What if I don't treat my PSA-detected prostate cancer? Answers from three natural history models
Cancer Epidemiology, Biomarkers & Prevention , Volume 20 - Issue 5 p. 740- 750
Background: Making an informed decision about treating a prostate cancer detected after a routine prostate-specific antigen (PSA) test requires knowledge about disease natural history, such as the chances that it would have been clinically diagnosed in the absence of screening and that it would metastasize or lead to death in the absence of treatment. Methods: We use three independently developed models of prostate cancer natural history to project risks of clinical progression events and disease-specific deaths for PSA-detected cases assuming they receive no primary treatment. Results: The three models project that 20%-33% of men have preclinical onset; of these 38%-50% would be clinically diagnosed and 12%-25% would die of the disease in the absence of screening and primary treatment. The risk that men age less than 60 at PSA detection with Gleason score 2-7 would be clinically diagnosed in the absence of screening is 67%-93% and would die of the disease in the absence of primary treatment is 23%-34%. For Gleason score 8 to 10 these risks are 90%-96% and 63%-83%. Conclusions: Risks of disease progression among untreated PSA-detected cases can be nontrivial, particularly for younger men and men with high Gleason scores. Model projections can be useful for informing decisions about treatment. Impact: This is the first study to project population-based natural history summaries in the absence of screening or primary treatment and risks of clinical progression events following PSA detection in the absence of primary treatment.
|Cancer Epidemiology, Biomarkers & Prevention|
|Organisation||Erasmus MC: University Medical Center Rotterdam|
Gulati, R, Wever, E.M, Tsodikov, A, Penson, D.F, Inoue, L.Y.T, Katcher, J, … Etzioni, R. (2011). What if I don't treat my PSA-detected prostate cancer? Answers from three natural history models. Cancer Epidemiology, Biomarkers & Prevention, 20(5), 740–750. doi:10.1158/1055-9965.EPI-10-0718