In the rapidly evolving healthcare environment, radiologists strive to establish their rightful place.Thus, there is a need for enhanced outpatient and clinical education within the Department of Radiology and exploration of its methodologies. Accordingly, the Korean Society of Radiology established a task force to investigate the clinical and outpatient practice status of radiologists overseas, current state of related education, involvement of other specialties in radiologic practices and education in Korea, and clinical and outpatient practice status among Korean radiologists. Furthermore, a survey on clinical competency enhancement was conducted among the members of the Korean Society of Radiology. These findings suggest the need for visibility and clinical competency enhancement in radiologists and methodologies for strengthening clinical competencies.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

In the rapidly evolving healthcare environment, radiologists strive to establish their rightful place.Thus, there is a need for enhanced outpatient and clinical education within the Department of Radiology and exploration of its methodologies. Accordingly, the Korean Society of Radiology established a task force to investigate the clinical and outpatient practice status of radiologists overseas, current state of related education, involvement of other specialties in radiologic practices and education in Korea, and clinical and outpatient practice status among Korean radiologists. Furthermore, a survey on clinical competency enhancement was conducted among the members of the Korean Society of Radiology. These findings suggest the need for visibility and clinical competency enhancement in radiologists and methodologies for strengthening clinical competencies.

Background and Objectives: The number of people with heart failure (HF) is increasing worldwide, and the social burden is increasing as HF has high mortality and morbidity. We aimed to provide updated trends on the epidemiology of HF in Korea to shape future social measures against HF. Methods: We used the National Health Information Database of the National Health Insurance Service to determine the prevalence, incidence, hospitalization rate, mortality rate, comorbidities, in-hospital mortality, and healthcare cost of patients with HF from 2002 to 2020 in Korea. Results: The prevalence of HF in the total Korean population rose from 0.77% in 2002 to 2.58% (1,326,886 people) in 2020. Although the age-standardized incidence of HF decreased over the past 18 years, the age-standardized prevalence increased. In 2020, the hospitalization rate for any cause in patients with HF was 1,166 per 100,000 persons, with a steady increase from 2002. In 2002, the HF mortality was 3.0 per 100,000 persons, which rose to 15.6 per 100,000 persons in 2020. While hospitalization rates and in-hospital mortality for patients with HF increased, the mortality rate for patients with HF did not (5.8% in 2020), and the one-year survival rate from the first diagnosis of HF improved. The total healthcare costs for patients with HF were approximately $2.4 billion in 2020, a 16-fold increase over the $0.15 billion in 2002. Conclusions The study’s results underscore the growing socioeconomic burden of HF in Korea, driven by an aging population and increasing HF prevalence.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Background: s/Aims: Reported incidence of extrahepatic bile duct cancer is higher in Asians than in Western populations. Korea, in particular, is one of the countries with the highest incidence rates of extrahepatic bile duct cancer in the world. Although research and innovative therapeutic modalities for extrahepatic bile duct cancer are emerging, clinical guidelines are currently unavailable in Korea. The Korean Society of Hepato-Biliary-Pancreatic Surgery in collaboration with related societies (Korean Pancreatic and Biliary Surgery Society, Korean Society of Abdominal Radiology, Korean Society of Medical Oncology, Korean Society of Radiation Oncology, Korean Society of Pathologists, and Korean Society of Nuclear Medicine) decided to establish clinical guideline for extrahepatic bile duct cancer in June 2021. Methods: Contents of the guidelines were developed through subgroup meetings for each key question and a preliminary draft was finalized through a Clinical Guidelines Committee workshop. Results: In November 2021, the finalized draft was presented for public scrutiny during a formal hearing. Conclusions The extrahepatic guideline committee believed that this guideline could be helpful in the treatment of patients.

Systemic sclerosis is an autoimmune disease characterized by inflammatory reactions and fibrosis. Myofibroblasts are considered therapeutic targets for preventing and reversing the pathogenesis of fibrosis in systemic sclerosis. Although the mechanisms that differentiate into myofibroblasts are diverse, transforming growth factor β (TGF-β) is known to be a key mediator of fibrosis in systemic sclerosis. This study investigated the effects of extracellular vesicles derived from human adipose stem cells (ASC-EVs) in an in vivo systemic sclerosis model and in vitro TGF-β1-induced dermal fibroblasts. The therapeutic effects of ASC-EVs on the in vivo systemic sclerosis model were evaluated based on dermal thickness and the number of α-smooth muscle actin (α-SMA)-expressing cells using hematoxylin and eosin staining and immunohistochemistry. Administration of ASC-EVs decreased both the dermal thickness and α-SMA expressing cell number as well as the mRNA levels of fibrotic genes, such as Acta2, Ccn2, Col1a1 and Comp. Additionally, we discovered that ASC-EVs can decrease the expression of α-SMA and CTGF and suppress the TGF-β pathway by inhibiting the activation of SMAD2 in dermal fibroblasts induced by TGF-β1. Finally, TGF-β1-induced dermal fibroblasts underwent selective death through ASC-EVs treatment. These results indicate that ASC-EVs could provide a therapeutic approach for preventing and reversing systemic sclerosis.