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.

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.

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.

Antiviral therapy is an essential treatment for chronic hepatitis B (CHB) infection. Although hypophosphatemia is an important adverse effect of antiviral agents, its clinical significance remains unclear. We investigated the incidence and clinical consequences of hypophosphatemia in a large cohort of CHB patients. Methods: This retrospective cohort study included CHB patients who started antiviral therapy between 2005 and 2015 and continued it for at least 1 year. Patients with decompensated liver cirrhosis, diabetes mellitus, hypertension, concomitant diuretic administration, and end-stage renal disease were excluded. The primary outcome was a change in renal function. Secondary outcomes included the incidence of infection and changes in serum potassium, uric acid, and total carbon dioxide (tCO2). Results: Among the 4,335 patients, hypophosphatemia developed in 75 (1.7%). During the median 2-year follow-up period, patients with hypophosphatemia showed a lower estimated glomerular filtration rate than those in the control group. The incidence of infection and changes in serum potassium, uric acid, and tCO2 were similar between groups. Conclusion: Hypophosphatemia was associated with a renal function decline in patients with CHB receiving antiviral therapy.

Purpose: Vascular endothelial growth factor tyrosine kinase inhibitors (TKIs) have been the standard of care for advanced and metastatic clear cell renal cell carcinoma (ccRCC). However, the therapeutic effect of TKI monotherapy remains unsatisfactory given the high rates of acquired resistance to TKI therapy despite favorable initial tumor response. Materials and Methods: To define the TKI-resistance mechanism and identify new therapeutic target for TKI-resistant ccRCC, an integrative differential gene expression analysis was performed using acquired resistant cohort and a public dataset. Sunitinib-resistant RCC cell lines were established and used to test their malignant behaviors of TKI resistance through in vitro and in vivo studies. Immunohistochemistry was conducted to compare expression between the tumor and normal kidney and verify expression of pathway-related proteins. Results: Integrated differential gene expression analysis revealed increased interferon-induced transmembrane protein 3 (IFITM3) expression in post-TKI samples. IFITM3 expression was increased in ccRCC compared with the normal kidney. TKI-resistant RCC cells showed high expression of IFITM3 compared with TKI-sensitive cells and displayed aggressive biologic features such as higher proliferative ability, clonogenic survival, migration, and invasion while being treated with sunitinib. These aggressive features were suppressed by the inhibition of IFITM3 expression and promoted by IFITM3 overexpression, and these findings were confirmed in a xenograft model. IFITM3-mediated TKI resistance was associated with the activation of TRAF6 and MAPK/AP-1 pathways. Conclusions These results demonstrate IFITM3-mediated activation of the TRAF6/MAPK/AP-1 pathways as a mechanism of acquired TKI resistance, and suggest IFITM3 as a new target for TKI-resistant ccRCC.

Background/Aims: Combi-elastography is a B-mode ultrasound-based method in which two elastography modalities are utilized simultaneously to assess metabolic dysfunction-associated steatotic liver disease (MASLD). However, the performance of combi-elastography for diagnosing metabolic dysfunction-associated steatohepatitis (MASH) and determining fibrosis severity is unclear. This study compared the diagnostic performances of combi-elastography and vibrationcontrolled transient elastography (VCTE) for identifying hepatic steatosis, fibrosis, and high-risk MASH. Methods: Participants who underwent combi-elastography, VCTE, and liver biopsy were selected from a prospective cohort of patients with clinically suspected MASLD. Combi-elastographyrelated parameters were acquired, and their performances were evaluated using area under the receiver-operating characteristic curve (AUROC) analysis. Results: A total of 212 participants were included. The diagnostic performance for hepatic steatosis of the attenuation coefficient adjusted by covariates from combi-elastography was comparable to that of the controlled attenuation parameter measured by VCTE (AUROC, 0.85 vs 0.85; p=0.925). The performance of the combi-elastography-derived fibrosis index adjusted by covariates for diagnosing significant fibrosis was comparable to that of liver stiffness measured by VCTE (AUROC, 0.77 vs 0.80; p=0.573). The activity index from combi-elastography adjusted by covariates was equivalent to the FibroScan-aspartate aminotransferase score in diagnosing high-risk MASH among participants with MASLD (AUROC, 0.72 vs 0.74; p=0.792). Conclusions The performance of combi-elastography is similar to that of VCTE when evaluating histology of MASLD.

Background/Aims: Combi-elastography is a B-mode ultrasound-based method in which two elastography modalities are utilized simultaneously to assess metabolic dysfunction-associated steatotic liver disease (MASLD). However, the performance of combi-elastography for diagnosing metabolic dysfunction-associated steatohepatitis (MASH) and determining fibrosis severity is unclear. This study compared the diagnostic performances of combi-elastography and vibrationcontrolled transient elastography (VCTE) for identifying hepatic steatosis, fibrosis, and high-risk MASH. Methods: Participants who underwent combi-elastography, VCTE, and liver biopsy were selected from a prospective cohort of patients with clinically suspected MASLD. Combi-elastographyrelated parameters were acquired, and their performances were evaluated using area under the receiver-operating characteristic curve (AUROC) analysis. Results: A total of 212 participants were included. The diagnostic performance for hepatic steatosis of the attenuation coefficient adjusted by covariates from combi-elastography was comparable to that of the controlled attenuation parameter measured by VCTE (AUROC, 0.85 vs 0.85; p=0.925). The performance of the combi-elastography-derived fibrosis index adjusted by covariates for diagnosing significant fibrosis was comparable to that of liver stiffness measured by VCTE (AUROC, 0.77 vs 0.80; p=0.573). The activity index from combi-elastography adjusted by covariates was equivalent to the FibroScan-aspartate aminotransferase score in diagnosing high-risk MASH among participants with MASLD (AUROC, 0.72 vs 0.74; p=0.792). Conclusions The performance of combi-elastography is similar to that of VCTE when evaluating histology of MASLD.