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. 2016 Sep;25(9):1361-6.
doi: 10.1158/1055-9965.EPI-16-0260. Epub 2016 Jul 6.

A Nomogram Derived by Combination of Demographic and Biomarker Data Improves the Noninvasive Evaluation of Patients at Risk for Bladder Cancer

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Free PMC article

A Nomogram Derived by Combination of Demographic and Biomarker Data Improves the Noninvasive Evaluation of Patients at Risk for Bladder Cancer

Sijia Huang et al. Cancer Epidemiol Biomarkers Prev. .
Free PMC article

Abstract

Background: Improvements in the noninvasive clinical evaluation of patients at risk for bladder cancer would be of benefit both to individuals and to health care 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 bladder cancer.

Methods: Logistic regression analysis was used to model the probability of bladder cancer burden in a cohort of 686 subjects (394 with bladder cancer) 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 (AUC) analyses revealed that demographic features alone predicted tumor burden with an accuracy of 0.806 [95% confidence interval (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, noninvasive evaluation of patients at risk of harboring bladder cancer. Further research is needed to evaluate the bladder cancer risk nomogram for potential clinical utility.

Impact: The application of such a nomogram may better inform the decision to perform invasive diagnostic procedures. Cancer Epidemiol Biomarkers Prev; 25(9); 1361-6. ©2016 AACR.

Conflict of interest statement

Disclose of potential conflict of interest All other authors have no potential conflicts to disclose.

Figures

Figure 1
Figure 1. Diagnostic nomogram for predicting bladder cancer
Locate the patient’s age on the age axis. Draw a straight line up to the point’s axis to determine how many points toward predicting bladder cancer the patient should receive. Repeat this process for each of the remaining axes, drawing a straight line each time to the point’s axis. Sum the points received for each predictive variable and locate this number on the total point’s axis. Then draw a straight line down from the total points to the predicted risk score, which depicts the risk the patient has of harboring bladder cancer.
Figure 2
Figure 2
Receiver operating characteristic (ROC) curves for key demographic data, key biomarker data, and the combination of both for predicting the presence of bladder cancer.
Figure 3
Figure 3. Calibration of the hybrid nomogram for bladder cancer
Dashed line is reference line where an ideal nomogram would lie. Dotted line is the performance of hybrid nomogram, while the solid line corrects for any bias in hybrid nomogram.
Figure 4
Figure 4. Decision curve analysis of hybrid nomogram
The Y-axis represents the net benefit, which is calculated by summing the benefits (gaining true positives) and subtracting weighted harms (deleting false positives). A model is of clinical value if it has the highest net benefit.

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