Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Purpose: We aimed to identify changes in surgical indications in patients with ulcerative colitis (UC) in the biologics era in a single tertiary center. Methods: In this retrospective observational study, 108 patients with UC who underwent abdominal surgery for UC at Seoul National University Hospital from 2000 to 2021 were included. We compared the total number of patients undergoing UC before and after the introduction of biologic therapy. Results: Of the 108 patients with UC (male, 59 and female, 49; mean age, 46.8 years), 30 (27.8%) underwent surgery for neoplasms and 78 (72.2%) for medical intractability without neoplasms. The duration between diagnosis and surgery varied significantly (126.00 months vs. 60.50 months, P = 0.001). A significant difference was also noted in the surgical indications according to time (P = 0.02). Between 2000 and 2010, 12 patients (19.4%) underwent surgery for UC with neoplasms and 50 (80.6%) for UC without neoplasms, while between 2011 and 2021, 18 (39.1%) and 28 patients (60.9%) underwent surgery for UC with and without neoplasms, respectively. Conclusion Since 2011, when biological agents were covered by insurance in South Korea, there has been a relative increase in the incidence of surgical indications for neoplasia cases. Focusing on closely monitoring individuals with longterm UC for neoplasms is necessary.

Subperiosteal ganglion is a rare lesion with an unclear pathogenesis that develops from the periosteum with cortical erosion. It most commonly occurs in the tibia and occurs less frequently in the upper extremities. We report a case of subperiosteal ganglion at the ulnar side of the metaphysis of the distal radius in a 27-year-old woman, and we describe the diagnosis and treatment.

Purpose: We aimed to identify changes in surgical indications in patients with ulcerative colitis (UC) in the biologics era in a single tertiary center. Methods: In this retrospective observational study, 108 patients with UC who underwent abdominal surgery for UC at Seoul National University Hospital from 2000 to 2021 were included. We compared the total number of patients undergoing UC before and after the introduction of biologic therapy. Results: Of the 108 patients with UC (male, 59 and female, 49; mean age, 46.8 years), 30 (27.8%) underwent surgery for neoplasms and 78 (72.2%) for medical intractability without neoplasms. The duration between diagnosis and surgery varied significantly (126.00 months vs. 60.50 months, P = 0.001). A significant difference was also noted in the surgical indications according to time (P = 0.02). Between 2000 and 2010, 12 patients (19.4%) underwent surgery for UC with neoplasms and 50 (80.6%) for UC without neoplasms, while between 2011 and 2021, 18 (39.1%) and 28 patients (60.9%) underwent surgery for UC with and without neoplasms, respectively. Conclusion Since 2011, when biological agents were covered by insurance in South Korea, there has been a relative increase in the incidence of surgical indications for neoplasia cases. Focusing on closely monitoring individuals with longterm UC for neoplasms is necessary.

Subperiosteal ganglion is a rare lesion with an unclear pathogenesis that develops from the periosteum with cortical erosion. It most commonly occurs in the tibia and occurs less frequently in the upper extremities. We report a case of subperiosteal ganglion at the ulnar side of the metaphysis of the distal radius in a 27-year-old woman, and we describe the diagnosis and treatment.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Purpose: We aimed to identify changes in surgical indications in patients with ulcerative colitis (UC) in the biologics era in a single tertiary center. Methods: In this retrospective observational study, 108 patients with UC who underwent abdominal surgery for UC at Seoul National University Hospital from 2000 to 2021 were included. We compared the total number of patients undergoing UC before and after the introduction of biologic therapy. Results: Of the 108 patients with UC (male, 59 and female, 49; mean age, 46.8 years), 30 (27.8%) underwent surgery for neoplasms and 78 (72.2%) for medical intractability without neoplasms. The duration between diagnosis and surgery varied significantly (126.00 months vs. 60.50 months, P = 0.001). A significant difference was also noted in the surgical indications according to time (P = 0.02). Between 2000 and 2010, 12 patients (19.4%) underwent surgery for UC with neoplasms and 50 (80.6%) for UC without neoplasms, while between 2011 and 2021, 18 (39.1%) and 28 patients (60.9%) underwent surgery for UC with and without neoplasms, respectively. Conclusion Since 2011, when biological agents were covered by insurance in South Korea, there has been a relative increase in the incidence of surgical indications for neoplasia cases. Focusing on closely monitoring individuals with longterm UC for neoplasms is necessary.

Subperiosteal ganglion is a rare lesion with an unclear pathogenesis that develops from the periosteum with cortical erosion. It most commonly occurs in the tibia and occurs less frequently in the upper extremities. We report a case of subperiosteal ganglion at the ulnar side of the metaphysis of the distal radius in a 27-year-old woman, and we describe the diagnosis and treatment.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

As fluoroscopy-guided interventional procedures gain popularity, the associated health threats from radiation exposure to interventionalists during these procedures are increasing. Therefore, an understanding of the potential risks of radiation and careful consideration on minimizing exposure to radiation during the procedures are of paramount importance. The Korean Pancreatobiliary Association has developed a clinical practice guideline to minimize radiation exposure during fluoroscopy-guided interventional procedures. This guideline provides recommendations to deal with the risk of radiation exposure to interventionalists who perform fluoroscopy-guided procedures, and emphasizes the importance of proper and practical approaches to avoid unnecessary radiation exposure.