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.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.

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 test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To assess the performance of novel qualitative diagnostic criteria using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to identify the pathologic complete response (pCR) of primary tumors in esophageal cancer after neoadjuvant chemoradiation (nCRT). Materials and Methods: Patients who underwent nCRT, subsequent MRI, positron emission tomography/computed tomography (PET/CT), endoscopy, or esophagectomy for esophageal cancer between October 2021 and October 2023 were retrospectively analyzed. The DCE-MRI response of primary tumors was interpreted using five grades by thoracic radiologists as follows: G1 (compatible with CR), G2 (probable CR), G3 (probable partial response [PR]), G4 (compatible with PR), and G5 (stable or progressive disease). The performances of MRI, PET/CT, endoscopy, and their combinations in diagnosing pCR in primary tumors were calculated. Results: A total of 52 patients (male:female, 46:6; age, 61.2 ± 8.0 years) were included. Surgical specimens revealed pCR (ypT0) in 34 patients. G1 as the MRI criterion for pCR of primary tumors yielded a positive predictive value (PPV), specificity of 100% (18/18), and low sensitivity (23.5% [8/34]). Combining G1 and G2 as the MRI criteria increased the sensitivity to 73.5% (25/34), with a specificity of 88.9% (16/18), accuracy of 78.8% (41/52), and PPV of 92.6% (25/27). Adding the DCEMRI results (G1-2) significantly improved accuracy for both PET/CT (from 65.4% [34/52] to 80.8% [42/52], P = 0.03) and endoscopy (from 55.8% [29/52] to 76.9% [40/52], P = 0.005), with increase in sensitivity (from 55.9% [19/34] to 82.4% [28/34] for PET/CT-based evaluation [P = 0.008] and from 47.1% [16/34] to 82.4% [28/34] for endoscopy-based evaluation [P = 0.001]). Conclusion DCE-MRI-based grading shows high diagnostic performance for identifying pCR in primary tumors, particularly in terms of PPV and specificity, and enhances response evaluation when combined with PET/CT and endoscopy.

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.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.