Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. Issue 5 (25th April 2013)

• Record Type: Journal Article
• Authors: Miyaki, Rie; Yoshida, Shigeto; Tanaka, Shinji; Kominami, Yoko; Sanomura, Yoji; Matsuo, Taiji; Oka, Shiro; Raytchev, Bisser; Tamaki, Toru; Koide, Tetsushi; Kaneda, Kazufumi; Yoshihara, Masaharu; Chayama, Kazuaki
• Is Part Of: Journal of gastroenterology and hepatology. Volume 28:Issue 5(2013:May)
• Page Start: 841
• Page End: 847
• Publication Date: 2013-04-25
• Subjects: endoscopy: upper GI -- gastric cancer: clinical practice and treatment (including surgery) -- gastroenterology -- imaging and advanced technology/applied therapeutics; Gastroenterology -- Periodicals; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; Gastroenterology -- Periodicals; Liver Diseases -- Periodicals; 616.33
• DOI: 10.1111/jgh.12149
• Languages: English
• ISSNs: 0815-9319

Abstract

Abstract: Background and Aim: 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). Conclusion: Further development of this system will allow for quantitative evaluation of mucosal gastric cancers on magnifying gastrointestinal endoscopy images obtained with FICE.