Cancer risk prediction is necessary for precision early detection, which matches screening intensity to risk. However, practical steps for translating risk predictions to risk-stratified screening policies are not well established. We use a validated population prostate cancer model to simulate the outcomes of strategies that increase intensity for men at high risk and reduce intensity for men at low risk. We define risk by the Prompt-Prostate Genetic Score (PGS)® (San Diego, California), a germline genetic test. We first recalibrate the model to reflect the disease incidence observed within risk strata using data from a large prevention trial where some participants were tested with Prompt-PGS®. We then simulate risk-stratified strategies in a population with the same risk distribution as the trial and evaluate the cost-effectiveness of risk-stratified screening versus universal (risk-agnostic) screening. Prompt-PGS® risk-adapted screening was more cost-effective when universal screening was conservative. Risk-stratified strategies improved outcomes at a cost of less than $100,000 per quality-adjusted life year compared to biennial screening starting at age 55 but risk stratification was not cost-effective compared to biennial screening starting at age 45. Heterogeneity of risk and fraction of the population within each stratum were also important determinants of cost-effectiveness.