There is a growing interest in research aimed at better understanding the disease status or predicting the prognosis of patients with simple blood tests associated with systemic inflammation. The neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), platelet-lymphocyte ratio (PLR), and mean platelet volume (MPV) can be used as factors to determine the prognosis of patients in various clinical situations. However, reference values for these attributes based on large, healthy populations have yet to be determined. From January 2014 to December 2016, data from routine blood analyses were collected from healthy patients in the checkup center of a tertiary hospital in Seoul, South Korea. Retrospective data review was then performed on an electronic medical record system. Data were treated anonymously as only age, sex, body mass index, medical history including cancer diagnosis, medications, and smoking status were considered. After the initial screen, we had a collection of 12,160 samples from patients without any medical history, including cancer treatment. This patient pool consisted of 6268 (51.5%, median age 47 years) and 5892 (48.5%, median age 46 years) male and female patients, respectively. The mean NLR across all ages was 1.65 (0.79), and the values for men and women were 1.63 (0.76) and 1.66 (0.82), respectively. The mean LMR, PLR, and MPV were 5.31 (1.68), 132.40 (43.68), and 10.02 (0.79), respectively. This study provides preliminary reference data on LMR, PLR, and MPV from different age and sex groups in South Korea. The results suggest that different cutoff values should be applied to the various patient populations.

Summary: Background: Tegoprazan is a novel potassium‐competitive acid blocker that has a fast onset of action and can control gastric pH for a prolonged period, which could offer clinical benefit in acid‐related disorders.Aim: To confirm the non‐inferiority of tegoprazan to esomeprazole in patients with erosive oesophagitis (EE).Methods: In this multicentre, randomised, double‐blind, parallel‐group comparison study, 302 Korean patients with endoscopically confirmed EE (Los Angeles Classification Grades A‐D) were randomly allocated to either tegoprazan (50 or 100 mg) or esomeprazole (40 mg) treatment groups for 4 or 8 weeks. The primary endpoint was the cumulative proportion of patients with healed EE confirmed by endoscopy up to 8 weeks from treatment initiation. Symptoms, safety and tolerability were also assessed.Results: The cumulative healing rates at week 8 were 98.9% (91/92), 98.9% (90/91) and 98.9% (87/88) for tegoprazan 50 mg, tegoprazan 100 mg and esomeprazole 40 mg, respectively. Both doses of tegoprazan were non‐inferior to esomeprazole 40 mg. The incidence of adverse events was comparable among the groups, and tegoprazan was well‐tolerated.Conclusion: Once daily administration of tegoprazan 50 or 100 mg showed non‐inferior efficacy in healing EE and tolerability to that of esomeprazole 40 mg.

In early gastric cancer (EGC), tumor invasion depth is an important factor for determining the treatment method. However, as endoscopic ultrasonography has limitations when measuring the exact depth in a clinical setting as endoscopists often depend on gross findings and personal experience. The present study aimed to develop a model optimized for EGC detection and depth prediction, and we investigated factors affecting artificial intelligence (AI) diagnosis. We employed a visual geometry group(VGG)-16 model for the classification of endoscopic images as EGC (T1a or T1b) or non-EGC. To induce the model to activate EGC regions during training, we proposed a novel loss function that simultaneously measured classification and localization errors. We experimented with 11,539 endoscopic images (896 T1a-EGC, 809 T1b-EGC, and 9834 non-EGC). The areas under the curves of receiver operating characteristic curves for EGC detection and depth prediction were 0.981 and 0.851, respectively. Among the factors affecting AI prediction of tumor depth, only histologic differentiation was significantly associated, where undifferentiated-type histology exhibited a lower AI accuracy. Thus, the lesion-based model is an appropriate training method for AI in EGC. However, further improvements and validation are required, especially for undifferentiated-type histology.

Background and study aims: The risk of post endoscopic submucosal dissection electrocoagulation syndrome (PEECS) is unknown. We aimed to investigate the incidence and clinicopathologic risk factors associated with PEECS after colorectal endoscopic submucosal dissection (ESD). Patients and methods: All patients treated with colorectal ESD between 2009 and 2011 by a single expert ESD endoscopist at Gangnam Severance Hospital, Seoul, Korea were included in this retrospective study. Patients who had fever, regional rebound tenderness, or marked leukocytosis after ESD were defined as having PEECS. Results: 89 patients were treated during the study period. Six patients with microperforation and one patient with overt perforation were excluded. Thus, 82 cases without perforation were analyzed. The risk of PEECS was 40.2 %. In the PEECS group, the mean size of resected specimens was larger and mean procedure time was longer than in the patients without PEECS. The risk of PEECS was significantly lower for patients with carcinoid tumors, and for ESD in the rectosigmoid area. Piecemeal resection was significantly associated with the development of PEECS. In multivariate analysis, lesion size larger than 3 cm (odds ratio [OR] 5.0, 95 % confidence interval [95 %CI] 1.2 – 21.7) and site other than rectosigmoid (OR 7.6, 95 %CI 2.1 – 27.9) were independent risk factors for PEECS. Conclusions: Large tumor size and tumor site other than rectosigmoid were independent risk factors related to PEECS. Patients with tumors larger than 3 cm, in colon areas other than the rectosigmoid, should be observed carefully after colorectal ESD.