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.

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.

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.

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.

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.

Purpose: There is increasing interest in the efficacy of stereotactic ablative radiotherapy (SABR) for treating colorectal cancer (CRC) patients with oligometastases (OM), recently. The purpose of this meta-analysis was to evaluate local control (LC), progression-free survival (PFS), and overall survival (OS) of CRC patients with pulmonary OM treated with SABR and toxicities. Materials and Methods: Studies that reported SABR for CRC patients with pulmonary OM were searched from MEDLINE and Embase. Treatment outcomes including LC, PFS, OS, and toxicities of grade 3 or higher were assessed. Results: A total of 19 studies with 1,668 patients were chosen for this meta-analysis. Pooled 1-, 2-, and 3-year LC rates were 83.1%, 69.3%, and 63.9%, respectively. PFS rates were 44.8%, 26.5%, and 21.5% at 1, 2, and 3 years, respectively. OS rates at 1-, 2-, and 3-year were 87.5%, 69.9%, and 60.5%, respectively. The toxicity rate of grade 3 or higher was 3.6%. The effect of dose escalation was meta-analyzed using available studies. Conclusion Application of SABR to CRC patients with pulmonary OM achieved modest local control with acceptable toxicity according to the present meta-analysis. Further studies establishing the clinical efficacy of SABR are guaranteed.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Purpose: Foot pressure measurement devices are used widely in clinical settings for plantar pressure assessments. Despite the availability of various devices, studies evaluating the inter-device reliability are limited. This study compared plantar pressure measurements obtained from HR Mat (Tekscan Inc.) and EMED-n50 (Novel GmbH). Materials and Methods: The study involved 38 healthy male volunteers. The participants were categorized into two groups based on the Meary’s angle in standing foot lateral radiographs: those with normal feet (angles ranging from –4° to 4°) and those with mild flatfeet (angles from –8° to –15°). The static and dynamic plantar pressures of the participants were measured using HR Mat and EMED-n50.The reliability of the contact area and mean force was assessed using the interclass correlation coefficient (ICC). Furthermore, the differences in measurements between the two devices were examined, considering the presence of mild flatfoot. Results: The ICC values for the contact area and mean force ranged from 0.703 to 0.947, indicating good-to-excellent reliability across all areas. EMED-n50 tended to record higher contact areas than HR Mat. The mean force was significantly higher in the forefoot region when measured with EMED-n50, whereas, in the hindfoot region, this difference was observed only during static measurements with HR Mat. Participants with mild flatfeet exhibited significantly higher contact areas in the midfoot region for both devices, with no consistent differences in the other parameters. Conclusion The contact area and mean force measurements of the HR Mat and EMED-n50 showed high reliability. On the other hand, EMED-n50 tended to record higher contact areas than HR Mat. In cases of mild flatfoot, an increase in contact area within the midfoot region was observed, but no consistent impact on the differences between the two devices was evident.

Purpose: This study aimed to analyze the treatment outcomes of combined definitive radiation therapy (RT) and androgen deprivation therapy (ADT) for clinically node-positive prostate cancer. Materials and Methods: Medical records of 60 patients with clinically suspected metastatic lymph nodes on radiological examination were retrospectively analyzed. Eight patients (13.3%) were suspected to have metastatic common iliac or para-aortic lymph nodes. All patients underwent definitive RT with a dose fractionation of 70 Gy in 28 fractions. ADT was initiated 2–3 months before RT and continued for at least 2 years. Biochemical failure rate (BFR), clinical failure rate (CFR), overall survival (OS), and prostate cancer-specific survival (PCSS) were calculated, and genitourinary and gastrointestinal adverse events were recorded. Results: The median follow-up period was 5.47 years. The 5-year BFR, CFR, OS, and PCSS rates were 19.1%, 11.3%, 89.0%, and 98.2%, respectively. The median duration of ADT was 2.30 years. BFR and CFR increased after 3 years, and 11 out of 14 biochemical failures occurred after the cessation of ADT. Grade 2 and beyond late genitourinary and gastrointestinal toxicity rates were 5.0% and 13.3%, respectively. However, only two grade 3 adverse events were reported, and no grade 4–5 adverse events were reported. Patients with non-regional lymph node metastases did not have worse BFR, CFR, or adverse event rates. Conclusion This study reported the efficacy and tolerable toxicity of hypofractionated definitive RT combined with ADT for clinically node-positive prostate cancer. Additionally, selected patients with adjacent non-regional lymph node metastases might be able to undergo definitive RT combined with ADT.