title: Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement.
abstract: OBJECTIVE
  Magnifying endoscopy with flexible spectral imaging color enhancement (FICE) is clinically useful in diagnosing gastric cancer and determining treatment options; however, there is a learning curve. Accurate FICE-based diagnosis requires training and experience. In addition, objectivity is necessary. Thus, a software program that can identify gastric cancer quantitatively was developed.
  
  
  METHODS
  A bag-of-features framework with densely sampled scale-invariant feature transform descriptors to magnifying endoscopy images of 46 mucosal gastric cancers was applied. Computer-based findings were compared with histologic findings. The probability of gastric cancer was calculated by means of logistic regression, and sensitivity and specificity of the system were determined.
  
  
  RESULTS
  The average probability was 0.78 ± 0.25 for the images of cancer and 0.31 ± 0.25 for the images of noncancer tissue, with a significant difference between the two groups. An optimal cut-off point of 0.59 was determined on the basis of the receiver operating characteristic curves. The computer-aided diagnosis system yielded a detection accuracy of 85.9% (79/92), sensitivity for a diagnosis of cancer of 84.8% (39/46), and specificity of 87.0% (40/46).
  
  
  CONCLUSIONS
  Further development of this system will allow for quantitative evaluation of mucosal gastric cancers on magnifying gastrointestinal endoscopy images obtained with FICE.
authors: Departments of Gastroenterology and Metabolism, Hiroshima University, Hiroshima, Japan.
authors: Rie
authors: R
authors: Miyaki
authors: Shigeto
authors: S
authors: Yoshida
authors: Shinji
authors: S
authors: Tanaka
authors: Yoko
authors: Y
authors: Kominami
authors: Yoji
authors: Y
authors: Sanomura
authors: Taiji
authors: T
authors: Matsuo
authors: Shiro
authors: S
authors: Oka
authors: Bisser
authors: B
authors: Raytchev
authors: Toru
authors: T
authors: Tamaki
authors: Tetsushi
authors: T
authors: Koide
authors: Kazufumi
authors: K
authors: Kaneda
authors: Masaharu
authors: M
authors: Yoshihara
authors: Kazuaki
authors: K
authors: Chayama
mesh_terms: D000368:Aged; D003116:Color; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D005773:Gastroscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D016015:Logistic Models; D008297:Male; D011237:Predictive Value of Tests; D011336:Probability; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012984:Software; D013274:Stomach Neoplasms / Q000175:diagnosis* / Q000473:pathology*