Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study aimed to characterize the clinical patterns and severity of brain injury in neonates who survived extracorporeal membrane oxygenation (ECMO) therapy for acute respiratory failure during the neonatal period, to evaluate their short-term neurodevelopmental outcomes, and to identify the factors associated with these outcomes. Methods: We retrospectively reviewed the medical records of neonates who survived ECMO between 2018 and 2024. Based on brain magnetic resonance imaging (MRI) findings, the patients were classified into two groups: no/mild and moderate/severe brain injury. Neurodevelopmental outcomes were assessed at 12–40 months of age using the Bayley Scale of Infant Development II/III and/or the Korean Developmental Screening Test. Results: Among the 19 neonates included in the study, 18 (94.7%) showed varying degrees of brain injury on MRI (mild: 12, moderate: 1, severe: 5). Neonates with moderate/severe brain injury had significantly longer durations of ECMO support and extended durations of mechanical ventilation and were more likely to receive continuous renal replacement therapy than those with no or mild injury. Developmental delay was identified in 36.8% of survivors and was significantly associated with prolonged mechanical ventilation, longer neonatal intensive care unit stays, and a higher incidence of seizures. Conclusion Brain injury is frequently observed on MRI in neonates treated with ECMO. However, its direct association with adverse neurodevelopmental outcomes is not definitive. Since MRI findings alone cannot predict developmental outcomes, clinical and environmental factors should be integrated into prognostic assessments.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: To evaluate the relationship between distal fusion level in correction and fusion surgery for adolescent idiopathic scoliosis (AIS) and radiologic changes in the sacroiliac (SI) joint. Materials and Methods: This retrospective cohort study evaluated patients who underwent correction and fusion for AIS between 2005 and 2017 with at least 5 years of follow-up. We categorized patients into two groups: Group 1 (distal fusion above L2, 74 patients) and Group 2 (distal fusion at L3 and below, 52 patients). Radiologic parameters and SI joint changes were evaluated on plain radiographs obtained from preoperative to 5 years postoperatively. We also investigated other risk factors for SI joint change. Results: Analysis of demographic factors revealed no significant difference between the two groups. There was a significant difference in the incidence of SI joint change between Group 1 (5 patients, 6.75%) and Group 2 (18 patients, 34.61%), with Group 2 showing a faster increase in incidence according to the Kaplan-Meier method (p<0.0001). Preoperative lumbar lordosis (LL) and ΔLL had a significant relationship with SI joint changes [preoperative LL, hazard ratio (HR)=0.77, 95% confidence interval (CI)=0.64– 0.93, p=0.008; ΔLL, HR=0.79, 95% CI=0.67–0.95, p=0.01). Conclusion After AIS surgery, patients who had fusion to the lower lumbar vertebrae (L3 or L4) experienced a higher incidence and faster progression of degenerative changes in the SI joint. Low preoperative LL and inadequate correction of LL during the operation were also risk factors for SI joint degeneration.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study aimed to characterize the clinical patterns and severity of brain injury in neonates who survived extracorporeal membrane oxygenation (ECMO) therapy for acute respiratory failure during the neonatal period, to evaluate their short-term neurodevelopmental outcomes, and to identify the factors associated with these outcomes. Methods: We retrospectively reviewed the medical records of neonates who survived ECMO between 2018 and 2024. Based on brain magnetic resonance imaging (MRI) findings, the patients were classified into two groups: no/mild and moderate/severe brain injury. Neurodevelopmental outcomes were assessed at 12–40 months of age using the Bayley Scale of Infant Development II/III and/or the Korean Developmental Screening Test. Results: Among the 19 neonates included in the study, 18 (94.7%) showed varying degrees of brain injury on MRI (mild: 12, moderate: 1, severe: 5). Neonates with moderate/severe brain injury had significantly longer durations of ECMO support and extended durations of mechanical ventilation and were more likely to receive continuous renal replacement therapy than those with no or mild injury. Developmental delay was identified in 36.8% of survivors and was significantly associated with prolonged mechanical ventilation, longer neonatal intensive care unit stays, and a higher incidence of seizures. Conclusion Brain injury is frequently observed on MRI in neonates treated with ECMO. However, its direct association with adverse neurodevelopmental outcomes is not definitive. Since MRI findings alone cannot predict developmental outcomes, clinical and environmental factors should be integrated into prognostic assessments.