PSA-based tool improves decision-making for prostate cancer screening and treatment

Prostate cancer is the second leading cause of cancer death in American men.

About 1 in 8 men will be diagnosed with prostate cancer in their lifetime, and the risk varies by age and race.

Prostate cancer is mainly controlled by the levels of prostate-specific antigen in the blood.

Although an estimated 10 million PSA tests are performed annually, there are few tools available to interpret the results and help patients decide what course of action to take.

University of Michigan researchers have developed a model that can help doctors and patients understand PSA results and what they mean for patients’ life expectancy.

“Current tools don’t take into account how long someone might live or the benefit a patient might get from treatment,” said Kristian Stensland, MD, MPH, MS, Assistant Professor of Urology.

“Our model is the first to integrate all these factors and help people understand whether they need further screening or treatment.”

Existing risk calculators are less accurate or predict prostate cancer risk through biopsy-based tests, which require tissue samples and additional processing time.

In a previous study, researchers showed that PSA scores can influence both physician and patient behavior, leading to biopsy referrals even when the risk of harm from prostate cancer is low.

With this model, they hope that only patients likely to benefit from further screening and treatment will receive referrals.

The new model is based on PSA scores and was developed using data from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, which enrolled more than 33,000 patients aged 55 to 74 from 1993 to 2001.

The researchers also took into account family history of prostate cancer, race, age, body mass index, smoking status, and history of hypertension, diabetes, or stroke.

After building the model, they tested it using PSA scores from more than 200,000 patients who received care at the Veterans Affairs Health System in the same age range from 2002 to 2006.

The model was able to predict the risk of prostate cancer-related mortality and highlight which patients would benefit from further treatment.

“It’s important to remember that we built and tested the model using data from two decades ago, and a lot has changed since then,” Stensland said.

“Although prostate cancer treatment is different now, our model improves on previous tools and can be used to decide how we do PSA testing.”

The researchers are now working to apply their model to clinical settings.