Diagnosis Of Early Gastric Cancer Research Articles

Targeted labeling with tissue marking dyes guided by magnifying endoscopy of endoscopic submucosal dissection specimen improves the accuracy of endoscopic and histopathological diagnosis of early gastric cancer: a before-after study.

Although histopathological evaluation after endoscopic submucosal dissection (ESD) is critical to assess the accuracy of endoscopic diagnosis, it is still challenging to perform precise endoscopic to pathological evaluation. We evaluated the importance of tissue marking dye (TMD)-targeted marking for post-ESD specimen guided by magnificent endoscope on histopathological accuracy and endoscopic-to-histopathological reconstruction. A total of 81 specimens resected by ESD [43 without TMD marking (N-TMD group), and 38 specimens with TMD-targeted cancerous areas marking guided by post-procedural magnifying endoscopy on resected specimens (TMD group)] between January 31, 2019, and January 31, 2022 at the Renmin Hospital of Wuhan University were included in the study. The baseline characteristics of patients, discrepancies between endoscopic and histopathological diagnosis, and the impact of TMD on histopathological diagnosis and reconstruction were analyzed. Discrepancies between endoscopic (pre-ESD) and histopathological (post-ESD) diagnosis increased significantly in TMD group (68.4% (26/38) for tumor areas, 26.3% (10/38) for tumor margins, and 26.3% (10/38) for tumor differentiations) when compared with N-TMD group (p < 0.0001). Deeper sections were achieved in all TMD-marked resected lesions and 27.9% (12/43) lesions in the N-TMD group (p < 0.001). More pathological evaluations in TMD group were changed from curative resection to non-curative resection [6/38(15.8%) vs 1/43(2.3%)] compared with N-TMD group (p < 0.0001). TMD-targeted marking also improved the efficiency of histopathological reconstruction on pre-procedural endoscopic images and benefit endoscopists training. TMD-targeted labeling on resected specimens could improve precise endoscopic-to-pathological diagnosis, reconstruction by point-to-point marking and benefit endoscopists training.

Endoscopic surveillance with systematic random biopsy for the early diagnosis of hereditary diffuse gastric cancer: a prospective 16-year longitudinal cohort study

Hereditary diffuse gastric cancer, generally caused by germline pathogenic variants in CDH1, presents with early-onset signet ring cell carcinoma. Prophylactic total gastrectomy is the definitive treatment. Endoscopic surveillance can inform the timing of prophylactic total gastrectomy through detection of microscopic signet ring cell carcinoma foci. However, evidence is scarce about the optimal endoscopic sampling technique and characterisation of signet ring cell carcinoma foci in hereditary diffuse gastric cancer. We aimed to formally assess the diagnostic yield of different sampling strategies and to identify criteria for the characterisation of endoscopic lesions. For this prospective longitudinal cohort study, we included individuals aged 18 years or older at the Cambridge University Hospitals National Health Service (NHS) Foundation Trust who fulfilled testing criteria for hereditary diffuse gastric cancer between June 1, 2005, and July 31, 2021. The primary outcome was detection of intramucosal signet ring cell carcinoma foci. We assessed the detection rate and anatomical location of signet ring cell carcinoma in random biopsy samples taken according to a systematic protocol compared with biopsies targeted to endoscopic findings. Endoscopic lesions were examined with white-light and narrow band imaging with magnification to assess the likelihood of cancerous foci. 145 individuals were included, of whom 68 (47%) were male and 92 (63%) carried the CDH1 pathogenic variant. 58 (40%) patients were diagnosed with invasive signet ring cell carcinoma over a median follow-up time of 51 months (IQR 18-80). The first diagnosis of signet ring cell carcinoma was most commonly made from random biopsies (29 [50%] of 58 patients), rather than targeted biopsies (15 [26%] patients). The anatomical distribution of signet ring cell carcinoma foci detected by random biopsies more accurately reflected those identified in prophylactic total gastrectomy specimens than did targeted biopsies. Omitting random biopsies in our cohort would have led to an under-diagnosis rate of 42%. Using a novel panel of endoscopic criteria, gastric lesions containing signet ring cell carcinoma were predicted with a sensitivity of 67·3% and a specificity of 90·2%. Random biopsies enhance the early detection of signet ring cell carcinoma and are complementary to targeted biopsies in surveillance of hereditary diffuse gastric cancer. This sampling method should be the standard of care when performing all surveillance endoscopies for individuals with hereditary diffuse gastric cancer. UK Medical Research Council.

A mucosal recovery software tool for endoscopic submucosal dissection in early gastric cancer.

Due to the limited diagnostic ability, the low detection rate of early gastric cancer (EGC) is a serious health threat. The establishment of the mapping between endoscopic images and pathological images can rapidly improve the diagnostic ability to detect EGC. To expedite the learning process of EGC diagnosis, a mucosal recovery map for the mapping between ESD mucosa specimen and pathological images should be performed in collaboration with endoscopists and pathologists, which is a time-consuming and laborious work. 20 patients at the Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College from March 2020 to July 2020 were enrolled in this study. We proposed the improved U-Net to obtain WSI-level segmentation results, and the WSI-level results can be mapped to the macroscopic image of the specimen. For the convenient use, a software pipeline named as "Pathology Helper" for integration the workflow of the construction of mucosal recovery maps was developed. The MIoU and Dice of our model can achieve 0.955 ± 0.0936 and 0.961 ± 0.0874 for WSI-level segmentation, respectively. With the help of "Pathology Helper", we can construct the high-quality mucosal recovery maps to reduce the workload of endoscopists and pathologists. "Pathology Helper" will accelerate the learning of endoscopists and pathologists, and rapidly improve their abilities to detect EGC. Our work can also improve the detection rate of early gastric cancer, so that more patients with gastric cancer will be treated in a timely manner.

Clinical Role of Magnifying Endoscopy with Narrow-band Imaging in the Diagnosis of Early Gastric Cancer

Clinical Role of Magnifying Endoscopy with Narrow-band Imaging in the Diagnosis of Early Gastric Cancer

Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection.

Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.

Data-independent acquisition mass spectrometry identification of extracellular vesicle biomarkers for gastric adenocarcinoma.

Early diagnosis of gastric adenocarcinoma (GAC) can effectively prevent the progression of the disease and significantly improve patient survival. Currently, protein markers in clinical practice barely meet patient needs; it is therefore imperative to develop new diagnostic biomarkers with high sensitivity and specificity. In this study, we extracted extracellular vesicles (EV) from the sera of 33 patients with GAC and 19 healthy controls, then applied data-independent acquisition (DIA) mass spectrometry to measure protein expression profiles. Differential protein expression analysis identified 23 proteins showing expression patterns across different cancer stages, from which 15 proteins were selected as candidate biomarkers for GAC diagnosis. From this subset of 15 proteins, up to 6 proteins were iteratively selected as features and logistic regression was used to distinguish patients from healthy controls. Furthermore, serum-derived EV from a new cohort of 12 patients with gastric cancer and 18 healthy controls were quantified using the same method. A classification panel consisting of GSN, HP, ORM1, PIGR, and TFRC showed the best performance, with a sensitivity and negative predictive value (NPV) of 0.83 and 0.82. The area under curve (AUC) of the receiver operating characteristic (ROC) is 0.80. Finally, to facilitate the diagnosis of advanced stage GAC, we identified a 3-protein panel consisting of LYZ, SAA1, and F12 that showed reasonably good performance with an AUC of 0.83 in the validation dataset. In conclusion, we identified new protein biomarker panels from serum EVs for early diagnosis of gastric cancer that worth further validation.

Metabolomics study identified bile acids as potential biomarkers for gastric cancer: A case control study.

Gastric cancer (GC) is a common lethal malignancy worldwide. Gastroscopy is an effective screening technique for decreasing mortality. However, there are still limited useful non-invasive markers for early detection of GC. Bile acids are important molecules for the modulation of energy metabolism. With an in-depth targeted method for accurate quantitation of 80 bile acids (BAs), we aimed to find potential biomarkers for the early screening of GC. A cohort with 280 participants was enrolled, including 113 GC, 22 benign gastric lesions (BGL) and 145 healthy controls. Potential markers were identified using a random forest machine algorithm in the discovery cohort (n=180), then validated in an internal validation cohort (n=78) and a group with 22 BGL. The results represented significant alterations in the circulating BA pool between GC and the controls. BAs also exhibited significant correlations with various clinical traits. Then, we developed a diagnostic panel that comprised six BAs or ratios for GC detection. The panel showed high accuracy for the diagnosis of GC with AUC of 1 (95%CI: 1.00-1.00) and 0.98 (95%CI: 0.93-1.00) in the discovery and validation cohort, respectively. This 6-BAs panel was also able to identify early GC with AUC of 1 (95%CI: 0.999-1.00) and 0.94 (95%CI: 0.83-1.00) in the discovery and validation cohort, respectively. Meanwhile, this panel achieved a good differential diagnosis between GC and BGL and the AUC was 0.873 (95%CI: 0.812-0.934). The alternations of serum bile acids are characteristic metabolic features of GC. Bile acids could be promising biomarkers for the early diagnosis of GC.

The study progress of relationship between upper gastrointestinal endoscopy and artificial intelligence-aided diagnosis in early gastric cancer

Gastric cancer was the third most deadly cancer in the world. However, the 5-year survival rate of patients, who was diagnosed with early gastric cancer

Early gastric mixed neuroendocrine–non-neuroendocrine neoplasms with endoscopic findings of neuroendocrine cell carcinoma components exposed on the mucosal surface: a case report

BackgroundGastric mixed neuroendocrine–non-neuroendocrine neoplasms are rare malignant tumors. The lack of specific findings makes it difficult to diagnose endoscopically. We report the case of early gastric mixed neuroendocrine–non-neuroendocrine neoplasms treated by endoscopic submucosal dissection.Case presentationAn 81-year-old Japanese female underwent esophagogastroduodenoscopy for screening and was treated with endoscopic submucosal dissection for the diagnosis of early gastric cancer. Histopathologically, the lesion was diagnosed as mixed neuroendocrine–non-neuroendocrine neoplasms (tubular adenocarcinoma 2 60%, endocrine cell carcinoma 40%), pT1b(submucosa (SM) 900 μm), pUL(−), Ly(+), v(−), pHM0, pVM0. After additional surgical resection without adjuvant chemotherapy, she has had no recurrences or metastases for 3 years.ConclusionsComparing narrow-band imaging magnified endoscopic findings with pathological findings, the depressed area with a lack of surface structure was consistent with the neuroendocrine cell carcinoma component, while narrow-band imaging magnification findings showed non-network vessels. In this case, we examined endoscopic findings of early stage mixed neuroendocrine—non-neuroendocrine neoplasms in detail and compared it with the pathological findings. We believe that these endoscopic findings contribute to the diagnosis of mixed neuroendocrine–non-neuroendocrine neoplasms and can lead to its early detection.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm−1, 1004 cm−1, 1449 cm−1, 1089–1128 cm−1, 1311–1367 cm−1 and 1585–1665 cm−1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400–1800 cm−1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

A literature review on the potential clinical implications of streptococci in gastric cancer.

Streptococcus is widely found in nature and the human body, and most species are not pathogenic. In recent years, studies have found that Streptococcus is associated with gastric cancer. Streptococcus was found to be enriched in the oral cavity, stomach and intestine of gastric cancer patients and found to be increased in gastric cancer tissues, suggesting that Streptococcus may be the pathogenic bacteria underlying gastric cancer. This review discusses the discovery of Streptococcus, the relationship between Streptococcus and gastric cancer, and the possible carcinogenic mechanism of Streptococcus and summarizes the progress of the research on the role of Streptococcus in gastric cancer to provide new ideas for the early detection, diagnosis and treatment of gastric cancer.

Automatic classification of gastric lesions in gastroscopic images using a lightweight deep learning model with attention mechanism and cost-sensitive learning

Magnification endoscopy with narrow-band imaging (ME-NBI) technology is widely used in the early diagnosis of precancerous lesions and gastric cancer, which is critical to reducing the incidence of gastric cancer and improving the survival rate of gastric cancer patients. The diagnosis based on ME-NBI image is mostly in manual way in clinics. In order to improve its objectivity and efficiency, here we proposed a lightweight attention mechanism deep learning model to automatically classify ME-NBI images for artificial intelligence (AI) diagnosis of early gastric cancer, low-grade intraepithelial neoplasia, and non-neoplasm. We collected 4,098 images from 990 patients for model training and validation and evaluated the performance of our model by comparisons with that of other models, such as the benchmark model. An additional 587 images from 296 patients were collected as an independent test set to further evaluate our method’s performance. The validation set showed that the overall accuracy, recall, precision, F1 score, and the area under the curve of our method were higher than those of other methods. The independent test set showed that our method achieved state-of-the-art classification for low-grade intraepithelial neoplasia (accuracy = 93.9%, sensitivity = 92.6%). Our method displayed the advantages of lightweight and high effectiveness in classifying effectiveness, which is the potential for AI diagnosis of early gastric cancer, low-grade intraepithelial neoplasia, and non-neoplasm.

Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.

Gastric cancer is one of the most common malignant tumors. Although some progress has been made in chemotherapy and surgery, it is still one of the highest mortalities in the world. Therefore, early detection, diagnosis and treatment are very important to improve the prognosis of patients. In recent years, with the proposal of the concept of radiomics, it has been gradually applied to histopathological grading, differential diagnosis, therapeutic efficacy and prognosis evaluation of gastric cancer, whose advantage is to comprehensively quantify the tumor phenotype using a large number of quantitative image features, so as to predict and diagnose the lesion area of gastric cancer early. The purpose of this review is to evaluate the research status and progress of radiomics in gastric cancer, and reviewed the workflow and clinical application of radiomics. The 27 original studies on the application of radiomics in gastric cancer were included from web of science database search results from 2017 to 2021, the number of patients included ranged from 30 to 1680, and the models used were based on the combination of radiomics signature and clinical factors. Most of these studies showed positive results, the median radiomics quality score (RQS) for all studies was 36.1%, and the development prospect and challenges of radiomics development were prospected. In general, radiomics has great potential in improving the early prediction and diagnosis of gastric cancer, and provides an unprecedented opportunity for clinical practice to improve the decision support of gastric cancer treatment at a low cost.

The Diagnosis of Early Gastric Cancer Based on Medical Imaging Technology and Mathematical Modeling.

The key to reducing the mortality of gastric cancer is early detection, early diagnosis, and early treatment of gastric cancer. Early diagnosis of gastric cancer is the key to early detection and diagnosis of gastric cancer. Early diagnosis and treatment of gastric cancer is of great significance for improving the curative effect and reducing mortality of gastric cancer. The purpose of this paper is to study the diagnosis of early gastric cancer based on medical imaging techniques and mathematical modeling. The effect of W-DeepLab network-assisted diagnosis of images under white light was analyzed, and the value of Narrow Band Imaging and Blue Laser Imaging in the diagnosis of early gastric cancer was compared. Because Blue Laser Imaging endoscopy can clearly observe the demarcation line and microvascular morphology; but when using Narrow Band Imaging observation, part of the demarcation line and microvascular morphology is not observed. The results show that Blue Laser Imaging is brighter than Narrow Band Imaging's endoscopic images, and it is easier to observe the microstructure of lesions under endoscopy, so as to accurately determine the nature of lesions.

S1599 Diagnostic Performance of Linked Color Image (LCI) and Blue Laser Imaging (BLI) for the Diagnosis of Early Gastric Cancer: A Systematic Review and Meta-Analysis

S1599 Diagnostic Performance of Linked Color Image (LCI) and Blue Laser Imaging (BLI) for the Diagnosis of Early Gastric Cancer: A Systematic Review and Meta-Analysis

데이터 선별을 통한 EfficientNetV2-L 기반 조기 위암 컴퓨터 보조 진단 시스템 연구

Gastric cancer is a common cancer worldwide, especially in Korea. Early diagnosis is very important to increase the full recovery rate. However, early gastric cancer has no special symptoms and is a disease that even experts find difficult to diagnose in gastroscopy. Therefore, in this paper proposed a computer-aided diagnosis(CADx) for early gastric cancer diagnosis using EfficientNetV2-L. Due to the nature of medical data, it is difficult to collect a large amount of data. The data used for training was augmented using Cifar10 policy of the Google

Deep learning for classification and localization of early gastric cancer in endoscopic images

Gastric cancer, as a malignant tumor, is one of the most common cancer-related deaths worldwide with high mortality and incidence rates. Therefore, the endoscopic detection of gastric cancer at an early stage is essential. In this paper, we propose an automatic diagnosis method of early gastric cancer (EGC) based on deep learning (DL) techniques. Specifically, with the new annotated endoscopic image dataset collected from a single-center, this paper designs several DL architectures to realize the automatic analysis of EGC images. Particularly, a guided-attention deep network was introduced, derived from ResNet-50, for the accurate score prediction of EGC and the extraction of feature information. Furthermore, we combined a lightweight attention module and multi-scale feature extractor with U-Net for estimating the pixel-level segmentation of EGC pathological regions. Experiment results on the presented dataset showed outstanding performance in the involved classification and segmentation assignments with an accuracy of 98.84% and an intersection over union (IOU) of 0.64, revealing the potential applications of DL in aiding and improving EGC diagnosis using endoscopic images.

Primary Investigation of Deep Learning Models for Japanese "Group Classification" of Whole-Slide Images of Gastric Endoscopic Biopsy.

Background Accurate pathological diagnosis of gastric endoscopic biopsy could greatly improve the opportunity of early diagnosis and treatment of gastric cancer. The Japanese “Group classification” of gastric biopsy corresponds well with the endoscopic diagnostic system and can guide clinical treatment. However, severe shortage of pathologists and their heavy workload limit the diagnostic accuracy. This study presents the first attempt to investigate the applicability and effectiveness of AI-aided system for automated Japanese “Group classification” of gastric endoscopic biopsy. Methods In total, 260 whole-slide images of gastric endoscopic biopsy were collected from Dalian Municipal Central Hospital from January 2015 to January 2021. These images were annotated by experienced pathologists according to the Japanese “Group classification.” Five popular convolutional neural networks, i.e., VGG16, VGG19, ResNet50, Xception, and InceptionV3 were trained and tested. The performance of the models was compared in terms of widely used metrics, namely, AUC (area under the receiver operating characteristic curve, i.e., ROC curve), accuracy, recall, precision, and F1 score. Results Results showed that ResNet50 achieved the best performance with accuracy 93.16% and AUC 0.994. Conclusion Our results demonstrated the applicability and effectiveness of DL-based system for automated Japanese “Group classification” of gastric endoscopic biopsy.

Feasibility of olfactomedin 4 as a molecular biomarker for early diagnosis of gastric neoplasia after intestinal metaplasia

Objectives This study discusses whether olfactomedin 4 (OLFM4) could be used as a sensitive and specific biomarker in the early diagnosis of gastric cancer (GC) after gastric intestinal metaplasia (GIM). Methods An integrative analysis combining data derived from the Gene Expression Omnibus (GEO) and cBioPortal databases was performed to investigate the potential molecular biomarker. Immunohistochemistry and quantitative real-time polymerase chain reactions were used to measure the expression of messenger ribonucleic acid (mRNA) and protein by OLFM4. In combination with the gastroscopic findings and the OLFM4 expression in GIM-GC, a predictive model was established. The receiver operator characteristic curve (ROC) was applied to assess the diagnostic value of the model for GIM-GC. Results According to the GEO and cBioPortal databases, OLFM4 was identified as a key gene in the diagnosis of GIM-GC. Higher protein expression of OLFM4 was found in GIM and GIM-GC compared with chronic superficial gastritis (GS) (p < 0.05). The positive expression rate of OLFM4 in paracancerous tissue (GCP) was higher than in GIM (p > 0.05). There was no significant difference between GIM-GC and GCP (p > 0.05). The mRNA expression of OLFM4 was similar to the protein expression, and the positive expression rate was higher in early GIM-GC than in GIM (p < 0.05). Conclusion Olfactomedin 4 could be used as a biomarker for the early diagnosis of GIM-GC, and the logistic predictive model could be an effective tool for increasing the early diagnostic rate.

Can Chromoendoscopy Improve the Early Diagnosis of Gastric Carcinoma in Dogs?

Simple SummaryCurrently, canine gastric carcinoma is mainly diagnosed in its late, incurable phase, and strategies for early diagnosis are lacking. In human medicine, chromoendoscopic (CE) methods such as staining the gastric mucosal surface with indigo carmine (IC), and narrow band imaging (NBI), have improved the diagnosis of precancerous gastric mucosal changes and early gastric carcinoma. This study aimed at investigating whether IC-CE and NBI-CE can improve the diagnostic yield of endoscopy in dogs. Belgian Shepherd dogs are predisposed to gastric carcinoma; thus, 30 dogs of the breed served as the study population. As a result, the study revealed that especially the combination of standard white light endoscopy (WLE) with NBI-CE allows better recognition of gastric mucosal structural changes than WLE alone. However, CE assessment templates used to predict the type of mucosal change in humans were not applicable in dogs. The value of the study lies in providing evidence that CE can improve the diagnosis of precancerous changes and early gastric carcinoma in dogs. However, current image assessment templates from human medicine need major adjustments to comprehend canine gastric mucosal conditions.Chromoendoscopy has improved the early diagnosis of gastric cancer in humans but its usefulness in dogs is unknown. This study aimed at assessing whether adding narrow band imaging (NBI) or indigo carmine (IC) chromoendoscopy (CE) can improve the diagnostic yield of standard white light endoscopy (WLE). We compared the real-time findings of canine WLE, NBI-CE, and IC-CE and corresponding histology reports with endoscopic mucosal pattern assessment templates used in human medicine. Belgian Shepherd dogs are predisposed to gastric carcinoma. Therefore, 30 dogs of this breed served as the study population. According to histology, 17/30 dogs had mucosal changes (mucous metaplasia, glandular dysplasia, and gastric carcinoma). Diagnostic yield was best when targeted biopsies were taken with WLE and NBI-CE combined (15/17 cases). WLE alone positively identified only 8/17 cases and missed a gastric carcinoma in 3/6 cases. CE assessment templates based on macroscopic mucosal patterns, broadly used in human medicine, were not readily applicable in dogs. In conclusion, the study provides evidence that using CE in dogs has the potential to improve the diagnosis of precancerous gastric mucosal pathology and early gastric carcinoma. However, current image assessment templates from human medicine need major adjustments to the patterns of canine gastric mucosa.