Optimization of Biomarker-Based Prostate Cancer Screening Policies

Mathematical models may be used to optimize the decision of when to screen for cancer and how invasive a test to use, for example a biopsy or a biomarker. Partially observable Markov decision process (POMDP) models may be used to optimize screening decisions based on a patient's belief state, which is calculated using Bayesian updating and comprises a patient's complete history of biomarker test results. POMDPs can be used to determine how, if at all, biomarkers should be used for cancer screening in order to maximize quality-adjusted life years, a population health measure of disease burden that incorporates both the quality and quantity of life.