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: 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: 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.

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.

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: Delayed perforation is a rare but serious adverse event of gastric endoscopic submucosal dissection (ESD). The aim of this study was to clarify the clinical features and appropriate management strategy of patients with delayed perforation. Methods: Among 11,531 patients who underwent gastric ESD, the clinical features and outcomes of patients who experienced delayed perforation were retrospectively reviewed and compared with those of the control group. Results: Delayed perforation occurred in 15 of 11,531 patients (0.13%). The patients with delayed perforation were significantly older than those without delayed perforation (p=0.027). The median time to diagnosis of delayed perforation was 28.8 hours (range, 14 to 71 hours). All 15 patients with delayed perforation complained of severe abdominal pain after gastric ESD and underwent subsequent chest X-rays (CXRs) for evaluation. In subsequent CXR, free air was found in 12 patients (80%). For three (20%) patients without free air in CXR, delayed perforation was finally diagnosed by computed tomography. Leukocytosis was significantly less frequent in the patients without free air in CXR (p=0.022). A perforation hole smaller than 1 cm in size was more frequently observed in the six patients who underwent successful non-surgical treatments than in the nine patients who underwent surgery (p<0.001). There was no mortality related to delayed perforation. Conclusions One-fifth of the patients with delayed perforation did not show free air in CXR and exhibited less leukocytosis than those with free air. Non-surgical treatments including endoscopic closure might be considered as an initial treatment modality for delayed perforation smaller than 1 cm.

Background/Aims: Lymphoepithelioma-like carcinoma (LELC) is a rare subtype of gastric cancer. We aimed to identify the clinicopathological features and rate of lymph node metastasis (LNM) to investigate the feasibility of endoscopic submucosal dissection for early gastric LELC confined to the mucosa or submucosa. Methods: We compared the clinicopathological characteristics of 116 early gastric LELC patients and 5,753 early gastric well- or moderately differentiated (WD or MD) tubular adenocarcinoma patients treated by gastrectomy. Results: Compared to WD or MD early gastric cancer (EGC) patients, early LELC patients were younger and had a higher prevalence of proximally located tumors. Despite more frequent deep submucosal invasion (86.2% vs 29.8%), lymphatic invasion was less frequent (6.0% vs 16.2%) in early LELC patients than in WD or MD EGC patients. Among tumors with deep submucosal invasion, the tumor size was smaller, lymphatic invasion was less frequent (6.0% vs 40.2%) and the rate of LNM was lower (10.0% vs 19.4%) in patients with LELC than in those with WD or MD EGC. The overall rate of LNM in early LELC patients was 8.6% (10/116). The risk of LNM in patients with mucosal, shallow submucosal invasive, or deep submucosal invasive LELC was 0% (0/6), 0% (0/10), and 10% (10/100), respectively. Conclusions Early LELC is a distinct subtype of EGC with more frequent deep submucosal invasion but less lymphatic invasion and LNM than WD or MD EGCs. Endoscopic submucosal dissection may be considered curative for patients with early LELC confined to the mucosa or shallow submucosa, given its negligible rate of LNM.