INTRODUCTION: Patients with bladder cancer face a variable risk of recurrence based on their clinical characteristics and the biology of their disease. Physicians need tools to accurately estimate the risk of recurrence and cancer-specific mortality to recommend individualized therapy and to design appropriate clinical trials. METHODS: A MEDLINE literature search was performed on bladder cancer nomograms from January 1966 to July 2007. We recorded input variables, prediction form, number of patients used to develop the prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed. Each prediction tool was classified into patient clinical disease state and the outcome being predicted. RESULTS: The literature search generated 11 published prediction tools that may be applied to patients in various clinical stages of bladder cancer. Of the 11 prediction tools, 8 have undergone validation. The following considerations need to be applied when designing and judging predictive models: predictive accuracy (internal and external validation), calibration, generalizability (reproducibility and transportability), and level of complexity, with the intent of determining whether the new model offers advantages relative to available alternatives. Studies comparing decision tools show that nomograms outperform other methodologies such as risk grouping. CONCLUSIONS: Nomograms provide the most accurate individualized risk estimations that facilitate management decisions. However, current nomograms still need to be refined. Potential advances may include the incorporation of biomarkers, validation in larger patient cohorts, and prospective data acquisition.
