Prostate Cancer Screening: Toward a Risk-Stratified Public Health Model to Reduce Overdiagnosis and Optimize Resource Allocation

Abstract

Introduction: PSA-based screening reduces prostate cancer mortality but is associated with significant overdiagnosis and overtreatment. This review evaluates the transition from age-based screening to risk-stratified models to optimize resource allocation.
Methods: We synthesized data from landmark randomized trials (ERSPC, Göteborg-1, CAP), recent clinical guidelines (AUA/SUO, NCCN, EAU), and emerging results from risk-stratified trials such as ProScreen and BARCODE1.
Results: Evidence suggests that integrating baseline PSA, multiparametric MRI (mpMRI), and blood-based biomarkers (e.g., Stockholm3) significantly reduces unnecessary biopsies and overdiagnosis. While Polygenic Risk Scores (PRS) show potential in identifying high-risk individuals, their clinical integration is currently limited to trial settings. Sequential screening algorithms demonstrate the feasibility of maintaining mortality benefits while reducing healthcare expenditure.
Conclusion: A shift toward risk-stratified public health models is essential to balance the benefits and harms of screening. Future efforts must focus on validating these models across diverse populations and ensuring scalability in resource-limited settings.