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Projecting Individualized Probabilities of Developing Bladder Cancer in White Individuals

Xifeng Wu, Jie Lin, H. Barton Grossman, Maosheng Huang, Jian Gu, Carol J. Etzel, Christopher I. Amos, Colin P. Dinney, Margaret R. Spitz

From the Departments of Epidemiology and Urology, The University of Texas M.D. Anderson Cancer Center, Houston, TX

Address reprint requests to Xifeng Wu, MD, PhD, Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, 1155 Hermann Pressler Blvd, Houston, TX 77030; e-mail: xwu{at}mdanderson.org

Purpose: There has been no risk assessment model for bladder cancer (BC). We developed the first model incorporating mutagen sensitivity and epidemiologic factors to predict BC risk.

Patients and Methods: We used epidemiologic and genetic data from a large case-control study to build the models and constructed receiver operating characteristic curves. The area under the curve (AUC) was used to evaluate model discriminatory ability. We also projected absolute risk of developing BC by taking into account competing causes of death.

Results: The study included 678 white BC patients and 678 controls. Significant risk factors in the epidemiologic model included pack-years smoked and exposures to diesel, aromatic amines, dry cleaning fluids, radioactive materials, and arsenic. This model yielded good discriminatory ability (AUC = 0.70; 95% CI, 0.67 to 0.73). When mutagen sensitivity data were incorporated, the AUC increased to 0.80 (95% CI, 0.72 to 0.82). The models showed excellent concordance in the internal validation. We also computed an easy to use ordinal risk score and provided examples for projecting absolute risk.

Conclusion: We have developed the first risk prediction model for BC. The enhanced model integrating the genetic factor exhibited excellent discriminatory ability. Our model only requires an individual to answer a few simple questions during a clinic visit to project individualized probability. This model may be used as a basis for developing a Web-based tool for BC risk assessment. Validation of our model in an external population is an essential next step towards practical use in the clinical setting.

Supported by National Cancer Institute Grants No. CA74880 and CA91846.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.