title: 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 (α) that can be estimated by the available follow-up data, in particular by the 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 postsurgical PSA values. This study offers a simple tool to predict prostate cancer relapse. Cancer Res; 76(17); 4941-7. ©2016 AACR.
authors: Department of Neuroscience, University of Turin, Torino, Italy. ilaria.stura@unito.it.
authors: Ilaria
authors: I
authors: Stura
authors: Department of Neuroscience, University of Turin, Torino, Italy.
authors: Domenico
authors: D
authors: Gabriele
authors: Department of Neuroscience, University of Turin, Torino, Italy.
authors: Caterina
authors: C
authors: Guiot
mesh_terms: D000368:Aged; D000726:Androgen Antagonists / Q000627:therapeutic use; D017024:Chemotherapy, Adjuvant; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009364:Neoplasm Recurrence, Local / Q000097:blood*; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000601:surgery
chemical_list: D000726:Androgen Antagonists; D017430:Prostate-Specific Antigen