Background/Aims: Endoscopic anti-reflux therapy is a therapeutic option for gastroesophageal reflux disease (GERD), providing durable effects. However, clinical data from Korea remain limited. This study evaluates the clinical outcomes of endoscopic radiofrequency Stretta therapy in Korean patients. Methods: A retrospective analysis was conducted on 71 patients with GERD who underwent Stretta therapy at 2 tertiary hospitals in Korea between November 2015 and July 2021. Clinical outcomes, including patient satisfaction, medication cessation or reduction, and complications, were evaluated. Pre- and post-procedural esophageal manometry and 24-hour pH monitoring test results were also analyzed. Results: Patient satisfaction rates at 1, 6, and 12 months post-procedure were 54.7% (35/64), 70.0% (28/40), and 75.0% (21/28), respectively. Medication cessation or reduction was achieved in 31.2% (20/64) at 1 month, 70.0% (28/40) at 6 months, and 67.9% (19/28) at 12 months. Esophageal manometry (n = 21) showed no significant changes in mean lower esophageal sphincter pressure (18.7 mmHg [2.5-52.9] vs 17.4 mmHg [0.0-43.0], P = 0.702) or mean integrated relaxation pressure (8.2 mmHg [0.0-28.0] vs 10.1 mmHg [0.0-31.0], P = 0.840). The 24-hour pH monitoring (n = 18) demonstrated a nonsignificant decrease in acid exposure time (pH < 4) from 2.3% (0.0-8.4) to 1.6% (0.0-7.3) (P = 0.182). Similarly, the DeMeester score decreased non-significantly from 8.4 (0.8-27.7) to 6.6 (0.8-21.8) (P = 0.352). No procedure-related complications occurred. Conclusion Endoscopic radiofrequency Stretta therapy appears to be a safe treatment option for GERD and may provide favorable patient satisfaction and medication reduction.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

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: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

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.

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: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Background/Aims: Tegoprazan is a novel potassium-competitive acid blocker that has beneficial effects on acid-related disorders such as gastroesophageal reflux and peptic ulcer diseases.This study aimed to validate the effect of tegoprazan on endoscopic submucosal dissection (ESD)-induced artificial ulcers. Methods: Patients from 16 centers in Korea who underwent ESD for gastric neoplasia were enrolled. After ESD, pantoprazole was administered intravenously for 48 hours. The patients were randomly allocated to either the tegoprazan or esomeprazole group. Tegoprazan 50 mg or esomeprazole 40 mg were administered for 4 weeks, after which gastroscopic evaluation was performed. If the artificial ulcer had not healed, the same dose of tegoprazan or esomeprazole was administered for an additional 4 weeks, and a gastroscopic evaluation was performed. Results: One hundred sixty patients were enrolled in this study. The healing rates of artificial ulcers at 4 weeks were 30.3% (23/76) and 22.1% (15/68) in the tegoprazan and esomeprazole groups, respectively (p=0.006). At 8 weeks after ESD, the cumulative ulcer healing rates were 73.7% (56/76) and 77.9% (53/68) in the tegoprazan and esomeprazole groups, respectively (p=0.210). Delayed bleeding occurred in two patients in the tegoprazan group (2.6%) and in one patient in the esomeprazole group (1.5%). Other adverse events were negligible in both groups. Conclusions Tegoprazan showed similar effects on post-ESD artificial ulcer healing in comparison with esomeprazole.