TY - JOUR
T1 - Predictive models before and after radical prostatectomy
AU - Capitanio, Umberto
AU - Briganti, Alberto
AU - Gallina, Andrea
AU - Suardi, Nazareno
AU - Karakiewicz, Pierre I.
AU - Montorsi, Francesco
AU - Scattoni, Vincenzo
PY - 2010/9/1
Y1 - 2010/9/1
N2 - 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.
AB - 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.
KW - Predictive models
KW - Prostate cancer
KW - Prostatectomy
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U2 - 10.1002/pros.21159
DO - 10.1002/pros.21159
M3 - Article
C2 - 20623635
AN - SCOPUS:77956373710
VL - 70
SP - 1371
EP - 1378
JO - Prostate
JF - Prostate
SN - 0270-4137
IS - 12
ER -