The work described herein was performed at the Department of Endoscopy and Medicine, Hiroshima University, Hiroshima, Japan.
Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement
Article first published online: 25 APR 2013
© 2013 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd
Journal of Gastroenterology and Hepatology
Volume 28, Issue 5, pages 841–847, May 2013
How to Cite
Miyaki, R., Yoshida, S., Tanaka, S., Kominami, Y., Sanomura, Y., Matsuo, T., Oka, S., Raytchev, B., Tamaki, T., Koide, T., Kaneda, K., Yoshihara, M. and Chayama, K. (2013), Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. Journal of Gastroenterology and Hepatology, 28: 841–847. doi: 10.1111/jgh.12149
- Issue published online: 25 APR 2013
- Article first published online: 25 APR 2013
- Accepted manuscript online: 21 FEB 2013 02:41AM EST
- Manuscript Accepted: 24 JAN 2013
- endoscopy: upper GI;
- gastric cancer: clinical practice and treatment (including surgery);
- imaging and advanced technology/applied therapeutics
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.
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.
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).
Further development of this system will allow for quantitative evaluation of mucosal gastric cancers on magnifying gastrointestinal endoscopy images obtained with FICE.