Diagnosis Of Early Gastric Cancer Research Articles

Exploring various carbon nanomaterials-based electrodes modified with polymelamine for the reagentless electrochemical immunosensing of Claudin18.2

Claudin18.2 (CLDN18.2) is a tight junction protein often overexpressed in various solid tumors, including gastrointestinal and esophageal cancers, serving as a promising target and potential biomarker for tumor diagnosis, treatment assessment, and prognosis. Despite its significance, no biosensor has been reported to date for the detection of CLDN18.2. Here, we present the inaugural immunosensor for CLDN18.2. In this study, an amine-rich conducting polymer of polymelamine (PM) was electrografted onto different carbon nanomaterial-based screen-printed electrodes (SPEs), including carbon (C), graphene (Gr), graphene oxide (GO), carbon nanotube (CNT), and carbon nanofiber (CNF) via cyclic voltammetry. A comparative study was performed to explore the best material for the preparation of the PM-modified electrodes to be used as in-situ redox substrate for the immunosensor fabrication. The surface chemistry and structural features of pristine and PM-deposited electrodes were analyzed using Raman and scanning electron microscopy (SEM) techniques. Our results showed that the PM deposited on Gr and CNT/SPEs exhibited the most significant and stable redox behavior in PBS buffer. The terminal amine moieties on the PM-modified electrode surfaces were utilized for immobilizing anti-CLDN18.2 monoclonal antibodies via N-ethyl-N′-(3-(dimethylamino)propyl)carbodiimide/N-hydroxysuccinimide chemistry to construct the electrochemical immunosensor platform. Differential pulse voltammetry-based immunosensing of CLDN18.2 protein on BSA/anti-CLDN18.2/PM-Gr/SPE and BSA/anti-CLDN18.2/PM-CNT/SPE exhibited excellent selectivity against other proteins such as CD1, PDCD1, and ErBb2. The limits of detection of these two immunosensor platforms were calculated to be 7.9 pg/mL and 0.104 ng/mL for the CNT and Gr immunosensors, respectively. This study demonstrated that the PM-modified Gr and CNT electrodes offer promising platforms not only for the reagentless signaling but also for covalent immobilization of biomolecules. Moreover, these platforms offer excellent sensitivity and selectivity for the detection of CLDN18.2 due to its enhanced stable redox activity. The immunosensor demonstrated promising results for the sensitive detection of CLDN18.2 in biological samples, addressing the critical need for early gastric cancer diagnosis.

Machine learning models to predict submucosal invasion in early gastric cancer based on endoscopy features and standardized color metrics

Conventional endoscopy is widely used in the diagnosis of early gastric cancers (EGCs), but the graphical features were loosely defined and dependent on endoscopists’ experience. We aim to establish a more accurate predictive model for infiltration depth of early gastric cancer including a standardized colorimetric system, which demonstrates promising clinical implication. A retrospective study of 718 EGC cases was performed. Clinical and pathological characteristics were included, and Commission Internationale de l’Eclariage (CIE) standard colorimetric system was used to evaluate the chromaticity of lesions. The predicting models were established in the derivation set using multivariate backward stepwise logistic regression, decision tree model, and random forest model. Logistic regression shows location, macroscopic type, length, marked margin elevation, WLI color difference and histological type are factors significantly independently associated with infiltration depth. In the decision tree model, margin elevation, lesion located in the lower 1/3 part, WLI a*color value, b*color value, and abnormal thickness in enhanced CT were selected, which achieved an AUROC of 0.810. A random forest model was established presenting the importance of each feature with an accuracy of 0.80, and an AUROC of 0.844. Quantified color metrics can improve the diagnostic precision in the invasion depth of EGC. We have developed a nomogram model using logistic regression and machine learning algorithms were also explored, which turned out to be helpful in decision-making progress.

Sensitive small extracellular vesicles associated circRNAs analysis combined with machine learning for precision identification of gastric cancer

Small extracellular vesicle associated circular RNAs (sEV-circRNAs) are emerging as promising biomarkers for gastric cancer diagnosis. Current research predominantly focuses on identifying these biomarkers through high-throughput sequencing. However, there has been insufficient exploration into the practical application of sEV-circRNAs for early gastric cancer diagnosis. In this study, we developed a sensitive electrochemical platform that leverages tetrahedron-Dox-AuNPs (TDA) tag and DNA tetrahedron-enhanced catalytic hairpin assembly (DT-CHA) to detect sEV-circRNAs. Based on the dual signal amplification of the TDA tag and DT-CHA, the platform can achieve low-concentration detection of the target, with a detection limit of 153.1 aM and a linear range from 1 fM to 1 nM. By profiling four sEV-circRNAs (circNRIP1, circRANGAP1, circCORO1C, and circSHKBP1) in a gastric cancer cohort and combining suitable ML diagnostic model, this platform performed well in distinguishing healthy donors from early GC patients. Thus, this confluence of a multi-biomarker approach with machine learning analysis, applied to plasma sEV-circRNAs, emerges as an important strategy for cancer screening.

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.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BackgroundGastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis. MethodsWe quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models. ResultsIn the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively. ConclusionsThis study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry.

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

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.

Autoimmune Atrophic Gastritis: A Clinical Review.

Autoimmune atrophic gastritis (AAG) is a chronic condition characterized by the presence of atrophy in the oxyntic mucosa due to anti-parietal cell antibodies. This review provides a comprehensive and up-to-date overview of autoimmune atrophic gastritis, reporting recent evidence on epidemiology, pathogenesis, diagnosis, clinical presentation, risk of malignancies, and management. The prevalence of AAG has been estimated at between 0.3% and 2.7% in the general population. The diagnosis of AAG is based on a combination of the serologic profile and the histological examination of gastric biopsies. Patients with AAG are often asymptomatic but can also have dyspeptic or reflux symptoms. The atrophy of the oxyntic mucosa leads to iron and vitamin B12 malabsorption, which may result in anemia and neurological affections. Autoimmune atrophic gastritis is associated with an increased risk of type I neuroendocrine tumors (NETs) and gastric cancer, with an incidence rate of 2.8% and 0.5% per person/year, respectively. Management is directed to reinstate vitamins and iron and to prevent malignancies with endoscopic surveillance. In conclusion, atrophic autoimmune gastritis is an infrequent condition, often asymptomatic and misdiagnosed, that requires an early diagnosis for appropriate vitamin supplementation and endoscopic follow-up for the early diagnosis of NETs and gastric cancer.

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.

Serum proteomic profiling of precancerous gastric lesions and early gastric cancer reveals signatures associated with systemic inflammatory response and metaplastic differentiation.

The noninvasive detection technique using serum for large-scale screening is useful for the early diagnosis of gastric cancer (GC). Herein, we employed liquid chromatography mass spectrometry to determine the serum proteome signatures and related pathways in individuals with gastric precancerous (pre-GC) lesions and GC and explore the effect of Helicobacter pylori (H. pylori) infection. Differentially expressed proteins in GC and pre-GC compared with non-atrophic gastritis (NAG) group were identified. APOA4, a protein associated with metaplastic differentiation, and COMP, an extracellular matrix protein, were increased in the serum of patients with pre-GC lesions and GC. In addition, several inflammation-associated proteins, such as component C3, were decreased in the GC and pre-GC groups, which highlight a tendency for the inflammatory response to converge at the gastric lesion site during the GC cascade. Moreover, the abundance of proteins associated with oxidant detoxification was higher in the GC group compared with that in the NAG group, and these proteins were also increased in the serum of the H. pylori-positive GC group compared with that in the H. pylori-negative GC patients, reflecting the importance of oxidative stress pathways in H. pylori infection. Collectively, the findings of this study highlight pathways that play important roles in GC progression, and may provide potential diagnostic biomarkers for the detection of pre-GC lesions.

Abstract 4794: Impact of prior upper endoscopy on gastric cancer stage and survival

Abstract Background: While gastric cancer (GC) incidence in the US has decreased, 5-year survival remains at 34.5%. Early diagnosis offers curative or treatable options, yet many cases are detected at an advanced stage with poor prognosis. In high-incidence East Asian countries with established screening programs, endoscopic screening demonstrates stage shift towards early GC diagnosis and reduced GC-related mortality. Analyses of these programs have reported benefit of endoscopic screening up to 36 months prior to diagnosis. This study explores the impact of esophagogastroduodenoscopy (EGD) in this timeframe on GC stage at diagnosis and survival in an older US population. Methods: The SEER-Medicare database was queried from 2000 to 2017 for patients 68 years of age or older with histologically confirmed GC with continuous Medicare enrollment for at least 36 months prior to GC diagnosis. We excluded patients with prior cancer history and those diagnosed by autopsy or death certificate. Medicare claims files were searched for EGD claims 6-36 months prior to GC diagnosis. We excluded claims in the 6 months prior to diagnosis to avoid capturing potential diagnostic EGD. We defined the exposed group as patients with an EGD claim 6-36 months prior to GC diagnosis (Prior EGD) and the unexposed group as patients with no EGD claim in this period (No Prior EGD). Propensity score analysis balanced baseline differences for patients with and without Prior EGD using covariates sex, age and year of diagnosis, race and ethnicity, rural/urban, comorbidity, SES, and marital status. The primary outcome was in situ/local-stage GC (early) at diagnosis compared to regional/distant-stage (advanced). Secondary outcomes were 5-year overall and GC-specific survival. The adjusted odds of having in situ/local-stage GC diagnosis compared to regional/distant-stage was estimated using logistic regression, and survival analysis was done using the Kaplan-Meier method. Results: In 10,572 patients with GC (57.5% male, mean age 78.8 years), 16.4% had Prior EGD (n=1,736). Patients with Prior EGD had a significantly higher proportion of GC diagnosed in local stage compared to patients with No Prior EGD (45.7% vs. 30.2%, p < 0.001). After controlling for confounders, Prior EGD group had two-fold increased odds of GC diagnosis at early-stage compared with No Prior EGD (OR=1.99, 95% CI: 1.88-2.10). Compared with No Prior EGD, Prior EGD was associated with higher overall survival (5-year survival 32.1% vs. 22.7%, p<0.001) and GC-specific survival (5-year survival 46.0% vs. 31.1%, p<0.001), both driven by early-stage disease. Conclusions: Our analysis demonstrates that EGD up to 36 months prior to the diagnosis of GC is associated with earlier stage at diagnosis and improved overall and GC-specific survival. This study of an older population suggests that endoscopic screening may be beneficial even in a low-incidence country such as the US and warrants further study. Citation Format: Sophie Wagner, Jennifer S. Ferris, Josephine Soddano, Ji Yoon Yoon, Sheila D. Rustgi, Haley Zylberberg, Jeong Yun Yang, Yongmei Huang, Ling Chen, Chin Hur. Impact of prior upper endoscopy on gastric cancer stage and survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4794.

Chinese national clinical practice guidelines on the prevention, diagnosis, and treatment of early gastric cancer.

Gastric cancer is one of the most common malignant tumors in the digestive system in China. Few comprehensive practice guidelines for early gastric cancer in China are currently available. Therefore, we created the Chinese national clinical practice guideline for the prevention, diagnosis, and treatment of early gastric cancer. This clinical practice guideline (CPG) was developed in accordance with the World Health Organization's recommended process and with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) in assessing evidence quality. We used the Evidence to Decision framework to formulate clinical recommendations to minimize bias and increase transparency in the CPG development process. We used the Reporting Items for practice Guidelines in HealThcare (RIGHT) statement and the Appraisal of Guidelines for Research and Evaluation II (AGREE II) as reporting and conduct guidelines to ensure completeness and transparency of the CPG. This CPG contains 40 recommendations regarding the prevention, screening, diagnosis, treatment, and follow-up of early gastric cancer based on available clinical studies and guidelines. We provide recommendations for the timing of Helicobacter pylori eradication, screening populations for early gastric cancer, indications for endoscopic resection and surgical gastrectomy, follow-up interval after treatment, and other recommendations. This CPG can lead to optimum care for patients and populations by providing up-to-date medical information. We intend this CPG for widespread adoption to increase the standard of prevention, screening, diagnosis, treatment, and follow-up of early gastric cancer; thereby, contributing to improving national health care and patient quality of life.

Can Nano Yield Big Insights? Oligonucleotide-Based Biosensors in Early Diagnosis of Gastric Cancer

Gastric cancer (GC) remains a significant global health challenge, with late-stage diagnosis impacting treatment options and decreased survival rates. To address this, there has been a growing interest in the development of innovative screening and diagnostic methods. Over the past 20 years, nanobiosensors have undergone multiple iterations and unveiled remarkable features that pledge to reshape patient care. Despite the excitement over the plethora of ground-breaking advancements for cancer detection, use-ready samples and streamlined healthcare information monitoring and usage, this technology is still awaiting entry into clinical trials, urging a closer gaze within the medical community. Oligonucleotide-based biosensors, leveraging DNA or RNA’s long-term storage of information, offer great specificity and sensitivity, as described throughout this paper. Consequently, this renders them as an ideal choice for revolutionizing GC diagnosis and facilitating early intervention. The aim of this review is to provide an overview of this cutting-edge, invaluable technology and its limitations across various aspects.

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.

Thinking and strategy selection on the quality control of early gastric cancer

With the developing technique of the diagnosis and treatment of early gastric cancer, the quality of early gastric cancer diagnosis and treatment is coming into focus, and is crucial to improve the overall management of gastric cancer. It is necessary to establish a quality control system to ensure the quality of diagnosis and treatment for EGC. Based on the summary of the diagnosis and treatment status and technological progress of early gastric cancer, this paper proposes the quality control strategy, content and plan for the diagnosis and treatment process of EGC from the aspects of multidisciplinary diagnosis and treatment, clinical diagnosis technology, endoscopic and surgical treatment, pathological diagnosis and follow-up, with a view to expound the rationality, standardization and quality guarantee of the diagnosis and treatment process for early gastric cancer.

Risk of gastric adenoma and adenocarcinoma in patients with familial adenomatous polyposis in Japan: a nationwide multicenter study

BackgroundPatients with familial adenomatous polyposis (FAP) have an increased risk of developing gastric neoplasms. However, the clinical course of FAP with these gastric lesions has not yet been fully clarified. The present study aimed to clarify the changes in the incidence risk of developing gastric adenoma or gastric cancer during the lifespan of patients with FAP.MethodsFour hundred forty-three patients with data regarding gastric adenoma and gastric cancer retrospectively registered in a nationwide Japanese multicenter study were enrolled. The cumulative incidences and hazard rates (HRs) of gastric neoplasms were evaluated.ResultsThe cumulative incidence rates in 50-year-old patients with FAP were 22.8% for gastric adenoma and 7.6% for gastric cancer, respectively. No significant association was found between gastric neoplasms and the colonic phenotype. The peak age for the HR of gastric adenoma was 65 years, with the highest HR (0.043). Regarding the incidence of gastric cancer, the HR increased moderately up to the age of 40 years, but the increase accelerated from the age of 50 years (HR = 0.0067).ConclusionCareful surveillance of the upper gastrointestinal tract in elderly patients with FAP, such as shortening the interval of follow-up according to age, may be helpful for early diagnosis of gastric cancer.

Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

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

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. 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. 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. 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.

&amp;nbsp;Mining of Gene Modules and Identification of Key Genes for Early Diagnosis of Gastric Cancer

&amp;nbsp;Mining of Gene Modules and Identification of Key Genes for Early Diagnosis of Gastric Cancer

Value of Serum Gastrin-17 Combined with Pepsinogen I, II and Helicobacter pylori C14 Detection in Early Diagnosis of Gastric Cancer

Value of Serum Gastrin-17 Combined with Pepsinogen I, II and Helicobacter pylori C14 Detection in Early Diagnosis of Gastric Cancer