Abstract
Prostatectomized patients are at risk of resurgence, and for this reason, during a follow-up period, they are monitored for prostate-specific antigen (PSA) growth, an indicator of tumor progression. The presence of tumors can be evaluated with an expensive exam, called positron emission tomography with prostate-specific membrane antigen (PET-PSMA). To justify the high cost of the PET-PSMA and, at the same time, to contain the risk for the patient, this exam should be recommended only when the evidence of tumor progression is strong. With the aim of estimating the optimal time to recommend the exam based on the patient's history and collected data, we build a hierarchical Bayesian model that describes, jointly, the PSA growth curve and the probability of a positive PET-PSMA. With our proposal, we process all past and present information about the patients PSA measurement and PET-PSMA results, in order to give an informed estimate of the optimal time, improving current practice.