis / Q000473:pathology*; D015179:Colorectal Neoplasms / Q000175:diagnosis / Q000473:pathology*; D003937:Diagnosis, Differential; D004311:Double-Blind Method; D005260:Female; D005858:Germany; D006801:Humans; D006965:Hyperplasia / Q000175:diagnosis / Q000473:pathology; D059032:Learning Curve; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011237:Predictive Value of Tests; D011446:Prospective Studies; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014481:United States; D014584:User-Computer Interface