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Background Differential diagnosis of single-nodule pulmonary metastasis (SNPM) and second primary lung cancer (SPLC) in patients with colorectal cancer (CRC) prior to lung surgery is relatively complex. Radiomics is an emerging technique for image information analysis, while it has not yet been applied to construct a differential diagnostic model between SNPM and SPLC in patients with CRC. In the present study, we aimed to extract radiomics signatures from thin-section computed tomography (CT) images of the chest. These radiomics signatures were combined with clinical features to construct a composite differential diagnostic model.Method A total of 91 patients with CRC, including 66 patients with SNPM and 25 patients with SPLC, were enrolled in this study. Patients were randomly assigned to the training cohort (n  =  63) and validation cohort (n  =  28) at a ratio of 7 to 3. Moreover, 107 radiomics features were extracted from the chest thin-section CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to filter these features, and clinical features were screened by univariate analysis. The screened radiomics and clinical features were combined to construct a multifactorial logistic regression composite model. The receiver operating characteristic (ROC) curves were adopted to evaluate the models, and the corresponding nomograms were created.Results A series of 6 radiomics characteristics was screened by LASSO. After univariate logistic regression analysis, the composite model finally included 4 radiomics features and 4 clinical features. In the training cohort, the area under the curve scores of ROC curves were 0.912 (95% confidence interval [CI]: 0.813-0.969), 0.884 (95% CI: 0.778-0.951), and 0.939 (95% CI: 0.848-0.984) for models derived from radiomics, clinical, and combined features, respectively. Similarly, these values were 0.756 (95% CI: 0.558-0.897), 0.888 (95% CI: 0.711-0.975), and 0.950 (95% CI: 0.795-0.997) in the validation cohort, respectively.Conclusions We constructed a model for differential diagnosis of SNPM and SPLC in patients with CRC using radiomics and clinical features. Moreover, our findings provided a new assessment tool for patients with CRC in the future.

Background The effect of high peritoneal dialysate glucose concentration (PDGC) on all-cause and cardiovascular disease (CVD) mortality in peritoneal dialysis (PD) patients is unclear.Objective Our study aimed to investigate the effect of high PDGC on all-cause and CVD mortality in continuous ambulatory PD (CAPD) patients.Methods The study enrolled 716 patients newly initiated on CAPD therapy between January 2006 and December 2010. We allocated the patients to low (<1.56%), medium (≥1.56% to <1.74%), and high (≥1.74%) average PDGC groups according to the tertile of average PDGC in the first 6 months after PD initiation. Cox regression and ordinal logistic regression were used to analyze determinants of mortality and of PDGC use respectively.Results Mean follow-up in the study cohort was 31 ± 15 months. The all-cause mortality was 4.7 events per 100 patient-years, and the leading cause of death was CVD. Patients with a higher PDGC had significantly higher cumulative rates of all-cause (log-rank p< 0.001) and CVD mortality (log-rank p< 0.001). In Cox regression analysis, high PDGC independently predicted higher all-cause (hazard ratio: 2.63; p= 0.004) and CVD mortality (hazard ratio: 2.78; p= 0.01). Compared with a lower PDGC, a higher PDGC was significantly associated with older age [odds ratio (OR): 1.02; p< 0.001], low residual renal function (OR: 0.91; p< 0.001), and high dialysate-to-plasma ratio of creatinine (OR: 28.61; p< 0.001) in ordinal logistic regression.Conclusions Higher PDGC is associated with higher all-cause and CVD mortality in CAPD patients.