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.

Purpose: Cancer survivors are at increased risk of diabetes mellitus (DM). Additionally, the prevalence of obesity, which is also a risk factor for DM, is increasing in cancer survivors. We investigated the associations between weight change after cancer diagnosis and DM risk. Materials and Methods: This retrospective cohort study used data from the Korean National Health Insurance Service. Participants who were newly diagnosed with cancer from 2010 to 2016 and received national health screening before and after diagnosis were included and followed until 2019. Weight change status after cancer diagnosis was categorized into four groups: sustained normal weight, obese to normal weight, normal weight to obese, or sustained obese. Cox proportional hazard analyses were performed to examine associations between weight change and DM. Results: The study population comprised 264,250 cancer survivors. DM risk was highest in sustained obese (adjusted hazard ratios [aHR], 2.17; 95% confidence interval [CI], 2.08 to 2.26), followed by normal weight to obese (aHR, 1.66; 95% CI, 1.54 to 1.79), obese to normal weight (aHR, 1.29; 95% CI, 1.21 to 1.39), and then sustained normal weight group (reference). In subgroup analyses according to cancer type, most cancers showed the highest risks in sustained obese group. Conclusion Obesity at any time point was related to increased DM risk, presenting the highest risk in cancer survivors with sustained obesity. Survivors who changed from obese to normal weight had lower risk than survivors with sustained obesity. Survivors who changed from normal weight to obese showed increased risk compared to those who sustained normal weight. Our finding supports the significance of weight management among cancer survivors.

Purpose: Cancer survivors are at increased risk of diabetes mellitus (DM). Additionally, the prevalence of obesity, which is also a risk factor for DM, is increasing in cancer survivors. We investigated the associations between weight change after cancer diagnosis and DM risk. Materials and Methods: This retrospective cohort study used data from the Korean National Health Insurance Service. Participants who were newly diagnosed with cancer from 2010 to 2016 and received national health screening before and after diagnosis were included and followed until 2019. Weight change status after cancer diagnosis was categorized into four groups: sustained normal weight, obese to normal weight, normal weight to obese, or sustained obese. Cox proportional hazard analyses were performed to examine associations between weight change and DM. Results: The study population comprised 264,250 cancer survivors. DM risk was highest in sustained obese (adjusted hazard ratios [aHR], 2.17; 95% confidence interval [CI], 2.08 to 2.26), followed by normal weight to obese (aHR, 1.66; 95% CI, 1.54 to 1.79), obese to normal weight (aHR, 1.29; 95% CI, 1.21 to 1.39), and then sustained normal weight group (reference). In subgroup analyses according to cancer type, most cancers showed the highest risks in sustained obese group. Conclusion Obesity at any time point was related to increased DM risk, presenting the highest risk in cancer survivors with sustained obesity. Survivors who changed from obese to normal weight had lower risk than survivors with sustained obesity. Survivors who changed from normal weight to obese showed increased risk compared to those who sustained normal weight. Our finding supports the significance of weight management among cancer survivors.

Purpose: Cancer survivors are at increased risk of diabetes mellitus (DM). Additionally, the prevalence of obesity, which is also a risk factor for DM, is increasing in cancer survivors. We investigated the associations between weight change after cancer diagnosis and DM risk. Materials and Methods: This retrospective cohort study used data from the Korean National Health Insurance Service. Participants who were newly diagnosed with cancer from 2010 to 2016 and received national health screening before and after diagnosis were included and followed until 2019. Weight change status after cancer diagnosis was categorized into four groups: sustained normal weight, obese to normal weight, normal weight to obese, or sustained obese. Cox proportional hazard analyses were performed to examine associations between weight change and DM. Results: The study population comprised 264,250 cancer survivors. DM risk was highest in sustained obese (adjusted hazard ratios [aHR], 2.17; 95% confidence interval [CI], 2.08 to 2.26), followed by normal weight to obese (aHR, 1.66; 95% CI, 1.54 to 1.79), obese to normal weight (aHR, 1.29; 95% CI, 1.21 to 1.39), and then sustained normal weight group (reference). In subgroup analyses according to cancer type, most cancers showed the highest risks in sustained obese group. Conclusion Obesity at any time point was related to increased DM risk, presenting the highest risk in cancer survivors with sustained obesity. Survivors who changed from obese to normal weight had lower risk than survivors with sustained obesity. Survivors who changed from normal weight to obese showed increased risk compared to those who sustained normal weight. Our finding supports the significance of weight management among cancer survivors.

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

Background: Hearing loss (HL) has been suggested to be associated with impaired microcirculation of the inner ear. This cross-sectional study aimed to evaluate an association between HL and serum lipid levels. Methods: The study comprised 10,356 Korean adults who participated in the fifth Korea National Health and Nutrition Examination Survey (2010–2012). We defined HL as the average hearing thresholds exceeding 25 dB at predetermined frequency levels by pure tone audiometry. Serum lipid levels were measured using an enzymatic assay. The associations between lipid levels and HL were evaluated using a multiple logistic regression model after adjusting for covariates including age, sex, hypertension, diabetes, smoking status, alcohol, physical activity, educational level, household income, and noise exposure. Stratified analyses were performed to examine the effect of the covariates on the association between lipid levels and HL. Results: The high-density lipoprotein cholesterol (HDL-C) level was inversely associated with high-frequency (HF)-HL, with an odds ratio (95% confidence interval) of 0.78 (0.64–0.96) for 1-mmol/L increase in the HDL-C level. Neither the triglyceride nor the low-density lipoprotein cholesterol level was associated with HF-HL. For low-frequency HL, association with any of the serum lipid components was absent. A stratified analysis showed that the inverse association between HDL-C levels and HF-HL was evident (P trend <0.05) in some subjects with specific characteristics such as older age (≥65 years), female sex, non-hypertensive state, and non-regular physical activity. However, a significant interaction between HDL-C levels and all of the stratified variables was absent (P for interaction >0.05). Conclusion The HDL-C level has a linear inverse association with the risk of HF-HL. Given the known protective role of HDL-C against atherosclerotic changes, this finding seems to support the concept of impaired microcirculation in the inner ear as a mechanism for HF-HL.