Use of tumor dynamics to clarify the observed variability among biochemical recurrence nomograms for prostate cancer

BACKGROUND: Nomograms for biochemical recurrence (BCR) of prostate cancer (PC) after radical prostatectomy can yield very different prognoses for individual patients. Since the nomograms are optimized on different cohorts, the variations may be due to differences in patient risk-factor distributions. In addition, the nomograms assign different relative scores to the same PC risk factors and rarely stratify for tumor growth rate. METHODS: We compared BCR-free probabilities from the GPSM model with a cell kinetics (CK) model that uses the individual's tumor state and growth rate. We first created a cohort of 143 patients that reproduced the GPSM patient distribution in Gleason score, Prostate specific antigen (PSA), Seminal vesicle involvement and Margin status since they form the GPSM score. We then performed 143 CK calculations to determine BCR-free probabilities for comparison with the GPSM results for all scores and with four other prominent nomograms for a high-risk patient. RESULTS: The BCR-free probabilities from the CK model agree within 10% with those from the GPSM study for all scores once the CK model parameters are stratified in terms of the GPSM risk factors and the PSA doubling time (PSADT). However, the probabilities from widely used nomograms vary significantly. CONCLUSIONS: The CK model reproduces the observed GPSM BCR-free probabilities with a broad stratification of model parameters for PC risk factors and can thus be used to describe PC progression for individual patients. The analysis suggests that nomograms should stratify for PSADT to be predictive.