Overview

The widespread adoption of PSA screening in the US has created an epidemic of low-risk prostate cancer. The vast majority of low-risk cases will not die of their disease yet they continue to seek curative treatment. Active surveillance (AS) has become a preferred option for managing low-risk prostate cancer, but no evidence-based standard exists for how to implement it. In AS, cases are closely monitored with intent to treat if there is any evidence of disease progression. Several AS studies are ongoing, but they use different approaches and their results cannot be readily compared or integrated.

This primary objective of this research is to determine an optimal approach to AS given patient characteristics and preferences. We are collaborating with investigators conducting four of the largest and most well-followed AS cohorts in North America to (a) integrate the data to produce a representative estimate of the risk of disease progression on AS and (b) develop models projecting the long-term outcomes of different AS policies for men with low-risk disease.

We have received and processed date from the four cohorts listed below (a total of over 3,000 cases) and have implemented two integrative analyses – Bayesian joint modeling and Hidden Markov modeling – designed to produce comparable estimates of the risk of biopsy upgrading from Gleason Score (GS) 6 to GS 7 and above. The derived estimates account for differing intervals between biopsies and different risks of competing treatment. The resulting estimates are interpretable as natural history estimates in that they represent the cumulative upgrading risk on an annual basis and in the absence of competing treatment. The results reveal persistent differences among cohorts likely reflecting differences in underlying risk. Thus, not all AS cohorts are comparable and we recommended against developing recommendations for AS strategies based on any single cohort.