Related Experiment Video
Updated: Jun 11, 2026
Retzius-Sparing Robot-Assisted Radical Prostatectomy
Published on: May 19, 2022
Predictive models before and after radical prostatectomy
Umberto Capitanio1, Alberto Briganti, Andrea Gallina
1Department of Urology, Hospital San Raffaele, University Vita-Salute, Milan, Italy. capitanio.umberto@hsr.it
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.
