Cancer staging provides a common language that is used to describe the severity of an individual's cancer, which plays a critical role in optimizing cancer treatment. Recursive partitioning analysis (RPA) is the most widely accepted method for cancer staging. Despite its widespread use, to date, only limited tools have been developed to implement the RPA algorithm for cancer staging. Moreover, most of the available tools can be accessed only from command lines and also lack visualization, making them difficult for clinical investigators without programing skills to use. Therefore, we developed a web server called autoRPA that is dedicated to supporting the construction of prognostic staging models and performance comparisons among different staging models. Based on the RPA algorithm and log-rank test statistics, autoRPA can establish a decision-making tree from survival data and provide clinicians an intuitive method to further prune the decision tree. Moreover, autoRPA can evaluate the contribution of each submitted covariate that is involved in the grouping process and help identify factors that significantly contribute to cancer staging. Four indicators, including hazard consistency, hazard discrimination, percentage of variation explained, and sample size balance, are introduced to validate the performance of the designed staging models. In addition, autoRPA can also be used to compare the performance of different prognostic staging models using a standard bootstrap evaluation method. The web server of autoRPA is freely available at http://rpa.renlab.org.

Cancer staging; Clinical predictive ability; Performance comparison; Recursive partitioning analysis; Web services