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Updated: Nov 9, 2025
A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
A population-based study to predict distant metastasis in patients with renal cell carcinoma
Yong Li1, Peng Chen2, Zhi Chen2
1Department of Urology, The Second Affiliated Hospital, University of South China, Hengyang, China.
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.
