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

234

222

The Japanese Gastroenterological Association, 2 Japan Gastroenterological Endoscopy Society, Tokyo, and 3 The Japanese Gastric Cancer Association, Kyoto, JapanGastric 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.

Section: Methodsmentioning

confidence: 99%

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

Muto

Yao

Kaise

et al. 2016

234

222

“…Even using small‐caliber endoscopy with low resolution, greater color differences are evident in FICE images, resulting in images with better contrast and possible facilitation in the detection of early gastric cancer. Gastric cancers have also been evaluated quantitatively by computers . The computer‐aided diagnosis system yielded a detection accuracy as high as 85.9%.…”

Section: Using Fice For Screening and Detailed Examinationsmentioning

confidence: 99%

Present and future status of flexible spectral imaging color enhancement and blue laser imaging technology

Osawa

Yamamoto

2013

143

119

The usefulness of flexible spectral imaging color enhancement (FICE) has been reported for evaluating the esophagus, stomach, and small and large intestine. Higher contrast is shown between cancer and the surrounding mucosa in the esophagus and stomach and may facilitate the detection of gastric cancers missed by white light imaging alone. The surface patterns of gastric mucosa are clearly visualized in non-malignant areas but are irregular and blurred in malignant areas, leading to clear demarcation. Capsule endoscopy with FICE detects angiodysplasia and erosions of the small intestine. The surface and vascular pattern with FICE is useful for the differential diagnosis of colorectal polyps. However, FICE remains somewhat poor at visualizing mucosal microvasculature on a tumor surface. Narrow-band imaging (NBI) is dark in observing whole gastric mucosa and poor at visualizing mucosal microstructure. Blue laser imaging (BLI) has the potential to resolve these limitations. Narrow-band laser light combined with white light shows irregular microvessels on both differentiated and undifferentiated gastric cancer similar to those using NBI. In addition, irregular surface patterns including minute white zones are clearly seen on the uneven surface of differentiated lesions, resulting in exclusion of undifferentiated lesions. Using both distant and close-up views, a high contrast between green intestinal metaplasia and brown gastric cancer may lead to early detection of gastric cancers and determination of a demarcation line. BLI produces high-contrast images in esophageal cancer with clear vision of intrapapillary capillary loops and also predicts the histopathological diagnosis and depth of invasion in colorectal neoplasms.

“…However, this optical diagnosis requires substantial expertise and experience, which prevents its general use in gastroscopy. Miyaki et al . developed software to automatically differentiate cancerous from non‐cancerous areas.…”

Section: Stomachmentioning

confidence: 99%

“…[34][35][36][37][38][39][40] However, this optical diagnosis requires substantial expertise and experience, which prevents its general use in gastroscopy. Miyaki et al 15 developed software to automatically differentiate cancerous from non-cancerous areas. The authors used a bag-of-features framework with densely sampled scale-invariant featuretransform descriptors to magnifying FICE images and validated the model using 46 intramucosal gastric cancers.…”

Section: Identification Of Gastric Cancermentioning

confidence: 99%

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective

Mori

Kudo

Mohmed

et al. 2019

112

With recent breakthroughs in artificial intelligence, computer‐aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early‐stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false‐negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real‐time gastroscopy to provide on‐site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.