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.

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: Habitual coffee consumption was inversely associated with type 2 diabetes (T2D) and hyperglycemia in observational studies, but the causality of the association remains uncertain. This study tested a causal association of genetically predicted coffee consumption with T2D using the Mendelian randomization (MR) method. SUBJECTS/METHODS: We used five single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) associated with habitual coffee consumption in a previous genome-wide association study among Koreans. We analyzed the associations between IVs and T2D, fasting blood glucose (FBG), 2h-postprandial glucose (2h-PG), and glycated haemoglobin (HbA1C) levels. The MR results were further evaluated by standard sensitivity tests for possible pleiotropism. RESULTS: MR analysis revealed that increased genetically predicted coffee consumption was associated with a reduced prevalence of T2D; ORs per one-unit increment of logtransformed cup per day of coffee consumption ranged from 0.75 (0.62–0.90) for the weighted mode-based method to 0.79 (0.62–0.99) for Wald ratio estimator. We also used the inverse-variance-weighted method, weighted median-based method, MR-Egger method, and MR-PRESSO method. Similarly, genetically predicted coffee consumption was inversely associated with FBG and 2h-PG levels but not with HbA1c. Sensitivity measures gave similar results without evidence of pleiotropy. CONCLUSIONS A genetic predisposition to habitual coffee consumption was inversely associated with T2D prevalence and lower levels of FBG and 2h-PG profiles. Our study warrants further exploration.

Objectives: We aimed to examine the association between the relative preference for vegetables and meat and cancer incidence, in a population-based retrospective cohort in Korea. Methods: We included 10,148,131 participants (5,794,124 men; 4,354,007 women) who underwent national health screening between 2004 and 2005 from the National Health Information Database of the National Health Insurance Service (NHIS-NHID). Participants were asked whether they preferred consuming 1) vegetables more often, 2) both vegetables and meat or 3) meat more often. Participants were followed up to Dec. 31, 2017. All cancer and eighteen common cancer cases were identified through the code from the International Classification of Diseases, 10th revision. We estimated sexspecific relative risks and 95% confidence intervals, adjusting for age, body mass index, alcohol consumption, smoking, physical activity, and income level. Results: During an average follow-up of 12.4 years, 714,170 cancer cases were documented. In men, consuming meat more often was associated with lower risk of esophageal, liver, and stomach cancers, but higher risk of lung and kidney cancers. Consuming both vegetables and meat was associated with higher risk of prostate cancer, but with lower risk of esophageal, liver, and stomach cancers in men. In women, consuming meat more often was associated with a higher risk of colorectal cancer and breast, endometrial, and cervical cancers diagnosed before the age of 50. Consuming both vegetables and meat was associated with lower risk of liver cancer in women. Conclusions Our study suggests a potential link between vegetable and meat intake and cancer incidence in the Korean population. Further investigation on the association between the intake of specific types of vegetables and meat and cancer risk in Korean prospective cohort studies is needed.

Objectives: We aimed to examine the association between the relative preference for vegetables and meat and cancer incidence, in a population-based retrospective cohort in Korea. Methods: We included 10,148,131 participants (5,794,124 men; 4,354,007 women) who underwent national health screening between 2004 and 2005 from the National Health Information Database of the National Health Insurance Service (NHIS-NHID). Participants were asked whether they preferred consuming 1) vegetables more often, 2) both vegetables and meat or 3) meat more often. Participants were followed up to Dec. 31, 2017. All cancer and eighteen common cancer cases were identified through the code from the International Classification of Diseases, 10th revision. We estimated sexspecific relative risks and 95% confidence intervals, adjusting for age, body mass index, alcohol consumption, smoking, physical activity, and income level. Results: During an average follow-up of 12.4 years, 714,170 cancer cases were documented. In men, consuming meat more often was associated with lower risk of esophageal, liver, and stomach cancers, but higher risk of lung and kidney cancers. Consuming both vegetables and meat was associated with higher risk of prostate cancer, but with lower risk of esophageal, liver, and stomach cancers in men. In women, consuming meat more often was associated with a higher risk of colorectal cancer and breast, endometrial, and cervical cancers diagnosed before the age of 50. Consuming both vegetables and meat was associated with lower risk of liver cancer in women. Conclusions Our study suggests a potential link between vegetable and meat intake and cancer incidence in the Korean population. Further investigation on the association between the intake of specific types of vegetables and meat and cancer risk in Korean prospective cohort studies is needed.

BACKGROUND: Pathologic diagnosis of central nervous system (CNS) neoplasms is made by comparing light microscopic, immunohistochemical, and molecular cytogenetic findings with clinicoradiologic observations. Intraoperative frozen cytology smears can improve the diagnostic accuracy for CNS neoplasms. Here, we evaluate the diagnostic value of cytology in frozen diagnoses of CNS neoplasms. METHODS: Cases were selected from patients undergoing both frozen cytology and frozen sections. Diagnostic accuracy was evaluated. RESULTS: Four hundred and fifty-four cases were included in this retrospective single-center review study covering a span of 10 years. Five discrepant cases (1.1%) were found after excluding 53 deferred cases (31 cases of tentative diagnosis, 22 cases of inadequate frozen sampling). A total of 346 cases of complete concordance and 50 cases of partial concordance were classified as not discordant cases in the present study. Diagnostic accuracy of intraoperative frozen diagnosis was 87.2%, and the accuracy was 98.8% after excluding deferred cases. Discrepancies between frozen and permanent diagnoses (n = 5, 1.1%) were found in cases of nonrepresentative sampling (n = 2) and misinterpretation (n = 3). High concordance was observed more frequently in meningeal tumors (97/98, 99%), metastatic brain tumors (51/52, 98.1%), pituitary adenomas (86/89, 96.6%), schwannomas (45/47, 95.8%), high-grade astrocytic tumors (47/58, 81%), low grade astrocytic tumors (10/13, 76.9%), non-neoplastic lesions (23/36, 63.9%), in decreasing frequency. CONCLUSIONS: Using intraoperative cytology and frozen sections of CNS tumors is a highly accurate diagnostic ancillary method, providing subtyping of CNS neoplasms, especially in frequently encountered entities.
Brain Neoplasms ; Central Nervous System Neoplasms ; Central Nervous System ; Cytogenetics ; Diagnosis ; Frozen Sections ; Humans ; Meningeal Neoplasms ; Methods ; Neurilemmoma ; Pituitary Neoplasms ; Retrospective Studies

OBJECTIVE: To compare the diagnostic performance of light emitting diode (LED) backlight monitors and cold cathode fluorescent lamp (CCFL) monitors for the interpretation of digital chest radiographs. MATERIALS AND METHODS: We selected 130 chest radiographs from health screening patients. The soft copy image data were randomly sorted and displayed on a 3.5 M LED (2560 x 1440 pixels) monitor and a 3 M CCFL (2048 x 1536 pixels) monitor. Eight radiologists rated their confidence in detecting nodules and abnormal interstitial lung markings (ILD). Low dose chest CT images were used as a reference standard. The performance of the monitor systems was assessed by analyzing 2080 observations and comparing them by multi-reader, multi-case receiver operating characteristic analysis. The observers reported visual fatigue and a sense of heat. Radiant heat and brightness of the monitors were measured. RESULTS: Measured brightness was 291 cd/m2 for the LED and 354 cd/m2 for the CCFL monitor. Area under curves for nodule detection were 0.721 +/- 0.072 and 0.764 +/- 0.098 for LED and CCFL (p = 0.173), whereas those for ILD were 0.871 +/- 0.073 and 0.844 +/- 0.068 (p = 0.145), respectively. There were no significant differences in interpretation time (p = 0.446) or fatigue score (p = 0.102) between the two monitors. Sense of heat was lower for the LED monitor (p = 0.024). The temperature elevation was 6.7degrees C for LED and 12.4degrees C for the CCFL monitor. CONCLUSION: Although the LED monitor had lower maximum brightness compared with the CCFL monitor, soft copy reading of the digital chest radiographs on LED and CCFL showed no difference in terms of diagnostic performance. In addition, LED emitted less heat.
Cold Temperature ; Data Display ; *Electrodes ; Equipment Design ; Humans ; *Image Interpretation, Computer-Assisted ; Lung Neoplasms/*radiography ; ROC Curve ; Radiographic Image Enhancement/*instrumentation ; Radiography, Thoracic/*instrumentation ; Retrospective Studies ; Tomography, X-Ray Computed/*instrumentation