Background/Aims: Multiple myeloma (MM) predominantly affects elderly individuals, but studies on older patients with MM are limited. The clinical characteristics and survival outcomes of patients with MM aged 80 years or over were retrospectively analyzed. Methods: This retrospective multicenter study was conducted to investigate the clinical characteristics, treatment patterns, and survival outcomes of patients aged 80 years or over who were newly diagnosed with MM at five academic hospitals in Daegu, Korea, between 2010 and 2019. Results: A total of 127 patients with a median age of 83 years (range, 80–93 yr) were enrolled: 52 (40.9%) with Eastern Cooperative Oncology Group Performance Status (ECOG PS) > 2, 84 (66.1%) with International Staging System (ISS) stage III disease, and 93 (73.2%) with a Charlson comorbidity index (CCI) > 4. Chemotherapy was administered to 86 patients (67.7%). The median overall survival was 9.3 months. Overall survival was significantly associated with ECOG PS > 2 (HR 2.26, 95% CI 1.43–3.59), ISS stage III (HR 1.99, 95% CI 1.18–3.34), and chemotherapy (HR 0.34, 95% CI 0.21–0.55). There was no statistically significant difference in event-free survival according to the type of anti-myeloma chemotherapy administered. The early mortality (EM) rate was 28.3%. Conclusions Even in patients with MM aged 80 years or over, chemotherapy can result in better survival outcomes than supportive care. Patients aged ≥ 80 years should not be excluded from chemotherapy based on age alone. However, reducing EM in elderly patients with newly diagnosed MM remains challenging.

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/Aims: Multiple myeloma (MM) predominantly affects elderly individuals, but studies on older patients with MM are limited. The clinical characteristics and survival outcomes of patients with MM aged 80 years or over were retrospectively analyzed. Methods: This retrospective multicenter study was conducted to investigate the clinical characteristics, treatment patterns, and survival outcomes of patients aged 80 years or over who were newly diagnosed with MM at five academic hospitals in Daegu, Korea, between 2010 and 2019. Results: A total of 127 patients with a median age of 83 years (range, 80–93 yr) were enrolled: 52 (40.9%) with Eastern Cooperative Oncology Group Performance Status (ECOG PS) > 2, 84 (66.1%) with International Staging System (ISS) stage III disease, and 93 (73.2%) with a Charlson comorbidity index (CCI) > 4. Chemotherapy was administered to 86 patients (67.7%). The median overall survival was 9.3 months. Overall survival was significantly associated with ECOG PS > 2 (HR 2.26, 95% CI 1.43–3.59), ISS stage III (HR 1.99, 95% CI 1.18–3.34), and chemotherapy (HR 0.34, 95% CI 0.21–0.55). There was no statistically significant difference in event-free survival according to the type of anti-myeloma chemotherapy administered. The early mortality (EM) rate was 28.3%. Conclusions Even in patients with MM aged 80 years or over, chemotherapy can result in better survival outcomes than supportive care. Patients aged ≥ 80 years should not be excluded from chemotherapy based on age alone. However, reducing EM in elderly patients with newly diagnosed MM remains challenging.

Background/Aims: Multiple myeloma (MM) predominantly affects elderly individuals, but studies on older patients with MM are limited. The clinical characteristics and survival outcomes of patients with MM aged 80 years or over were retrospectively analyzed. Methods: This retrospective multicenter study was conducted to investigate the clinical characteristics, treatment patterns, and survival outcomes of patients aged 80 years or over who were newly diagnosed with MM at five academic hospitals in Daegu, Korea, between 2010 and 2019. Results: A total of 127 patients with a median age of 83 years (range, 80–93 yr) were enrolled: 52 (40.9%) with Eastern Cooperative Oncology Group Performance Status (ECOG PS) > 2, 84 (66.1%) with International Staging System (ISS) stage III disease, and 93 (73.2%) with a Charlson comorbidity index (CCI) > 4. Chemotherapy was administered to 86 patients (67.7%). The median overall survival was 9.3 months. Overall survival was significantly associated with ECOG PS > 2 (HR 2.26, 95% CI 1.43–3.59), ISS stage III (HR 1.99, 95% CI 1.18–3.34), and chemotherapy (HR 0.34, 95% CI 0.21–0.55). There was no statistically significant difference in event-free survival according to the type of anti-myeloma chemotherapy administered. The early mortality (EM) rate was 28.3%. Conclusions Even in patients with MM aged 80 years or over, chemotherapy can result in better survival outcomes than supportive care. Patients aged ≥ 80 years should not be excluded from chemotherapy based on age alone. However, reducing EM in elderly patients with newly diagnosed MM remains challenging.

Background/Aims: Multiple myeloma (MM) predominantly affects elderly individuals, but studies on older patients with MM are limited. The clinical characteristics and survival outcomes of patients with MM aged 80 years or over were retrospectively analyzed. Methods: This retrospective multicenter study was conducted to investigate the clinical characteristics, treatment patterns, and survival outcomes of patients aged 80 years or over who were newly diagnosed with MM at five academic hospitals in Daegu, Korea, between 2010 and 2019. Results: A total of 127 patients with a median age of 83 years (range, 80–93 yr) were enrolled: 52 (40.9%) with Eastern Cooperative Oncology Group Performance Status (ECOG PS) > 2, 84 (66.1%) with International Staging System (ISS) stage III disease, and 93 (73.2%) with a Charlson comorbidity index (CCI) > 4. Chemotherapy was administered to 86 patients (67.7%). The median overall survival was 9.3 months. Overall survival was significantly associated with ECOG PS > 2 (HR 2.26, 95% CI 1.43–3.59), ISS stage III (HR 1.99, 95% CI 1.18–3.34), and chemotherapy (HR 0.34, 95% CI 0.21–0.55). There was no statistically significant difference in event-free survival according to the type of anti-myeloma chemotherapy administered. The early mortality (EM) rate was 28.3%. Conclusions Even in patients with MM aged 80 years or over, chemotherapy can result in better survival outcomes than supportive care. Patients aged ≥ 80 years should not be excluded from chemotherapy based on age alone. However, reducing EM in elderly patients with newly diagnosed MM remains challenging.

Background/Aims: Multiple myeloma (MM) predominantly affects elderly individuals, but studies on older patients with MM are limited. The clinical characteristics and survival outcomes of patients with MM aged 80 years or over were retrospectively analyzed. Methods: This retrospective multicenter study was conducted to investigate the clinical characteristics, treatment patterns, and survival outcomes of patients aged 80 years or over who were newly diagnosed with MM at five academic hospitals in Daegu, Korea, between 2010 and 2019. Results: A total of 127 patients with a median age of 83 years (range, 80–93 yr) were enrolled: 52 (40.9%) with Eastern Cooperative Oncology Group Performance Status (ECOG PS) > 2, 84 (66.1%) with International Staging System (ISS) stage III disease, and 93 (73.2%) with a Charlson comorbidity index (CCI) > 4. Chemotherapy was administered to 86 patients (67.7%). The median overall survival was 9.3 months. Overall survival was significantly associated with ECOG PS > 2 (HR 2.26, 95% CI 1.43–3.59), ISS stage III (HR 1.99, 95% CI 1.18–3.34), and chemotherapy (HR 0.34, 95% CI 0.21–0.55). There was no statistically significant difference in event-free survival according to the type of anti-myeloma chemotherapy administered. The early mortality (EM) rate was 28.3%. Conclusions Even in patients with MM aged 80 years or over, chemotherapy can result in better survival outcomes than supportive care. Patients aged ≥ 80 years should not be excluded from chemotherapy based on age alone. However, reducing EM in elderly patients with newly diagnosed MM remains challenging.

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.

Patients with cancer experience psychological and social problems; in particular, older patients with cancer face many difficulties during the cancer treatment process owing to aging and underlying diseases. Furthermore, the lives of individuals may be impacted by the COVID-19 pandemic. Therefore, the purpose of this study is to describe the experiences of older patients with cancer during the cancer diagnosis and treatment process amid the COVID-19 pandemic. Methods: This study employed a qualitative, descriptive phenomenological approach to explore and analyze the experiences of the participants. The participants in this study consisted of patients aged over 65 who were diagnosed with cancer, and data were collected from May 4 to June 30, 2022 through in-depth individual interviews. The collected data were analyzed using Giorgi’s phenomenological analysis. Results: Participants were six older patients with cancer with an average age of 69.66 years. Five theme clusters and 15 themes were generated. The theme clusters were “psychological adaptation to cancer diagnosis,” “receiving social and medical support,” “difficulty in the treatment process,” “fear of the COVID-19 pandemic,” and “living through difficulties.” Conclusion: This study revealed that older patients with cancer demonstrated a unique psychology about how to accept the shock of the diagnosis and the resulting coping patterns and feelings of depression. Additionally, it was verified that older patients with cancer, susceptible to infections due to their advanced age and weakened immunity, also face an increased vulnerability to COVID-19. Therefore, a support system tailored to the characteristics of older patients with cancer should be established.