Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: Evaluation of regional left ventricle function using two-dimensional echocardiography (2DE) in patients with ischemic heart disease has limitations due to its low objectivity and qualitative nature. In addition, 2DE is limited because multiple acoustic windows are used to obtain the image, whereas three-dimensional echocardiography (3DE) uses a single window. This study aims to demonstrate the clinical utility of 3DE segmental volume analysis for evaluating regional wall motion abnormality (RWMA). Methods: This retrospective study included 33 patients with ischemic heart disease and single-vessel territory RWMA confirmed on coronary angiography. RWMA was visually assessed using 2DE, generating 17-segment bull’s-eye polar maps, and 3DE. In the 3DE study, two independent observers analyzed segmental volumes and segmental volume ejection fractions (SVEFs) using QLAB 3D quantification software. The optimal SVEF cutoff value differentiating normal from abnormal was determined using receiver operating curve analysis. The accuracy of 3DE in predicting culprit coronary arteries was compared with that of 2DE using Cohen κ coefficients, which also were used for interobserver and intraobserver variability assessments. Results: Mean 3DE SVEFs were significantly lower in segments showing RWMA on 2DE. The optimal SVEF cutoff value was 44%, with sensitivity of 75.0% and specificity of 73.9% (area under the curve, 0.801; 95% CI, 0.763–0.838; P < 0.001).The reliability of 3DE-derived bull’s-eye predictions of culprit coronary arteries was 81.8% (κ = 0.672; 95% CI, 0.555– 0.789; P < 0.001). Interobserver and intraobserver variabilities were 97.0% (κ = 0.947; 95% CI, 0.894–1.00; P < 0.001) and 93.9% (κ = 0.897; 95% CI, 0.827–0.967; P < 0.001), respectively. Conclusions The 3DE segmental volume analysis effectively quantified regional left ventricle function and aligned well with 2DE and coronary angiography findings in predicting culprit coronary arteries. Thus, 3DE segmental volume analysis can serve as a quantitative and objective tool for RWMA assessment in patients with ischemic heart disease.

Background: Evaluation of regional left ventricle function using two-dimensional echocardiography (2DE) in patients with ischemic heart disease has limitations due to its low objectivity and qualitative nature. In addition, 2DE is limited because multiple acoustic windows are used to obtain the image, whereas three-dimensional echocardiography (3DE) uses a single window. This study aims to demonstrate the clinical utility of 3DE segmental volume analysis for evaluating regional wall motion abnormality (RWMA). Methods: This retrospective study included 33 patients with ischemic heart disease and single-vessel territory RWMA confirmed on coronary angiography. RWMA was visually assessed using 2DE, generating 17-segment bull’s-eye polar maps, and 3DE. In the 3DE study, two independent observers analyzed segmental volumes and segmental volume ejection fractions (SVEFs) using QLAB 3D quantification software. The optimal SVEF cutoff value differentiating normal from abnormal was determined using receiver operating curve analysis. The accuracy of 3DE in predicting culprit coronary arteries was compared with that of 2DE using Cohen κ coefficients, which also were used for interobserver and intraobserver variability assessments. Results: Mean 3DE SVEFs were significantly lower in segments showing RWMA on 2DE. The optimal SVEF cutoff value was 44%, with sensitivity of 75.0% and specificity of 73.9% (area under the curve, 0.801; 95% CI, 0.763–0.838; P < 0.001).The reliability of 3DE-derived bull’s-eye predictions of culprit coronary arteries was 81.8% (κ = 0.672; 95% CI, 0.555– 0.789; P < 0.001). Interobserver and intraobserver variabilities were 97.0% (κ = 0.947; 95% CI, 0.894–1.00; P < 0.001) and 93.9% (κ = 0.897; 95% CI, 0.827–0.967; P < 0.001), respectively. Conclusions The 3DE segmental volume analysis effectively quantified regional left ventricle function and aligned well with 2DE and coronary angiography findings in predicting culprit coronary arteries. Thus, 3DE segmental volume analysis can serve as a quantitative and objective tool for RWMA assessment in patients with ischemic heart disease.

Background: Evaluation of regional left ventricle function using two-dimensional echocardiography (2DE) in patients with ischemic heart disease has limitations due to its low objectivity and qualitative nature. In addition, 2DE is limited because multiple acoustic windows are used to obtain the image, whereas three-dimensional echocardiography (3DE) uses a single window. This study aims to demonstrate the clinical utility of 3DE segmental volume analysis for evaluating regional wall motion abnormality (RWMA). Methods: This retrospective study included 33 patients with ischemic heart disease and single-vessel territory RWMA confirmed on coronary angiography. RWMA was visually assessed using 2DE, generating 17-segment bull’s-eye polar maps, and 3DE. In the 3DE study, two independent observers analyzed segmental volumes and segmental volume ejection fractions (SVEFs) using QLAB 3D quantification software. The optimal SVEF cutoff value differentiating normal from abnormal was determined using receiver operating curve analysis. The accuracy of 3DE in predicting culprit coronary arteries was compared with that of 2DE using Cohen κ coefficients, which also were used for interobserver and intraobserver variability assessments. Results: Mean 3DE SVEFs were significantly lower in segments showing RWMA on 2DE. The optimal SVEF cutoff value was 44%, with sensitivity of 75.0% and specificity of 73.9% (area under the curve, 0.801; 95% CI, 0.763–0.838; P < 0.001).The reliability of 3DE-derived bull’s-eye predictions of culprit coronary arteries was 81.8% (κ = 0.672; 95% CI, 0.555– 0.789; P < 0.001). Interobserver and intraobserver variabilities were 97.0% (κ = 0.947; 95% CI, 0.894–1.00; P < 0.001) and 93.9% (κ = 0.897; 95% CI, 0.827–0.967; P < 0.001), respectively. Conclusions The 3DE segmental volume analysis effectively quantified regional left ventricle function and aligned well with 2DE and coronary angiography findings in predicting culprit coronary arteries. Thus, 3DE segmental volume analysis can serve as a quantitative and objective tool for RWMA assessment in patients with ischemic heart disease.

Background/Aims: Optimal risk stratification based on simplified geriatric assessment to predict treatment-related toxicity and survival needs to be clarified in older patients with diffuse large B-cell lymphoma (DLBCL). Methods: This multicenter prospective cohort study enrolled newly diagnosed patients with DLBCL (≥ 65 yr) between September 2015 and April 2018. A simplified geriatric assessment was performed at baseline using Activities of Daily Living (ADL), Instrumental ADL (IADL), and Charlson’s Comorbidity Index (CCI). The primary endpoint was event-free survival (EFS). Results: The study included 249 patients, the median age was 74 years (range, 65-88), and 125 (50.2%) were female. In multivariable Cox analysis, ADL, IADL, CCI, and age were independent factors for EFS; an integrated geriatric score was derived and the patients stratified into three geriatric categories: fit (n = 162, 65.1%), intermediate-fit (n = 25, 10.0%), and frail (n = 62, 24.9%). The established geriatric model was significantly associated with EFS (fit vs. intermediate-fit, HR 2.61, p < 0.001; fit vs. frail, HR 4.61, p < 0.001) and outperformed each covariate alone or in combination. In 87 intermediate-fit or frail patients, the relative doxorubicin dose intensity (RDDI) ≥ 62.4% was significantly associated with worse EFS (HR, 2.15, 95% CI 1.30–3.53, p = 0.002). It was related with a higher incidence of grade ≥ 3 symptomatic non-hematologic toxicities (63.2% vs. 27.8%, p < 0.001) and earlier treatment discontinuation (34.5% vs. 8.0%, p < 0.001) in patients with RDDI ≥ 62.4% than in those with RDDI < 62.4%. Conclusions This model integrating simplified geriatric assessment can risk-stratify older patients with DLBCL and identify those who are highly vulnerable to standard dose-intensity chemoimmunotherapy.

Purpose: This study aimed to compare the short-term outcomes and safety profiles of androgen-deprivation therapy (ADT)+abiraterone/prednisone with those of ADT+docetaxel in patients with de novo metastatic hormone-sensitive prostate cancer (mHSPC). Materials and Methods: A web-based database system was established to collect prospective cohort data for patients with mHSPC in Korea. From May 2019 to November 2022, 928 patients with mHSPC from 15 institutions were enrolled. Among these patients, data from 122 patients who received ADT+abiraterone/prednisone or ADT+docetaxel as the primary systemic treatment for mHSPC were collected. The patients were divided into two groups: ADT+abiraterone/prednisone group (n=102) and ADT+docetaxel group (n=20). We compared the demographic characteristics, medical histories, baseline cancer status, initial laboratory tests, metastatic burden, oncological outcomes for mHSPC, progression after mHSPC treatment, adverse effects, follow-up, and survival data between the two groups. Results: No significant differences in the demographic characteristics, medical histories, metastatic burden, and baseline cancer status were observed between the two groups. The ADT+abiraterone/prednisone group had a lower prostate-specific antigen (PSA) progression rate (7.8% vs. 30.0%; p=0.011) and lower systemic treatment discontinuation rate (22.5% vs. 45.0%; p=0.037). No significant differences in adverse effects, oncological outcomes, and total follow-up period were observed between the two groups. Conclusions ADT+abiraterone/prednisone had lower PSA progression and systemic treatment discontinuation rates than ADT+docetaxel. In conclusion, further studies involving larger, double-blinded randomized trials with extended follow-up periods are necessary.

Objective: Early identification of COVID-19 in patients is important to prevent significant worsening of the disease. This study was undertaken to verify whether MEWS (Modified Early Warning Score), NEWS(National Early Warning Score), ROX index, and CURB-65, which are early diagnostic tools for severe respiratory diseases, could be applied to patients visiting the emergency room for COVID-19. Methods: This retrospective observational study included patients who visited an emergency medical center from September 1 to October 31, 2020, and from January 1 to February 28, 2021. Based on the vital signs and blood tests during the emergency room visit, severity evaluation tools and early diagnostic tools for severe cases were used and compared according to their area under the curve (AUC) values. The primary outcome was in-hospital mortality, while the secondary outcomes were intensive care unit admission rate and the need for mechanical ventilation based on these four tools (MEWS, NEWS, ROX index, and CURB-65). Results: A total of 667 patients were analyzed. No significant difference was determined between the non-survivor group and survivor group in the MEWS values (P=0.13), but statistically significant differences were observed for NEWS (5 vs. 1, P<0.05), CURB-65 (2 vs. 1, P<0.05), and ROX index (16.61 vs. 23.1, P<0.01). The AUC value of NEWS for death prediction indicated a good predictive power at 0.80, while that of MEWS showed a low predictive power at 0.57, which was statistically significant. Moreover, the AUC values of CURB-65 and ROX index did not differ significantly from values obtained for NEWS. Conclusion As early diagnostic tools for predicting death in COVID-19 patients, NEWS, ROX index, and CURB-65 showed excellent discrimination ability, whereas MEWS showed statistically and significantly lower discrimination ability.