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.

Background: Recent guidelines about preventing surgical site infections (SSIs) recommend against the administration of prophylactic antibiotics after surgery. However, many colorectal surgeons still prefer prolonged use of prophylactic antibiotics. While minimally invasive surgery (MIS) has become the standard for colorectal cancer surgery, there were few studies about proper dose of prophylactic antibiotics in minimally invasive colorectal surgery. Methods: This is a retrospective study. All patients underwent elective colorectal cancer surgery using MIS. Intravenous cefotetan was administered as a prophylactic antibiotic.Two groups were classified according to the dose of prophylactic antibiotics: a group using a single dose preoperatively (single-dose group) and a group using a preoperative single dose plus additional doses within 24 hours after surgery (multiple-dose group). The SSI rates between the two groups were compared before and after propensity score matching (PSM).Risk factors of SSIs were assessed using univariate and multivariable analysis. Results: There were 902 patients in the single-dose group and 330 patients in the multipledose group. After PSM, 320 patients were included in each group. There were no differences in baseline characteristics and surgical outcomes except the length of hospital stay. SSI rates were not different between the two groups before and after PSM (before 2.0% vs. 2.1%, P = 0.890; after 0.9% vs. 1.9%, P = 0.505). In multivariable analysis, American Society of Anesthesiologists class 3, rectal surgery, intraoperative transfusion, and larger tumor size were identified as independent factors associated with SSI incidence. Conclusion A single preoperative dose of prophylactic antibiotics may be sufficient to prevent SSIs in elective MIS for colorectal cancer.