1.Transradial Approach for Neurovascular Interventions : A Literature Review
Hoon KIM ; Young Woo KIM ; Hyeong Jin LEE ; Seon Woong CHOI ; Sunghan KIM ; Jae Sang OH ; Sang-Hyuk IM ; Jai Ho CHOI ; Seong-Rim KIM
Journal of Korean Neurosurgical Society 2025;68(2):113-126
		                        		
		                        			
		                        			 The femoral artery is the preferred access route for neurointerventions. The transfemoral approach (TFA) offers advantages such as a large diameter and easy access. However, it also entails disadvantages such as patient discomfort and high risk of complications. Following the initial report of coronary angiography using the transradial approach (TRA) in 1989, cardiologists discovered the advantages of TRA over the TFA and gradually replaced it with the TRA. In 1997, Matsumoto et al. used the TRA for cerebral angiography and neurointervention. Thereafter, the adoption of TRA for neurointervention gradually increased and good outcomes were reported. However, despite these developments, the adoption rate of TRA is relatively low. We reviewed the relevant studies to increase the accessibility of TRA for neurointerventionists. 
		                        		
		                        		
		                        		
		                        	
2.Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application
Jinyoung KIM ; Min-Hee KIM ; Dong-Jun LIM ; Hankyeol LEE ; Jae Jun LEE ; Hyuk-Sang KWON ; Mee Kyoung KIM ; Ki-Ho SONG ; Tae-Jung KIM ; So Lyung JUNG ; Yong Oh LEE ; Ki-Hyun BAEK
Endocrinology and Metabolism 2025;40(2):216-224
		                        		
		                        			 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. 
		                        		
		                        		
		                        		
		                        	
3.Transradial Approach for Neurovascular Interventions : A Literature Review
Hoon KIM ; Young Woo KIM ; Hyeong Jin LEE ; Seon Woong CHOI ; Sunghan KIM ; Jae Sang OH ; Sang-Hyuk IM ; Jai Ho CHOI ; Seong-Rim KIM
Journal of Korean Neurosurgical Society 2025;68(2):113-126
		                        		
		                        			
		                        			 The femoral artery is the preferred access route for neurointerventions. The transfemoral approach (TFA) offers advantages such as a large diameter and easy access. However, it also entails disadvantages such as patient discomfort and high risk of complications. Following the initial report of coronary angiography using the transradial approach (TRA) in 1989, cardiologists discovered the advantages of TRA over the TFA and gradually replaced it with the TRA. In 1997, Matsumoto et al. used the TRA for cerebral angiography and neurointervention. Thereafter, the adoption of TRA for neurointervention gradually increased and good outcomes were reported. However, despite these developments, the adoption rate of TRA is relatively low. We reviewed the relevant studies to increase the accessibility of TRA for neurointerventionists. 
		                        		
		                        		
		                        		
		                        	
4.Erratum: Korean Gastric Cancer Association-Led Nationwide Survey on Surgically Treated Gastric Cancers in 2023
Dong Jin KIM ; Jeong Ho SONG ; Ji-Hyeon PARK ; Sojung KIM ; Sin Hye PARK ; Cheol Min SHIN ; Yoonjin KWAK ; Kyunghye BANG ; Chung-sik GONG ; Sung Eun OH ; Yoo Min KIM ; Young Suk PARK ; Jeesun KIM ; Ji Eun JUNG ; Mi Ran JUNG ; Bang Wool EOM ; Ki Bum PARK ; Jae Hun CHUNG ; Sang-Il LEE ; Young-Gil SON ; Dae Hoon KIM ; Sang Hyuk SEO ; Sejin LEE ; Won Jun SEO ; Dong Jin PARK ; Yoonhong KIM ; Jin-Jo KIM ; Ki Bum PARK ; In CHO ; Hye Seong AHN ; Sung Jin OH ; Ju-Hee LEE ; Hayemin LEE ; Seong Chan GONG ; Changin CHOI ; Ji-Ho PARK ; Eun Young KIM ; Chang Min LEE ; Jong Hyuk YUN ; Seung Jong OH ; Eunju LEE ; Seong-A JEONG ; Jung-Min BAE ; Jae-Seok MIN ; Hyun-dong CHAE ; Sung Gon KIM ; Daegeun PARK ; Dong Baek KANG ; Hogoon KIM ; Seung Soo LEE ; Sung Il CHOI ; Seong Ho HWANG ; Su-Mi KIM ; Moon Soo LEE ; Sang Hyun KIM ; Sang-Ho JEONG ; Yusung YANG ; Yonghae BAIK ; Sang Soo EOM ; Inho JEONG ; Yoon Ju JUNG ; Jong-Min PARK ; Jin Won LEE ; Jungjai PARK ; Ki Han KIM ; Kyung-Goo LEE ; Jeongyeon LEE ; Seongil OH ; Ji Hun PARK ; Jong Won KIM ;
Journal of Gastric Cancer 2025;25(2):400-402
		                        		
		                        		
		                        		
		                        	
5.Korean Gastric Cancer AssociationLed Nationwide Survey on Surgically Treated Gastric Cancers in 2023
Dong Jin KIM ; Jeong Ho SONG ; Ji-Hyeon PARK ; Sojung KIM ; Sin Hye PARK ; Cheol Min SHIN ; Yoonjin KWAK ; Kyunghye BANG ; Chung-sik GONG ; Sung Eun OH ; Yoo Min KIM ; Young Suk PARK ; Jeesun KIM ; Ji Eun JUNG ; Mi Ran JUNG ; Bang Wool EOM ; Ki Bum PARK ; Jae Hun CHUNG ; Sang-Il LEE ; Young-Gil SON ; Dae Hoon KIM ; Sang Hyuk SEO ; Sejin LEE ; Won Jun SEO ; Dong Jin PARK ; Yoonhong KIM ; Jin-Jo KIM ; Ki Bum PARK ; In CHO ; Hye Seong AHN ; Sung Jin OH ; Ju-Hee LEE ; Hayemin LEE ; Seong Chan GONG ; Changin CHOI ; Ji-Ho PARK ; Eun Young KIM ; Chang Min LEE ; Jong Hyuk YUN ; Seung Jong OH ; Eunju LEE ; Seong-A JEONG ; Jung-Min BAE ; Jae-Seok MIN ; Hyun-dong CHAE ; Sung Gon KIM ; Daegeun PARK ; Dong Baek KANG ; Hogoon KIM ; Seung Soo LEE ; Sung Il CHOI ; Seong Ho HWANG ; Su-Mi KIM ; Moon Soo LEE ; Sang Hyun KIM ; Sang-Ho JEONG ; Yusung YANG ; Yonghae BAIK ; Sang Soo EOM ; Inho JEONG ; Yoon Ju JUNG ; Jong-Min PARK ; Jin Won LEE ; Jungjai PARK ; Ki Han KIM ; Kyung-Goo LEE ; Jeongyeon LEE ; Seongil OH ; Ji Hun PARK ; Jong Won KIM ; The Information Committee of the Korean Gastric Cancer Association
Journal of Gastric Cancer 2025;25(1):115-132
		                        		
		                        			 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. 
		                        		
		                        		
		                        		
		                        	
6.Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application
Jinyoung KIM ; Min-Hee KIM ; Dong-Jun LIM ; Hankyeol LEE ; Jae Jun LEE ; Hyuk-Sang KWON ; Mee Kyoung KIM ; Ki-Ho SONG ; Tae-Jung KIM ; So Lyung JUNG ; Yong Oh LEE ; Ki-Hyun BAEK
Endocrinology and Metabolism 2025;40(2):216-224
		                        		
		                        			 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. 
		                        		
		                        		
		                        		
		                        	
7.Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application
Jinyoung KIM ; Min-Hee KIM ; Dong-Jun LIM ; Hankyeol LEE ; Jae Jun LEE ; Hyuk-Sang KWON ; Mee Kyoung KIM ; Ki-Ho SONG ; Tae-Jung KIM ; So Lyung JUNG ; Yong Oh LEE ; Ki-Hyun BAEK
Endocrinology and Metabolism 2025;40(2):216-224
		                        		
		                        			 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. 
		                        		
		                        		
		                        		
		                        	
8.Erratum: Korean Gastric Cancer Association-Led Nationwide Survey on Surgically Treated Gastric Cancers in 2023
Dong Jin KIM ; Jeong Ho SONG ; Ji-Hyeon PARK ; Sojung KIM ; Sin Hye PARK ; Cheol Min SHIN ; Yoonjin KWAK ; Kyunghye BANG ; Chung-sik GONG ; Sung Eun OH ; Yoo Min KIM ; Young Suk PARK ; Jeesun KIM ; Ji Eun JUNG ; Mi Ran JUNG ; Bang Wool EOM ; Ki Bum PARK ; Jae Hun CHUNG ; Sang-Il LEE ; Young-Gil SON ; Dae Hoon KIM ; Sang Hyuk SEO ; Sejin LEE ; Won Jun SEO ; Dong Jin PARK ; Yoonhong KIM ; Jin-Jo KIM ; Ki Bum PARK ; In CHO ; Hye Seong AHN ; Sung Jin OH ; Ju-Hee LEE ; Hayemin LEE ; Seong Chan GONG ; Changin CHOI ; Ji-Ho PARK ; Eun Young KIM ; Chang Min LEE ; Jong Hyuk YUN ; Seung Jong OH ; Eunju LEE ; Seong-A JEONG ; Jung-Min BAE ; Jae-Seok MIN ; Hyun-dong CHAE ; Sung Gon KIM ; Daegeun PARK ; Dong Baek KANG ; Hogoon KIM ; Seung Soo LEE ; Sung Il CHOI ; Seong Ho HWANG ; Su-Mi KIM ; Moon Soo LEE ; Sang Hyun KIM ; Sang-Ho JEONG ; Yusung YANG ; Yonghae BAIK ; Sang Soo EOM ; Inho JEONG ; Yoon Ju JUNG ; Jong-Min PARK ; Jin Won LEE ; Jungjai PARK ; Ki Han KIM ; Kyung-Goo LEE ; Jeongyeon LEE ; Seongil OH ; Ji Hun PARK ; Jong Won KIM ;
Journal of Gastric Cancer 2025;25(2):400-402
		                        		
		                        		
		                        		
		                        	
9.Korean Gastric Cancer AssociationLed Nationwide Survey on Surgically Treated Gastric Cancers in 2023
Dong Jin KIM ; Jeong Ho SONG ; Ji-Hyeon PARK ; Sojung KIM ; Sin Hye PARK ; Cheol Min SHIN ; Yoonjin KWAK ; Kyunghye BANG ; Chung-sik GONG ; Sung Eun OH ; Yoo Min KIM ; Young Suk PARK ; Jeesun KIM ; Ji Eun JUNG ; Mi Ran JUNG ; Bang Wool EOM ; Ki Bum PARK ; Jae Hun CHUNG ; Sang-Il LEE ; Young-Gil SON ; Dae Hoon KIM ; Sang Hyuk SEO ; Sejin LEE ; Won Jun SEO ; Dong Jin PARK ; Yoonhong KIM ; Jin-Jo KIM ; Ki Bum PARK ; In CHO ; Hye Seong AHN ; Sung Jin OH ; Ju-Hee LEE ; Hayemin LEE ; Seong Chan GONG ; Changin CHOI ; Ji-Ho PARK ; Eun Young KIM ; Chang Min LEE ; Jong Hyuk YUN ; Seung Jong OH ; Eunju LEE ; Seong-A JEONG ; Jung-Min BAE ; Jae-Seok MIN ; Hyun-dong CHAE ; Sung Gon KIM ; Daegeun PARK ; Dong Baek KANG ; Hogoon KIM ; Seung Soo LEE ; Sung Il CHOI ; Seong Ho HWANG ; Su-Mi KIM ; Moon Soo LEE ; Sang Hyun KIM ; Sang-Ho JEONG ; Yusung YANG ; Yonghae BAIK ; Sang Soo EOM ; Inho JEONG ; Yoon Ju JUNG ; Jong-Min PARK ; Jin Won LEE ; Jungjai PARK ; Ki Han KIM ; Kyung-Goo LEE ; Jeongyeon LEE ; Seongil OH ; Ji Hun PARK ; Jong Won KIM ; The Information Committee of the Korean Gastric Cancer Association
Journal of Gastric Cancer 2025;25(1):115-132
		                        		
		                        			 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. 
		                        		
		                        		
		                        		
		                        	
10.Transradial Approach for Neurovascular Interventions : A Literature Review
Hoon KIM ; Young Woo KIM ; Hyeong Jin LEE ; Seon Woong CHOI ; Sunghan KIM ; Jae Sang OH ; Sang-Hyuk IM ; Jai Ho CHOI ; Seong-Rim KIM
Journal of Korean Neurosurgical Society 2025;68(2):113-126
		                        		
		                        			
		                        			 The femoral artery is the preferred access route for neurointerventions. The transfemoral approach (TFA) offers advantages such as a large diameter and easy access. However, it also entails disadvantages such as patient discomfort and high risk of complications. Following the initial report of coronary angiography using the transradial approach (TRA) in 1989, cardiologists discovered the advantages of TRA over the TFA and gradually replaced it with the TRA. In 1997, Matsumoto et al. used the TRA for cerebral angiography and neurointervention. Thereafter, the adoption of TRA for neurointervention gradually increased and good outcomes were reported. However, despite these developments, the adoption rate of TRA is relatively low. We reviewed the relevant studies to increase the accessibility of TRA for neurointerventionists. 
		                        		
		                        		
		                        		
		                        	
            
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