BackgroundPancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM.MethodsPC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves.ResultsA total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power.ConclusionWe developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

The relationship between the accumulation of fat in visceral or subcutaneous tissue and bone mineral density (BMD) remains unclear. Our primary objective in this study was to illuminate this relationship by conducting an investigation on a vast scale, encompassing a nationally representative population in the United States. A weighted multiple linear regression model was established to evaluate the relationship between visceral fat, subcutaneous fat, and BMD. Additionally, the exploration of the potential nonlinear relationship was conducted employing the methodology of smooth curve fitting. In order to determine potential inflection points, a two-stage linear regression model was utilized. A total of 10,455 participants between the ages of 20 and 59 were included in this study. Various weighted multiple linear regression models revealed a negative correlation between lumbar BMD and visceral mass index (VMI) and subcutaneous mass index (SMI). However, the association between VMI and lumbar BMD displayed a U-shaped pattern upon employing the smooth curve fitting, and the inflection point of 0.304 kg/m2was determined using a two-stage linear regression model. Our findings indicated a negative association between subcutaneous fat and BMD. A U-shaped relationship was observed between visceral fat and BMD.

BackgroundBone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.MethodsThe study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.ResultsOur prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083–0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979–0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.ConclusionOur developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

The risk of osteoporosis in breast cancer patients is higher than that in healthy populations. The fracture and death rates increase after patients are diagnosed with osteoporosis. We aimed to develop machine learning-based models to predict the risk of osteoporosis as well as the relative fracture occurrence and prognosis. We selected 749 breast cancer patients from two independent Chinese centers and applied six different methods of machine learning to develop osteoporosis, fracture and survival risk assessment models. The performance of the models was compared with that of current models, such as FRAX, OSTA and TNM, by applying ROC, DCA curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Three models were developed. The XGB model demonstrated the best discriminatory performance among the models. Internal and external validation revealed that the AUCs of the osteoporosis model were 0.86 and 0.87, compared with the FRAX model (0.84 and 0.72)/OSTA model (0.77 and 0.66), respectively. The fracture model had high AUCs in the internal and external cohorts of 0.93 and 0.92, which were higher than those of the FRAX model (0.89 and 0.86). The survival model was also assessed and showed high reliability via internal and external validation (AUC of 0.96 and 0.95), which was better than that of the TNM model (AUCs of 0.87 and 0.87). Our models offer a solid approach to help improve decision making.

No abstract

Objective: The relationship between fat accumulation in visceral or subcutaneous tissue and bone mineral density (BMD) remains unclear. In this study, we aim to shed light on this relationship by examining a large, nationally representative population. Methods: A weighted multiple linear regression model was established to evaluate the relationship between visceral fat, subcutaneous fat and BMD. Additionally, the potential nonlinear relationship was explored using smooth curve fitting method. Results: A total of 10455 participants between the ages of 20 and 59 were included in this study. Various weighted multiple linear regression models revealed a negative correlation between lumbar BMD T-score and visceral mass index (VMI) and subcutaneous mass index (SMI). However, the association between VMI and lumbar BMD T-score was U-shaped when we did smooth curve fitting, and the inflection point of 0.304kg/m² was determined using a two-stage linear regression model. Conclusions: Subcutaneous fat and BMD T-score were found to have a negative association, and visceral fat and BMD T-score were discovered to have a U-shaped connection. It is the significance of taking body composition and weight control into account while treating and preventing osteoporosis.

No abstract