Predictive models before and after radical prostatectomy.
BACKGROUND
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.


METHODS
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.


RESULTS
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.
Capitanio
Umberto
U
Department of Urology, Hospital San Raffaele, University Vita-Salute, Milan, Italy. capitanio.umberto@hsr.it
Briganti
Alberto
A
Gallina
Andrea
A
Suardi
Nazareno
N
Karakiewicz
Pierre I
PI
Montorsi
Francesco
F
Scattoni
Vincenzo
V
D003657:Decision Making; D003661:Decision Support Techniques; D018572:Disease-Free Survival; D006801:Humans; D008297:Male; D009415:Nerve Net; D049451:Nomograms; D011237:Predictive Value of Tests; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000601:surgery*; D015203:Reproducibility of Results