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Active surveillance for prostate cancer can be individualized based on risk parameters


Personalized approach would reduce the number of biopsies some patients need to receive.

The intensity level of active surveillance for a man with low-risk prostate cancer can be personalized based on his individual risk factors, according to research published in JAMA Oncology.1

“Active surveillance as currently practiced and currently described in most of the guidelines involves a combination of periodic PSA assessments and tumor assessments. And classically, the tumor assessments have all required prostate biopsy on a regular basis. Initially, it was every year; most guidelines now say every 1 to 2 years. And that timing is what we are now really trying to tailor,” lead researcher Matthew R. Cooperberg, MD, said in an interview with JAMA Oncology made simultaneously available with the publication.2

“The frequency of the biopsies in particular in active surveillance is a concern. Every time we do a biopsy of the prostate, there is discomfort, there is risk of bleeding and there's a low risk of a significant infection. Patients don't like them, they're expensive, and they're not without risk. So there's been a lot of interest in alternatives to biopsy. MRI has not yet been shown to be a safe replacement for biopsy; [although] it can help guide the decision in some select cases. And it's a combination of the desire to avoid multiple biopsies and the uncertainty associated with active surveillance that still discourages some men and I think some clinicians in particular, from embracing active surveillance more universally,” added Cooperberg, who is professor of urology; epidemiology & biostatistics, and Helen Diller Family Chair in Urology at the University of California, San Francisco (UCSF).

Given this situation, Cooperberg et al sought to determine if there were clinical parameters that could be used to implement an individualized approach to active surveillance, whereby patients with lower risk could undergo a less intensive management program. To accomplish this, the investigators developed a prediction model and used it to assess data from men enrolled in the Canary Prostate Active Surveillance Study (PASS). To validate the model, the investigators also assessed a group of men not enrolled in PASS who were treated at UCSF.

The multicenter, prospective active surveillance cohort PASS study was launched in 2008. The study enrolled men receiving active surveillance for prostate cancer at 9 academic medical centers in North America. Patients received blood tests and biopsies on a defined schedule for a minimum of 5 years following enrollment.

Cooperberg et al identified 850 men enrolled in PASS prior to 2017 who had been diagnosed since 2003 with Gleason grade group 1 prostate cancer and had at least 1 confirmatory biopsy post-diagnosis. The median age of this group was 64 (range, 58-68), 91% were White, and 5% were Black. The UCSF validation cohort included 533 patients identified according to the same criteria as the PASS group. In the UCSF cohort, themedian age was 61 (range, 57-65), 79% were White, and 2% were black.

The study defined reclassification as subsequent biopsy revealing a Gleason grade group of 2 or higher. Cooperberg et al’s model identified several parameters predictive of reclassification on multivariable analysis: body mass index (HR, 1.08; P < .001); prostate size (HR, 0.40; P <.001); maximum percent positive cores (HR, 1.30; P = .004); PSA kinetics (HR, 1.46; P <.001); time since diagnosis (HR, 1.62; P <.001); PSA at diagnosis (HR, 1.51; P = .003); and history of any negative biopsy after diagnosis (1 vs 0: HR, 0.52; P <.001; and ≥2 vs 0: HR, 0.18; P <.001).

The researchers also found that the area under the receiver operating curve was 0.70 for prediction of nonreclassification at 4 years for both the PASS cohort and the validation cohort. The authors also wrote that the model “achieved a negative predictive value of 0.88 (95% CI, 0.83-0.94) for those in the bottom 25th percentile of risk and of 0.95 (95% CI, 0.89-1.00) for those in the bottom 10th percentile.”

Using their model, Cooperberg el al created an online calculator to that allows patients with low-risk disease to enter their information and determine more precisely where they are at on the spectrum of risk.

“We do have an online calculator, which will generate the results, [but] it's important to stress that…the model is not going to tell [an individual] what to do; it's not going to tell them whether or not to get a biopsy. The idea is to provide a more personalized assessment of risk,” explained Cooperberg.


1. Cooperberg MR, Zheng Y, Faino AV, et al. Tailoring Intensity of Active Surveillance for Low-Risk Prostate Cancer Based on Individualized Prediction of Risk Stability [published online August 27, 2020]. JAMA Oncol. doi: 10.1001/jamaoncol.2020.3187

2. JN learning. Interview with Matthew R. Cooperberg, MD, author of Tailoring Intensity of Active Surveillance for Low-Risk Prostate Cancer Based on Individualized Prediction of Risk Stability [made available August 27, 2020]. JAMA Oncol. Accessed September 2, 2020. https://bit.ly/3jCno3R.

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