Classification Of Gastric Cancer Research Articles

Machine Learning Driven Feature Extraction and Dimensionality Reduction for Image Classification

Cancer is the leading cause of death globally, affecting various organs in the human body. Early diagnosis of gastric cancer is essential for improving survival rates. However, traditional diagnosis methods are time-consuming, require multiple tests, and rely on specialist availability. This motivates the development of automated techniques for diagnosing gastric cancer using image analysis. While existing computerized techniques have been proposed, challenges remain. These include difficulty distinguishing healthy from cancerous regions in images and extracting irrelevant features during analysis. This research addresses these challenges by proposing a novel deep learning-based method for gastric cancer classification. The method utilizes deep feature extraction, dimensionality reduction, and classification techniques applied to a gastric cancer image dataset. This approach achieves high accuracy (99.32%), sensitivity (99.13%), and specificity (99.64%) in classifying gastric cancer.

Chicken swarm algorithm with deep convolutional neural network based tongue image analysis for gastric cancer classification

Chicken swarm algorithm with deep convolutional neural network based tongue image analysis for gastric cancer classification

A pathway for detection of gastric cancer biomarkers via using a layer-by-layer coated D-shaped grinding long-period fiber grating sensor

Gastric cancer significantly contributes to global cancer mortality, often leading to inoperable stages and high recurrence rates post-surgery. Elevated levels of G-17 and G-gly have been identified as potential risk factors, particularly in patients with duodenal ulcers. This study introduces an innovative D-shaped grinding long-period fiber grating sensor (D-LLPFGs) designed for non-invasive detection of the gastrin G-17 antigen, employing a layer-by-layer chemical self-assembly to bond G-17 antibodies onto the fiber surface through hydrogen bonding. The D-LLPFGs sensor demonstrated significant spectral shifts within one minute of antigen-antibody interaction, highlighting its rapid detection capability. At an optimized antibody concentration of 4 μg/ml, antigen testing across different concentrations (10, 12.5, 20, 50 μg/ml) showed peak changes at 12.5 μg/ml antigen, with a 1.186 nm shift and 0.503 dB loss. The sensor exhibited a wavelength sensitivity of 0.095 nm/μg/ml, indicating its high sensitivity and potential in gastric cancer classification, diagnosis, and treatment. This research concludes that the D-shaped fiber sensor is an effective and sensitive tool for detecting G-17 antigen levels, presenting a significant advancement in non-invasive gastric cancer diagnosis. Its quick response time and high sensitivity highlight its potential for broad biomedical applications, offering a new avenue for early cancer detection and improving patient prognosis. The success of this study opens the door to further exploration and implementation of fiber optic sensors in clinical settings, marking a significant step forward in the fight against gastric cancer.

Research advances in the molecular classification of gastric cancer.

Gastric cancer (GC) is a malignant tumor with one of the lowest five-year survival rates. Traditional first-line treatment regimens, such as platinum drugs, have limited therapeutic efficacy in treating advanced GC and significant side effects, greatly reducing patient quality of life. In contrast, trastuzumab and other immune checkpoint inhibitors, such as nivolumab and pembrolizumab, have demonstrated consistent and reliable efficacy in treating GC. Here, we discuss the intrinsic characteristics of GC from a molecular perspective and provide a comprehensive review of classification and treatment advances in the disease. Finally, we suggest several strategies based on the intrinsic molecular characteristics of GC to aid in overcoming clinical challenges in the development of precision medicine and improve patient prognosis.

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases

Rationale and ObjectivesTo develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. Materials and MethodsIn total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). ResultsThe classification model constructed by the DLR algorithm showed excellent performance in the classification task compared with ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. ConclusionThe DLR model is an effective method for identifying the primary source of liver metastases.

Comparable prognosis of early gastric cancer between intestinal type and diffuse type in patients of age 75 and older: a SEER-based cohort study.

The prognostic significance of Lauren's classification in elderly early gastric cancer (EGC) patients remains largely unknown. We aim to investigate the characteristics and clinical implications of Lauren's classification in elderly EGC patients. Patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database based on the inclusion and exclusion criteria. Univariate and multivariate Cox regression, propensity score matching, inverse-probability-weighted analysis, and propensity-score adjustment were utilized to evaluate the association between Lauren's classification and cancer-specific survival (CSS) in elderly EGC patients. Stratification and interaction analyses were used to reveal the effects of confounding factors on the association between Lauren's classification and CSS. The diffuse type (median, 41.0 months) showed a similar survival (37.0 months), and was mainly distributed in female group (62.5% vs. 42.2%) with poorly differentiated or undifferentiated components (89.1% vs. 27.0%) compared with intestinal type in elderly EGC patients. Analyses of univariate and multivariate Cox regression, propensity score matching, inverse-probability-weighted analysis, and propensity-score adjustment showed that Lauren's classification was not significantly CSS in elderly EGC patients (P>0.05). Subgroup and interaction analyses confirmed the stability of the results. Diffuse type was mainly distributed in female patients with more poorly differentiated/undifferentiated components and similar prognosis compared with intestinal type in age 75 and older EGC patients. No significant association was observed between diffuse type and CSS of the elderly EGC patients.

Implementing an integrated molecular classification for gastric cancer from endoscopic biopsies using on-slide tests.

The availability of more effective biological therapy can improve outcomes of gastric cancer (GC), but most patients do not have access to personalized treatment. GC molecular classification helps identify patients suitable for specific therapies and provides useful prognostic information. To date, only a small number of patients have access to molecular classification. We proposed a working molecular classification that can be delivered using on-slide tests available in most histopathology laboratories. We used eight on-slide tests [in situ hybridization (ISH) for Epstein-Barr virus-encoded small ribonucleic acid (EBER) and immunohistochemistry (IHC) for MutL homolog 1 (MLH1), PMS1 homolog 2 (PMS2), MutS homolog 2 (MSH2), MutS homolog 6 (MSH6), E-cadherin, β-catenin and p53] to classify GC into one of six categories: GC associated with Epstein-Barr virus (GC-EBV), GC mismatch repair deficient (GC-dMMR), GC with epithelial-mesenchymal transition (GC-EMT), GC with chromosomal instability (GC-CIN), GC genomically stable (GC-GS) and GC not otherwise specified (GC-NOS)∕indeterminate. The classification has provision also for current and future on-slide companion diagnostic (CDx) tests necessary to select specific biological therapies and, as proof of principle, in this study we used three CDx tests currently required for the management of GC [human epidermal growth factor receptor 2 (Her2), programmed cell death-ligand 1 (PD-L1) 22C3 and Claudin18.2 (CLDN18.2)]. This paper describes the necessary tissue pathways and laboratory workflow and assesses the feasibility of using this classification prospectively on small endoscopic biopsies of gastric and gastroesophageal junction adenocarcinoma. This work demonstrates that such molecular classification can be implemented in the context of a histopathology diagnostic routine with little impact on turnaround times and laboratory capacity. The widespread adoption of a molecular classification for GC will help refine prognosis and guide the choice of more appropriate biological therapy for these patients.

Gastro-BaseNet: A Specialized Pre-Trained Model for Enhanced Gastroscopic Data Classification and Diagnosis of Gastric Cancer and Ulcer.

Most of the development of gastric disease prediction models has utilized pre-trained models from natural data, such as ImageNet, which lack knowledge of medical domains. This study proposes Gastro-BaseNet, a classification model trained using gastroscopic image data for abnormal gastric lesions. To prove performance, we compared transfer-learning based on two pre-trained models (Gastro-BaseNet and ImageNet) and two training methods (freeze and fine-tune modes). The effectiveness was verified in terms of classification at the image-level and patient-level, as well as the localization performance of lesions. The development of Gastro-BaseNet had demonstrated superior transfer learning performance compared to random weight settings in ImageNet. When developing a model for predicting the diagnosis of gastric cancer and gastric ulcers, the transfer-learned model based on Gastro-BaseNet outperformed that based on ImageNet. Furthermore, the model's performance was highest when fine-tuning the entire layer in the fine-tune mode. Additionally, the trained model was based on Gastro-BaseNet, which showed higher localization performance, which confirmed its accurate detection and classification of lesions in specific locations. This study represents a notable advancement in the development of image analysis models within the medical field, resulting in improved diagnostic predictive accuracy and aiding in making more informed clinical decisions in gastrointestinal endoscopy.

Classification and localization of gastric cancer using Multi Information Fusion Network

Classification and localization of gastric cancer using Multi Information Fusion Network

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored cancer classification using machine learning and deep learning techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake a comprehensive review of colorectal and gastric cancer detection specifically employing traditional ML classifiers. This review emphasizes the mathematical underpinnings of cancer detection, encompassing preprocessing techniques, feature extraction, machine learning classifiers, and performance assessment metrics. We provide mathematical formulations for these key components. Our analysis is limited to peer-reviewed articles published between 2017 and 2023, exclusively considering medical imaging datasets. Benchmark and publicly available imaging datasets for colorectal and gastric cancers are presented. This review synthesizes findings from 20 articles on colorectal cancer and 16 on gastric cancer, culminating in a total of 36 research articles. A significant focus is placed on mathematical formulations for commonly used preprocessing techniques, features, ML classifiers, and assessment metrics. Crucially, we introduce our optimized methodology for the detection of both colorectal and gastric cancers. Our performance metrics analysis reveals remarkable results: 100% accuracy in both cancer types, but with the lowest sensitivity recorded at 43.1% for gastric cancer.

Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.

Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.

Investigating Effective Data Augmentation Techniques for Accurate Gastric Classification in the Development of a Deep Learning-Based Computer-Aided Diagnosis System

Gastric cancer is a significant health concern, particularly in Korea, and its accurate detection is crucial for effective treatment. However, a gastroscopic biopsy can be time-consuming and may, thus, delay diagnosis and treatment. Thus, this study proposed a gastric cancer diagnostic method, CADx, to facilitate a more efficient image analysis. Owing to the challenges in collecting medical image data, small datasets are often used in this field. To overcome this limitation, we used AutoAugment’s ImageNet policy and applied cut-and-paste techniques using a sliding window algorithm to further increase the size of the dataset. The results showed an accuracy of 0.8317 for T-stage 1 and T-stage 4 image classification and an accuracy of 0.8417 for early gastric cancer and normal image classification, indicating improvements of 7 and 9%, respectively. Furthermore, through the application of test-time augmentation to the early gastric cancer and normal image datasets, the image classification accuracy was improved by 5.8% to 0.9000. Overall, the results of this study demonstrate the effectiveness of the proposed augmentation methods for enhancing gastric cancer classification performance.

Effect of preoperative chemotherapy on the histopathological classification of gastric cancer.

In the era of individualized gastric cancer (GC) treatment, accurate determination of histological subtype becomes increasingly relevant. As yet, it is unclear whether preoperative chemotherapy may affect the histological subtype. The aim of this study was to assess concordance in histological subtype between pretreatment biopsies and surgical resection specimens before and after the introduction of perioperative treatment. Histological subtype was centrally determined in paired GC biopsies and surgical resection specimens of patients treated with either surgery alone (SA) in the Dutch D1/D2 study or with preoperative chemotherapy (CT) in the CRITICS trial. The histological subtype as determined in the resection specimen was considered the gold standard. Concordance rates and sensitivity and specificity of intestinal, diffuse, mixed, and "other" subtypes of GC were analyzed. In total, 105 and 515 pairs of GC biopsies and resection specimens of patients treated in the SA and CT cohorts, respectively, were included. Overall concordance in the histological subtype was 72% in the SA and 74% in the CT cohort and substantially higher in the diffuse subtype (83% and 86%) compared to the intestinal (70% and 74%), mixed (21% and 33%) and "other" subtypes (54% and 54%). In the SA cohort, sensitivities and specificities were 0.88 and 0.71 in the intestinal, 0.67 and 0.93 in the diffuse, 0.20 and 0.98 in the mixed, and 0.50 and 0.93 in the "other" subtypes, respectively. Our results suggest that accurate determination of histological subtype on gastric cancer biopsies is suboptimal but that the impact of preoperative chemotherapy on histological subtype is negligible.

The central role of gastrin in gastric cancer.

The prevalence of gastric cancer has markedly declined, but due to the high mortality rates associated with gastric cancer, it is still a serious disease. The preferred classification of gastric cancer is according to Lauren into either the intestinal type, which has a glandular growth pattern, or the diffuse type, which does not have glandular structures. Both types have been classified as adenocarcinomas, with the latter type based on periodic acid-Schiff (PAS) positivity presumed to reflect mucin. However, the presence of mucin in the diffuse type, in contrast to neuroendocrine/enterochromaffin-like (ECL) cell markers, has not been confirmed by immunohistochemistry and in situ hybridization. The ECL cells are probably prone to becoming cancerous because they do not express E-cadherin. Gastric cancer is unique in that a bacterium, Helicobacter pylori, is thought to be its main cause. H. pylori predisposes infected individuals to cancer only after having caused oxyntic atrophy leading to gastric hypoacidity and hypergastrinemia. No single H. pylori factor has been convincingly proved to be carcinogenic. It is probable that gastrin is the pathogenetic factor for gastric cancer due to H. pylori, autoimmune gastritis, and long-term prolonged inhibition of gastric acid secretion. Hypergastrinemia induces ECL cell hyperplasia, which develops into neuroendocrine tumors (NETs) and then into neuroendocrine carcinomas in rodents, a sequence that has also been described in humans. During carcinogenesis, the tumor cells lose specific traits, requiring that sensitive methods be used to recognize their origin. Gastric cancer occurrence may hopefully be prevented by H. pylori eradication at a young age, and by the reduced use of inhibitors of acid secretion and use of a gastrin antagonist in those with previous long-term H. pylori infection and those with autoimmune gastritis.

COL5A2 is a prognostic-related biomarker and correlated with immune infiltrates in gastric cancer based on transcriptomics and single-cell RNA sequencing

BackgroundThere is still a therapeutic challenge in treating gastric cancer (GC) due to its high incidence and poor prognosis. Collagen type V alpha 2 (COL5A2) is increased in various cancers, yet it remains unclear how it contributes to the prognosis and immunity of GC.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to download transcriptome profiling (TCGA-STAD; GSE84437), single-cell RNA sequencing (scRNA-seq) data (GSE167297) and clinical information. COL5A2 expression and its relationship with clinicopathological factors were analyzed. We conducted survival analysis and Cox regression analysis to evaluate the prognosis and independent factors of GC. Co-expressed analysis was also performed. To identify the underlying mechanism, we conducted analyses of differentially expressed genes (DEGs) and functional enrichment. The correlations between COL5A2 expression and immune cell infiltration levels and immune infiltrate gene marker sets were further explored. Additionally, we analyzed the association of COL5A2 expression with immunological checkpoint molecules. Furthermore, the relationship between COL5A2 expression and immunotherapy sensitivity was also investigated.ResultsCOL5A2 expression was elevated in GC. More than this, the scRNA-seq analysis revealed that COL5A2 expression had a spatial gradient. The upregulated COL5A2 was associated with worse overall survival. A significant correlation was found between COL5A2 overexpression and age, T classification and clinical stage in GC. COL5A2 was found to be an independent factor for the unfortunate outcome in Cox regression analysis. The co-expressed genes of COL5A2 were associated with tumor stage or poor survival. Enrichment analysis revealed that the DEGs were mainly associated with extracellular matrix (ECM)-related processes, PI3K-AKT signaling pathway, and focal adhesion. GSEA analyses revealed that COL5A2 was associated with tumor progression-related pathways. Meanwhile, COL5A2 expression was correlated with tumor-infiltrating immune cells. Moreover, immunophenoscore (IPS) analysis and PRJEB25780 cohorts showed that patients with low COL5A2 expression were highly sensitive to immunotherapy.ConclusionsCOL5A2 might act as a prognostic biomarker of GC prognosis and immune infiltration and may provide a therapeutic intervention strategy.

Proposal for T3 classification of esophagogastric junction carcinoma based on the interconnection of extramural anatomical structures.

Classification of extramural invasion of esophagogastric junction carcinoma (EGJC) is not yet established. The anatomy surrounding the EGJ alters between the mediastinum and the abdominal cavity. This review proposed a T3 classification of EGJC based on anatomical continuity. Analysis of endoscopic ultrasound images, review of intraoperative images, and detailed observation of surgical specimens were followed by a review of the literature. In the EGJ, the muscularis propria of the esophagus is enclosed in mediastinal adipose tissue called the adventitia, which is surrounded by the diaphragmatic crus and contains the paraesophageal lymph nodes (LNs). After passing through the esophageal hiatus along with the vagus nerves and blood vessels, the adventitia joins the adipose tissue containing the paracardial LNs, which is covered by the peritoneum, and then further divides into the lesser and greater omentum. The connective tissue outside the muscularis propria of the stomach, including the adipose tissue of the omentum, is called the subserosa. According to the TNM classification, T3 esophageal and gastric cancer is defined as invasion of the adventitia and subserosa, respectively. Given that the adventitia is anatomically continuous with the subserosa, T3 tumors of the EGJ can be described as those that extend through the muscularis propria but do not invade the peritoneum or diaphragmatic crus. We propose classifying T3 EGJC as "tumor extends through muscularis propria" rather than using the separate terms "adventitia" and "submucosa". T4 could be "tumor perforates serosa or invades adjacent structures", as per the current gastric cancer classification.

A consolidated working classification of gastric cancer for histopathologists (Review).

Gastric cancer (GC) remains a disease with poor prognosis despite increasing availability of more effective targeted treatment. This may be in part due to the difficulty in selecting patients for appropriate treatment. Conventional taxonomic classifications of GC are ill-suited to make full use of recent advances in personalised therapy. In the past decade a number of molecular classifications have been proposed to address this; however, to date, there has been little implementation in the diagnostic routine. The lack of harmonisation between these classifications, the complexity and unavailability of some of the tests required plus the demands on time and resources, all contribute to poor uptake in the diagnostic routine. In the present study, these classifications were reviewed and an inclusive working classification that includes their main points, focuses on prognosis and treatment options and can be delivered using four on-slide tests (in situ hybridization for Epstein-Barr encoding region and immunohistochemistry for mismatch repair, E-cadherin and p53) is proposed. These tests can be performed on paraffin-embedded tissue and could be available in the majority of histopathology laboratories. The proposed classification also includes reflex testing for specific biomarkers relevant to treatment selection.

Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map

ObjectiveTo investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer.MethodsA retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance.ResultsGender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802).ConclusionThe R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer.Critical relevance statementThe radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.Graphical abstract

Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics.

Cancer-associated fibroblasts (CAFs) are essential tumoral components of gastric cancer (GC), contributing to the development, therapeutic resistance and immune-suppressive tumor microenvironment (TME) of GC. This study aimed to explore the factors related to matrix CAFs and establish a CAF model to evaluate the prognosis and therapeutic effect of GC. Sample information from the multiply public databases were retrieved. Weighted gene co-expression network analysis was used to identify CAF-related genes. EPIC algorithm was used to construct and verify the model. Machine-learning methods characterized CAF risk. Gene set enrichment analysis was employed to elucidate the underlying mechanism of CAF in the development of GC. A three-gene (GLT8D2, SPARC and VCAN) prognostic CAF model was established, and patients were markedly divided according to the riskscore of CAF model. The high-risk CAF clusters had significantly worse prognoses and less significant responses to immunotherapy than the low-risk group. Additionally, the CAF risk score was positively associated with CAF infiltration in GC. Moreover, the expression of the three model biomarkers were significantly associated with the CAF infiltration. GSEA revealed significant enrichment of cell adhesion molecules, extracellular matrix receptors and focal adhesions in patients at a high risk of CAF. The CAF signature refines the classifications of GC with distinct prognosis and clinicopathological indicators. The three-gene model could effectively aid in determining the prognosis, drug resistance and immunotherapy efficacy of GC. Thus, this model has promising clinical significance for guiding precise GC anti-CAF therapy combined with immunotherapy.

The significance of endoscopic Kyoto classification of gastritis in the gastric cancer risk assessment: A systematic review and meta‑analysis.

Kyoto Classification of Gastritis is a newly proposed gastric cancer risk assessment in recent years. It selects important gastroscopic manifestations that have been reported and calculates score values. Although it has been extensively employed in clinical practice, there is no thorough review or systematic summary of its usage. We looked for works published before May 2022 on the correlation between the Kyoto Classification of Gastritis and gastric cancer (GC) risk in Web of Science, EMBASE, China National Knowledge Infrastructure, PubMed, Wanfang database, and other suitable sources. Statistical analysis was carried out using Stata 14.0 and RevMan 5.40. Two statistical methods were employed. Eight case-control studies involving 6927 patients (continuous variables group: 1961 patients; dichotomy variables group: 4966 patients) were included, and the meta-analysis results showed a significant association between Kyoto Classification of Gastritis and GC. A Kyoto classification score ≥ 4 might indicate a risk of GC (odds ratios 7.30; 95% confidence intervals [CI] 3.62-14.72; P < .00001. There was a significant difference between gastritis and GC scores (mean difference [MD] 0.86; 95% CI 0.73-0.99; P < .00001). Moreover, we found that intestinal metaplasia and atrophy highly affected the Kyoto Classification score (MD = 0.35, MD = 0.72 95% CI 0.20-0.50,0.56-0.88). However, there was considerable heterogeneity in both statistical analyses. We found the source of heterogeneity in the first analysis method but failed to find it in the second analysis method, which may be due to the small number of studies. The Kyoto Classification of gastritis score is crucial for detecting early stomach cancer. A score >4 suggests a significant risk for gastric cancer, with atrophy and intestinal metaplasia having the most impact. This score may be promoted at primary hospitals; however, because of the small number and quality of included studies, the results mentioned above need to be verified by randomized control trials with large samples and high-quality methods.