Optimizing Surveillance of Low-Risk Prostate Cancer
*Rebecca Yates Coley, Johns Hopkins Bloomberg School of Public Health
Keywords: precision medicine, Bayesian analysis, hierarchical modeling, latent variable analysis
We present a project from the Johns Hopkins Individualized Health Initiative to support a personalized prostate cancer management program. For individuals with a diagnosis of low risk prostate cancer, active surveillance offers an alternative to early curative intervention. The success of surveillance depends on being able to effectively distinguish indolent tumors from those with metastatic potential, a characteristic that cannot be directly observed without surgical removal of the prostate. We have developed a joint hierarchical Bayesian model for prediction of an individual's latent cancer state. Predictions can be updated in real time and communicated with patients and clinicians through a decision support tool.