Diagnosis of early gastric cancer using image enhanced endoscopy: a systematic approach

Risk and prediction of multiple primary malignancies after early gastric cancer

BACKGROUND

Patients with early gastric cancer have increased risk of developing multiple primary malignancies (MPM) due to improved survival rates. The purpose of this study was to evaluate the clinicopathological features of MPM and to generate a useful tool for predicting the development of MPM after early gastric cancer.

METHODS

We selected 1025 early gastric cancer patients with complete medical records for a retrospective analysis. The Cox proportional risk regression model was used to analyze the independent risk factors for the development of MPM in early gastric cancer. RStudio software was used to compare the OS of early gastric cancer patients with and without MPM, and a nomogram was established to predict the probability of MPM 1-, 2-, 3-year after early gastric cancer. The predictive effectiveness of the nomogram was evaluated by the C-index and calibration curve. Decision curve analysis (DCA) measured the applicability of the nomogram to clinical practice.

RESULTS

Of the 1025 patients with early gastric cancer, 66 patients (6.4%) had 69 primary cancers other than early gastric cancer. They had a median follow-up of 41 months, and their cumulative incidence of MPM was 4.9%, 5.4% and 5.9% after 1-, 2-, and 3- year, respectively. Oesophageal cancer was the most frequently detected MPM, followed by lung and colorectal cancers. Male (p=0.038), age ≥65 years (p=0.003), smoking history (p=0.036), and lymph node metastasis (p=0.013) were independent risk factors for MPM in patients with early gastric cancer. Patients with early gastric cancer with MPM had a worse OS prognosis than patients with early gastric cancer without MPM (p<0.001). The internally validated nomogram predicted the probability of developing MPM after early gastric cancer (C index= 0.697). The calibration chart showed that the predicted probability of MPM in early gastric cancer was similar to the observed result, and the DCA showed strong clinical practicability.

CONCLUSION

After the diagnosis and treatment of early gastric cancer, we should be alert to the possibility of MPM and perform regular and careful monitoring.

Endoscopic diagnosis and treatment in gastric cancer: Current evidence and new perspectives

Gastric cancer is the fifth most common cause of cancer related deaths worldwide. Despite advancement in endoscopic techniques, the majority of the cases are diagnosed at late stage, when the curative treatment options are very limited. The early gastric cancer (EGC) on the other side is potentially curable, and in selected cases endoscopic resection techniques offer similar survival rates then surgical resection. The detection of EGC is endoscopically challenging and requires high quality examination. Recent data show that close to 10% of the gastric cancer cases had a previous negative endoscopy. This highlights the urgent need to improve the quality of the endoscopy services, what can be achieved by increasing the awareness of gastroenterologists and continuously monitoring the key performance indicators of upper gastrointestinal endoscopy. Newer endoscopic imaging techniques are also becoming commonly available to aid the detection of gastric premalignant lesions and EGC. High-definition endoscopy with image enhancement techniques is preferred over white light endoscopy to recognize these lesions, and they are also useful to determine the invasion depth of EGC. The endoscopic optical characterization of lesions is necessary for the selection of proper resection method and decide whether endoscopic resection techniques can be considered. Artificial intelligence systems aid the detection of EGC and can help to determine the depth of invasion. Endoscopic mucosal resection and endoscopic submucosal dissection requires centralized care and tertiary referral centers with appropriate expertise to ensure proper patient selection, high success rate and low adverse event rate. Appropriately scheduled endoscopic surveillance of high-risk patients, premalignant lesions and after resection of EGC is also important in the early detection and successful treatment of gastric cancer.

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

Gastric Cancer: Emerging Trends in Prevention, Diagnosis, and Treatment

Gastric adenocarcinoma (GC) is the fourth leading cause of global cancer mortality, and the leading infection-associated cancer. Helicobacter pylori is the dominant risk factor for GC and classified as an IARC class I carcinogen. Surveillance of gastric premalignant conditions is now indicated in high-risk patients. Upper endoscopy is the gold standard for GC diagnosis, and image-enhanced endoscopy increases the detection of gastric premalignant conditions and early gastric cancer (EGC). Clinical staging is crucial for treatment approach, defining early gastric cancer, operable locoregional disease, and advanced GC. Endoscopic submucosal dissection is the treatment of choice for most EGC. Targeted therapies are rapidly evolving, based on biomarkers including MSI/dMMR, HER2, and PD-L1. These advancements in surveillance, diagnostic and therapeutic strategies are expected to improve GC survival rates in the near term.

Early gastric cancer: A challenge in Western countries

Early gastric cancer (EGC) is an invasive carcinoma involving only the stomach mucosa or submucosa, independently of lymph node status. EGC represents over 50% of cases in Japan and in South Korea, whereas it accounts only for approximately 20% of all newly diagnosed gastric cancers in Western countries. The main classification systems of EGC are the Vienna histopathologic classification and the Paris endoscopic classification of polypoid and non-polypoid lesions. A careful endoscopic assessment is fundamental to establish the best treatment of EGC. Generally, EGCs are curable if the lesion is completely removed by endoscopic resection or surgery. Some types of EGC can be resected endoscopically; for others the most appropriate treatment is surgical resection and D2 lymphadenectomy, especially in Western countries. The favorable oncological prognosis, the extended lymphadenectomy and the reconstruction of the intestinal continuity that excludes the duodenum make the prophylactic cholecystectomy mandatory to avoid the onset of biliary complications.

Endoscopic Classifications of Early Gastric Cancer: A Literature Review

Simple Summary

Throughout the years, endoscopic technologies have advanced to facilitate better assessment of gastric lesions and early detection of gastric cancer. With improvements in conventional white light endoscopy, we have also witnessed the development of newer endoscopic diagnostic modalities, giving rise to several classifications for early gastric cancer. Different endoscopic classifications of early gastric based on several endoscopic diagnostic modalities were included in this review. In addition to this, newer and novel endoscopic classifications that were specifically developed for the stomach for assessing and diagnosing gastric lesions have also been included. Illustrative representations of each classification have also been provided to aid readers in better understanding of these endoscopic classifications of early gastric cancer.

Abstract

Endoscopic technologies have been continuously advancing throughout the years to facilitate improvement in the detection and diagnosis of gastric lesions. With the development of different endoscopic diagnostic modalities for EGC, several classifications have been advocated for the evaluation of gastric lesions, aiming for an early detection and diagnosis. Sufficient knowledge on the appearance of EGC on white light endoscopy is fundamental for early detection and management. On the other hand, those superficial EGC with subtle morphological changes that are challenging to be detected with white light endoscopy may now be clearly defined by means of image-enhanced endoscopy (IEE). By combining magnifying endoscopy and IEE, irregularities in the surface structures can be evaluated and highlighted, leading to improvements in EGC diagnostic accuracy. The main scope of this review article is to offer a closer look at the different classifications of EGC based on several endoscopic diagnostic modalities, as well as to introduce readers to newer and novel classifications, specifically developed for the stomach, for the assessment and diagnosis of gastric lesions.

pCLE highlights distinctive vascular patterns in early gastric cancer and in gastric diseases with high risk of malignant complications

Endoscopy is widely used to detect and diagnose precancerous lesions and gastric cancer (GC). The probe-based Confocal Laser Endomicroscopy (pCLE) is an endoscopic technique suitable for subcellular resolution and for microvasculature analyses. The aim of this study was to use pCLE to identify specific vascular patterns in high-risk and early stage GC. Mucosal architecture, vessel tortuosity, enlargements and leakage were assessed in patients with autoimmune gastritis and early gastric cancer (EGC). We were able to stratify gastritis patients by identifying distinct vascular profiles: gastritis was usually associated with increased vascularization characterized by a high number of tortuous vessels, which were also found in atrophic autoimmune disease. Leaky and tortuous vessels, distributed in a spatially irregular network, characterized the atrophic metaplastic mucosa. The mucosal vasculature of EGC patients displayed tortuous vessels, but unlike what detected in atrophic gastritis, they appeared patchy, as is in neoplastic gastric tissue. Very importantly, we detected vascular changes even in areas without lesions, supporting the contention that vascular alterations may provide a favorable microenvironment for carcinogenesis. This report confirms that pCLE is a valid endoscopic approach to improve the definition of patients with malignant lesions or at increased risk for GC by assessing vascular changes.

Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI’s efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.