Related Experiment Video
Updated: Oct 20, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and Validation of Prognostic Nomogram for Young Patients with Kidney Cancer
Shengwei Tian1,2, Si Sun1,2, Weipu Mao1,2
1Department of Urology, Zhongda Hospital, Southeast University, Nanjing, 210009, People's Republic of China.
Background:
The aim of this study was to establish a nomogram model to evaluate the prognosis of early-onset kidney cancer (EOKC) in terms of overall survival (OS) and cancer-specific survival (CSS).
Methods:
Patients with EOKC diagnosed between 2004 and 2015 were collected from Surveillance, Epidemiology and End Results (SEER) and randomly assigned to the training and validation set at a ratio of 2 to 1. Important variables for constructing nomograms were screened by univariate and multivariate Cox analysis. The nomogram model was evaluated using concordance index (C-index), decision curve analysis (DCA) curves, and receiver operating characteristic (ROC) curves.
Results:
A total of 12,526 EOKC patients were included in the study. OS nomogram was constructed based on gender, age, race, grade, AJCC stage, TNM stage, histology, chemotherapy and radiotherapy. CSS nomogram was constructed based on listed above except gender. In the external validation, the C-index for the OS nomogram was 0.855 (95% CI 0.834-0.976), and the C-index for the CSS nomogram was 0.938 (0.925-0.951). High-quality calibration curves were noted in both OS and CSS nomogram models. ROC and DCA curves showed that nomograms had better predictive performance than TNM stage and SEER stage.
Conclusion:
The nomogram model provides an applicable tool for evaluating the OS and CSS prognosis of EOKC.
More Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
Dosage Regimen Designs: Nomograms and Tabulations
Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
Acute Kidney Injury IV: Diagnostic Studies and Prevention
Kidney Transplant I: Introduction
Kidney Transplant III: Nursing Management
Cancer Survival Analysis