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.

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.

Background: The diagnosis of tuberculous pleural effusion (TPE) often relies on pleural fluid adenosine deaminase (ADA) levels. The diagnostic utility of ADA, however, is influenced by the prevalence of tuberculosis (TB) in local populations. Malignant pleural effusion (MPE) cases can exhibit moderately elevated ADA levels comparable to those seen in TPE. As population aging potentially impacts ADA levels, global TB incidence is decreasing whereas the burden of malignancy is on the rise. Consequently, epidemiological shifts and temporal changes in ADA distribution complicate the differential diagnosis between TPE and MPE when ADA levels are within the 40–70 IU/L range. Nonetheless, data specific to this subset are scarce. Methods: This retrospective study included consecutive patients aged > 18 years with confirmed TPE and MPE, spanning from 2012 to 2023. ADA levels in pleural fluid were categorized into three groups: < 40 IU/L, 40–70 IU/L, and > 70 IU/L. The study examined annual trends in the frequency of new cases and ADA level distributions over time and identified discriminating factors between TPE and MPE in cases with ADA levels of 40–70 IU/L. Results: In total, 297 TPE and 369 MPE cases were included in this study. Over the study period, the frequency of TPE progressively declined, while that of MPE increased. In the most recent four-year period, new TPE and MPE cases with ADA levels of 40–70 IU/L occurred at comparable numbers. Multivariable analysis identified pleural fluid carcinoembryonic antigen (CEA) levels and the number of focal pleural nodules as independent predictors for MPE. Specifically, the presence of either CEA levels > 15.7 ng/mL or more than eight pleural nodules yielded the highest diagnostic accuracy with a sensitivity of 88%, specificity of 100%, and an area under the curve of 0.95. Conclusion The differential diagnosis between TPE and MPE with pleural ADA levels of 40–70 IU/L has become increasingly critical due to evolving epidemiological patterns and ADA distribution changes over time. Pleural fluid CEA levels and the characteristics of pleural nodules may offer valuable guidance in distinguishing between TPE and MPE within this diagnostic gray zone.

Background: The diagnosis of tuberculous pleural effusion (TPE) often relies on pleural fluid adenosine deaminase (ADA) levels. The diagnostic utility of ADA, however, is influenced by the prevalence of tuberculosis (TB) in local populations. Malignant pleural effusion (MPE) cases can exhibit moderately elevated ADA levels comparable to those seen in TPE. As population aging potentially impacts ADA levels, global TB incidence is decreasing whereas the burden of malignancy is on the rise. Consequently, epidemiological shifts and temporal changes in ADA distribution complicate the differential diagnosis between TPE and MPE when ADA levels are within the 40–70 IU/L range. Nonetheless, data specific to this subset are scarce. Methods: This retrospective study included consecutive patients aged > 18 years with confirmed TPE and MPE, spanning from 2012 to 2023. ADA levels in pleural fluid were categorized into three groups: < 40 IU/L, 40–70 IU/L, and > 70 IU/L. The study examined annual trends in the frequency of new cases and ADA level distributions over time and identified discriminating factors between TPE and MPE in cases with ADA levels of 40–70 IU/L. Results: In total, 297 TPE and 369 MPE cases were included in this study. Over the study period, the frequency of TPE progressively declined, while that of MPE increased. In the most recent four-year period, new TPE and MPE cases with ADA levels of 40–70 IU/L occurred at comparable numbers. Multivariable analysis identified pleural fluid carcinoembryonic antigen (CEA) levels and the number of focal pleural nodules as independent predictors for MPE. Specifically, the presence of either CEA levels > 15.7 ng/mL or more than eight pleural nodules yielded the highest diagnostic accuracy with a sensitivity of 88%, specificity of 100%, and an area under the curve of 0.95. Conclusion The differential diagnosis between TPE and MPE with pleural ADA levels of 40–70 IU/L has become increasingly critical due to evolving epidemiological patterns and ADA distribution changes over time. Pleural fluid CEA levels and the characteristics of pleural nodules may offer valuable guidance in distinguishing between TPE and MPE within this diagnostic gray zone.

Background: and Purpose To determine the clinical phenotypes, relapse timing, treatment responses, and outcomes of children with relapsing myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Methods: We collected the demographic, clinical, laboratory, and radiological data of patients aged <18 years who had been diagnosed with MOGAD at Seoul National University Children’s Hospital between January 2010 and January 2022; 100 were identified as positive for MOG antibodies, 43 of whom experienced relapse. Results: The median age at onset was 7 years (range 2–16 years). The median number of relapses was 2 (range 1–8), and patients were followed up for a median of 65 months (range 5–214 months). The first relapse was experienced before 3 months from onset by 15 patients (34.9%). The most-common initial phenotypes were acute disseminated encephalomyelitis (n=17, 39.5%) and optic neuritis (ON; n=11, 25.6%). The most-common relapse phenotypes were neuromyelitis optica spectrum disorder (n=9, 20.9%), relapsing ON (n=6, 14.0%), and multiphasic disseminated encephalomyelitis (n=6, 14.0%). Many of the patients (n=18, 41.9%) were not specifically categorized. A high proportion of these patients had non-acute disseminated encephalomyelitis encephalitis. Atypical phenotypes such as prolonged fever or hemiplegic migraine-like episodes were also noted. Mycophenolate mofetil and cyclic immunoglobulin treatment significantly reduced the annual relapse rates. Conclusions Our 43 pediatric patients with relapsing MOGAD showed a tendency toward early relapse and various relapse phenotypes. The overall prognoses of these patients were good regardless of phenotype or response to second-line immunosuppressant treatment.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

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.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.