Background: Researchers have proposed that there is a potential link between folliclestimulating hormone (FSH) and cognitive function, yet the evidence remains inconclusive.The current study aims to identify the association between serum FSH levels and cognitive performance, and to examine whether this association varies by cognitive diagnosis, serum estradiol (E2) levels, or cognitive domain. Methods: This multicenter cross-sectional study used a clinical database comprising female visitors to memory clinics at three referral hospitals in Korea. Venous blood samples were collected to determine serum FSH and E2 concentrations via immunoradiometric assay.Cognitive performance was evaluated using either the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease or the Seoul Neuropsychological Screening Battery, while cognitive diagnoses were made via clinical diagnostic interviews. Results: Among the 159 participants (normal cognition [NC], n = 70; mild cognitive impairment [MCI], n = 52; Alzheimer’s disease [AD] dementia, n = 37), there were no significant differences in serum FSH levels associated with cognitive diagnosis. In women with NC, serum FSH levels were found to be positively correlated with cognitive performance in global cognition, nonverbal memory, and executive function, even after adjusting for serum E2 level and its interaction with serum FSH level. However, no significant correlations were observed in women with MCI and AD dementia. Conclusion The association between circulating FSH and cognition may be independent from circulating E2, but it may depend on disease progression or cognitive domains. This suggests a potential role of gonadotropin in cognitive decline in elderly women.

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: Researchers have proposed that there is a potential link between folliclestimulating hormone (FSH) and cognitive function, yet the evidence remains inconclusive.The current study aims to identify the association between serum FSH levels and cognitive performance, and to examine whether this association varies by cognitive diagnosis, serum estradiol (E2) levels, or cognitive domain. Methods: This multicenter cross-sectional study used a clinical database comprising female visitors to memory clinics at three referral hospitals in Korea. Venous blood samples were collected to determine serum FSH and E2 concentrations via immunoradiometric assay.Cognitive performance was evaluated using either the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease or the Seoul Neuropsychological Screening Battery, while cognitive diagnoses were made via clinical diagnostic interviews. Results: Among the 159 participants (normal cognition [NC], n = 70; mild cognitive impairment [MCI], n = 52; Alzheimer’s disease [AD] dementia, n = 37), there were no significant differences in serum FSH levels associated with cognitive diagnosis. In women with NC, serum FSH levels were found to be positively correlated with cognitive performance in global cognition, nonverbal memory, and executive function, even after adjusting for serum E2 level and its interaction with serum FSH level. However, no significant correlations were observed in women with MCI and AD dementia. Conclusion The association between circulating FSH and cognition may be independent from circulating E2, but it may depend on disease progression or cognitive domains. This suggests a potential role of gonadotropin in cognitive decline in elderly women.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

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: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

Background: Researchers have proposed that there is a potential link between folliclestimulating hormone (FSH) and cognitive function, yet the evidence remains inconclusive.The current study aims to identify the association between serum FSH levels and cognitive performance, and to examine whether this association varies by cognitive diagnosis, serum estradiol (E2) levels, or cognitive domain. Methods: This multicenter cross-sectional study used a clinical database comprising female visitors to memory clinics at three referral hospitals in Korea. Venous blood samples were collected to determine serum FSH and E2 concentrations via immunoradiometric assay.Cognitive performance was evaluated using either the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease or the Seoul Neuropsychological Screening Battery, while cognitive diagnoses were made via clinical diagnostic interviews. Results: Among the 159 participants (normal cognition [NC], n = 70; mild cognitive impairment [MCI], n = 52; Alzheimer’s disease [AD] dementia, n = 37), there were no significant differences in serum FSH levels associated with cognitive diagnosis. In women with NC, serum FSH levels were found to be positively correlated with cognitive performance in global cognition, nonverbal memory, and executive function, even after adjusting for serum E2 level and its interaction with serum FSH level. However, no significant correlations were observed in women with MCI and AD dementia. Conclusion The association between circulating FSH and cognition may be independent from circulating E2, but it may depend on disease progression or cognitive domains. This suggests a potential role of gonadotropin in cognitive decline in elderly women.