Confocal Laser Endomicroscopy for Detection of Early Upper Gastrointestinal Cancer

Confocal laser endomicroscopy as a new diagnostic tool for poorly differentiated gastric adenocarcinoma

Gastric cancer (GC) is a multifactorial disease, where both environmental and genetic features can have an impact on its occurrence and development. GC represents one of the leading causes of cancer-related deaths worldwide. GC is most frequent in males and is believed to arise from a series of premalignant lesions. The detection of GC at an early stage is crucial because early GC, which is an invasive stomach cancer confined to the mucosal or submucosal lining, may be curable with a reported 5-year survival rate of more than 90%. Advanced GC usually has a poor prognosis despite current treatment standards. The diagnostic efficacy of conventional endoscopy (with light endoscopy) is currently limited. Confocal laser endomicroscopy is a novel imaging technique that allows real-time in vivo histological examination of mucosal surfaces during endoscopy. Confocal laser endomicroscopy may be of great importance in the surveillance of precancerous gastric lesions and in the diagnosis of GC. In this editorial we commented on the article about this topic published by Lou et al in the recent issue of the World Journal of Clinical Cases.

Confocal laser endomicroscopy for gastric neoplasm

Confocal laser endomicroscopy (CLE) is a novel endoscopic modality that provides real-time histological information via high-resolution magnified view of the mucosa. CLE has a higher sensitivity, specificity, and diagnostic accuracy in detecting atrophic gastritis as compared to chromoendoscopy and narrow-band imaging. It can even predict low-grade and high-grade intraepithelial neoplasia by analyzing gastric pit patterns. CLE may have some advantages over the standard biopsy protocol, such as higher diagnostic yield and fewer biopsy requirements. Its diagnostic accuracy in detecting superficial gastric cancer is higher than that of white-light endoscopy. Inherent limitations, such as a narrow field of vision, can be surpassed by technological advancements and integration with other detection methods. Artificial intelligence holds promise in automated analysis of histopathological images. Thus, CLE can be helpful in screening for early gastric cancer and may help reduce the risk of complications from repeated biopsies, such as mucosal damage, bleeding, and infection.

Challenges in early detection and endoscopic resection of esophageal cancer: There is a long way to go

The publication by Qu et al provided a comprehensive discussion about the epidemiology, etiology, histopathology, early detection, and endoscopic treatment of esophageal carcinoma (EC) and summarized the progress in the advanced technologies for screening and endoscopic resection for EC. In this editorial, we will provide deeper insight into the challenges that hinder practical application of these advanced technologies along with the role of these technologies in upper endoscopy quality. More efforts need to be made to overcome the challenges and add the value of these technologies in upper endoscopy quality. Clinical outcomes of management strategies after noncurative endoscopic dissection for early EC patients need further investigation. The experiences with noncurative endoscopic resection of other organs may have certain implications for noncurative resection of early EC.

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Progress in research of tumor biomarkers and molecular imaging probes for gastric cancer

Gastric cancer is a malignant tumor still associated with high morbidity and mortality worldwide. Its onset is relatively insidious, and when detected, it is already at an advanced stage, lacks effective individualized treatments, and has a poor prognosis. If gastric cancer can be diagnosed at an early stage, the survival rate of patients can be greatly improved. However, traditional imaging modalities lack specificity and sensitivity. In recent years, molecular imaging technology is booming, which can non-invasively and dynamically monitor gastric cancer at the cellular and molecular levels, and provide more reference information for clinical selection of treatment options and assessment of efficacy and prognosis. This article reviews the biomarkers of gastric cancer and molecular probes in various imaging modalities.

Predicting early gastric cancer risk using machine learning: A population-based retrospective study

Background

Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators.

Methods

Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results.

Results

The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses.

Conclusions

The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.

Recent trends in Helicobacter pylori management: harnessing the power of AI and other advanced approaches

Background

Helicobacter pylori (H. pylori) is a bacterial infection that is prevalent and affects more than half of the world's population, causing stomach disorders such as gastritis, peptic ulcer disease, and gastric cancer.

Main body

The diagnosis of H. pylori infection relies on invasive and non-invasive techniques emerging artificial intelligence, and antibiotic therapy is available, but antibiotic resistance is a growing concern. The development of a vaccine is crucial in preventing H. pylori-associated diseases, but it faces challenges due to the bacterium's variability and immune escape mechanisms. Despite the challenges, ongoing research into H. pylori's virulence factors and immune escape mechanisms, as well as the development of potential vaccine targets, provides hope for more effective management and prevention of H. pylori-associated diseases. Recent research on H. pylori's immune escape mechanisms and novel immune checkpoint inhibitors could also lead to biomarkers for early cancer detection. Therefore, experts have suggested a combination of traditional and herbal medicine with artificial intelligence to potentially eradicate H. pylori.

Short conclusion

H. pylori infection remains a significant global health problem, but ongoing research into its properties and advanced technologies in addition to the combination of traditional and herbal medicine with artificial intelligence may also lead to the eradication of H. pylori-associated diseases.

Graphical abstract

Endoscopic Imaging for the Diagnosis of Neoplastic and Pre-Neoplastic Conditions of the Stomach

Gastric cancer is an aggressive disease with low long-term survival rates. An early diagnosis is essential to offer a better prognosis and curative treatment. Upper gastrointestinal endoscopy is the main tool for the screening and diagnosis of patients with gastric pre-neoplastic conditions and early lesions. Image-enhanced techniques such as conventional chromoendoscopy, virtual chromoendoscopy, magnifying imaging, and artificial intelligence improve the diagnosis and the characterization of early neoplastic lesions. In this review, we provide a summary of the currently available recommendations for the screening, surveillance, and diagnosis of gastric cancer, focusing on novel endoscopy imaging technologies.