Prostate cancer is the second-leading cause of cancer death in American men, with approximately 1 in 8 men diagnosed during their lifetime. Screening for prostate cancer primarily involves measuring prostate-specific antigen (PSA) levels in the blood. Although an estimated 10 million PSA tests are conducted annually, tools for interpreting results and guiding patient action have been limited.
Researchers at the University of Michigan have developed a model to assist doctors and patients in understanding PSA results and their implications for patient life expectancy. Kristian Stensland, M.D., M.P.H., M.S., Assistant Professor of Urology, stated that the model incorporates factors such as patient life expectancy and potential treatment benefits, distinguishing it from prior tools. The aim is to help individuals determine the necessity of further screening or treatment.
Existing risk calculators are noted as less accurate or predict prostate cancer risk using biopsy-based tests that require tissue samples. A previous study by the researchers indicated that PSA scores could influence medical decisions, leading to biopsy referrals even when prostate cancer risk was low. The new model seeks to refer only patients who are likely to benefit from additional screening and treatment.
The model relies on PSA scores and was developed using data from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, which enrolled over 33,000 patients aged 55 to 74 years between 1993 and 2001. Factors considered in the model also included family history of prostate cancer, race, age, body mass index, smoking status, and a history of hypertension, diabetes, or stroke.
Following its development, the model was tested using PSA scores from more than 200,000 patients within the Veterans Affairs Healthcare System, in the same age range, from 2002 to 2006. The model demonstrated the ability to predict the risk for prostate cancer-specific mortality and identify patients who would benefit from additional treatment.
Stensland acknowledged that the model was created and tested using data from two decades ago, and prostate cancer treatment has evolved since then. However, he emphasized that the model improves upon previous tools and can be utilized to refine PSA screening protocols. The researchers are currently working on implementing the model in clinical settings.