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.

OBJECTIVES: Unequal access to cancer clinical trials is an important issue, given the potential benefits of participation for cancer patients. We evaluated regional disparities in access to cancer clinical trials in Korea. METHODS: From the Ministry of Food and Drug Safety database, we extracted 2,465 records of all cancer clinical trials approved between January 2012 and April 2023. To measure disparities in cancer clinical trial access, we calculated the ratio of clinical trials open to non-capital areas relative to those open to capital areas. We then analyzed temporal trends in this ratio, which we termed the trial geographical equity index (TGEI). RESULTS: Disparities in access to cancer clinical trials, as indicated by the TGEI, did not significantly improve during the study period (regression coefficient, 0.002; p=0.59). However, for phase II/III trials sponsored by global pharmaceutical companies, the TGEI improved significantly (regression coefficient, 0.021; p<0.01). In contrast, the TGEI deteriorated for trials initiated by investigators or those testing domestically developed therapeutics (regression coefficient, -0.015; p=0.05). Furthermore, the increasing trend of TGEI for phase II/III trials sponsored by global companies began to reverse after 2019, coinciding with the outbreak of coronavirus disease 2019 (COVID-19). CONCLUSIONS Over the past decade, access to cancer clinical trials has improved in Korea, particularly for phase II/III trials evaluating therapeutics from global companies. However, this increase in accessibility has not extended to trials initiated by investigators or those assessing domestically developed therapeutics. Additionally, the impact of COVID-19 on disparities in clinical trial access should be closely monitored.

PURPOSE: We investigated the effect of the protease inhibitor, aprotinin, on mean arterial pressure (MAP), hematocrit (Hct), blood loss, and survival rate in rats with experimental splenic injury. METHODS: We created an experimental splenic injury model in anesthetized rats by cutting the splenic parenchyma into three fragments. We analyzed the effect of aprotinin on three different treatment groups. The aprotinin treatment group received a single dose of 30,000 U/kg of aprotinin in 10 ml/kg normal saline, the tranexamic acid group was treated with a single dose of 100 mg/kg of tranexamic acid in 10ml/kg normal saline, and the saline control group was treated with only 10 ml/kg normal saline. In addition, a sham-operated group (laparotomy without splenectomy) was treated with 10 ml/kg normal saline. RESULTS: MAP was higher in the sham-operated group and the aprotinin group than in the other groups. There were no significant differences for hematocrit except that the saline group exhibited a lower level than the other groups at the six-hour time point. The amount of intraperitoneal blood loss in the sham-operated and aprotinin groups due to splenic injury was significantly lower than in the tranexamic acid and saline groups. The survival rate in the aprotinin group was similar to the tranexamic acid group, but, the survival rate of the aprotinin-treated group was statistically higher than that of the saline control group. CONCLUSION: Hemodynamic changes resulting from splenic injury can be diminished by aprotinin treatment. Aprotinin could be considered in preference to other drugs as a first line treatment in hemodynamically unstable splenic injury patients.
Animals ; Aprotinin* ; Arterial Pressure ; Hematocrit ; Hemodynamics ; Hemoperitoneum ; Hemorrhage* ; Humans ; Protease Inhibitors ; Rats* ; Splenic Rupture ; Survival Rate ; Tranexamic Acid

Hydrothorax complicating continuous ambulatory peritoneal dialysis (CAPD) occurs approximately 2% of continuous ambulatory peritoneal dialysis. Management might begin with interruption of peritoneal dialysis for 2-6 weeks. But, approximately half of the patients failed to respond to the conservative approach, as thus some authors advocated the combined use of intrapleural sclerosing agents and discontinuation of CAPD. As a rule of thumb, a 10-day wait is recommended after pleurodesis before resuming CAPD. As opposed to closed pleurodesis, the diaphragmatic defects can be identified and repaired under direct vision with surgical approach. But, limited eligibility of dialysis patients for open thoracostomy or video-assisted thoracostomy has been a major impediment for this definitive treatment of choice. From 1999 to 2003, among patients undergoing CAPD in Korea University Hospital, hydrothorax developed in four patients. Discontinuation of CAPD and conventional pleurodesis were performed. Three patients were treated successfully. A patient who interrupted peritoneal dialysis only for four days recurred after resuming CAPD. In conclusion, when pleural effusion complicates in a CAPD patient, chemical pleurodesis and cessation of CAPD during at least 10 days might be an initial tratement of choice.
Dialysis ; Humans ; Hydrothorax* ; Korea ; Peritoneal Dialysis* ; Peritoneal Dialysis, Continuous Ambulatory ; Pleural Effusion ; Pleurodesis* ; Sclerosing Solutions ; Thoracostomy ; Thumb

Purpose: Patient-derived tumor cells can be a powerful resource for studying pathophysiological mechanisms and developing robust strategies for precision medicine. However, establishing organoids from patient-derived cells is challenging because of limited access to tissue specimens. Therefore, we aimed to establish organoids from malignant ascites and pleural effusions. Materials and Methods: Ascitic or pleural fluid from pancreatic, gastric, and breast cancer patients was collected and concentrated to culture tumor cells ex vivo. Organoids were considered to be successfully cultured when maintained for five or more passages. Immunohistochemical staining was performed to compare the molecular features, and drug sensitivity was assayed to analyze the clinical responses of original patients. Results: We collected 70 fluid samples from 58 patients (pancreatic cancer, n=39; gastric cancer, n=21; and breast cancer, n=10). The overall success rate was 40%; however, it differed with types of malignancy, with pancreatic, gastric, and breast cancers showing 48.7%, 33.3%, and 20%, respectively. Cytopathological results significantly differed between successful and failed cases (p=0.014). Immunohistochemical staining of breast cancer organoids showed molecular features identical to those of tumor tissues. In drug sensitivity assays, pancreatic cancer organoids recapitulated the clinical responses of the original patients. Conclusion Tumor organoids established from malignant ascites or pleural effusion of pancreatic, gastric, and breast cancers reflect the molecular characteristics and drug sensitivity profiles. Our organoid platform could be used as a testbed for patients with pleural and peritoneal metastases to guide precision oncology and drug discovery.

Purpose: Li-Fraumeni syndrome (LFS) is a hereditary disorder caused by germline mutation in TP53. Owing to the rarity of LFS, data on its clinical features are limited. This study aimed to evaluate the clinical characteristics and prognosis of Korean patients with LFS. Materials and Methods: Patients who underwent genetic counseling and confirmed with germline TP53 mutation in the National Cancer Center in Korea between 2011 and 2022 were retrospectively reviewed. Data on family history with pedigree, types of mutation, clinical features, and prognosis were collected. Results: Fourteen patients with LFS were included in this study. The median age at diagnosis of the first tumor was 32 years. Missense and nonsense mutations were observed in 13 and one patients, respectively. The repeated mutations were p.Arg273His, p.Ala138Val, and pPro190Leu. The sister with breast cancer harbored the same mutation of p.Ala138Val. Seven patients had multiple primary cancers. Breast cancer was most frequently observed, and other types of tumor included sarcoma, thyroid cancer, pancreatic cancer, brain tumor, adrenocortical carcinoma, ovarian cancer, endometrial cancer, colon cancer, vaginal cancer, skin cancer, and leukemia. The median follow-up period was 51.5 months. Two and four patients showed local recurrence and distant metastasis, respectively. Two patients died of leukemia and pancreatic cancer 3 and 23 months after diagnosis, respectively. Conclusion This study provides information on different characteristics of patients with LFS, including types of mutation, types of cancer, and prognostic outcomes. For more appropriate management of these patients, proper genetic screening and multidisciplinary discussion are required.