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.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Background/Aims: In Korea, the incidence of primary diffuse large B-cell lymphoma of the central nervous system (PCNSL) is increasing and autologous stem cell transplantation (ASCT) has improved the survival of younger patients. We explored our real-world experience with PCNSL at Asan Medical Center (AMC). Methods: We used the AMC lymphoma registry to collect patient data prospectively. We analyzed 279 patients diagnosed from 2002 until August 2019. Results: The PCNSL incidence at AMC increased progressively and comprised 7.4-8.9% of new non-Hodgkin lymphoma patients annually during the most recent 4 years. The median age was 60 years (range, 17-85) and males comprised 55%. Patients under 65 years of age (n = 183) had no significant differences in characteristics compared to those aged 65 years or over, with the exception of less occipital lobe involvement and lower beta-2 microglobulin levels. Rituximab, methotrexate, procarbazine, and vincristine (R-MPV) combination induction had the best overall response, of 95%. The median overall survival was 3.8 years with 5- and 10-year survival rates of 41.5% and 30.2%, respectively. Survival was better in younger patients and those treated with ASCT. Thiotepa, busulfan, and cytoxan (TBC) conditioning chemotherapy had better survival than other combinations. The International Extranodal Lymphoma Study Group and Memorial Sloan Kettering Cancer Center prognostic score systems were valid in this cohort. Age and performance status were independent prognostic factors. Exclusive extra-central nervous system failure occurred in six patients (5.6%) among 107 failures. Conclusions The incidence of PCNSL is rising. R-MPV induction therapy followed by ASCT with TBC has improved the survival of young, fit PCNSL patients.

Background/Aims: In Korea, the incidence of primary diffuse large B-cell lymphoma of the central nervous system (PCNSL) is increasing and autologous stem cell transplantation (ASCT) has improved the survival of younger patients. We explored our real-world experience with PCNSL at Asan Medical Center (AMC). Methods: We used the AMC lymphoma registry to collect patient data prospectively. We analyzed 279 patients diagnosed from 2002 until August 2019. Results: The PCNSL incidence at AMC increased progressively and comprised 7.4-8.9% of new non-Hodgkin lymphoma patients annually during the most recent 4 years. The median age was 60 years (range, 17-85) and males comprised 55%. Patients under 65 years of age (n = 183) had no significant differences in characteristics compared to those aged 65 years or over, with the exception of less occipital lobe involvement and lower beta-2 microglobulin levels. Rituximab, methotrexate, procarbazine, and vincristine (R-MPV) combination induction had the best overall response, of 95%. The median overall survival was 3.8 years with 5- and 10-year survival rates of 41.5% and 30.2%, respectively. Survival was better in younger patients and those treated with ASCT. Thiotepa, busulfan, and cytoxan (TBC) conditioning chemotherapy had better survival than other combinations. The International Extranodal Lymphoma Study Group and Memorial Sloan Kettering Cancer Center prognostic score systems were valid in this cohort. Age and performance status were independent prognostic factors. Exclusive extra-central nervous system failure occurred in six patients (5.6%) among 107 failures. Conclusions The incidence of PCNSL is rising. R-MPV induction therapy followed by ASCT with TBC has improved the survival of young, fit PCNSL patients.

Background/Aims: In Korea, the incidence of primary diffuse large B-cell lymphoma of the central nervous system (PCNSL) is increasing and autologous stem cell transplantation (ASCT) has improved the survival of younger patients. We explored our real-world experience with PCNSL at Asan Medical Center (AMC). Methods: We used the AMC lymphoma registry to collect patient data prospectively. We analyzed 279 patients diagnosed from 2002 until August 2019. Results: The PCNSL incidence at AMC increased progressively and comprised 7.4-8.9% of new non-Hodgkin lymphoma patients annually during the most recent 4 years. The median age was 60 years (range, 17-85) and males comprised 55%. Patients under 65 years of age (n = 183) had no significant differences in characteristics compared to those aged 65 years or over, with the exception of less occipital lobe involvement and lower beta-2 microglobulin levels. Rituximab, methotrexate, procarbazine, and vincristine (R-MPV) combination induction had the best overall response, of 95%. The median overall survival was 3.8 years with 5- and 10-year survival rates of 41.5% and 30.2%, respectively. Survival was better in younger patients and those treated with ASCT. Thiotepa, busulfan, and cytoxan (TBC) conditioning chemotherapy had better survival than other combinations. The International Extranodal Lymphoma Study Group and Memorial Sloan Kettering Cancer Center prognostic score systems were valid in this cohort. Age and performance status were independent prognostic factors. Exclusive extra-central nervous system failure occurred in six patients (5.6%) among 107 failures. Conclusions The incidence of PCNSL is rising. R-MPV induction therapy followed by ASCT with TBC has improved the survival of young, fit PCNSL patients.