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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Development and Validation of Nomograms Predicting the 5- and 8-Year Overall and Cancer-Specific Survival of Bladder
Peng Wen1, Jiao Wen2, Xiaolong Huang1,3
1Department of Urology, People's Hospital of Hechuan Chongqing, Chongqing 401520, China.
Background:
Bladder cancer is often prone to recurrence and metastasis. We sought to construct nomogram models to predict the overall survival (OS) and cancer-specific survival (CSS) of bladder cancer patients.
Methods:
A reliable random split-sample approach was used to divide patients into two groups: modeling and validation cohorts. Uni-variate and multivariate survival analyses were used to obtain the independent prognostic risk factors based on the modeling cohort. A nomogram was constructed using the R package, "rms". Harrell's concordance index (C-index), calibration curves and receiver operating characteristic (ROC) curves were applied to evaluate the discrimination, sensitivity and specificity of the nomograms using the R packages "hmisc", "rms" and "timeROC". A decision curve analysis (DCA) was used to evaluate the clinical value of the nomograms via R package "stdca.R".
Results:
10,478 and 10,379 patients were assigned into nomogram modeling and validation cohorts, respectively (split ratio ≈ 1:1). For OS and CSS, the C-index values for internal validation were 0.738 and 0.780, respectively, and the C-index values for external validation were 0.739 and 0.784, respectively. The area under the ROC curve (AUC) values for 5- and 8-year OS and CSS were all greater than 0.7. The calibration curves show that the predicted probability values of 5- and 8-year OS and CSS are close to the actual OS and CSS. The decision curve analysis revealed that the two nomograms have a positive clinical benefit.
Conclusion:
We successfully constructed two nomograms to forecast OS and CSS for bladder cancer patients. This information can help clinicians conduct prognostic evaluations in an individualized manner and tailor personalized treatment plans.
