Helicobacter pylori infection is one of the most common infectious diseases worldwide. Although the prevalence of H. pylori is gradually decreasing, approximately half of the world's population still becomes infected with this disease. H. pylori is responsible for substantial gastrointestinal morbidity worldwide, with a high disease burden. It is the most common cause of gastric and duodenal ulcers and gastric cancer. Since the revision of the H. pylori clinical practice guidelines in 2013 in Korea, the eradication rate of H. pylori has gradually decreased with the use of a clarithromycin-based triple therapy for 7 days. According to a nationwide randomized controlled study conducted by the Korean College of Helicobacter and Upper Gastrointestinal Research released in 2018, the intention-to-treat eradication rate was only 63.9%, which was mostly due to increased antimicrobial resistance, especially from clarithromycin. The clinical practice guidelines for the treatment of H. pylori were updated according to evidence-based medicine from a meta-analysis conducted on a target group receiving the latest level of eradication therapy. The draft recommendations developed based on the meta-analysis were finalized after an expert consensus on three recommendations regarding the indication for treatment and eight recommendations for the treatment itself. These guidelines were designed to provide clinical evidence for the treatment (including primary care treatment) of H. pylori infection to patients, nurses, medical school students, policymakers, and clinicians. These may differ from current medical insurance standards and will be revised if more evidence emerges in the future.

Background Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist’s role. The use of machine learning for the recognition and differentiation of images has been increasingly adopted in clinical practice. This study aimed to establish convolutional neural network (CNN) models to automatically classify gastric neoplasms based on endoscopic images. Methods Endoscopic white-light images of pathologically confirmed gastric lesions were collected and classified into five categories: advanced gastric cancer, early gastric cancer, high grade dysplasia, low grade dysplasia, and non-neoplasm. Three pretrained CNN models were fine-tuned using a training dataset. The classifying performance of the models was evaluated using a test dataset and a prospective validation dataset. Results A total of 5017 images were collected from 1269 patients, among which 812 images from 212 patients were used as the test dataset. An additional 200 images from 200 patients were collected and used for prospective validation. For the five-category classification, the weighted average accuracy of the Inception-Resnet-v2 model reached 84.6 %. The mean area under the curve (AUC) of the model for differentiating gastric cancer and neoplasm was 0.877 and 0.927, respectively. In prospective validation, the Inception-Resnet-v2 model showed lower performance compared with the endoscopist with the best performance (five-category accuracy 76.4 % vs. 87.6 %; cancer 76.0 % vs. 97.5 %; neoplasm 73.5 % vs. 96.5 %; P  < 0.001). However, there was no statistical difference between the Inception-Resnet-v2 model and the endoscopist with the worst performance in the differentiation of gastric cancer (accuracy 76.0 % vs. 82.0 %) and neoplasm (AUC 0.776 vs. 0.865). Conclusion The evaluated deep-learning models have the potential for clinical application in classifying gastric cancer or neoplasm on endoscopic white-light images.

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ObjectiveThe association between proton pump inhibitor (PPI) use and gastric cancer related to Helicobacter pylori eradication has not been fully investigated in geographical regions with high risk of gastric cancer. We aimed to evaluate the association between PPIs and gastric cancer in Korea.DesignThis study analysed the original and common data model versions of the Korean National Health Insurance Service database from 2002 to 2013. We compared the incidence rates of gastric cancer after 1-year drug exposure, between new users of PPIs and other drugs excluding PPIs, by Cox proportional hazards model. We also analysed the incidence of gastric cancer among PPI users after H. pylori eradication.ResultsThe analysis included 11 741 patients in matched PPI and non-PPI cohorts after large-scale propensity score matching. During a median follow-up of 4.3 years, PPI use was associated with a 2.37-fold increased incidence of gastric cancer (PPI≥30 days vs non-PPI; 118/51 813 person-years vs 40/49 729 person-years; HR 2.37, 95% CI 1.56 to 3.68, p=0.001). The incidence rates of gastric cancer showed an increasing trend parallel to the duration of PPI use. In H. pylori-eradicated subjects, the incidence of gastric cancer was significantly associated with PPI use over 180 days compared with the non-PPI group (PPI≥180 days vs non-PPI; 30/12 470 person-years vs 9/7814 person-years; HR 2.22, 95% CI 1.05 to 4.67, p=0.036).ConclusionPPI use was associated with gastric cancer, regardless of H. pylori eradication status. Long-term PPIs should be used with caution in high-risk regions for gastric cancer.