Prediction of Helicobacter pylori status by conventional endoscopy, narrow-band imaging magnifying endoscopy in stomach after endoscopic resection of gastric cancer

Novel endoscopic techniques for the diagnosis of gastric Helicobacter pylori infection: a systematic review and network meta-analysis

Objective

This study aimed to conduct a network meta-analysis to compare the diagnostic efficacy of diverse novel endoscopic techniques for detecting gastric Helicobacter pylori infection.

Methods

From inception to August 2023, literature was systematically searched across Pubmed, Embase, and Web of Science databases. Cochrane's risk of bias tool assessed the methodological quality of the included studies. Data analysis was conducted using the R software, employing a ranking chart to determine the most effective diagnostic method comprehensively. Convergence analysis was performed to assess the stability of the results.

Results

The study encompassed 36 articles comprising 54 observational studies, investigating 14 novel endoscopic techniques and involving 7,230 patients diagnosed with gastric H. pylori infection. Compared with the gold standard, the comprehensive network meta-analysis revealed the superior diagnostic performance of two new endoscopic techniques, Magnifying blue laser imaging endoscopy (M-BLI) and high-definition magnifying endoscopy with i-scan (M-I-SCAN). Specifically, M-BLI demonstrated the highest ranking in both sensitivity (SE) and positive predictive value (PPV), ranking second in negative predictive value (NPV) and fourth in specificity (SP). M-I-SCAN secured the top position in NPV, third in SE and SP, and fifth in PPV.

Conclusion

After thoroughly analyzing the ranking chart, we conclude that M-BLI and M-I-SCAN stand out as the most suitable new endoscopic techniques for diagnosing gastric H. pylori infection.

Systematic review registration

https://inplasy.com/inplasy-2023-11-0051/, identifier INPLASY2023110051.

Statistical Analysis of Gastric Cancer Cells Response to Broadband Terahertz Radiation with and without Contrast Nanoparticles

The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging.

The Diagnostic Performance of Linked Color Imaging Compared to White Light Imaging in Endoscopic Diagnosis of Helicobacter pylori Infection: A Systematic Review and Meta-Analysis

Background/Aims

Recognizing Helicobacter pylori infection during endoscopy is important because it can lead to the performance of confirmatory testing. Linked color imaging (LCI) is an image enhancement technique that can improve the detection of gastrointestinal lesions. The purpose of this study was to compare LCI to conventional white light imaging (WLI) in the endoscopic diagnosis of H. pylori infection.

Methods

We conducted a comprehensive literature search using PubMed, Embase, and the Cochrane Library. All studies evaluating the diagnostic performance of LCI or WLI in the endoscopic diagnosis of H. pylori were eligible. Studies on magnifying endoscopy, chromoendoscopy, and artificial intelligence were excluded.

Results

Thirty-four studies were included in this meta-analysis, of which 32 reported the performance of WLI and eight reported the performance of LCI in diagnosing H. pylori infection. The pooled sensitivity and specificity of WLI in the diagnosis of H. pylori infection were 0.528 (95% confidence interval [CI], 0.517 to 0.540) and 0.821 (95% CI, 0.811 to 0.830), respectively. The pooled sensitivity and specificity of LCI in the diagnosis of H. pylori were 0.816 (95% CI, 0.790 to 0.841) and 0.868 (95% CI, 0.850 to 0.884), respectively. The pooled diagnostic odds ratios of WLI and LCI were 15.447 (95% CI, 8.225 to 29.013) and 31.838 (95% CI, 15.576 to 65.078), respectively. The areas under the summary receiver operating characteristic curves of WLI and LCI were 0.870 and 0.911, respectively.

Conclusions

LCI showed higher sensitivity in the endoscopic diagnosis of H. pylori infection than standard WLI.

Diagnostic Accuracy of H. pylori Status by Conventional Endoscopy: Time-Trend Change After Eradication and Impact of Endoscopic Image Quality

AIM

To assess the time trend of diagnostic accuracy of pre- and post-eradication H. pylori status and interobserver agreement of gastric atrophy grading.

METHODS

A series 100 of conventional endoscopic image sets taken from subjects undergoing gastric cancer screening at a polyclinic were evaluated by 5 experienced assessors. Each assessor independently examined endoscopic findings according to the Kyoto classification and then determined the H. pylori status (never, current, or past infected). Gastric atrophy was assessed according to the Kimura-Takemoto classification and classified into 3 grades (none/mild, moderate, or severe). The image series that ≥3 assessors considered to have good quality were arbitrarily defined as high-quality image (HQI) series, and the rest were defined as low-quality image (LQI) series.

RESULTS

The overall diagnostic accuracy of H. pylori status was 83.0%. It was lowest in subjects with current infection (54%), gradually increased at 1 year (79%, P < 0.001) and 3 years (94.0%, P = 0.002), but then did not significantly change at 5 years (91.0%, P = 0.420) after eradication. The rate of LQI series was 28%. The overall diagnostic accuracy of H. pylori status dropped from 88.9% to 67.9% (P < 0.001), and the mean kappa value on gastric atrophy grading dropped from 0.730 to 0.580 (P = 0.002) in the HQI and LQI series, respectively.

CONCLUSIONS

Diagnostic accuracy of H. pylori status increased over time after eradication. LQI series badly affected the diagnostic accuracy of H. pylori status and the level of agreement when grading gastric atrophy.

Association between mucosal surface pattern under near focus technology and Helicobacter pylori infection

BACKGROUND

Many studies evaluated magnification endoscopy (ME) to correlate changes on the gastric mucosal surface with Helicobacter pylori (H. pylori) infection. However, few studies validated these concepts with high-definition endoscopy without ME.

AIM

To access the association between mucosal surface pattern under near focus technology and H. pylori infection status in a western population.

METHODS

Cross-sectional study including all patients referred to routine upper endoscopy. Endoscopic exams were performed using standard high definition (S-HD) followed by near focus (NF-HD) examination. Presence of erythema, erosion, atrophy, and nodularity were recorded during S-HD, and surface mucosal pattern was classified using NF-HD in the gastric body. Biopsies were taken for rapid urease test and histology.

RESULTS

One hundred and eighty-seven patients were analyzed from August to November 2019. Of those, 47 (25.1%) were H. pylori+, and 42 (22.5%) had a previous H. pylori treatment. In the examination with S-HD, erythema had the best sensitivity for H. pylori detection (80.9%). Exudate (99.3%), nodularity (97.1%), and atrophy (95.7%) demonstrated better specificity values, but with low sensitivity (6.4%-19.1%). On the other hand, the absence of erythema was strongly associated with H. pylori- (negative predictive value = 92%). With NF-HD, 56.2% of patients presented type 1 pattern (regular arrangement of collecting venules, RAC), and only 5.7% of RAC+ patients were H. pylori+. The loss of RAC presented 87.2% sensitivity for H. pylori detection, 70.7% specificity, 50% positive predictive value, and 94.3% negative predictive value, indicating that loss of RAC was suboptimal to confirm H. pylori infection, but when RAC was seen, H. pylori infection was unlikely.

CONCLUSION

The presence of RAC at the NF-HD exam and the absence of erythema at S-HD were highly predictive of H. pylori negative status. On the other hand, the loss of RAC had a suboptimal correlation with the presence of H. pylori.

Assessment of Helicobacter pylori status by examination of gastric mucosal patterns: diagnostic accuracy of white-light endoscopy and narrow-band imaging

Objectives

Helicobacter pylori infection is a common cause of chronic gastritis worldwide and an established risk factor for developing gastric malignancy. The endoscopic appearances predicting H. pylori status are an ongoing area of research, as are their diagnostic accuracies. This study aimed to establish the diagnostic accuracy of several mucosal features predictive of H. pylori negative status and formulate a simple prediction model for use at the time of endoscopy.

Design

Patients undergoing high-definition upper gastrointestinal (GI) endoscopy without magnification were recruited prospectively. During the endoscopy, the presence or absence of specific endoscopic findings was noted. Sydney protocol biopsies were used as the diagnostic reference standard, and urease test if taken. The results informed a logistic regression model used to produce a simple diagnostic approach. This model was subsequently validated using a further cohort of 30 patients.

Results

153 patients were recruited and completed the study protocol. The prevalence of active H. pylori infection was 18.3% (28/153). The overall diagnostic accuracy of the simple prediction model was 80.0%, and 100% of patients with active H. pylori infection were correctly classified. The presence of regular arrangement of collecting venules (RAC) showed a positive predictive value for H. pylori naïve status of 90.7%, rising to 93.6% for patients under the age of 60.

Conclusion

A simple endoscopic model may be accurate for predicting H. pylori status of a patient, and the need for biopsy-based tests. The presence of RAC in the stomach is an accurate predictor of H. pylori negative status, particularly in patients under the age of 60.

Trial registration number

The study was registered with ClinicalTrials.gov, No. NCT02385045.

Diagnosis of Helicobacter pylori Infection and Recent Advances

BACKGROUND

Helicobacter pylori (H. pylori) infects approximately 50% of the world population. Its infection is associated with gastropathies, extra-gastric digestive diseases, and diseases of other systems. There is a canonical process from acute-on-chronic inflammation, chronic atrophic gastritis (CAG), intestinal metaplasia (IM), dysplasia, and intraepithelial neoplasia, eventually to gastric cancer (GC). H. pylori eradication abolishes the inflammatory response and early treatment prevents the progression to preneoplastic lesions.

METHODS

the test-and-treat strategy, endoscopy-based strategy, and screen-and-treat strategy are recommended to prevent GC based on risk stratification, prevalence, and patients' clinical manifestations and conditions. Challenges contain false-negative results, increasing antibiotic resistance, decreasing eradication rate, and poor retesting rate. Present diagnosis methods are mainly based on invasive endoscopy and noninvasive laboratory testing.

RESULTS

to improve the accuracy and effectiveness and reduce the missed diagnosis, some advances were achieved including newer imaging techniques (such as image-enhanced endoscopy (IEE), artificial intelligence (AI) technology, and quantitative real-time polymerase chain reaction (qPCR) and digital PCR (dPCR).

CONCLUSION

in the article, we summarized the diagnosis methods of H. pylori infection and recent advances, further finding out the opportunities in challenges.

The endoscopic predictors of Helicobacter pylori status: a meta-analysis of diagnostic performance

Objective:

The endoscopic findings associated with Helicobacter pylori–naïve status, current infection or past infection are an area of ongoing interest. Previous studies have investigated parameters with a potential diagnostic value. The aim of this study was to perform meta-analysis of the available literature to validate the diagnostic accuracy of mucosal features proposed in the Kyoto classification.

Data sources:

The databases of MEDLINE and Embase, clinicalTrials.gov and the Cochrane Library were systematically searched for relevant studies from October 1999 to October 2019.

Methods:

A bivariate random effects model was used to produce pooled diagnostic accuracy calculations for each of the studied endoscopic findings. Diagnostic odds ratios and sensitivity and specificity characteristics were calculated to identify significant predictors of H pylori status.

Results:

Meta-analysis included 4380 patients in 15 studies. The most significant predictor of an H pylori-naïve status was a regular arrangement of collecting venules (diagnostic odds ratio 55.0, sensitivity 78.3%, specificity 93.8%). Predictors of active H pylori infection were mucosal oedema (18.1, 63.7%, 91.1%) and diffuse redness (14.4, 66.5%, 89.0%). Map-like redness had high specificity for previous H pylori eradication (99.0%), but poor specificity (13.0%).

Conclusion:

The regular arrangement of collecting venules, mucosal oedema, diffuse redness and map-like redness are important endoscopic findings for determining H pylori status. This meta-analysis provides a tentative basis for developing future endoscopic classification systems.

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy

BACKGROUND

Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification.

OBJECTIVE

This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images.

METHODS

Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed.

RESULTS

Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images.

CONCLUSIONS

An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome.

TRIAL REGISTRATION

PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957.

Comparative study of magnifying narrow-band imaging and conventional white light endoscopy in the diagnosis of Helicobacter pylori status after eradication therapy

Predicting Helicobacter pylori (Hp) status by endoscopic finding would be useful in recent clinical condition that the use of proton-pump inhibitors, anti-platelet, and anti-coagulant have become widespread. We aimed to elucidate the diagnostic accuracy of magnifying narrow-band imaging (M-NBI) endoscopy in distinguishing Hp status in patients with or without history of successful Hp eradication and compare this accuracy to the diagnostic accuracy of conventional white light (WL) endoscopy.Two hundred seven endoscopic examinations before and after Hp eradication were performed in prospective 163 patients. Endoscopic images by using the M-NBI and conventional WL were stored electronically and randomly allocated to 2 readers for evaluation. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were assessed by reference to Hp status assessed by conventional clinical test.Sensitivity, specificity, PPV, NPV, and accuracy for predicting Hp status for the conventional WL was 72.2%, 75.5%, 72.2%, 75.5%, and 73.9% for the first reader; 86.6%, 57.3%, 64.1%, 82.9%, and 71.0% for the second reader. On the other hand, sensitivity, specificity, PPV, NPV, and accuracy for predicting Hp status for the M-NBI was 96.9%, 93.6%, 93.1%, 97.1%, and 95.2% for the first reader; 92.8%, 93.6%, 92.8%, 93.6%, and 93.2% for the second reader, respectively. The diagnostic accuracy of M-NBI was significantly higher than that of WL (P < .0001 for both readers). Inter-observer agreement of M-NBI (k = 0.83) was also better than that of WL (k = 0.53).M-NBI was capable of distinguishing Hp status before and after eradication therapy.

Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: a single-center prospective study

Background

Deep learning is a type of artificial intelligence (AI) that imitates the neural network in the brain. We generated an AI to diagnose Helicobacter pylori (H. pylori) infection using blue laser imaging (BLI)-bright and linked color imaging (LCI). The aim of this pilot study was to establish an AI diagnosing system that predicts H. pylori infection status using endoscopic images to improve the accuracy and productivity of endoscopic examination.

Methods

A total of 222 enrolled subjects (105 H. pylori-positive) underwent esophagogastroduodenoscopy and a serum test for H. pylori IgG antibodies. During esophagogastroduodenoscopy, an endoscopist sequentially took 3 still images of the lesser curvature of the stomach using white light imaging (WLI), BLI-bright, and LCI. EG-L580NW endoscopic equipment (FUJIFILM Co., Japan) was used for the study. The specifications of the AI were as follows: operating system, Linux; neural network, GoogLeNet; framework, Caffe; graphic processor unit, Geforce GTX TITAN X (NVIDIA Co., USA).

Results

The area under the curve (AUC) on receiver operating characteristics analysis was 0.66 for WLI. In contrast, the AUCs of BLI-bright and LCI were 0.96 and 0.95, respectively. The AUCs obtained for BLI-bright and LCI were significantly larger than those for WLI (P<0.01).

Conclusions

The results demonstrate that the developed AI has an excellent ability to diagnose H. pylori infection using BLI-bright and LCI. AI technology with image-enhanced endoscopy is likely to become a useful image diagnostic tool.

Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images

BACKGROUND AND AIMS

The role of artificial intelligence in the diagnosis of Helicobacter pylori gastritis based on endoscopic images has not been evaluated. We constructed a convolutional neural network (CNN), and evaluated its ability to diagnose H. pylori infection.

METHODS

A 22-layer, deep CNN was pre-trained and fine-tuned on a dataset of 32,208 images either positive or negative for H. pylori (first CNN). Another CNN was trained using images classified according to 8 anatomical locations (secondary CNN). A separate test data set (11,481 images from 397 patients) was evaluated by the CNN, and 23 endoscopists, independently.

RESULTS

The sensitivity, specificity, accuracy, and diagnostic time were 81.9%, 83.4%, 83.1%, and 198s, respectively, for the first CNN, and 88.9%, 87.4%, 87.7%, and 194s, respectively, for the secondary CNN. These values for the 23 endoscopists were 79.0%, 83.2%, 82.4%, and 230±65min (85.2%, 89.3%, 88.6%, and 253±92min by 6 board-certified endoscopists), respectively. The secondary CNN had a significantly higher accuracy than endoscopists (by 5.3%; 95% CI, 0.3-10.2).

CONCLUSION

H. pylori gastritis could be diagnosed based on endoscopic images using CNN with higher accuracy and in a considerably shorter time compared to manual diagnosis by endoscopists.

Diagnostic Performance of Magnifying Endoscopy for Helicobacter pylori Infection: A Meta-Analysis

Background

Diagnosis of Helicobacter pylori (H. pylori) infection using magnifying endoscopy offers advantages over conventional invasive and noninvasive tests.

Objective

This meta-analysis aimed to assess the diagnostic performance of magnifying endoscopy in the prediction of H. pylori infection.

Methods

A literature search of the PubMed, Medline, EMBASE, Science Direct and the Cochrane Library databases was performed. A random-effects model was used to calculate the diagnostic efficiency of magnifying endoscopy for H. pylori infection. A summary receiver operator characteristic curve was plotted, and the area under the curve (AUC) was calculated.

Results

A total of 18 studies involving 1897 patients were included. The pooled sensitivity and specificity of magnifying endoscopy to predict H. pylori infection were 0.89 [95% confidence interval (CI) 0.87–0.91] and 0.82 (95%CI 0.79–0.85), respectively, with an AUC of 0.9461. When targeting the gastric antrum, the pooled sensitivity and specificity were 0.82 (95%CI 0.78–0.86) and 0.72 (95%CI 0.66–0.78), respectively. When targeting the gastric corpus, the pooled sensitivity and specificity were 0.92 (95%CI 0.90–0.94) and 0.86 (95%CI 0.82–0.88), respectively. The pooled sensitivity and specificity using magnifying white light endoscopy were 0.90 (95%CI 0.87–0.91) and 0.81 (95%CI 0.77–0.84), respectively. The pooled sensitivity and specificity using magnifying chromoendoscopy were 0.87 (95%CI 0.83–0.91) and 0.85 (95%CI 0.80–0.88), respectively. The “pit plus vascular pattern” classification in the gastric corpus observed by magnifying endoscopy was able to accurately predict the status of H. pylori infection, as indicated by a pooled sensitivity and specificity of 0.96 (95%CI 0.94–0.97) and 0.91 (95%CI 0.87–0.93), respectively, with an AUC of 0.9872.

Conclusions

Magnifying endoscopy was able to accurately predict the status of H. pylori infection, either in magnifying white light endoscopy or magnifying chromoendoscopy mode. The “pit plus vascular pattern” classification in the gastric corpus is an optimum diagnostic criterion.

Evidence based review of the impact of image enhanced endoscopy in the diagnosis of gastric disorders

Gastric cancer is the third most common cause of cancer-related death. Advanced stages of gastric cancers generally have grim prognosis. But, good prognosis can be achieved if such cancers are detected, diagnosed and resected at early stages. However, early gastric cancers and its precursors often produce only subtle mucosal changes and therefore quite commonly remain elusive at the conventional examination with white light endoscopy. Image-enhanced endoscopy makes mucosal lesions more conspicuous and can therefore potentially yield earlier and more accurate diagnoses. Recent years have seen growing work of research in support of various types of image enhanced endoscopy (IEE) techniques (e.g., dye-chromoendoscopy; magnification endoscopy; narrow-band imaging; flexible spectral imaging color enhancement; and I-SCAN) for a variety of gastric pathologies. In this review, we will examine the evidence for the utilization of various IEE techniques in the diagnosis of gastric disorders.

Diagnosis of Helicobacter pylori infection: Current options and developments

Accurate diagnosis of Helicobacter pylori (H. pylori) infection is a crucial part in the effective management of many gastroduodenal diseases. Several invasive and non-invasive diagnostic tests are available for the detection of H. pylori and each test has its usefulness and limitations in different clinical situations. Although none can be considered as a single gold standard in clinical practice, several techniques have been developed to give the more reliable results. Invasive tests are performed via endoscopic biopsy specimens and these tests include histology, culture, rapid urease test as well as molecular methods. Developments of endoscopic equipment also contribute to the real-time diagnosis of H. pylori during endoscopy. Urea breathing test and stool antigen test are most widely used non-invasive tests, whereas serology is useful in screening and epidemiological studies. Molecular methods have been used in variable specimens other than gastric mucosa. More than detection of H. pylori infection, several tests are introduced into the evaluation of virulence factors and antibiotic sensitivity of H. pylori, as well as screening precancerous lesions and gastric cancer. The aim of this article is to review the current options and novel developments of diagnostic tests and their applications in different clinical conditions or for specific purposes.

Magnifying endoscopy simple diagnostic algorithm for early gastric cancer (MESDA-G)

Gastric cancer is the third leading cause of cancer death worldwide. Early detection and accurate diagnosis of mucosal cancer is desirable in order to achieve decreased mortality; cause-specific survival of patients with early gastric cancer is reported to exceed 95%. Endoscopy is the functional modality to detect early cancer; however, the procedure is not definitive when using conventional white-light imaging. In contrast, magnifying narrow-band imaging (M-NBI), a novel endoscopic technology, is a powerful tool for characterizing gastric mucosal lesions because it can visualize the microvascular architecture and microsurface structure. To date, many reports on the diagnosis of early gastric cancer by M-NBI, including multicenter prospective randomized studies conducted in Japan, have been published in peer-reviewed international journals. Based on these published data, we devised a proposal for a diagnostic strategy for gastric mucosal cancer using M-NBI to simplify the process of diagnosis and improve accuracy. Herein, we recommend a diagnostic algorithm for early gastric cancer using magnifying endoscopy.

Endoscopic and histological features of gastric cancers after successful Helicobacter pylori eradication therapy

BACKGROUND

Gastric cancer after successful Helicobacter pylori eradication therapy is often difficult to diagnose by endoscopy because of its indistinct borderline or lack of obviously cancerous characteristics. Furthermore, it has become evident that non-neoplastic epithelium covers cancerous areas in gastric cancer after eradication. Here, we investigated these endoscopic features and their relationship to histological findings.

METHODS

We studied 24 and 47 gastric cancers in patients who had (eradication group) and had not (control group) undergone H. pylori eradication, respectively. A gastritis-like appearance revealed by conventional endoscopy was defined as a mucosal pattern with no marked difference from the surrounding non-cancerous area and that revealed by narrow-band imaging (NBI)-magnifying endoscopy (ME) as the mucosal pattern observed in H. pylori-associated atrophic gastritis. We investigated a gastritis-like appearance revealed by conventional endoscopy (A), a gastritis-like appearance at the margin (B) and within (C) the cancerous area revealed by NBI-ME, and the histological characteristics of the overlying non-neoplastic epithelium. We also evaluated the relationship between endoscopic and histological findings in the eradication group.

RESULTS

Endoscopy showed that features A, B and C were significantly more frequent in the eradication group (P = 0.031, P < 0.001, P < 0.001, respectively). Non-neoplastic epithelium covered more than 10 % of the cancerous area more frequently in the eradication group. In the eradication group, more than 90 % of cancers showing a gastritis-like appearance had non-neoplastic epithelium extending over 10 % of the cancerous area.

CONCLUSION

Gastric cancer after successful H. pylori eradication tends to have gastritis-like features due to non-neoplastic epithelium covering the cancerous tissue.

Helicobacter Pylori Infection: Diagnostic Strategies in Primary Diagnosis and After Therapy

Accurate diagnosis of Helicobacter pylori infection pre- and post-treatment is mandatory in the current era of decreasing prevalence and increasing antibiotic resistance. The diagnostic performance of most tests is poorer in clinical situations with low bacterial density which is seen in conditions such as atrophic gastritis or intake of antisecretory and antibiotic medications. Noninvasive tests require less cost and resource but provide excellent accuracy; however, endoscopy with testing of gastric biopsy specimens is indicated where alarming symptoms are present or antibiotic susceptibility testing by culture is desired. Newer modalities such as polymerase chain reaction testing provide additional virulence and antibiotic sensitivity profiling. This article outlines new developments and the key parameters of each test, as careful selection of test modality within the clinical context is required for adequate management of infected symptomatic patients.

Diagnosis of Helicobacter pylori infection: Current options and developments

Accurate diagnosis of Helicobacter pylori (H. pylori) infection is a crucial part in the effective management of many gastroduodenal diseases. Several invasive and non-invasive diagnostic tests are available for the detection of H. pylori and each test has its usefulness and limitations in different clinical situations. Although none can be considered as a single gold standard in clinical practice, several techniques have been developed to give the more reliable results. Invasive tests are performed via endoscopic biopsy specimens and these tests include histology, culture, rapid urease test as well as molecular methods. Developments of endoscopic equipment also contribute to the real-time diagnosis of H. pylori during endoscopy. Urea breathing test and stool antigen test are most widely used non-invasive tests, whereas serology is useful in screening and epidemiological studies. Molecular methods have been used in variable specimens other than gastric mucosa. More than detection of H. pylori infection, several tests are introduced into the evaluation of virulence factors and antibiotic sensitivity of H. pylori, as well as screening precancerous lesions and gastric cancer. The aim of this article is to review the current options and novel developments of diagnostic tests and their applications in different clinical conditions or for specific purposes.

Gastric cancer associated with pregnancy: A case report

In recent years, the incidence rate of malignant tumors associated with pregnancy has increased gradually due to an increase in childbearing age. This paper reports a rare case of gastric carcinoma during pregnancy. We also discuss and summarize the relationship between pregnancy and the pathogenesis of gastric cancer, medical examinations, diagnosis and treatment of this condition.

Diagnosis of Helicobacter pylori infection

The present manuscript focuses on the new information that was published in the field of diagnosis of Helicobacter pylori this past year. While there is little news about the invasive tests, more data are coming concerning the endoscopic features of H. pylori infection. Major efforts were also done to improve molecular detection of the mutations involved in antibiotic resistance. New antibody-based tests (stool antigen test or indirect antibody tests) were also developed.

Diagnosing Helicobacter pylori infection in vivo by novel endoscopic techniques

Infection with Helicobacter pylori (H. pylori) is a worldwide problem. Endoscopic observation of H. pylori infection in vivo would be helpful to obtain an immediate diagnosis. The aim of this review is to describe recent advances in endoscopic technology and to review the available literature pertaining to its clinical application in H. pylori infection. Endoscopic visualization of H. pylori infection is not always feasible using conventional endoscopy. Thus, advanced endoscopic techniques have been developed with the aim of providing a precise and ‘‘real-time’’ endoscopic diagnosis. Recently, new endoscopic techniques such as magnifying endoscopy, narrow band imaging, I-Scan, endocytoscopy and endomicroscopy help focus examination of the stomach to diagnose disease in a time-efficient manner, and the analysis of mucosal surface details is beginning to resemble histologic examination. The new detailed images have enabled endoscopists to observe microscopic structures, such as gastric pit patterns, microvessels and cell morphology. Accordingly, endoscopic prediction of H. pylori infection is possible by analysis of surface architecture of the mucosa, which influences the clinical management. These endoscopic techniques might lead us to easier diagnosis and treatment of H. pylori-related diseases.