Background: Improvements in the non-invasive clinical evaluation of patients at risk for bladder cancer (BCa) would be of benefit both to individuals and to healthcare systems. We investigated the potential utility of a hybrid nomogram that combined key demographic features with the results of a multiplex urinary biomarker assay in hopes of identifying patients at risk of harboring BCa. Methods: Logistic regression analysis was used to model the probability of BCa burden in a cohort of 686 subjects (394 with BCa) using key demographic features alone, biomarker data alone and the combination of demographic features and key biomarker data. We examined discrimination, calibration and decision curve analysis techniques to evaluate prediction model performance. Results: Area under the receiver operating characteristic curve (AUROC) analyses revealed that demographic features alone predicted tumor burden with an accuracy of 0.806 [95% CI: 0.76-0.85], while biomarker data had an accuracy of 0.835 [95% CI: 0.80-0.87]. The addition of molecular data into the nomogram improved the predictive performance to 0.891 [95% CI: 0.86-0.92]. Decision curve analyses showed that the hybrid nomogram performed better than demographic or biomarker data alone. Conclusion: A nomogram construction strategy that combines key demographic features with biomarker data may facilitate the accurate, non-invasive evaluation of patients at risk of harboring BCa. Further research is needed to evaluate the BCa risk nomogram for potential clinical utility. Impact: The application of such a nomogram may better inform the decision to perform invasive diagnostic procedures.
- Received March 30, 2016.
- Revision received June 22, 2016.
- Accepted June 23, 2016.
- Copyright ©2016, American Association for Cancer Research.