Background: This study analyzed the epidemiology and treatment outcomes of germ cell tumorpatients at a single institution. Methods: A retrospective analysis was conducted on intracranial germ cell tumor (iGCT) pa-tients treated at a single tertiary hospital from 2004 to 2019. Patients were categorized based on treatment modality: Korean Society for Pediatric Neuro-Oncology (KSPNO) protocol or bleomycin, etoposide, and cisplatin with radiation therapy. Results: Forty-nine iGCT patients treated with combined chemotherapy and radiotherapywere analyzed. The median age was 19 years (range: 6–40), with a median follow-up duration of 148.0 months (range: 10.5–265.5). Tumors were most common in the pineal gland (51.0%). Although no significant differences in outcomes were observed between treatment modalities, outcomes varied significantly by pathological type. The 10-year progression-free survival rates for germinoma and non-germinomatous germ cell tumors (NGGCTs) were 88.1% and 32.7%, respectively (p=0.003), while the 10-year overall survival rates were 92.9% and 67.5%, respectively (p<0.001). Fourteen patients experienced CTCAE (Common Terminology Criteria for Adverse Events) grade ≥3 adverse events, with one eventrelated death. Conclusion Pure germinoma demonstrated higher survival and lower recurrence rates comparedto NGGCT. The KSPNO protocol appears to be an acceptable and safe treatment option for iGCT patients. Further multi-institutional studies with larger cohorts are warranted.

Background: This study analyzed the epidemiology and treatment outcomes of germ cell tumorpatients at a single institution. Methods: A retrospective analysis was conducted on intracranial germ cell tumor (iGCT) pa-tients treated at a single tertiary hospital from 2004 to 2019. Patients were categorized based on treatment modality: Korean Society for Pediatric Neuro-Oncology (KSPNO) protocol or bleomycin, etoposide, and cisplatin with radiation therapy. Results: Forty-nine iGCT patients treated with combined chemotherapy and radiotherapywere analyzed. The median age was 19 years (range: 6–40), with a median follow-up duration of 148.0 months (range: 10.5–265.5). Tumors were most common in the pineal gland (51.0%). Although no significant differences in outcomes were observed between treatment modalities, outcomes varied significantly by pathological type. The 10-year progression-free survival rates for germinoma and non-germinomatous germ cell tumors (NGGCTs) were 88.1% and 32.7%, respectively (p=0.003), while the 10-year overall survival rates were 92.9% and 67.5%, respectively (p<0.001). Fourteen patients experienced CTCAE (Common Terminology Criteria for Adverse Events) grade ≥3 adverse events, with one eventrelated death. Conclusion Pure germinoma demonstrated higher survival and lower recurrence rates comparedto NGGCT. The KSPNO protocol appears to be an acceptable and safe treatment option for iGCT patients. Further multi-institutional studies with larger cohorts are warranted.

Background: This study analyzed the epidemiology and treatment outcomes of germ cell tumorpatients at a single institution. Methods: A retrospective analysis was conducted on intracranial germ cell tumor (iGCT) pa-tients treated at a single tertiary hospital from 2004 to 2019. Patients were categorized based on treatment modality: Korean Society for Pediatric Neuro-Oncology (KSPNO) protocol or bleomycin, etoposide, and cisplatin with radiation therapy. Results: Forty-nine iGCT patients treated with combined chemotherapy and radiotherapywere analyzed. The median age was 19 years (range: 6–40), with a median follow-up duration of 148.0 months (range: 10.5–265.5). Tumors were most common in the pineal gland (51.0%). Although no significant differences in outcomes were observed between treatment modalities, outcomes varied significantly by pathological type. The 10-year progression-free survival rates for germinoma and non-germinomatous germ cell tumors (NGGCTs) were 88.1% and 32.7%, respectively (p=0.003), while the 10-year overall survival rates were 92.9% and 67.5%, respectively (p<0.001). Fourteen patients experienced CTCAE (Common Terminology Criteria for Adverse Events) grade ≥3 adverse events, with one eventrelated death. Conclusion Pure germinoma demonstrated higher survival and lower recurrence rates comparedto NGGCT. The KSPNO protocol appears to be an acceptable and safe treatment option for iGCT patients. Further multi-institutional studies with larger cohorts are warranted.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

OBJECTIVES: This study aimed to examine the association between the food-based index of dietary inflammatory potential (FBDI) and the risk of mild cognitive impairment (MCI) in Korean older adults. METHODS: The subjects were 798 Korean adults aged 60 years and older. The FBDI was calculated based on the intake of 7 anti-inflammatory and 3 inflammatory food groups. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. A general linear model and multiple logistic regression were applied to assess the association between FBDI and the risk of MCI. RESULTS: As the FBDI increased, the intake of white rice, cookies/candies, and sweetened drinks tended to increase, but the intake of niacin, β-carotene, calcium, and potassium tended to decrease (p for trend<0.05). The highest FBDI group had a higher MCI risk (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.01 to 2.52) than the lowest FBDI group, adjusted for gender, age, and education level; and this trend was significant in a fully adjusted model (p for trend=0.039). No significant associations were found in men after adjusting for confounding factors. Among women, MCI risk increased as the FBDI increased (p for trend=0.007); and the highest FBDI group had a higher MCI risk (OR, 2.22; 95% CI, 1.04 to 4.74) than the lowest FBDI group in a fully adjusted model. CONCLUSIONS These results suggest that the appropriate intake of anti-inflammatory foods and nutrients may be associated with a reduced risk of MCI among older adults.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

OBJECTIVES: This study aimed to examine the association between the food-based index of dietary inflammatory potential (FBDI) and the risk of mild cognitive impairment (MCI) in Korean older adults. METHODS: The subjects were 798 Korean adults aged 60 years and older. The FBDI was calculated based on the intake of 7 anti-inflammatory and 3 inflammatory food groups. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. A general linear model and multiple logistic regression were applied to assess the association between FBDI and the risk of MCI. RESULTS: As the FBDI increased, the intake of white rice, cookies/candies, and sweetened drinks tended to increase, but the intake of niacin, β-carotene, calcium, and potassium tended to decrease (p for trend<0.05). The highest FBDI group had a higher MCI risk (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.01 to 2.52) than the lowest FBDI group, adjusted for gender, age, and education level; and this trend was significant in a fully adjusted model (p for trend=0.039). No significant associations were found in men after adjusting for confounding factors. Among women, MCI risk increased as the FBDI increased (p for trend=0.007); and the highest FBDI group had a higher MCI risk (OR, 2.22; 95% CI, 1.04 to 4.74) than the lowest FBDI group in a fully adjusted model. CONCLUSIONS These results suggest that the appropriate intake of anti-inflammatory foods and nutrients may be associated with a reduced risk of MCI among older adults.

OBJECTIVES: This study aimed to examine the association between the food-based index of dietary inflammatory potential (FBDI) and the risk of mild cognitive impairment (MCI) in Korean older adults. METHODS: The subjects were 798 Korean adults aged 60 years and older. The FBDI was calculated based on the intake of 7 anti-inflammatory and 3 inflammatory food groups. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. A general linear model and multiple logistic regression were applied to assess the association between FBDI and the risk of MCI. RESULTS: As the FBDI increased, the intake of white rice, cookies/candies, and sweetened drinks tended to increase, but the intake of niacin, β-carotene, calcium, and potassium tended to decrease (p for trend<0.05). The highest FBDI group had a higher MCI risk (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.01 to 2.52) than the lowest FBDI group, adjusted for gender, age, and education level; and this trend was significant in a fully adjusted model (p for trend=0.039). No significant associations were found in men after adjusting for confounding factors. Among women, MCI risk increased as the FBDI increased (p for trend=0.007); and the highest FBDI group had a higher MCI risk (OR, 2.22; 95% CI, 1.04 to 4.74) than the lowest FBDI group in a fully adjusted model. CONCLUSIONS These results suggest that the appropriate intake of anti-inflammatory foods and nutrients may be associated with a reduced risk of MCI among older adults.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.