We introduce a survival analysis framework and deployable system to predict hazard risk of metastatic, castration-resistant prostate cancer patients from their baseline clinicopathological features. Currently, the research in this area signalizes the clinicopathological predictors that can be used to better understand patient prognosis, but there is no visual predictive model with good prediction power that can be presented to assist healthcare workers. Interactive visualization by assistive manipulative models allows to clinicians to move on from the traditional scoring protocols (such as Apgar ...). In addition, innovative modeling and visualization tools for data analysis, monitoring, and interpretation are needed for better understanding from different cancers and also for standard and robust designing of data collection systems. Our system would lead to better data collection, further enhancing the diagnosis and prognosis models and the visualization tool.