Predictive models before and after radical prostatectomy

CONTEXT. In the last 10 years, several user-friendly predictive tools have been developed to help clinicians in decision-making process before and after radical prostatectomy. OBJECTIVE. To review the most known and used predictive models in pre-operative and post-operative setting. EVIDENCE ACQUISITION. A structured, comprehensive literature review was performed using data retrieved from recent review articles, original articles, and abstracts. Used keywords were predictive models, nomograms, look-up tables, classification and regression-tree analysis, artificial neural networks, and radical prostatectomy. EVIDENCE SYNTHESIS. A great amount of predictive models has been provided in oncology setting: nomograms, look-up tables, classification and regression-tree analysis, propensity scores, risk-group stratification models, and artificial neural networks. Pre-surgery predictive tools offer the opportunity of getting the most evidence-based and individualized selection of available treatment alternatives. Post-operative predictive models usually provide higher accuracy relative to the pre-surgery models. CONCLUSIONS. Decisions and treatment should be tailored to each individual patient and to the specific characteristics of patients. A number of available predictive models represent a tool to provide accurate prediction of cancer natural history and to improve patients' care.