Refined Analysis of Prostate-specific Antigen Kinetics to Predict Prostate Cancer Active Surveillance Outcomes.

Cooperberg MR¹, Brooks JD², Faino AV³, Newcomb LF⁴, Kearns JT⁵, Carroll PR⁶, Dash A⁵, Etzioni R³, Fabrizio MD⁷, Gleave ME⁸, Morgan TM⁹, Nelson PS¹⁰, Thompson IM¹¹, Wagner AA¹², Lin DW⁴, Zheng Y³.

Author information:
1. Department of Urology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA. Electronic address: [email protected].
2. Department of Urology, Stanford University, Stanford, CA, USA.
3. Biostatistics Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
4. Cancer Prevention Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Urology, University of Washington, Seattle, WA, USA.
5. Department of Urology, University of Washington, Seattle, WA, USA.
6. Department of Urology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
7. Department of Urology, Eastern Virginia Medical School, Virginia Beach, VA, USA.
8. Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.
9. Department of Urology, University of Michigan, Ann Arbor, MI, USA.
10. Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
11. CHRISTUS Medical Center Hospital, San Antonio, TX, USA.
12. Division of Urology, Beth Israel Deaconess Medical Center, Boston, MA, USA.

Comment in

• Re: Refined Analysis of Prostate-specific Antigen Kinetics to Predict Prostate Cancer Active Surveillance Outcomes. [Eur Urol. 2018]

Abstract

BACKGROUND:

For men on active surveillance for prostate cancer, utility of prostate-specific antigen (PSA) kinetics (PSAk) in predicting pathologic reclassification remains controversial.

OBJECTIVE:

To develop prediction methods for utilizing serial PSA and evaluate frequency of collection.

DESIGN, SETTING, AND PARTICIPANTS:

Data were collected from men enrolled in the multicenter Canary Prostate Active Surveillance Study, for whom PSA data were measured and biopsies performed on prespecified schedules. We developed a PSAk parameter based on a linear mixed-effect model (LMEM) that accounted for serial PSA levels.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:

The association of diagnostic PSA and/or PSAk with time to reclassification (increase in cancer grade and/or volume) was evaluated using multivariable Cox proportional hazards models.

RESULTS AND LIMITATIONS:

A total of 851 men met the study criteria; 255 (30%) had a reclassification event within 5 yr. Median follow-up was 3.7 yr. After adjusting for prostate size, time since diagnosis, biopsy parameters, and diagnostic PSA, PSAk was a significant predictor of reclassification (hazard ratio for each 0.10 increase in PSAk=1.6 [95% confidence interval 1.2-2.1, p<0.001]). The PSAk model improved stratification of risk prediction for the top and bottom deciles of risk over a model without PSAk. Model performance was essentially identical using PSA data measured every 6 mo to those measured every 3 mo. The major limitation is the reliability of reclassification as an end point, although it drives most treatment decisions.

CONCLUSIONS:

PSAk calculated using an LMEM statistically significantly predicts biopsy reclassification. Models that use repeat PSA measurements outperform a model incorporating only diagnostic PSA. Model performance is similar using PSA assessed every 3 or 6 mo. If validated, these results should inform optimal incorporation of PSA trends into active surveillance protocols and risk calculators.

PATIENT SUMMARY:

In this report, we looked at whether repeat prostate-specific antigen (PSA) measurements, or PSA kinetics, improve prediction of biopsy outcomes in men using active surveillance to manage localized prostate cancer. We found that in a large multicenter active surveillance cohort, PSA kinetics improves the prediction of surveillance biopsy outcome.

Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.

PMCID: PMC6263168 [Available on 2019-08-01]