Gastric cancer is the third leading cause of cancer death worldwide. Early detection and accurate diagnosis of mucosal cancer is desirable in order to achieve decreased mortality; cause-specific survival of patients with early gastric cancer is reported to exceed 95%. Endoscopy is the functional modality to detect early cancer; however, the procedure is not definitive when using conventional white-light imaging. In contrast, magnifying narrow-band imaging (M-NBI), a novel endoscopic technology, is a powerful tool for characterizing gastric mucosal lesions because it can visualize the microvascular architecture and microsurface structure. To date, many reports on the diagnosis of early gastric cancer by M-NBI, including multicenter prospective randomized studies conducted in Japan, have been published in peer-reviewed international journals. Based on these published data, we devised a proposal for a diagnostic strategy for gastric mucosal cancer using M-NBI to simplify the process of diagnosis and improve accuracy. Herein, we recommend a diagnostic algorithm for early gastric cancer using magnifying endoscopy.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/den.12638

Magnifying endoscopy simple diagnostic algorithm for early gastric cancer (MESDA-G).

Combined magnification endoscopy with chromoendoscopy for the evaluation of Barrett's esophagus.

Combined magnification endoscopy with chromoendoscopy in the evaluation of patients with suspected malabsorption

https://www.giejournal.org/article/S0016-5107(08)02489-9/pdf

Evaluation and validation of computed virtual chromoendoscopy in early gastric cancer

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.

Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement.

Diagnosis of gastric polypoid lesions by magnifying endoscopy and dye endoscopy.

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Diagnosis of gastric polypoid lesions by magnifying endoscopy and dye endoscopy.