title: Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video).
abstract: BACKGROUND
  Narrow-band imaging (NBI) classification of colorectal lesions is clinically useful in determining treatment options for colorectal tumors. There is a learning curve, however. Accurate NBI-based diagnosis requires training and experience. In addition, objective diagnosis is necessary. Thus, we developed a computerized system to automatically classify NBI magnifying colonoscopic images.
  
  
  OBJECTIVE
  To evaluate the utility and limitations of our automated NBI classification system.
  
  
  METHODS
  Retrospective study.
  
  
  METHODS
  Department of endoscopy, university hospital.
  
  
  METHODS
  Performance of our computer-based system for classification of NBI magnifying colonoscopy images in comparison to classification by two experienced endoscopists and to histologic findings.
  
  
  RESULTS
  For the 371 colorectal lesions depicted on validation images, the computer-aided classification system yielded a detection accuracy of 97.8% (363/371); sensitivity and specificity of types B-C3 lesions for a diagnosis of neoplastic lesion were 97.8% (317/324) and 97.9% (46/47), respectively. Diagnostic concordance between the computer-aided classification system and the two experienced endoscopists was 98.7% (366/371), with no significant difference between methods.
  
  
  CONCLUSIONS
  Retrospective, single-center in this initial report.
  
  
  CONCLUSIONS
  Our new computer-aided system is reliable for predicting the histology of colorectal tumors by using NBI magnifying colonoscopy.
authors.affiliation: Department of Medicine and Molecular Science, Hiroshima University, Hiroshima, Japan.
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Takemura
authors.forename: Shigeto
authors.initials: S
authors.lastname: Yoshida
authors.forename: Shinji
authors.initials: S
authors.lastname: Tanaka
authors.forename: Rie
authors.initials: R
authors.lastname: Kawase
authors.forename: Keiichi
authors.initials: K
authors.lastname: Onji
authors.forename: Shiro
authors.initials: S
authors.lastname: Oka
authors.forename: Toru
authors.initials: T
authors.lastname: Tamaki
authors.forename: Bisser
authors.initials: B
authors.lastname: Raytchev
authors.forename: Kazufumi
authors.initials: K
authors.lastname: Kaneda
authors.forename: Masaharu
authors.initials: M
authors.lastname: Yoshihara
authors.forename: Kazuaki
authors.initials: K
authors.lastname: Chayama
mesh_terms: D000236:Adenoma / Q000145:classification / Q000473:pathology*; D002277:Carcinoma / Q000145:classification / Q000473:pathology*; D003113:Colonoscopy / Q000379:methods*; D015179:Colorectal Neoplasms / Q000145:classification / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted; D007249:Inflammation / Q000473:pathology; D007413:Intestinal Mucosa / Q000473:pathology; D009361:Neoplasm Invasiveness; D011237:Predictive Value of Tests; D012189:Retrospective Studies