Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

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: 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.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Purpose: Cancer survivors are at increased risk of diabetes mellitus (DM). Additionally, the prevalence of obesity, which is also a risk factor for DM, is increasing in cancer survivors. We investigated the associations between weight change after cancer diagnosis and DM risk. Materials and Methods: This retrospective cohort study used data from the Korean National Health Insurance Service. Participants who were newly diagnosed with cancer from 2010 to 2016 and received national health screening before and after diagnosis were included and followed until 2019. Weight change status after cancer diagnosis was categorized into four groups: sustained normal weight, obese to normal weight, normal weight to obese, or sustained obese. Cox proportional hazard analyses were performed to examine associations between weight change and DM. Results: The study population comprised 264,250 cancer survivors. DM risk was highest in sustained obese (adjusted hazard ratios [aHR], 2.17; 95% confidence interval [CI], 2.08 to 2.26), followed by normal weight to obese (aHR, 1.66; 95% CI, 1.54 to 1.79), obese to normal weight (aHR, 1.29; 95% CI, 1.21 to 1.39), and then sustained normal weight group (reference). In subgroup analyses according to cancer type, most cancers showed the highest risks in sustained obese group. Conclusion Obesity at any time point was related to increased DM risk, presenting the highest risk in cancer survivors with sustained obesity. Survivors who changed from obese to normal weight had lower risk than survivors with sustained obesity. Survivors who changed from normal weight to obese showed increased risk compared to those who sustained normal weight. Our finding supports the significance of weight management among cancer survivors.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

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: 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.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

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.