Introduction

Volatile organic compounds (VOCs), as comparatively novel and non-invasive biomarkers, provide a promising and attractive approach for the screening of tumors in the digestive system.

Aims & Methods

The aim of this study was to characterize the breath chemical patterns associated with gastric cancer in patients and controls to identify potential expiratory markers of this disease.
Volatilomic signatures of breath samples obtained from 98 gastric cancer patients and 214 controls were characterized using gas chromatography with mass spectrometric detection (GC-MS). Study subjects were recruited from three different geographical areas (Latvia, Ukraine, Colombia). Breath sampling, sample storage and transport followed strict and standardized protocol to compensate for some confounding factors. A Mann-Whitney U test was run to compare the levels of the measured VOCs. A comparison was made between gastric cancer patients versus non-gastric cancer subjects and p<0.05 was taken as significant threshold.

Results

A total of 3288 distinct compounds were identified in the breath samples provided by all subjects. Amongst these, 1543 were identified in breath samples obtained from gastric cancer patients and 2165 from controls. The levels of 14 compounds differed in a statistically significant manner between the groups of interest. Twelve of them appeared in breath of the cancer patients group at significantly higher levels than in the control group, and hence the other 2 showed decreased levels. Those VOCs with increased levels are two ketones (2-pentanone and 6-methyl-5-hepten-2-one), two aromatics (p-xylene and ethylbenzene), three alcohols (1-octanol, 1-decanol and 2-ethyl-1-hexanol), three hydrocarbons (decane, dodecane and tridecane) and two aldehydes (hexanal and decanal).

Conclusion

The comparison of breath chemical patterns of gastric cancer patients and controls resulted in the identification of 12 VOCs that were at significantly higher levels in breath of gastric cancer patients. These compounds can be potentially responsible for the breath fingerprint changes related to gastric cancer. Consequently, targeting these specie in breath using portable sensor-based devices can be a promising screening method for early gastric neoplasia.

Introduction

Gastric cancer has been identified as a serious global public health problem, and early detection of gastric cancer is an effective means of curbing the risk of gastric cancer. However, accurate, efficient and economical detection of early gastric cancer (EGC) remains a challenge.

Aims & Methods

We aim to fully explore the characteristics of various endoscopic images and EGC diagnostic requirements to boost the accuracy and efficiency of deep learning-based EGC detection and enable its public availability, so as to improve the survival rate and life quality of patients. A new lightweight EGC fusion diagnosis framework based on heterogeneous neural networks (FusionWN-Net) is proposed. FusionWN-Net consists of two stages: Model construction and Decision-level fusion. In the Model construction, two lightweight EGC classification models (WLI-Net and NBI-Net) are designed for the imaging characteristics of WLI and NBI to obtain accurate unimodal diagnostic results while guaranteeing low computational costs; in the Decision-level fusion, the Dempster-Shaffer algorithm is utilized to further fuse the diagnostic results of WLI-Net and NBI-Net to reach the final diagnosis. The proposed framework is trained and evaluated on a dataset of gastric endoscopic images collected and established from 1,055 patients of six hospitals, including Fuzhou University Affiliated Provincial Hospital. On this basis, we use Android Studio to develop a portable EGC diagnosis system (PEGCS) which can run smoothly on the mobile device.

Results

Extensive experiments show that the proposed framework exhibits superior performance in terms of both computational efficiency and diagnostic reliability. WLI-Net achieves 83.31% diagnostic accuracy with only 3.182M parameters and 0.471G FLOPs, and 37.04 FPS on the mobile device; NBI-Net achieves 82.85% diagnostic accuracy with 3.596M parameters and 0.609G FLOPs, and 24.39 FPS on the mobile device; the further obtained FusionWN-Net increases the accuracy to 86.52% with 6.778M parameters and 1.080G FLOPs, reaching the FPS of 14.08 on the mobile device, which is expected to be used in clinical practice.

Conclusion

We proposed WLI-Net and NBI-Net that can be used for white light endoscopic images and narrow band endoscopic images respectively, and a lightweight and efficient multimodal framework for EGC diagnosis, which are tailored for the unique characteristics and diagnostic requirements of different endoscopic images. Besides, we established the PEGCS to put the framework into practice. This work not only takes a big step forward in improving the EGC diagnostic accuracy, but also provides a general and efficient solution for obtaining high-quality EGC diagnosis results in resource-limited application scenarios, which demonstrates significant potential of clinical application.

Disclosure

The authors declare no conflicts of interest, financial or otherwise, related to the preparation or publication of this work.

Introduction

The asymptomatic nature of precancerous lesions, namely low- and high-grade dysplasia (LGD and HGD, respectively), constitutes a major clinical challenge that precludes a timely detection of gastric cancer (GC) in curable stages. The identification of novel and accurate biomarkers holds the promise of a better risk assessment and citizens stratification for an early diagnosis. In this exploratory study, we proposed to characterize the transcriptomic landscape of gastric lesions (GL) towards the definition of a molecular profile for early screening of GC in Western populations.

Aims & Methods

RNA-Sequencing analysis was conducted on RNA extracted from 39 formalin-fixed paraffin-embedded (FFPE) samples: 20 “normal-appearing” mucosa controls and 19 gastric lesions (GL), including LGD, HGD, and adenocarcinoma. Differential expression analysis, functional enrichment analysis, least absolute shrinkage and selection operator (Lasso) regression, and the machine-learning extreme gradient boosting (XGBoost) algorithm were employed to identify potential biomarkers and assess the predictive value of the molecular signature of the top dysregulated genes.

Results

A total of 1171 differentially expressed genes (DEGs) were identified in GL. Remarkably, 867 of these genes (74%) showed exclusive dysregulation in lesions compared to stage I GC samples from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) cohort. These unique biomarkers predominantly participate in translation, as well as metabolic and biosynthetic processes. In contrast, DEGs from TCGA-STAD cohort were primarily enriched in immunity-related processes. Notably, the eight best genes model, comprising ADAMTSL1, CLDN2, ENC1, GSR, IQNC, PER1, PRR33, and a novel transcript corresponding to a long non-coding RNA (lncRNA, ENSG00000227218), achieved an area under the curve (AUC) of 0.938 (95% CI: 0.815-1.000), with a sensitivity of 87.5% and specificity of 100% to classify GL samples.

Conclusion

This preliminary study identified over 800 DEGs exclusively in GL compared to stage I GC. Moreover, it unveiled a molecular-based signature that effectively discriminates patients with GL in an intermediate-risk population. This discovery paves the way for enhancing early GC screening strategies, particularly for those who can be treated through minimally invasive, nonsurgical procedures.

Introduction

Gastric cancer (GC) is a major cause of cancer-related death worldwide. Although immune checkpoint inhibitors (ICIs) have improved outcomes in a subset of patients, only 20–30% achieve long-term benefit. Identifying robust biomarkers to predict immunotherapy response remains a critical need. With the rise of digital pathology and deep learning (DL), it is now possible to derive prognostic features from whole-slide images (WSIs), offering a novel means of evaluating the tumor immune microenvironment.

Aims & Methods

This study aimed to develop a Digital Pathology-based Immunotherapy Response Signature (DPIRS) using DL to predict prognosis in GC patients treated with ICIs. We retrospectively analyzed 110 GC patients receiving ICIs at The First Hospital of Jilin University between 2019–2023. H&E-stained WSIs were segmented into 256×256 pixel tiles. A convolutional neural network (ResNet18) was trained to detect immune-relevant histopathological patterns. The DPIRS score for each patient was defined as the proportion of positively scored image tiles. DPIRS was correlated with progression-free survival (PFS), overall survival (OS), immune checkpoint molecule expression, and immune infiltration characteristics.

Results

The DL model showed excellent predictive performance (AUC >0.85) across internal and TCGA validation cohorts. Patients with high DPIRS scores had significantly longer PFS and OS (p<0.01), as well as higher PD-L1 expression and greater tumor-infiltrating lymphocyte (TIL) density. Multivariate Cox regression confirmed DPIRS as an independent prognostic factor for immunotherapy efficacy.

Conclusion

DPIRS is a novel, non-invasive biomarker derived from deep learning-based analysis of digital pathology, enabling precise prediction of immunotherapy response in gastric cancer. This tool holds strong potential for clinical application in personalized treatment strategies.

Introduction

Gastric cancer continues to be one of the main challenges worldwide, with a predictable increase in the number of new cases in the coming decades. Staging systems for gastric precancerous lesions: Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastric Intestinal Metaplasia (OLGIM) are considered to be the best predictors for gastric cancer development. Only OLGA/OLGIM stages III-IV are considered high-risk lesions with a significant potential of progressing towards gastric cancer.

Aims & Methods

The purpose of this study was to evaluate the predictive role of OLGA and OLGIM staging in gastric cancer development.
We conducted a retrospective cohort study by matching gastric adenocarcinoma cases registered in the Latvian Cancer Registry between 2005 and 2022 to the pathology reports in the database of Academic Histology Laboratory from upper endoscopies performed in the Digestive Diseases Centre GASTRO, Jurmala hospital and Riga 1^st hospital. Only cases with antral and corpus biopsies (as the minimum) available from the index endoscopy allowing OLGA / OLGIM staging were included in the study. If data from several endoscopies were available for the same patient, the case with the highest OLGA/OLGIM stage was used as the index endoscopy; if OLGA/OLGIM staging was identical across several endoscopies, the first endoscopy was used as the index case. The presence of dysplasia in any of the previous pathology reports was also considered. Patients with suspected cancer at the enrolment or having been recorded in the cancer registry less than 6 months after the index endoscopy were excluded from the study.

Results

Altogether 54 patients were available for the analysis; 38.9% were male. The mean age at the time gastric cancer detection 69.2 years, range 40-90. The median time between the index endoscopy and gastric cancer diagnosis was 1403.5 days (IQR: 826.5-2793.3). Forty-one patients (75.9%) had one endoscopy, eleven (20.4%) had two, one (1.9%) had four and one (1.85%) – 8 endoscopies. Biopsies from incisura angularis of the stomach were available for 43 patients (79.6%).
Altogether 16 patients (29.6%) had dysplasia reported at one of the preceding endoscopies, of those 14 were low grade, 2 – high grade. Patient distribution per OLGA and OLGIM stages at the index endoscopy is shown in Table 1. Among patients ultimately diagnosed with gastric cancer, only 11 (20.4%) had OLGA III-IV stages and 10 (18.6%) had OLGIM III-IV stages at the time of the index endoscopy. Excluding cases with dysplasia reported during any of the preceding endoscopies did not make a substantial change in the OLGA and OLGIM stage distribution at the index endoscopy.

Table 1. OLGA and OLGIM stages at the time of the index upper endoscopy in patients having developed gastric cancer thereafter.

Conclusion

The majority of patients having undergone upper endoscopy before the diagnosis of gastric cancer were in low-risk OLGA and OLGIM stages. Continued research is warranted to identify the best approach for risk stratification of gastric precancerous lesions. Yet, the current study demonstrates that the assessment should not be solely based on OLGA or OLGIM staging.

Introduction

Gastric cancer remains a major global cause of cancer mortality. Early detection and accurate risk stratification are crucial for improving prognosis. Noninvasive, affordable serum tests are essential for mass screening. We compared the updated ABC method (pepsinogen I/II and gastrin-17) and a novel gastric cancer scoring system (“new scoring system”) regarding their effectiveness in screening early gastric cancer and precancerous lesions, risk stratification, and missed diagnosis prevention.

Aims & Methods

From April to November 2024, 7,304 asymptomatic individuals from Yuhang District, Hangzhou, China, participated in community-based screening. Serum pepsinogen I (PGI), PGII, PGI/II ratio (PGR), gastrin-17 (G-17), and H. pylori antibody were tested. Participants were stratified into low-, medium-, or high-risk groups using the ABC method and the new scoring system. A total of 654 medium- and high-risk participants underwent endoscopy and histopathology. Detection rates of gastric cancer, intraepithelial neoplasia, and atrophic gastritis were compared. Missed diagnosis rates in low-risk groups were analyzed assuming no endoscopy.

Results

The ABC method classified 90.55% as low-risk, 7.69% medium-risk, and 1.75% high-risk. The new scoring system classified 59.42% as low-risk, 32.67% medium-risk, and 7.91% high-risk. Endoscopy detected 11 gastric cancers (1.68%, 10 early-stage), 21 intraepithelial neoplasias (3.21%), and 98 atrophic gastritis cases (14.98%).
Gastric cancer detection rates in medium/high-risk groups were similar: ABC 1.58% (3/190), new scoring 1.77% (10/565) (p=1.000). Atrophic gastritis rates were comparable (ABC 14.21%, new scoring 15.04%, p=0.872). In the high-risk subgroup, the new scoring system detected significantly more atrophic gastritis cases (29.87% vs ABC 16.33%).
In low-risk groups, the ABC method would miss 8 of 11 gastric cancers (72.73%) without endoscopy; the new scoring system would miss only 1 case (9.09%, p=0.009). ROC analysis showed higher AUC for the new scoring system (0.60 vs 0.51). Decision curve analysis indicated superior net clinical benefit for the new scoring system at threshold probabilities above 0.2. The new scoring system showed higher sensitivity for early-stage disease detection, whereas ABC had slightly higher specificity.

Conclusion

Both systems showed similar performance in detecting gastric cancer and precancerous lesions among medium- and high-risk populations. The new scoring system significantly reduced missed diagnoses in low-risk groups and improved identification of high-risk precancerous conditions. Incorporating ROC, DCA, and accuracy metrics, the new scoring system offers enhanced flexibility for risk stratification, making it potentially more effective for population-based gastric cancer screening, especially in resource-limited settings. Further validation in larger cohorts is warranted.

Disclosure

Nothing to disclose.

Introduction

Gastric cancer (GC) incidence based on the endoscopic Kyoto classification of gastritis has not been systematically investigated using time-to-event analysis. Therefore, we examined GC incidence in an endoscopic surveillance cohort.

Aims & Methods

This study was retrospectively conducted at the Toyoshima Endoscopy Clinic. Patients who underwent two or more esophagogastroduodenoscopies (EGDs) were enrolled. GC incidence was based on Kyoto classification scores, such as atrophy, intestinal metaplasia (IM), enlarged folds (EF), nodularity, diffuse redness (DR), and total Kyoto scores. Atrophy was classified using the Kimura-Takemoto classification. Non-atrophy and Closed I, Closed II and III, and Open I to III were scored as atrophy scores of 0, 1, and 2, respectively. IM scores of 0, 1, and 2 were defined as the absence of IM, IM limited to the antrum, and IM extending into the corpus, respectively. The absence and presence of EF were scored as 0 and 1, respectively. The absence and presence of nodularity were scored 0 and 1, respectively. DR scores of 0, 1, and 2 were defined as the absence of DR, mild DR and/or DR with partial regular arrangement of collecting venules (RAC), and severe DR without RAC, respectively. The endoscopists diagnosed the Kyoto classification scores on-site during the index EGD. Hazard ratios [HRs] adjusted for age and sex were calculated using a Cox hazard model. This study was approved by the institutional review board of the Yoyogi Mental Clinic (approval no. RKK227).

Results

A total of 6718 patients were enrolled (mean age 55.0 years; men 44.2%). Of them, 3754, 2264, 700 patients were in H. pylori uninfected, eradicated, and currently infected status, respectively. Patients were followed up for up to 5.02 years. The mean Kyoto classification scores for atrophy, IM, EF, nodularity, DR, and total Kyoto were 0.585, 0.284, 0.039, 0.026, 0.312, and 1.247, respectively. During the follow-up period, 37 GCs occurred in 34 patients. All GCs were superficial and within the submucosal depth. Lauren’s intestinal type made up 89.1% of GCs. The annual incidence rates of GC were 0.04%, 0.17%, and 0.73% for atrophy scores of 0, 1, and 2; 0.07%, 0.25%, and 1.10% for IM scores of 0, 1, and 2; 0.17% and 0.92% for EF scores of 0 and 1; and 0.06%, 0.55%, and 0.74% for DR scores of 0, 1, and 2, respectively. The annual incidence rate of GC was 0.19%. The GC incidence rates were 0.05, 0.07, 0.47, and 1.27%/year for the total Kyoto scores of 0-1, 2-3, 4, and 5-8, respectively. Multivariate analysis showed that Kyoto atrophy scores 1 (HR with score 0 as reference: 3.66, 95% confidence interval [CI]: 1.06-12.61), 2 (11.60, 3.82-35.27), IM score 2 (9.92, 4.37-22.54), EF score 1 (4.03, 1.63-9.96), DR scores 1 (6.22, 2.65-14.56), and 2 (10.01, 3.73-26.86) were associated with GC incidence, whereas nodularity scores were not. The total Kyoto scores of 4 (HR with total Kyoto scores 0-1 as reference: 6.23, 95% CI: 1.93-20.13) and 5-8 (16.45, 6.29-43.03) were more likely to develop GC, whereas the total Kyoto scores 2-3 were not, as shown in table. The HR of the total Kyoto score for developing GC per 1 rank was 1.75 (95% CI: 1.46-2.09, P<0.001).

Conclusion

A high total Kyoto score (≥ 4) was associated with GC incidence. The endoscopy-based diagnosis of gastritis can stratify GC risk.

Disclosure

All authors disclosed no personal conflicts of interest.