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: 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: 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/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

BACKGROUND/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

BACKGROUND/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

BACKGROUND/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

BACKGROUND/OBJECTIVES: As awareness of climate change increases, the relevance of environmental education in dietary choices gains prominence. Although diversely defined, food literacy (FL) is increasingly recognized as the ability to make food choices with an awareness of environmental sustainability. This study aims to conduct a pilot implementation and assess the effectiveness of a program developed to improve FL among university students. SUBJECTS/METHODS: The study spanned from August 2022 to February 2023, involving 92 participants (42 in the intervention group and 50 in the control group). Over 11 weeks, the program included cooking classes, local farm visits, and environmental impact lectures developed through extensive literature reviews and interviews with students and experts. FL was measured using a 33-item survey along with basic sociodemographic factors. After the intervention, both groups participated in qualitative interviews. All statistical analyses were carried out in Stata/SE version 17.0, and interview data were analyzed in Microsoft Excel using the framework analysis method. RESULTS: The FL scores of the intervention group improved significantly from an average of 65.8 to 69.6 points (P = 0.015), with notable gains in the socio-ecological domain in FL from 65.3 to 71.5 points (P < 0.001). A linear regression analysis comparing FL between the intervention and control groups found that only the knowledge items were marginally significant (P = 0.054), with no statistically significant difference in the practice aspect before and after the intervention (P = 0.657). The interviews revealed that the intervention group experienced broadened perspectives and heightened environmental consciousness, although translating these into practice was challenged by unchanged daily routines. CONCLUSION This pilot program effectively enhanced some aspects of FL-related knowledge of participants. High satisfaction among participants and no dropouts indicated its potential for scaling. Future programs will benefit from strategies that facilitate the transition from educational improvement to practical application.

Purpose: This study aimed to assess the prognostic significance of bulky nodal involvement in patients with anal squamous cell carcinoma treated with definitive chemoradiotherapy. Materials and Methods: We retrospectively analyzed medical records of patients diagnosed with anal squamous cell carcinoma who underwent definitive chemoradiotherapy at three medical centers between 2004 and 2021. Exclusion criteria included distant metastasis at diagnosis, 2D radiotherapy, and salvage treatment for local relapse. Bulky N+ was defined as nodes with a long diameter of 2 cm or greater. Results: A total of 104 patients were included, comprising 51 with N0, 46 with non-bulky N+, and seven with bulky N+. The median follow-up duration was 54.0 months (range, 6.4 to 162.2 months). Estimated 5-year progression-free survival (PFS), loco-regional recurrence-free survival (LRRFS), and overall survival (OS) rates for patients with bulky N+ were 42.9%, 42.9%, and 47.6%, respectively. Bulky N+ was significantly associated with inferior PFS, LRRFS, and OS compared to patients without or with non-bulky N+, even after multivariate analysis. We proposed a new staging system incorporating bulky N+ as N2 category, with estimated 5-year LRRFS, PFS, and OS rates of 81.1%, 80.6%, and 86.2% for stage I, 67.7%, 60.9%, and 93.3% for stage II, and 42.9%, 42.9%, and 47.6% for stage III disease, enhancing the predictability of prognosis. Conclusion Patients with bulky nodal disease treated with standard chemoradiotherapy experienced poor survival outcomes, indicating the potential necessity for further treatment intensification.

OBJECTIVES: Stroke remains the second leading cause of death in Korea. This study was designed to estimate the crude, age-adjusted and age-specific incidence rates, as well as the case fatality rate of stroke, in Korea from 2011 to 2020. METHODS: We utilized data from the National Health Insurance Services from January 1, 2002 to December 31, 2020, to calculate incidence rates and 30-day and 1-year case fatality rates of stroke. Additionally, we determined sex and age-specific incidence rates and computed age-standardized incidence rates by direct standardization to the 2005 population. RESULTS: The crude incidence rate of stroke hovered around 200 (per 100,000 person-years) from 2011 to 2015, then surged to 218.4 in 2019, before marginally declining to 208.0 in 2020. Conversely, the age-standardized incidence rate consistently decreased by 25% between 2011 and 2020. When stratified by sex, the crude incidence rate increased between 2011 and 2019 for both sexes, followed by a decrease in 2020. Age-standardized incidence rates displayed a downward trend throughout the study period for both sexes. Across all age groups, the 30-day and 1-year case fatality rates of stroke consistently decreased from 2011 to 2019, only to increase in 2020. CONCLUSIONS Despite a decrease in the age-standardized incidence rate, the total number of stroke events in Korea continues to rise due to the rapidly aging population. Moreover, 2020 witnessed a decrease in incidence but an increase in case fatality rates.