Early Detection Of Gastric Cancer Research Articles

Comparison of effective imaging modalities for detecting gastric neoplasms: A randomized three-arm phase II trial.

The early detection of gastric neoplasms (GNs) leads to favorable treatment outcomes. The latest endoscopic system, EVIS X1, includes third-generation narrowband imaging (3G-NBI), texture and color enhancement imaging (TXI), and high-definition white-light imaging (WLI). Therefore, this randomized phase II trial aimed to identify the most promising imaging modality for GN detection using 3G-NBI and TXI. Patients with scheduled surveillance endoscopy after a history of esophageal cancer or GN or preoperative endoscopy for known esophageal cancer or GN were randomly assigned to the 3G-NBI, TXI, or WLI groups. Endoscopic observations were performed to detect new GN lesions, and all suspected lesions were biopsied. The primary endpoint was the GN detection rate during primary observation. Secondary endpoints were the rate of missed GNs, early gastric cancer (EGC) detection rate, and positive predictive value (PPV) for a GN diagnosis. The decision rule had a higher GN detection rate between 3G-NBI and TXI, outperforming WLI by >1.0%. Finally, 901 patients were enrolled and assigned to the 3G-NBI, TXI, and WLI groups (300, 300, and 301 patients, respectively). GN detection rates in the 3G-NBI, TXI, and WLI groups were 7.3, 5.0, and 5.6%, respectively. The rates of missed GNs were 1.0, 0.7, and 1.0%, the detection rates of EGC were 5.7, 4.0, and 5.6%, and the PPVs for the diagnosis of GN were 36.5, 21.3, and 36.8% in the 3G-NBI, TXI, and WLI groups, respectively. Compared to TXI and WLI, 3G-NBI is a more promising modality for GN detection.

A Review of Type III Gastroesophageal Junction Adenocarcinoma and the Importance of Early Detection of Gastric Cancer

A Review of Type III Gastroesophageal Junction Adenocarcinoma and the Importance of Early Detection of Gastric Cancer

Analysis of the detection rate and clinical characteristics of early gastric cancer by painless gastroscopy and ordinary gastroscopy.

To investigate the difference of early gastric cancer (EGC) detection rate and endoscopic characteristics between painless and ordinary electronic gastroscopy, and summarize the clinical data of gastric cancer (GC) patients. Clinical data of 72,000 patients who underwent gastroscopy in the First People Hospital of Huzhou (Zhejiang, China) from January 2016 to December 2021 were retrospectively analyzed. The patients were divided into painless gastroscopy group (observation group, 36,000 cases) and ordinary gastroscopy group (control group, 36,000 cases) according to the examination methods. The detection rate of EGC between the 2 groups and the endoscopic characteristics of EGC lesions between the 2 groups were compared, and the clinical data of GC were summarized. Painless gastroscopy is safer than ordinary gastroscopy. The detection rate of GC and EGC in the observation group was significantly higher than that in the control group (P < .05); the difference between the 2 groups in the detection rate of advanced GC was not statistically significant. The average length of EGC lesions in the observation group was significantly shorter than that in the control group (P < .05). The proportion of EGC with lesion length <2.0 cm in the observation group was significantly higher than that in the control group (P < .05). The proportion of EGC lesions with type II morphology, normal or pallor mucosal color, and no rupture in mucosa in the control group were significantly lower than that in the observation group, respectively (P < .05). The proportion of EGC distributed in the cardia, fundus and corpus was higher in the observation group than in the control group (P < .05). The incidence of helicobacter pylori (HP) infection, precancerous diseases, first-degree relatives of GC patients, and risk factors in patients with GC was significantly higher than that in non-GC patients (P < .05), multivariate logistic regression analysis showed that these were independent influencing factors for the occurrence of GC. Painless gastroscopy can effectively improve the screening and diagnostic efficiency of EGC, especially for EGC lesions that are not easy to expose the site, small in size, superficial, without obvious mucosal color change or without mucosal breakage. Therefore, the value of painless gastroscopy in EGC screening is worth further promotion and research.

Stool protein mass spectrometry identifies biomarkers for the early detection of diffuse-type gastric cancer.

There is a high unmet need for early detection approaches for diffuse gastric cancer (DGC). We examined whether the stool proteome of mouse models of GC or individuals with hereditary diffuse GC (HDGC) have utility as biomarkers for early detection. Proteomic mass spectrometry of stool from a genetically engineered mouse model driven by oncogenic KrasG12D and loss of p53 and Cdh1 in gastric parietal cells (known as TCON mice) identified differentially abundant proteins compared to littermate controls. Immunoblot assays validated a panel of proteins including actinin alpha 4 (ACTN4), N-acylsphingosine amidohydrolase 2 (ASAH2), dipeptidyl peptidase 4 (DPP4), and valosin-containing protein (VCP) as enriched in TCON stool compared to littermate control stool. Immunofluorescence analysis of these proteins in TCON stomach sections revealed increased protein expression as compared to littermate controls. Proteomic mass spectrometry of stool obtained from HDGC patients with CDH1 mutations identified increased expression of ASAH2, DPP4, VCP, lactotransferrin (LTF), and tropomyosin-2 (TPM2) relative to stool from healthy sex and age-matched donors. Chemical inhibition of ASAH2 using C6-urea ceramide was toxic to GC cell lines and patient derived-GC organoids. This toxicity was reversed by adding downstream products of the S1P synthesis pathway, suggesting a dependency on ASAH2 activity in GC. An exploratory analysis of the HDGC stool microbiome identified features which correlated with patient tumors. Here we provide evidence supporting the potential of analyzing stool biomarkers for the early detection of DGC.

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images

Background and objective: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis.Methods: To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features.Results: The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs.Conclusions: The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.

Genome-wide 5-hydroxymethylcytosines in circulating cell-free DNA as noninvasive diagnostic markers for gastric cancer.

5-Hydroxymethylcytosine-enriched gene profiles and regions show tissue-specific and tumor specific. There is a potential value to explore cell-free DNA 5-hydroxymethylcytosine feature biomarkers for early gastric cancer detection. A matched case‒control study design with 50 gastric cancer patients and 50 controls was performed to sequence the different 5-hydroxymethylcytosine modification features of cell free DNA. Significantly differential 5-hydroxymethylcytosine modification genes were identified to construct a gastric cancer diagnostic model. Data set from GEO was used as an external testing set to test the robustness of the diagnostic model. Accounting for more than 90% of 5-hydroxymethylcytosine peaks were distributed in the gene body in both the gastric cancer and control groups. The diagnostic model was developed based on five different 5-hydroxymethylcytosine modification genes, FBXL7, PDE3A, TPO, SNTG2 and STXBP5. The model could effectively distinguish gastric cancer patients from controls in the training (AUC = 0.95, sensitivity = 88.6%, specificity = 94.3%), validation (AUC = 0.87, sensitivity = 73.3%, specificity = 93.3%) and testing (AUC = 0.90, sensitivity = 81.9%, specificity = 90.2%) sets. The risk scores of the controls from the model were significantly lower than those of gastric cancer patients in bothour own data (P < 0.001) and GEO external testing data (P < 0.001), and no significant difference between different TNM stage patients (P = 0.09 and 0.66). Furthermore, there was no significant difference between the healthy control and benign gastric disease patients in the testing set from GEO (P = 0.10). The characteristics of 5-hydroxymethylcytosine in cell free DNA are specific to gastric cancer patients, and the diagnostic model constructed by five genes' 5-hydroxymethylcytosine features could effectively identify gastric cancer patients.

Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images

Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a “Bi-directional feature extraction and fusion module” and a “Purification module for feature channel and space” based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.

Deep Learning Based Gastric Cancer and Disease Detection System

Abstract: Gastric cancer, commonly known as stomach cancer, is a leading cause of cancer-related deaths worldwide. Early detection of gastric cancer is crucial for improving patient outcomes and reducing mortality rates. In recent years, advances in medical imaging and machine learning techniques have shown great promise in aiding the early diagnosis of gastric cancer. This abstract presents an innovative approach for gastric cancer detection using machine learning algorithms. Our proposed system leverages state-of-the-art convolutional neural networks (CNNs) for the automatic analysis of endoscopic images, to identify potential malignancies. Preprocessing techniques are employed to enhance the quality of input images and reduce noise. Feature extraction methods are then applied to extract relevant information from the images, and these features are used to train a machine learning model. The machine learning model is trained on a large dataset of gastric cancer cases, including various subtypes, to ensure robust performance. We employ cross-validation techniques to assess the model's accuracy, sensitivity, specificity, and overall performance. The proposed system demonstrates high accuracy in identifying gastric cancer lesions, even in cases where subtle or early-stage abnormalities may be difficult to detect by human experts. In conclusion, our advanced machine learning approach for gastric cancer detection shows promise in revolutionizing early diagnosis, improving patient outcomes, and reducing the burden on healthcare systems. Further research and clinical validation are necessary to fully assess the system's efficacy in a real-world clinical setting.

Matrix metalloproteinase-1 as a potential biomarker for early gastric cancer detection and its effect on gastric cancer cell proliferation and migration

The present study aimed to investigate the association between matrix metalloproteinase-1 (MMP-1) and early gastric cancer (EGC), while also evaluating the effect of MMP-1 on gastric cancer cell proliferation and migration. Transcriptome RNA sequencing and database analysis were conducted to assess the relationship between MMP-1 expression and EGC. Differences in MMP-1 expression between clinical EGC samples and paracancerous tissues were detected using fluorescence quantitative polymerase chain reaction (PCR). In N87 gastric cancer cells, changes in proliferation- and migration-related indicator expression were determined. Gene sequencing revealed differential expression of MMP-1 in early and advanced gastric cancers. Furthermore, enhanced MMP-1 expression was observed in early and advanced gastric cancer tissues, exhibiting a positive correlation with the malignant phenotype in gastric cancer cell lines. Fluorescence quantitative PCR revealed considerably higher MMP-1 expression in EGC tissues than in paracancerous tissues. CCK8 and EdU assays demonstrated a significant increase in N87 cell proliferation on MMP-1 upregulation and a decrease on its downregulation. The scratch assay results demonstrated a corresponding enhancement in N87 cell migratory capacity with MMP-1 upregulation, which was attenuated on its downregulation. Western blot experiments revealed a decrease in the expression of the epithelial-mesenchymal transition-related protein E-cadherin after MMP-1 upregulation, while vimentin expression significantly increased. Conversely, the downregulation of MMP-1 led to opposite outcomes. Overall, MMP-1 emerges as a potential biomarker for EGC diagnosis and plays a crucial role in the regulation of N87 gastric cancer cell proliferation and migration.

Impact of endoscopic surveillance on the early diagnosis and endoscopic resection likelihood of gastric cancer.

Endoscopy could help detect early gastric cancer (EGC) and improve the prognosis of patients. The aim of this study was to analyze the impact of endoscopy and endoscopic surveillance on the early detection of gastric cancer (GC), GC staging, and treatment selection. Patients with GC diagnosed at our center from 2010 to 2022 were retrospectively analyzed and allocated to the short-interval group (had received endoscopy within 3years before diagnosis), the long-interval group (had received endoscopy more than 3years before diagnosis), and the unchecked group (had not received endoscopy before diagnosis). The differences in GC staging and treatment modalities among the three groups were analyzed, and the differences in the clinical and pathological features of EGC were further analyzed. One thousand and twenty-five GC patients were included, with 395 cases of EGC and 630 cases of advanced GC. The proportions of EGC in the short-interval, long-interval, and unchecked groups were 98.0%, 84.2%, and 29.8%, respectively (p < 0.001). Among the 387 lesions of 367 EGC patients were resected by endoscopic submucosal dissection (ESD), 341 (88.1%) exhibited curative resection, and 46 (11.9%) involved noncurative resections. Lesions of EGC differed significantly in diameter, depth of invasion, and curative resection rate (p = 0.033, 0.019, and 0.005, respectively). In the short-interval group, 87.8% of the lesions were ≤ 2cm, 95.6% of the invasion depths were confined to the mucosal layer, and 96.7% of the eCura scores were A or B. Compared with the unchecked group, they had smaller diameters (RR = 0.419, 95% CI 0.234-0.752), shallower invasion depths (RR = 0.286, 95% CI 0.105-0.777), and a higher curative resection rate (RR = 0.215, 95% CI 0.068-0.676). Endoscopic surveillance at 3-year intervals can help detect EGC, and the EGC lesions found have smaller diameters and shallower depths of invasion, helping improve the curative resection rate of ESD.

Revolutionizing gastric cancer diagnosis through advanced machine learning approaches

&lt;p class="Keywords"&gt;Early detection of gastric cancer through a Computer-Aided Detection (CAD) system has the potential to significantly reduce the mortality rate associated with this disease. This study aims to investigate the effects of class imbalance on the performance of machine learning classifiers in this context. Using a dataset of 145,787 screening records from NHS Liverpool Hospital, we employed stratified sampling to create balanced and unbalanced datasets and evaluated the performance of four machine learning algorithms—Logistic Regression, Support Vector Machine, Naive Bayes, and Multilayer Perceptron—under five different test conditions. The study’s novelty lies in its detailed examination of class imbalance in gastric cancer diagnosis, emphasizing the crucial role of balanced datasets in machine learning-based early detection systems. For the MLP model under 10-fold cross-validation, the Class 0 sensitivity (non-cancer cases) of the unbalanced dataset was 0.968, higher than the balanced dataset’s 0.902. However, the Class 1 sensitivity (cancer cases) and Positive Predictive Value (PPV) of the unbalanced dataset were much lower (0.383 and 0.527) than those of the balanced dataset (0.959 and 0.907), indicating a significant improvement in identifying true positive cases when using a balanced dataset. These findings highlight the negative effect of class imbalance on prediction accuracy for positive cancer cases and underscore the importance of addressing this imbalance for more reliable and accurate predictions in medical diagnosis and screening. This approach has the potential to improve patient outcomes and may contribute to strategies aimed at reducing the mortality rate associated with gastric cancer.&lt;/p&gt;

Diagnostic performance of blue laser imaging for early detection of gastric cancer: A systematic review and meta-analysis.

Gastric cancer (GC) is associated with a significant global health burden and high mortality rates when diagnosed at later stages. The diagnosis often occurs at advanced stages when treatment options are limited and less effective. Early detection strategies are crucial to improving survival rates and outcomes for patients. Blue laser imaging (BLI) is an image-enhanced endoscopy technique that utilizes white light and narrow-band light to detect pathological changes in the mucosal architecture. This study aims at investigating the diagnostic performance of BLI for the detection of GC. A comprehensive search was conducted across multiple databases from inception until March 2023. Studies assessing the diagnostic efficacy of BLI for GC detection were included. The sensitivity, specificity and accuracy of BLI were calculated using pooled proportions and 95% confidence intervals (CI) with a random-effects model. Heterogeneity among the included studies was assessed using the I2 statistic. Six studies were included in the pooled analysis. There were 708 patients with 380 GC lesions. Most of the lesions involved the lower two-thirds of the stomach. The pooled performance metrics of BLI for GC detection were as follows: sensitivity of 91.9% (95% CI 83.3-96.3%; I2 = 82.3%), specificity of 93.4% (95% CI 82.0-97.8%; I2 = 87.9%) and accuracy of 95.4% (95% CI 72.6-99.8%; I2 = 73.6%). BLI demonstrates high diagnostic efficacy for the detection of GC. BLI can be a valuable tool in clinical practice. However, large-scale, randomized controlled studies are needed to further establish the role of BLI in routine clinical practice for GC detection.

A novel approach for early gastric cancer detection using a hybrid of alexnet, extreme learning machine, and adjusted gorilla troops optimization

Gastric cancer is a prevalent and highly lethal form of cancer that affects individuals worldwide. The early detection of gastric cancer plays a vital role in ensuring successful treatment and improved patient outcomes. However, identifying this malignancy at an early stage can be challenging, and conventional diagnostic techniques often exhibit limited accuracy. To address this issue, a groundbreaking study proposes a novel methodology that combines the utilization of the Alexandria Net (Alexnet), Extreme Learning Machine (ELM), and a modified metaheuristic algorithm. The integration of these techniques aims to enhance the accuracy and efficiency of gastric cancer detection. Additionally, a Modified version of the Dynamic Gorilla Troops Optimizer (DGTO) is incorporated as a dynamic metaheuristic algorithm. This algorithm optimizes the arrangement and selection of features within the model, significantly contributing to improved performance. To evaluate the effectiveness of the proposed methodology, the Alexnet/ELM-DGTO model is applied to a practical dataset known as GasHisSDB. Moreover, this approach outperforms several state-of-the-art methods in terms of precision, accuracy, recall, specificity, F1-score, and Area Under the Curve (AUC). This research paper makes a significant contribution to the field of early gastric cancer detection by introducing a novel methodology that overcomes the limitations of existing approaches. It offers a unique solution to the optimization problem of selecting the most suitable features for machine learning models. The effectiveness of the proposed methodology is further validated through its successful application on a practical dataset, highlighting its superiority over alternative methods. The results underscore the potential impact of utilizing the Alexnet/ELM-DGTO combination in the field of gastric cancer detection.

Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer.

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. Several studies have been conducted to investigate early biomarkers of gastric cancer using genomics, transcriptomics, proteomics, and metabolomics, respectively. However, endogenous substances associated with various omics are concurrently altered during gastric cancer development. Furthermore, environmental exposures and family history can also induce modifications in endogenous substances. Therefore, in this study, we primarily investigated alterations in DNA mutation, DNA methylation, mRNA, lncRNA, miRNA, circRNA, and protein, as well as glucose, amino acid, nucleotide, and lipid metabolism levels in the context of GC development, employing genomics, transcriptomics, proteomics, and metabolomics. Additionally, we elucidate the impact of exposure factors, including HP, EBV, nitrosamines, smoking, alcohol consumption, and family history, on diagnostic biomarkers of gastric cancer. Lastly, we provide a summary of the application of machine learning in integrating multi-omics data. Thus, this review aims to elucidate: i) the biomarkers of gastric cancer related to genomics, transcriptomics, proteomics, and metabolomics; ii) the influence of environmental exposure and family history on multi- -omics data; iii) the integrated analysis of multi-omics data using machine learning techniques.

DNA methylation biomarkers for early detection of gastric and colorectal cancers.

DNA methylation biomarkers for early detection of gastric and colorectal cancers.

Investigating FGFR2 gene as a blood-based epigenetic biomarker in gastric cancer.

Gastric adenocarcinoma is a prevalent form of cancer that often remains undetected in its early stages due to the lack of specific symptoms. This delayed diagnosis leads to poor clinical outcomes, underscoring the need for an effective and non-invasive method for early detection. Recent advances in cancer epigenetics have led to the identification of biomarkers that have the potential to revolutionize the early detection and monitoring of this disease. One such promising biomarker is the methylation of the FGFR2 promoter. This study aims to measure the methylation levels of a specific CpG site in the FGFR2 promoter gene in DNA extracted from blood leukocytes from patients with intestinal metaplasia, gastric cancer, and healthy control. The CpG site of the FGFR2 gene promoter was identified in its control region. Methylation alteration of the selected FGFR2 CpG site was determined through the (methylation-sensitive restriction enzyme) MSRE-qPCR. Genomic DNA was extracted from one hundred twenty-five participants. The normal group had mean methylation levels of 93.23 ± 4.929%, while the IM group had a level of 69.85 ± 27.15%. In GC patients, the levels varied, with 25.96 ± 18.98% in the intestinal type and 28.30 ± 16.07% in the diffuse type. The methylation levels in the IM and GC patients were significantly lower than those in the normal control group. However, no significant difference was observed between the methylation status of the intestinal type of GC and the diffuse type. The Receiver operating characteristic (ROC) curve analysis showed that FGFR2 CpG methylation levels in GC patients compared to normal controls had a high sensitivity of 100% and specificity of 100%, with a cut-off of < 74.25%; when GC patients were compared to IM patients, the sensitivity was 85%, and the specificity was 80%, with a cut-off < 44.45%. The potential of the FGFR2 methylation status as a non-invasive biomarker lies in its ability to be detected in blood leukocytes, which makes it a promising tool for the early detection of intestinal metaplasia and gastric cancer. This could significantly improve the detection and management of these gastric conditions.

Metachronous remnant gastric cancer after proximal gastrectomy.

260 Background: Proximal gastrectomy is frequently selected for early gastric cancer located at the upper third of the stomach. Annual postoperative endoscopy is considered to be useful for the early detection of metachronous remnant gastric cancer (MRGC). Despite that, we sometimes have also encountered cases that are not suitable for endoscopic resection (ER). The aim of this study is to identify the risk factors for the patients who developed MRGC after proximal gastrectomy, received annual endoscopy, but are not eligible for ER (non-ER). Methods: We retrospectively analyzed 191 patients who had received annual endoscopic surveillance after undergoing PG for cT1 gastric cancer at NCCH between 2006-2015. MRGCs were categorized into two groups: ER group and non-ER group. The remnant stomach was defined as three locations: pseudo-fornix (PF), corpus and antrum. Results: The median observation period was 73 (range 12-168) months. MRGC was observed in 29 cases with 32 lesions. ER was eligible for 20 lesions (ER group, 62.5%), but 12 were not (non-ER group, 37.5%). We compared the characteristics between of ER group and non-ER group in terms of sex, age (&gt;65 years), interval from the initial surgery (&gt;60months), the location (PF vs non-PF), and cross-sectional circumference (posterior wall vs. others). In both univariate and multivariate logistic regression analysis, only the location at PF was identified as a risk factor for non-ER group (OR 25.0, 95% CI: 1.8–339, p = 0.015). Among 12 lesions of the non-ER group, the location of PF was found in 6 (50%) lesions. As compared with MRGC at the non-PF, that at the PF (n = 6) was characterized by younger patients (57.5 (range 37-69) years vs. 69 (56-77) years), larger size (36 (7-57) mm vs. 9 (9-24) mm), deeper depth (MP or deeper; 5/6 vs. 0/6), more frequent undifferentiated-type histology (4/6 vs. 0/6) and frequent nodal metastasis (3/6 vs. 0/6). The interval to detect MRGC appears to be longer in the PF (78 (31-96) months than that of the non-PF (60 (31-96) months), but the difference was not statistically significant (p = 0.86). Then, we examined the visibility and the mucosal normality of the area developing MRGC in the annual follow-up endoscopy one year before the detection of MRGC (n =32). In 25 lesions at the non-PF, the visibility and the mucosal normality was secured in 21 (84%). while 5 PF lesions were difficult to observe (71.4%, p = 0.01) due to food residues in 4 (57%, p = 0.047) and insufficient expansion of the gastric mucosa in 4 (57%, p = 0.001). Conclusions: Detection of the MRGC at the PF at an early stage is crucial and challenging. With annual follow-up endoscopy, complete clearance and adequately extending the gastric mucosa could lead to the early detection of MRGC at the PF.

Cell-free DNA methylation profiles enable early detection of colorectal and gastric cancer

Cell-free DNA methylation profiles enable early detection of colorectal and gastric cancer

THE RESULTS OF THE JAPANESE GASTRIC CANCER ASSOCIATION MORPHOLOGICAL CLASSIFICATION ADAPTATION FOR THE KAZAKH POPULATION

Relevance: Gastric cancer is a heterogeneous group of malignant epithelial tumors arising from the cells of the gastric mucosa, one of the most common forms of malignant neoplasms in many countries of the world. But, despite repeated attempts, there are no convincing approaches to early large-scale (screening) detection technology for this form of cancer. This causes a high rate of late detection of advanced forms of gastric cancer in most countries of the world, a high one-year mortality rate of patients, and a low five-year survival rate. Currently, full screening for gastric cancer is carried out only in Japan, Korea, and China – countries with high incidence rates.&#x0D; The study aimed to was a retrospective analysis of literary data on the epidemiology of gastric cancer in the world, individual countries, but, above all, our own materials, summarized by specialists of the Joint Stock Company “Kazakh Research Institute of Oncology and Radiology” (JSC “KazNIIOiR”) on the situation with cancer stomach in Kazakhstan; analysis of information on the types and results of screenings for the early detection of gastric cancer in the world.&#x0D; Methods:For the analysis, we used available epidemiological indicators on GC from the specialized literature, data obtained from annual reporting forms No. 7, approved by the Ministry of Health of the Republic of Kazakhstan, in the country code (ICD 10 – C16), publications with statistical and analytical materials on Kazakhstan. Morbidity and mortality rates were calculated based on data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan website on the average annual population by region of Kazakhstan.&#x0D; Results: A long-term decrease in the incidence and mortality from gastric cancer in Kazakhstan since 2021 has been replaced by a stable increase in the incidence of gastric cancer against the backdrop of a high frequency of advanced forms, relatively high mortality, and low fiveyear survival of patients. At the same time, there is experience in the world of certain approaches to screening the population for early detection of gastric cancer.&#x0D; Conclusion: The high morbidity of gastric cancer in most regions of the country in recent years requires the search and development of optimal forms of screening for its early detection; this will reduce mortality from gastric cancer and increase the five-year survival rate of patients.

GASTRIC CANCER: EPIDEMIOLOGY AND PROSPECTS FOR THE DEVELOPMENT AND IMPLEMENTATION OF INNOVATIVE TECHNOLOGIES FOR EARLY DETECTION AND TREATMENT

Relevance: Gastric cancer is a heterogeneous group of malignant epithelial tumors arising from the cells of the gastric mucosa, one of the most common forms of malignant neoplasms in many countries of the world. But, despite repeated attempts, there are no convincing approaches to early large-scale (screening) detection technology for this form of cancer. This causes a high rate of late detection of advanced forms of gastric cancer in most countries of the world, a high one-year mortality rate of patients, and a low five-year survival rate. Currently, full screening for gastric cancer is carried out only in Japan, Korea, and China – countries with high incidence rates.&#x0D; The study aimed to assess the epidemiologically disadvantaged regions of Kazakhstan for cancer to select optimal early detection techniques to improve the treatment results.&#x0D; Methods:For the analysis, we used available epidemiological indicators on GC from the specialized literature, data obtained from annual reporting forms No. 7, approved by the Ministry of Health of the Republic of Kazakhstan, in the country code (ICD 10 – C16), publications with statistical and analytical materials on Kazakhstan. Morbidity and mortality rates were calculated based on data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan website on the average annual population by region of Kazakhstan.&#x0D; Results: A long-term decrease in the incidence and mortality from gastric cancer in Kazakhstan since 2021 has been replaced by a stable increase in the incidence of gastric cancer against the backdrop of a high frequency of advanced forms, relatively high mortality, and low fiveyear survival of patients. At the same time, there is experience in the world of certain approaches to screening the population for early detection of gastric cancer.&#x0D; Conclusion: The high morbidity of gastric cancer in most regions of the country in recent years requires the search and development of optimal forms of screening for its early detection; this will reduce mortality from gastric cancer and increase the five-year survival rate of patients.