Purpose: To propose improvements for promoting eyeball donation and managing donated corneas, this study analyzed the current system in Korea and reviewed relevant Korean laws, international standards, and foreign practices. Methods: To understand the current situation in Korea, annual reports published by the Korean Network for Organ Sharing and existing Korean laws were examined. For the international context, references were made to the National Organ Transplant Act, 21 CFR Part 1271 (Code of Federal Regulations Title 21 Part 1271), Current Good Tissue Practice guidelines of the Food and Drug Administration, and the Medical Standards of the Eye Bank Association of America in the United States. Opinions on promoting eyeball donation, improving the monitoring system for donated corneas and revising laws related to cornea management were gathered. The perspectives of 31 experts affiliated with the Korea Cornea Society were collected through a survey. Results: Currently, there are no laws or regulations that can be appropriately applied to the cornea which has properties of both organs and tissue. Additionally, there is no law regulating imported corneas. Therefore, there is a need to legislate or revise the current law; all experts who conducted the survey agreed on this. Furthermore, the current system faces limitations in the efficient procurement, stable supply, and management of donor corneas, as well as in donation promotion. To address these issues, the establishment of independent legislation for managing donated corneas and a National Central Eye Bank was proposed. This central body would oversee continuous personnel training, education, and monitoring, along with ensuring stable procurement, processing, and supply of corneas within a structured management system. Sixty-eight percent of the surveyed experts agreed with this proposal. Conclusions To establish a safe and efficient Korean corneal supply and demand system, it is imperative to enact cornea-specific laws, including the establishment of a National Central Eye Bank.

Objectives: This study aimed to achieve expert consensus on menopause management in the Asia-Pacific region, taking into account patient diversity, the latest evidence, and current treatment options. Methods: A focused literature search was performed to identify clinical practice statements on menopause management. Menopause experts were nominated by members of the Asia-Pacific Menopause Federation (APMF) society. A modified Delphi methodology, involving iterative rounds of anonymous surveys, was employed until consensus was reached for each statement. Consensus was defined as ≥ 70% of experts voting ‘agree’ or ‘strongly agree’ for a given clinical practice statement. Results: A total of 39 participants from 14 different APMF member societies were involved. Eighty-five clinical practice statements reached a consensus. Based on the clinical practice statements, an algorithm was created as a tool to guide clinicians on menopause management. APMF experts agreed that, in addition to vasomotor symptoms, Asian women experiencing somatic or psychological symptoms may also benefit from treatment with menopausal hormone therapy (MHT). MHT should also be considered for the prevention of osteoporosis in asymptomatic peri- and postmenopausal women. Conclusions This APMF consensus statement supersedes the previous one published in 2008. It provides guidance to gynecologists, endocrinologists, family physicians, and other healthcare professionals in delivering optimal care to menopausal women in the ethnically and culturally diverse Asia-Pacific region.

Background: Cardiovascular diseases are the leading cause of mortality after liver transplantation (LT). Although the impact of secondary tricuspid regurgitation (TR) with severe pulmonary hypertension (PH) is well investigated, the impact of primary TR with tricuspid valve incompetence (TVI) on LT outcomes remains unclear. We aimed to investigate the prevalence and impact of primary TR with TVI on LT outcomes in a large-volume LT center. Methods: We retrospectively examined 5 512 consecutive LT recipients who underwent routine pretransplant echocardiography between 2008 and 2020. Patients were categorized based on the presence of anatomical TVI, specifically defined by incomplete coaptation, coaptation failure, prolapse, and flail leaflets of tricuspid valve (TV). Propensity score (PS)-based inverse probability weighting (IPW) was used to balance clinical and cardiovascular risk variables. The outcomes were one-year cumulative all-cause mortality and 30-day major adverse cardiovascular events (MACE). Results: Anatomical TVI was identified in 14 patients (0.3%). Although rare, these patients exhibited significantly lower post-LT one-year survival rates (64.3% vs. 91.5%, P < 0.001) and higher 30-day MACE rates (42.9% vs. 16.9%, P = 0.026) than patients without TVI. They also had worse survival irrespective of echocardiographic evidence of PH (P < 0.001) and exhibited higher one-year mortality (IPW-adjusted hazard ratio: 4.09, P = 0.002) and increased 30-day MACE rates (IPW-adjusted odds ratio: 1.24, P = 0.048). Conclusions Primary TR with anatomical TVI was associated with significantly reduced one-year survival and increased post-LT MACE rates. These patients should be prioritized similarly to those with secondary TR with severe PH, with appropriate pretransplant evaluations and treatments to improve survival outcomes.

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/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: 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/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.

Objectives: This study aimed to achieve expert consensus on menopause management in the Asia-Pacific region, taking into account patient diversity, the latest evidence, and current treatment options. Methods: A focused literature search was performed to identify clinical practice statements on menopause management. Menopause experts were nominated by members of the Asia-Pacific Menopause Federation (APMF) society. A modified Delphi methodology, involving iterative rounds of anonymous surveys, was employed until consensus was reached for each statement. Consensus was defined as ≥ 70% of experts voting ‘agree’ or ‘strongly agree’ for a given clinical practice statement. Results: A total of 39 participants from 14 different APMF member societies were involved. Eighty-five clinical practice statements reached a consensus. Based on the clinical practice statements, an algorithm was created as a tool to guide clinicians on menopause management. APMF experts agreed that, in addition to vasomotor symptoms, Asian women experiencing somatic or psychological symptoms may also benefit from treatment with menopausal hormone therapy (MHT). MHT should also be considered for the prevention of osteoporosis in asymptomatic peri- and postmenopausal women. Conclusions This APMF consensus statement supersedes the previous one published in 2008. It provides guidance to gynecologists, endocrinologists, family physicians, and other healthcare professionals in delivering optimal care to menopausal women in the ethnically and culturally diverse Asia-Pacific region.

Background: Cardiovascular diseases are the leading cause of mortality after liver transplantation (LT). Although the impact of secondary tricuspid regurgitation (TR) with severe pulmonary hypertension (PH) is well investigated, the impact of primary TR with tricuspid valve incompetence (TVI) on LT outcomes remains unclear. We aimed to investigate the prevalence and impact of primary TR with TVI on LT outcomes in a large-volume LT center. Methods: We retrospectively examined 5 512 consecutive LT recipients who underwent routine pretransplant echocardiography between 2008 and 2020. Patients were categorized based on the presence of anatomical TVI, specifically defined by incomplete coaptation, coaptation failure, prolapse, and flail leaflets of tricuspid valve (TV). Propensity score (PS)-based inverse probability weighting (IPW) was used to balance clinical and cardiovascular risk variables. The outcomes were one-year cumulative all-cause mortality and 30-day major adverse cardiovascular events (MACE). Results: Anatomical TVI was identified in 14 patients (0.3%). Although rare, these patients exhibited significantly lower post-LT one-year survival rates (64.3% vs. 91.5%, P < 0.001) and higher 30-day MACE rates (42.9% vs. 16.9%, P = 0.026) than patients without TVI. They also had worse survival irrespective of echocardiographic evidence of PH (P < 0.001) and exhibited higher one-year mortality (IPW-adjusted hazard ratio: 4.09, P = 0.002) and increased 30-day MACE rates (IPW-adjusted odds ratio: 1.24, P = 0.048). Conclusions Primary TR with anatomical TVI was associated with significantly reduced one-year survival and increased post-LT MACE rates. These patients should be prioritized similarly to those with secondary TR with severe PH, with appropriate pretransplant evaluations and treatments to improve survival outcomes.

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.