Identification of a Risk Stratification Model to Predict Overall Survival and Surgical Benefit in Clear Cell Renal Cell Carcinoma With Distant Metastasis. Chen Jiasheng,Cao Nailong,Li Shouchun,Wang Ying Frontiers in oncology Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma and has different prognoses, especially in patients with metastasis. Here, we aimed to establish a novel model to predict overall survival (OS) and surgical benefit of ccRCC patients with distant metastasis. Using data from the Surveillance, Epidemiology, and End Results (SEER) databases, we identified 2185 ccRCC patients with distant metastasis diagnosed from 2010 to 2015. Univariate and multivariate Cox analysis were used to identify significant prognostic clinicopathological variables. By integrating these variables, a prognostic nomogram was constructed and evaluated using C-indexes and calibration curves. The discriminative ability of the nomogram was measured by analyses of receiver operating characteristic (ROC) curve. A risk stratification model was built according to each patient's total scores. Kaplan-Meier curves were performed in the low-, intermediate- and high-risk groups to evaluate the survival benefit of surgery. Eight clinicopathological variables were included as independent prognostic factors in the nomogram: grade, marital status, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. The nomogram had a better discriminative ability for predicting OS than Tumor-Node-Metastasis (TNM) stage. The C-index was 0.71 (95% CI 0.68-0.74) in the training cohort. The calibration plots demonstrated that the nomogram-based predictive outcomes had good consistency with the actual prognosis results. Total nephrectomy improved prognosis in both the low-risk and intermediate-risk groups, but partial nephrectomy could only benefit the low-risk group. We constructed a predictive nomogram and risk stratification model to evaluate prognosis in ccRCC patients with distant metastasis, which was valuable for prognostic stratification and making therapeutic decisions. 10.3389/fonc.2021.630842

A population-based study to predict distant metastasis in patients with renal cell carcinoma. Li Yong,Chen Peng,Chen Zhi Annals of palliative medicine BACKGROUND:Nomogram is potentially applied for quantitatively evaluating the probability of distant metastasis. The objective of our research was to establish a nomogram to predict distant metastasis in renal cell carcinoma (RCC) patients. METHODS:We conducted a retrospective analysis on 37,190 RCC cases diagnosed between 2010 and 2015 in the Surveillance Epidemiology and End Results (SEER) database. A multivariate logistic regression model-based nomogram was applied for predicting the risk factors concerning distant metastasis of RCC individuals. The concordance index (C-index) and calibration curves were utilized to internally validate the discrimination of nomogram. Decision curve analysis (DCA) was applied for comparing the predictive performance and clinically practical values between nomogram and conventional clinicopathologic risk factors. RESULTS:The nomogram incorporated seven clinical variables and achieved a predictive accuracy with a C-index of 0.863. The calibration plots illustrated optimal accordance between model prediction and practical observation. The DCA indicated the nomogram-based clinical utility. Receiver operating characteristic (ROC) curves also demonstrated an area under the curve (AUC) of 0.901 [95% confidence interval (CI): 0.894-0.908] in the training cohort and 0.892 (95% CI: 0.881-0.903) in the testing cohort. CONCLUSIONS:Our proposed novel nomogram potentially serves as an accurate and user-friendly clinical tool to predict occurrence of distant metastases in RCC patients. 10.21037/apm-20-2481