Recurrences of prostate cancer affect approximately one-quarter of patients who have undergone radical prostatectomy. Reliable factors to predict time to relapse in specific individuals are lacking. Here we present a mathematical model that evaluates a biologically sensible parameter ($\alpha$) which can be estimated by the available follow-up data, in particular by the prostate specific antigen (PSA) series. This parameter is robust and highly predictive for the time to relapse, also after administration of adjuvant androgen deprivation therapies. We present a practical computational method based on the collection of only four post-surgical PSA values. This study offers a simple tool to predict PCa relapse.

### A simple PSA-based computational approach predicts the timing of cancer relapse in prostatectomized patients

#### Abstract

Recurrences of prostate cancer affect approximately one-quarter of patients who have undergone radical prostatectomy. Reliable factors to predict time to relapse in specific individuals are lacking. Here we present a mathematical model that evaluates a biologically sensible parameter ($\alpha$) which can be estimated by the available follow-up data, in particular by the prostate specific antigen (PSA) series. This parameter is robust and highly predictive for the time to relapse, also after administration of adjuvant androgen deprivation therapies. We present a practical computational method based on the collection of only four post-surgical PSA values. This study offers a simple tool to predict PCa relapse.
##### Scheda breve Scheda completa Scheda completa (DC)
2016
76
17
4941
4947
http://cancerres.aacrjournals.org/content/76/17/4941.long
mathematical model, prostate cancer, von Bertalanffy
Stura Ilaria; Gabriele Domenico; Guiot Caterina
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1593967