BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

Objective: : Although Republic of Korea is an advanced country in medical technology with a successful treatment rate for serious diseases, such as cancer, and has improved technology for highly difficult surgery, many excellent medical doctors and physicians are struggling due to the recent unreasonable medical environment. Specialization in brain tumor surgery also faces challenges in Republic of Korea, including low financial incentives, legal threats, and limited career prospects. In response, the Korean Brain Tumor Society (KBTS) formed the Future Strategy Committee to assess these obstacles and propose solutions. Methods: : A survey was conducted among the KBTS members to understand their perceptions and concerns across different career stages. Results: : The findings revealed a decline in interest among chief residents in brain tumor surgery, owing to limited job opportunities and income prospects. Neurosurgical fellows expressed neutral satisfaction but highlighted challenges, such as low patient numbers and income. Faculty members with varying levels of experience echoed similar concerns, emphasizing the need for improved financial incentives and job stability. Despite these challenges, the respondents expressed dedication to the field and suggested strategies for improvement. Conclusion : The KBTS outlines a vision that focuses on practical excellence, comprehensive research, professional education, responsibilities, and member satisfaction. Addressing these challenges requires collaborative efforts among healthcare institutions, professional societies, and policymakers to support brain tumor specialists and enhance patient care.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

Objective: : Although Republic of Korea is an advanced country in medical technology with a successful treatment rate for serious diseases, such as cancer, and has improved technology for highly difficult surgery, many excellent medical doctors and physicians are struggling due to the recent unreasonable medical environment. Specialization in brain tumor surgery also faces challenges in Republic of Korea, including low financial incentives, legal threats, and limited career prospects. In response, the Korean Brain Tumor Society (KBTS) formed the Future Strategy Committee to assess these obstacles and propose solutions. Methods: : A survey was conducted among the KBTS members to understand their perceptions and concerns across different career stages. Results: : The findings revealed a decline in interest among chief residents in brain tumor surgery, owing to limited job opportunities and income prospects. Neurosurgical fellows expressed neutral satisfaction but highlighted challenges, such as low patient numbers and income. Faculty members with varying levels of experience echoed similar concerns, emphasizing the need for improved financial incentives and job stability. Despite these challenges, the respondents expressed dedication to the field and suggested strategies for improvement. Conclusion : The KBTS outlines a vision that focuses on practical excellence, comprehensive research, professional education, responsibilities, and member satisfaction. Addressing these challenges requires collaborative efforts among healthcare institutions, professional societies, and policymakers to support brain tumor specialists and enhance patient care.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

Objective: : Although Republic of Korea is an advanced country in medical technology with a successful treatment rate for serious diseases, such as cancer, and has improved technology for highly difficult surgery, many excellent medical doctors and physicians are struggling due to the recent unreasonable medical environment. Specialization in brain tumor surgery also faces challenges in Republic of Korea, including low financial incentives, legal threats, and limited career prospects. In response, the Korean Brain Tumor Society (KBTS) formed the Future Strategy Committee to assess these obstacles and propose solutions. Methods: : A survey was conducted among the KBTS members to understand their perceptions and concerns across different career stages. Results: : The findings revealed a decline in interest among chief residents in brain tumor surgery, owing to limited job opportunities and income prospects. Neurosurgical fellows expressed neutral satisfaction but highlighted challenges, such as low patient numbers and income. Faculty members with varying levels of experience echoed similar concerns, emphasizing the need for improved financial incentives and job stability. Despite these challenges, the respondents expressed dedication to the field and suggested strategies for improvement. Conclusion : The KBTS outlines a vision that focuses on practical excellence, comprehensive research, professional education, responsibilities, and member satisfaction. Addressing these challenges requires collaborative efforts among healthcare institutions, professional societies, and policymakers to support brain tumor specialists and enhance patient care.

Objective: : Although Republic of Korea is an advanced country in medical technology with a successful treatment rate for serious diseases, such as cancer, and has improved technology for highly difficult surgery, many excellent medical doctors and physicians are struggling due to the recent unreasonable medical environment. Specialization in brain tumor surgery also faces challenges in Republic of Korea, including low financial incentives, legal threats, and limited career prospects. In response, the Korean Brain Tumor Society (KBTS) formed the Future Strategy Committee to assess these obstacles and propose solutions. Methods: : A survey was conducted among the KBTS members to understand their perceptions and concerns across different career stages. Results: : The findings revealed a decline in interest among chief residents in brain tumor surgery, owing to limited job opportunities and income prospects. Neurosurgical fellows expressed neutral satisfaction but highlighted challenges, such as low patient numbers and income. Faculty members with varying levels of experience echoed similar concerns, emphasizing the need for improved financial incentives and job stability. Despite these challenges, the respondents expressed dedication to the field and suggested strategies for improvement. Conclusion : The KBTS outlines a vision that focuses on practical excellence, comprehensive research, professional education, responsibilities, and member satisfaction. Addressing these challenges requires collaborative efforts among healthcare institutions, professional societies, and policymakers to support brain tumor specialists and enhance patient care.

Background: The diagnostic accuracy of preoperative radiologic findings in predicting the tumor characteristics and clinical outcomes of papillary thyroid microcarcinoma (PTMC) was evaluated across all risk groups. Methods: In total, 939 PTMC patients, comprising both low-risk and non-low-risk groups, who underwent surgery were enrolled. The preoperative tumor size and lymph node metastasis (LNM) were evaluated by ultrasonography within 6 months before surgery and compared with the postoperative pathologic findings. Discrepancies between the preoperative and postoperative tumor sizes were analyzed, and clinical outcomes were assessed. Results: The agreement rate between radiological and pathological tumor size was approximately 60%. Significant discrepancies were noted, including an increase in tumor size in 24.3% of cases. Notably, in 10.8% of patients, the postoperative tumor size exceeded 1 cm, despite being initially classified as 0.5 to 1.0 cm based on preoperative imaging. A postoperative tumor size >1 cm was associated with aggressive pathologic factors such as multiplicity, microscopic extrathyroidal extension, and LNM, as well as a higher risk of distant metastasis. In 30.1% of patients, LNM was diagnosed after surgery despite not being suspected before the procedure. This group was characterized by smaller metastatic foci and lower risks of distant metastasis or recurrence than patients with LNM detected both before and after surgery. Conclusion Among all risk groups of PTMCs, a subset showed an increase in tumor size, reaching 1 cm after surgery. These cases require special consideration due to their association with adverse clinical outcomes, including an elevated risk of distant metastasis.