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/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

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/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

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/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

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.

Concomitant renal cell carcinomas (RCC) of both native and allograft kidneys are extremely rare, and only a few cases have been reported in the available English literature. A particularly rare variant within the adult population is the Xp11.2 translocation/transcription factor E3 (TFE3)-rearranged RCC. Although few case reports of TFE3-rearranged RCC have been reported in children who underwent kidney transplantation (KT), no case of adults with TFE3-rearranged RCC following KT has been reported. Herein, we presented the radiological and pathological findings of a rare metachronous papillary RCC in the allograft kidney and TFE3-rearranged RCC in the native kidney. The TFE3-rearranged RCC in the native kidney exhibited slow expansion in size over five years. Radiologically, it appeared as a slightly enhanced, lobulated mass on contrast-enhanced CT. MRI revealed high signal intensity on T1-weighted images and low signal intensity on T2-weighted images.