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. (c) 2010 Wiley-Liss, Inc.
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. (c) 2010 Wiley-Liss, Inc.
Authors: Jack Cuzick; Gregory P Swanson; Gabrielle Fisher; Arthur R Brothman; Daniel M Berney; Julia E Reid; David Mesher; V O Speights; Elzbieta Stankiewicz; Christopher S Foster; Henrik Møller; Peter Scardino; Jorja D Warren; Jimmy Park; Adib Younus; Darl D Flake; Susanne Wagner; Alexander Gutin; Jerry S Lanchbury; Steven Stone Journal: Lancet Oncol Date: 2011-03 Impact factor: 41.316
Authors: George Rodrigues; Padraig Warde; Tom Pickles; Juanita Crook; Michael Brundage; Luis Souhami; Himu Lukka Journal: Can Urol Assoc J Date: 2012-04 Impact factor: 1.862