Related Experiment Video
Updated: Feb 14, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Nomogram for predicting survival in patients with pancreatic cancer
Wei Song1, Dong-Liu Miao1, Lei Chen1
1Department of Intervention and Vascular Surgery, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou Cancer Medical Center, Suzhou, People's Republic of China.
Background:
The purpose of this study was to develop a nomogram to predict cancer-specific survival (CSS) in pancreatic cancer (PC).
Patients And Methods:
We used the Surveillance, Epidemiology, and End Results (SEER) database to analyze 53,028 patients diagnosed with PC from 2004 to 2014 and randomly divided them into the training (n=26,583) cohort and validation (n=26,445) cohort. Univariate and multivariate analyses were used to select independent prognostic factors. We used significant prognostic factors for constructing a nomogram based on Cox regression analyses. Validation of the nomogram was assessed by discrimination and calibration.
Results:
According to the multivariate models of training cohort, a nomogram that combined age, race, tumor location, marital status, tumor size, TNM stage, tumor grade, and surgery was constructed for predicting CSS. The internally validated and externally validated C-indexes were 0.741 and 0.734, respectively. The calibration curves showed that the nomogram was able to predict 1-, 3-, and 5-year CSS accurately.
Conclusion:
A nomogram effectively predicts survival in patients with PC. This prognostic model may be considered for use in clinical practice.
Related Concept Videos
Cancer Survival Analysis
Predicting Molecular Geometry
Dosage Regimen Designs: Nomograms and Tabulations
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Survival Tree
Building a Survival Tree
Constructing a...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

