Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: Autoimmune gastritis (AIG) is a corpus-dominant atrophic gastritis in which patients are positive for antiparietal cell antibody (APCA) and/or anti-intrinsic factor antibody. The risk of developing gastric cancer in patients with AIG remains unclear, and reliable frequency data of AIG in patients with gastric cancer are lacking. Methods: We included 624 Korean patients with gastric tumors (612 gastric cancers and 12 neuroendocrine tumors) who had APCA results and were available for AIG evaluation. In patients with positive APCA results, endoscopy and histology findings were reviewed to diagnose AIG. Results: Of the 624 patients, 37 (5.9%) tested positive for APCA, and ultimately, 11 (1.8%) met the diagnostic criteria for AIG (5 both endoscopy and histology findings, 4 endoscopy-only findings, 2 histology-only findings). The frequency of AIG in patients with gastric cancer was 1.3% (8/612), and that in patients with neuroendocrine tumors was 25.0% (3/12). Of the 11 patients with AIG, serum Helicobacter pylori antibody was positive in six patients (54.5%), all of whom had gastric cancer. Histologically, three patients showed pure AIG, four patients exhibited concurrent AIG and H. pylori gastritis, and the findings for four were indefinite for AIG. The pepsinogen (PG) I levels and PG I/II ratio were significantly lower in patients with gastric cancer with AIG than in patients with gastric cancer without AIG (p=0.042 and p=0.016, respectively). Conclusions The frequency of AIG in gastric cancer patients was very low compared to that in patients with neuroendocrine tumors. Rather, concurrent AIG and H. pylori gastritis was common in patients with AIG with gastric cancer.

Background/Aims: Chronic gastroesophageal reflux disease (GERD) requires symptom relief and treatment of associated conditions. In this study, we aimed to compare anti-reflux mucosectomy (ARMS) and Stretta radiofrequency (SRF) for treating patients with chronic GERD who are unresponsive to proton pump inhibitors (PPIs) and to identify the indications for each procedure. Methods: Data of patients who underwent ARMS or SRF between March 2021 and April 2023 were analyzed. Changes in GERD questionnaire (GERDQ) scores, endoscopic Los Angeles (LA) grade, flap valve grade (FVG) based on Hill’s type, EndoFLIP distensibility index (DI), endoscopic Barrett’s epithelium (BE) resolution rate, and PPI withdrawal rate were compared between the two groups. Results: Improvements in the GERDQ scores and PPI withdrawal rates were similar between the groups. The ARMS group showed significantly better changes in endoscopic LA grade, FVG, and EndoFLIP DI than the SRF group. The complications were more prevalent in the ARMS group than in the SRF group. Conclusions The change in endoscopic LA grade before and after the procedure was significantly higher in the ARMS group than in the SRF group. Significant improvements in endoscopic FVG, BE resolution, and EndoFLIP DI were observed only with the ARMS group.

Purpose: Pulmonary tumor thrombotic microangiopathy (PTTM) is a fatal complication of gastric cancer (GC). This study aimed to evaluate the clinical characteristics, outcomes, and immunohistochemical profiles of patients with GC-induced PTTM. Materials and Methods: From 2011 to 2023, 8 patients were clinically diagnosed with PTTM associated with GC antemortem. Clinical features and outcomes were reviewed, and immunohistochemical staining for c-erbB-2, MutL protein homolog 1, and programmed cell death ligand-1 was performed. Results: The median patient age was 56 years (range, 34–66 years). In all the patients, the tumors exhibited either ulceroinfiltrative or diffusely infiltrative gross morphology.The median tumor size was 5.8 cm (range, 2.0 cm–15.0 cm). Poorly differentiated adenocarcinoma was the most common histological type (6/8, 75%), followed by signet ring cell carcinoma (1/8, 12.5%) and moderately differentiated adenocarcinoma (1/8, 12.5%).Chest computed tomography revealed ground-glass opacities (7/8, 87.5%) or tree-in-bud signs (2/8, 25.0%) without definite evidence of pulmonary thromboembolism. Disseminated intravascular coagulation was present in 62.5% (5/8) of the patients diagnosed with PTTM.C-erbB-2 was positive in one patient (1/8, 12.5%). One patient who received palliative chemotherapy after developing PTTM survived for 35 days, whereas the other 7 patients who did not receive chemotherapy after developing PTTM survived for 7 days or less after PTTM diagnosis. Conclusions Most patients with GC-induced PTTM had an undifferentiated-type histology, infiltrative morphology, and extremely poor survival. Palliative chemotherapy may benefit patients with GC-induced PTTM; however, further studies are needed to explore the potential of targeted therapy in these patients.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: Autoimmune gastritis (AIG) is a corpus-dominant atrophic gastritis in which patients are positive for antiparietal cell antibody (APCA) and/or anti-intrinsic factor antibody. The risk of developing gastric cancer in patients with AIG remains unclear, and reliable frequency data of AIG in patients with gastric cancer are lacking. Methods: We included 624 Korean patients with gastric tumors (612 gastric cancers and 12 neuroendocrine tumors) who had APCA results and were available for AIG evaluation. In patients with positive APCA results, endoscopy and histology findings were reviewed to diagnose AIG. Results: Of the 624 patients, 37 (5.9%) tested positive for APCA, and ultimately, 11 (1.8%) met the diagnostic criteria for AIG (5 both endoscopy and histology findings, 4 endoscopy-only findings, 2 histology-only findings). The frequency of AIG in patients with gastric cancer was 1.3% (8/612), and that in patients with neuroendocrine tumors was 25.0% (3/12). Of the 11 patients with AIG, serum Helicobacter pylori antibody was positive in six patients (54.5%), all of whom had gastric cancer. Histologically, three patients showed pure AIG, four patients exhibited concurrent AIG and H. pylori gastritis, and the findings for four were indefinite for AIG. The pepsinogen (PG) I levels and PG I/II ratio were significantly lower in patients with gastric cancer with AIG than in patients with gastric cancer without AIG (p=0.042 and p=0.016, respectively). Conclusions The frequency of AIG in gastric cancer patients was very low compared to that in patients with neuroendocrine tumors. Rather, concurrent AIG and H. pylori gastritis was common in patients with AIG with gastric cancer.

Background/Aims: Chronic gastroesophageal reflux disease (GERD) requires symptom relief and treatment of associated conditions. In this study, we aimed to compare anti-reflux mucosectomy (ARMS) and Stretta radiofrequency (SRF) for treating patients with chronic GERD who are unresponsive to proton pump inhibitors (PPIs) and to identify the indications for each procedure. Methods: Data of patients who underwent ARMS or SRF between March 2021 and April 2023 were analyzed. Changes in GERD questionnaire (GERDQ) scores, endoscopic Los Angeles (LA) grade, flap valve grade (FVG) based on Hill’s type, EndoFLIP distensibility index (DI), endoscopic Barrett’s epithelium (BE) resolution rate, and PPI withdrawal rate were compared between the two groups. Results: Improvements in the GERDQ scores and PPI withdrawal rates were similar between the groups. The ARMS group showed significantly better changes in endoscopic LA grade, FVG, and EndoFLIP DI than the SRF group. The complications were more prevalent in the ARMS group than in the SRF group. Conclusions The change in endoscopic LA grade before and after the procedure was significantly higher in the ARMS group than in the SRF group. Significant improvements in endoscopic FVG, BE resolution, and EndoFLIP DI were observed only with the ARMS group.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.