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.
© 2013 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.