Purpose: The technique for transanal resection of rectal tumors has evolved from conventional methods to minimally invasive approaches. However, the research comparing long-term results between these approaches is limited. Methods: Between 2016 and 2022, a total of 133 patients who underwent transanal excision were analyzed. Patients were classified into 2 groups according to surgical approach: conventional transanal approach (CTA) and minimally invasive transanal approach (MTA). Medical records were analyzed to compare surgical and oncological outcomes between the 2 groups. Results: There were no significant differences observed in patient’s demographics and tumor characteristics, except the MTA group exhibited a statistically longer distance from the anal verge. Although statistical significance was not reached, the MTA group demonstrated a 100% margin-negative rate in contrast to the CTA group, which had worse outcomes for both margin status and fragmentation. Recurrence was observed only in the CTA group containing pT1 rectal cancer and grade 1 neuroendocrine tumor, with negative margins and no fragmentation. Conclusion The minimally invasive approach did not demonstrate statistical superiority but showed technical feasibility through the absence of margin-positive cases and the use of the clip handle method. Further studies are needed to validate these findings and assess broader applicability.

Purpose: The technique for transanal resection of rectal tumors has evolved from conventional methods to minimally invasive approaches. However, the research comparing long-term results between these approaches is limited. Methods: Between 2016 and 2022, a total of 133 patients who underwent transanal excision were analyzed. Patients were classified into 2 groups according to surgical approach: conventional transanal approach (CTA) and minimally invasive transanal approach (MTA). Medical records were analyzed to compare surgical and oncological outcomes between the 2 groups. Results: There were no significant differences observed in patient’s demographics and tumor characteristics, except the MTA group exhibited a statistically longer distance from the anal verge. Although statistical significance was not reached, the MTA group demonstrated a 100% margin-negative rate in contrast to the CTA group, which had worse outcomes for both margin status and fragmentation. Recurrence was observed only in the CTA group containing pT1 rectal cancer and grade 1 neuroendocrine tumor, with negative margins and no fragmentation. Conclusion The minimally invasive approach did not demonstrate statistical superiority but showed technical feasibility through the absence of margin-positive cases and the use of the clip handle method. Further studies are needed to validate these findings and assess broader applicability.

Purpose: The technique for transanal resection of rectal tumors has evolved from conventional methods to minimally invasive approaches. However, the research comparing long-term results between these approaches is limited. Methods: Between 2016 and 2022, a total of 133 patients who underwent transanal excision were analyzed. Patients were classified into 2 groups according to surgical approach: conventional transanal approach (CTA) and minimally invasive transanal approach (MTA). Medical records were analyzed to compare surgical and oncological outcomes between the 2 groups. Results: There were no significant differences observed in patient’s demographics and tumor characteristics, except the MTA group exhibited a statistically longer distance from the anal verge. Although statistical significance was not reached, the MTA group demonstrated a 100% margin-negative rate in contrast to the CTA group, which had worse outcomes for both margin status and fragmentation. Recurrence was observed only in the CTA group containing pT1 rectal cancer and grade 1 neuroendocrine tumor, with negative margins and no fragmentation. Conclusion The minimally invasive approach did not demonstrate statistical superiority but showed technical feasibility through the absence of margin-positive cases and the use of the clip handle method. Further studies are needed to validate these findings and assess broader applicability.

Purpose: The technique for transanal resection of rectal tumors has evolved from conventional methods to minimally invasive approaches. However, the research comparing long-term results between these approaches is limited. Methods: Between 2016 and 2022, a total of 133 patients who underwent transanal excision were analyzed. Patients were classified into 2 groups according to surgical approach: conventional transanal approach (CTA) and minimally invasive transanal approach (MTA). Medical records were analyzed to compare surgical and oncological outcomes between the 2 groups. Results: There were no significant differences observed in patient’s demographics and tumor characteristics, except the MTA group exhibited a statistically longer distance from the anal verge. Although statistical significance was not reached, the MTA group demonstrated a 100% margin-negative rate in contrast to the CTA group, which had worse outcomes for both margin status and fragmentation. Recurrence was observed only in the CTA group containing pT1 rectal cancer and grade 1 neuroendocrine tumor, with negative margins and no fragmentation. Conclusion The minimally invasive approach did not demonstrate statistical superiority but showed technical feasibility through the absence of margin-positive cases and the use of the clip handle method. Further studies are needed to validate these findings and assess broader applicability.

Purpose: The technique for transanal resection of rectal tumors has evolved from conventional methods to minimally invasive approaches. However, the research comparing long-term results between these approaches is limited. Methods: Between 2016 and 2022, a total of 133 patients who underwent transanal excision were analyzed. Patients were classified into 2 groups according to surgical approach: conventional transanal approach (CTA) and minimally invasive transanal approach (MTA). Medical records were analyzed to compare surgical and oncological outcomes between the 2 groups. Results: There were no significant differences observed in patient’s demographics and tumor characteristics, except the MTA group exhibited a statistically longer distance from the anal verge. Although statistical significance was not reached, the MTA group demonstrated a 100% margin-negative rate in contrast to the CTA group, which had worse outcomes for both margin status and fragmentation. Recurrence was observed only in the CTA group containing pT1 rectal cancer and grade 1 neuroendocrine tumor, with negative margins and no fragmentation. Conclusion The minimally invasive approach did not demonstrate statistical superiority but showed technical feasibility through the absence of margin-positive cases and the use of the clip handle method. Further studies are needed to validate these findings and assess broader applicability.

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.