Speaker
Description
Prostate cancer (PCa) remains a major global health burden, with more than 1.5 million new cases diagnosed annually worldwide. In the United States alone, over 300,000 men are expected to be diagnosed in 2026, with more than 36,000 deaths. Prostate-specific antigen (PSA) is widely used as a surrogate marker of tumor burden, and its temporal dynamics are closely associated with disease progression.
Improving our understanding of patient-specific PSA kinetics and the underlying drivers of progression is critical for advancing PCa treatment. In this study, we developed and analyzed two simple kinetic pharmacodynamic models of PSA dynamics, a latent variable and transit compartment linking to PSA dynamics. We evaluated each model’s ability to describe longitudinal PSA data from 55 patients undergoing hormone therapy. Model performance was assessed using goodness-of-fit metrics, diagnostic plots, and predictive accuracy for patient-specific trajectories.
Both models were able to capture key features of PSA dynamics, describing both responsive and progressive patient dynamics. However, the transit model demonstrated superior predictive performance and greater stability in estimating patient-specific parameters. These findings highlight the potential of simple mechanistic models to characterize disease progression and support personalized treatment strategies in prostate cancer.
