4.6 Article

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

期刊

EUROPEAN UROLOGY
卷 74, 期 2, 页码 211-217

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.eururo.2018.01.017

关键词

Prostate-specific antigen; Kinetics; Prostate cancer; Active surveillance; Outcomes

资金

  1. Department of Defense Prostate Cancer Research Program [W81XWH1110489, W81XWH1410595]
  2. Canary Foundation
  3. U.S. Department of Defense (DOD) [W81XWH1410595, W81XWH1110489] Funding Source: U.S. Department of Defense (DOD)

向作者/读者索取更多资源

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-upwas 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. (C) 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.

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