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

Diagnostic Accuracy of Narrow-Band Imaging in Predicting Helicobacter pylori Gastritis in Patients With Dyspepsia

Background Helicobacter pylori (H. pylori) is one of the most prevalent causes of chronic gastritis that can lead to gastric cancer if left untreated. Currently, endoscopy and histology are the gold standard tests for the diagnosis of H. pylori gastritis. Recently, studies have shown the utility of narrow-band imaging (NBI) in predicting H. pylori gastritis. Therefore, we aimed to determine the diagnostic accuracy of NBI in predicting H. pylori gastritis in patients with dyspepsia. Methodology After obtaining approval from the Ethical Review Committee, Sindh Institute of Urology and Transplantation, this cross-sectional study was conducted in the outpatient Clinic of Hepatogastroenterology of the institute. Inclusion criteria involved all patients of either gender aged 18 to 65 years with dyspeptic symptoms. We excluded patients with a history of proton pump inhibitor use within two weeks before endoscopy, heart failure, previous gastrectomy, portal gastropathy, cirrhosis, use of antiplatelet medications, non-steroidal anti-inflammatory drugs or anticoagulant medication, and hemorrhagic or thrombophilia disorders. Each patient underwent endoscopy-guided NBI studies followed by biopsies from the antrum and body of the stomach. Multivariate logistic regression analysis was performed for the type of NBI pattern predicting H. pylori infection. The diagnostic accuracy was obtained individually for each NBI type and then for the presence of either two or all three NBI types in predicting H. pylori gastritis. Results Out of the total 775 patients enrolled in the study, abnormal NBI patterns were observed in 401 (51.7%) patients. The presence of abnormal NBI antral mucosal pattern on endoscopy was significantly associated with H. pylori infection (p < 0.001) with excellent diagnostic accuracy. Among the three NBI types, individually, NBI type III had excellent specificity and better diagnostic accuracy in predicting H. pylori gastritis than the other two types. Furthermore, the presence of all three abnormal NBI patterns (I+II+III) together was significantly associated with the presence of H. pylori gastritis with a sensitivity of 94.54%, specificity of 86.55%, and diagnostic accuracy of 90.32%. Conclusions NBI on endoscopy shows excellent diagnostic accuracy in identifying H. pylori gastritis in patients with dyspepsia. However, multicenter studies are required not only to validate our results but also to predict the pre-cancerous lesions on NBI in patients with H. pylori gastritis.

Simultaneous detection of Helicobacter pylori infection comparing between white light and image-enhanced endoscopy

BACKGROUND

Helicobacter pylori (H. pylori) is associated with gastric cancer. Early and accurate diagnosis of H. pylori infection can reduce risk of gastric cancer. Conventional white light imaging (WLI) and image-enhanced endoscopic (IEE) techniques such as narrow-band imaging (NBI), linked color imaging (LCI) and blue laser imaging (BLI) plays pivotal role in H. pylori diagnosis. This study aimed to determine diagnostic performance of real-time endoscopy between WLI and other IEE techniques for diagnosis of H. pylori infection.

METHODS

This prospective study compared endoscopic images by gastroscopy using WLI and IEE techniques (LCI, Magnifying-BLI, and Magnifying-NBI) at Thammasat University Hospital, Thailand between January 2020, and July 2021. All participants underwent gastroscopy. Three biopsies at gastric antrum and two biopsies at body were obtained for H.pylori diagnosis. H. pylori infection was defined as a positive test of either one of the following tests: rapid urease test, histopathology, H. pylori culture.

RESULTS

Of 167 dyspeptic patients undergoing gastroscopy, 100 were enrolled in this study. Overall H. pylori infection was 40%. Patients had the mean age of 59.1 years and 53% were males. Enlarged gastric folds and antral nodularity can predict H. pylori infection with 100% PPV, while fundic gland polyps and red streak provided 100% PPV for exclusion of H. pylori infection on WLI. Sensitivity, specificity, PPV, NPV and accuracy for diagnosis of H. pylori infection for WLI were 80%, 71.7%, 65.3%, 84.3% and 75% respectively, while those for LCI were 90%, 70%, 66.7%, 91.3% and 78% respectively. M-NBI and M-BLI endoscopy demonstrated elongated pits in H. pylori-positive patients. Sensitivity, specificity, PPV, NPV and accuracy for M-BLI were 95%, 80%, 76%, 96% and 86% respectively, whereas those for M-NBI were 92.5%, 86.7%, 82.2%, 94.6% and 89% respectively. Sensitivity of M-BLI was better than WLI, while sensitivities of LCI and M-NBI were also numerically higher than WLI without statistical difference (M-BLI 95%vs.WLI 80%, p = 0.03; M-NBI 92.5%vs.WLI 80%, p = 0.13; LCI 90%vs.WLI 80%, p = 0.22). Sensitivities of all IEE modes were not different from one another (LCI 90%vs.M-BLI 95%, p = 0.50; LCI 90%vs.M-NBI 92.5%, p = 1.00, M-BLI 95%vs.M-NBI 92.5%, p = 1.00).

CONCLUSIONS

M-BLI significantly improved sensitivity of real-time endoscopic diagnosis of H. pylori infection compared with WLI. Enlarged gastric folds and antral nodularity could be reliable predictors for H. pylori infection, while fundic gland polyps and red streak could be important endoscopic findings for H. pylori-negative mucosa.

Clinical usefulness of linked color imaging in identifying Helicobacter pylori infection: A systematic review and meta-analysis

BACKGROUND

Accurate diagnosis of Helicobacter pylori (H. pylori) infection status is a crucial premise for eradication therapy, as well as evaluation of risk for gastric cancer. Recent progress on imaging enhancement endoscopy (IEE) made it possible to not only detect precancerous lesions and early gastrointestinal cancers but also to predict H. pylori infection in real time. As a novel IEE modality, linked color imaging (LCI) has exhibited its value on diagnosis of lesions of gastric mucosa through emphasizing minor differences of color tone.

AIM

To compare the efficacy of LCI for H. pylori active infection vs conventional white light imaging (WLI).

METHODS

PubMed, Embase, Embase and Cochrane Library were searched up to the end of April 11, 2022. The random-effects model was adopted to calculate the diagnostic efficacy of LCI and WLI. The calculation of sensitivity, specificity, and likelihood ratios were performed; symmetric receiver operator characteristic (SROC) curves and the areas under the SROC curves were computed. Quality of the included studies was chosen to assess using the quality assessment of diagnostic accuracy studies-2 tool.

RESULTS

Seven original studies were included in this study. The pooled sensitivity, specificity, positive likelihood rate, and negative likelihood rate of LCI for the diagnosis of H. pylori infection of gastric mucosa were 0.85 [95% confidence interval (CI): 0.76-0.92], 0.82 (95%CI: 0.78-0.85), 4.71 (95%CI: 3.7-5.9), and 0.18 (95%CI: 0.10-0.31) respectively, with diagnostic odds ratio = 26 (95%CI: 13-52), SROC = 0.87 (95%CI: 0.84-0.90), which showed superiority of diagnostic efficacy compared to WLI.

CONCLUSION

Our results showed LCI can improve efficacy of diagnosis on H. pylori infection, which represents a useful endoscopic evaluation modality for clinical practice.

Establishment of a modified Kyoto classification scoring model and its significance in the diagnosis of Helicobacter pylori current infection

BACKGROUND AND AIMS

We aimed to establish a modified model of the Kyoto classification score and verify its accuracy for predicting Helicobacter pylori (HP) infection during endoscopy.

METHODS

Patients who underwent gastroscopy from June 2020 to March 2021 were included in this study. Atrophy, intestinal metaplasia, hypertrophy of the gastric fold, nodularity, diffuse redness, sticky mucus, spotty redness, xanthoma, map-like redness, fundic gland polyp, and regular arrangement of collecting venules (RAC) were recorded according to the Kyoto classification of gastritis. The HP infection status of participants was determined by a ¹³C breath test, anti-HP antibody, and histopathologic hematoxylin and eosin staining. The modified Kyoto classification scoring model was established based on univariate analysis and logistic regression analysis. The modified scoring model was used to judge the status of HP infection in patients undergoing gastroscopy from July to September 2021 and to evaluate the accuracy of the prediction.

RESULTS

Of 667 participants in the derivation dataset, 326 cases had HP infection and 341 cases did not. Atrophy, hypertrophy of the gastric fold, nodularity, diffuse redness, sticky mucus, and spotty redness were associated with HP current infection. Thus, a new scoring model, termed the modified Kyoto classification scoring model, was constructed that included atrophy, hypertrophy of the gastric fold, nodularity, diffuse redness, sticky mucus, spotty redness, fundic gland polyp, and RAC as indicators. To test the model, 808 subjects, including 251 HP-positive patients, comprised the validation dataset.

CONCLUSIONS

The modified Kyoto classification scoring model improved the accuracy of endoscopic determination of HP current infection and has clinical application potential in the Chinese population.

An explainable artificial intelligence system for diagnosing Helicobacter Pylori infection under endoscopy: a case-control study

Background

Changes in gastric mucosa caused by Helicobacter pylori (H. pylori) infection affect the observation of early gastric cancer under endoscopy. Although previous researches reported that computer-aided diagnosis (CAD) systems have great potential in the diagnosis of H. pylori infection, their explainability remains a challenge.

Objective

We aim to develop an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) and giving diagnostic basis under endoscopy.

Design

A case-control study.

Methods

We retrospectively obtained 47,239 images from 1826 patients between 1 June 2020 and 31 July 2021 at Renmin Hospital of Wuhan University for the development of EADHI. EADHI was developed based on feature extraction combining ResNet-50 and long short-term memory networks. Nine endoscopic features were used for H. pylori infection. EADHI's performance was evaluated and compared to that of endoscopists. An external test was conducted in Wenzhou Central Hospital to evaluate its robustness. A gradient-boosting decision tree model was used to examine the contributions of different mucosal features for diagnosing H. pylori infection.

Results

The system extracted mucosal features for diagnosing H. pylori infection with an overall accuracy of 78.3% [95% confidence interval (CI): 76.2-80.3]. The accuracy of EADHI for diagnosing H. pylori infection (91.1%, 95% CI: 85.7-94.6) was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7-21.3) in internal test. And it showed a good accuracy of 91.9% (95% CI: 85.6-95.7) in external test. Mucosal edema was the most important diagnostic feature for H. pylori positive, while regular arrangement of collecting venules was the most important H. pylori negative feature.

Conclusion

The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs.

Plain language summary

An explainable AI system for Helicobacter pylori with good diagnostic performance Helicobacter pylori (H. pylori) is the main risk factor for gastric cancer (GC), and changes in gastric mucosa caused by H. pylori infection affect the observation of early GC under endoscopy. Therefore, it is necessary to identify H. pylori infection under endoscopy. Although previous research showed that computer-aided diagnosis (CAD) systems have great potential in H. pylori infection diagnosis, their generalization and explainability are still a challenge. Herein, we constructed an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) using images by case. In this study, we integrated ResNet-50 and long short-term memory (LSTM) networks into the system. Among them, ResNet50 is used for feature extraction, LSTM is used to classify H. pylori infection status based on these features. Furthermore, we added the information of mucosal features in each case when training the system so that EADHI could identify and output which mucosal features are contained in a case. In our study, EADHI achieved good diagnostic performance with an accuracy of 91.1% [95% confidence interval (CI): 85.7-94.6], which was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7-21.3%) in internal test. In addition, it showed a good diagnostic accuracy of 91.9% (95% CI: 85.6-95.7) in external tests. The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. However, we only used data from a single center to develop EADHI, and it was not effective in identifying past H. pylori infection. Future, multicenter, prospective studies are needed to demonstrate the clinical applicability of CADs.

Kyoto classification of gastritis: Advances and future perspectives in endoscopic diagnosis of gastritis

This editorial provides an update of the recent evidence on the endoscopy-based Kyoto classification of gastritis, clarifying the shortcomings of the Kyoto classification, and providing prospects for future research, with particular focus on the histological subtypes of gastric cancer (GC) and Helicobacter pylori (H. pylori) infection status. The total Kyoto score is designed to express GC risk on a score ranging from 0 to 8, based on the following five endoscopic findings: Atrophy, intestinal metaplasia (IM), enlarged folds (EF), nodularity, and diffuse redness (DR). The total Kyoto score reflects H. pylori status as follows: 0, ≥ 2, and ≥ 4 indicate a normal stomach, H. pylori-infected gastritis, and gastritis at risk for GC, respectively. Regular arrangement of collecting venules (RAC) predicts non-infection; EF, nodularity, and DR predict current infection; map-like redness (MLR) predicts past infection; and atrophy and IM predict current or past infection. Atrophy, IM, and EF all increase the incidence of H. pylori-infected GC. MLR is a specific risk factor for H. pylori-eradicated GC, while RAC results in less GC. Diffuse-type GC can be induced by active inflammation, which presents as EF, nodularity, and atrophy on endoscopy, as well as neutrophil and mononuclear cell infiltration on histology. In contrast, intestinal-type GC develops via atrophy and IM, and is consistent between endoscopy and histology. However, this GC risk-scoring design needs to be improved.

A prospective study for evaluating the effect of gastric targeted biopsy sampling with I‐scan optical enhancement on the diagnostic yield of CLOtest for Helicobacter pylori infection

Background and Aim of the Work

Helicobacter pylorigastritis can cause serious adverse effects in the short and long term. I‐scan optical enhancement (OE) has a potential role to distinguish areas of infected mucosa and allow for targeted biopsy. It improves visual contrast and mucosal pattern characterization. The work aims to determine if the diagnostic yield of the CLOtest could be improved by using endoscopic I‐scan OE technology for targeted gastric biopsy sampling.

Patients and Methods

A prospective study recruited 112 adult patients with active H. pylori infection diagnosed by C¹³ UBT at Nizwa General Hospital from March 2021 to January 2022. The patients underwent a careful examination by nonmagnifying upper endoscopy and I‐scan OE 3 moods, then randomly allocated into two groups. Group A: nontargeted double biopsies from the antrum and mid corpus. Group B: I‐scan OE‐directed targeted biopsy from abnormal mucosal patterns. The biopsy specimens were inoculated into CLOtest kits; the reading time of the positive results was at 1, 4, and 24 h.

Results

Group B had a 92.8% positive CLOtest compared to 89.3% in group A (p = 0.501). One‐hour CLOtest was positive in 78.5% of the patients in group B compared to 60.7% in group A (p = 0.047), while group A had a significantly more positive CLOtest at 24 h.

Conclusion

Sampling a targeted gastric biopsy with the aid of I‐scan ‐OE for CLOtest significantly hastens the positive reading time with high sensitivity.

Consistency between the endoscopic Kyoto classification and pathological updated Sydney system for gastritis: A cross-sectional study

BACKGROUND

Two methods are used to evaluate gastritis: the updated Sydney system (USS) with pathology and Kyoto classification, a new endoscopy-based diagnostic criterion for which evidence is accumulating. However, the consistency of their results is unclear. This study investigated the consistency of their results.

METHODS

Patients who underwent esophagogastroduodenoscopy and were evaluated for Helicobacter pylori infection for the first time were eligible. The association between corpus and antral USS scores (neutrophil activity, chronic inflammation, atrophy, and intestinal metaplasia) and Kyoto classification scores (atrophy, intestinal metaplasia, enlarged folds, nodularity, and diffuse redness) was assessed.

RESULTS

Seven-hundred-seventeen patients (mean age, 49.2 years; female sex, 57.9%; 450 H. pylori-positive and 267 H. pylori-negative patients) were enrolled. All endoscopic gastritis cases in the Kyoto classification were associated with high corpus and antral USS scores for neutrophil activity and chronic inflammation. A subanalysis was performed for H. pylori-positive patients. Regarding atrophy and intestinal metaplasia, endoscopic findings were associated with USS scores. Enlarged folds, nodularity, and diffuse redness were associated with high corpus USS scores for neutrophil activity and chronic inflammation, but with low antral USS scores for atrophy and intestinal metaplasia. The Kyoto classification scores were also associated with the pathological topographic distribution of neutrophil activity and intestinal metaplasia.

CONCLUSIONS

Among H. pylori-positive individuals, endoscopic and pathological diagnoses were consistent with atrophy and intestinal metaplasia. Enlarged folds, nodularity, and diffuse redness were associated with pathological inflammation (neutrophil activity and chronic inflammation) of the corpus; however, they were inversely associated with pathological atrophy and intestinal metaplasia. The endoscopy-based Kyoto classification of gastritis partially reflects pathology.

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.