Evidence map›Paper›PMID 29433975›Full record

ArticleEuropean urology2018

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

Matthew R Cooperberg, James D Brooks, Anna V Faino, Lisa F Newcomb, James T Kearns, Peter R Carroll, Atreya Dash, Ruth Etzioni, Michael D Fabrizio, Martin E Gleave and 6 more

Abstract readMulticenter Study
In one paragraph

Article in European urology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Article
  2. Article
  3. [European journal of nuclear medicine and molecular imaging · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Is Active Surveillance Too Active?Current urology reports · 2023
    Review
  13. Article
  14. Article
  15. Article
  16. Liquid biomarkers in active surveillance.World journal of urology · 2022
    Review
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Matthew R CooperbergDepartment 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: matthew.cooperberg@ucsf.edu.
James D BrooksDepartment of Urology, Stanford University, Stanford, CA, USA.
Anna V FainoBiostatistics Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Lisa F NewcombCancer Prevention Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Urology, University of Washington, Seattle, WA, USA.
James T KearnsDepartment of Urology, University of Washington, Seattle, WA, USA.
Peter R CarrollDepartment of Urology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
Atreya DashDepartment of Urology, University of Washington, Seattle, WA, USA.
Ruth EtzioniBiostatistics Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Michael D FabrizioDepartment of Urology, Eastern Virginia Medical School, Virginia Beach, VA, USA.
Martin E GleaveDepartment of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.
Todd M MorganDepartment of Urology, University of Michigan, Ann Arbor, MI, USA.
Peter S NelsonDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Ian M ThompsonCHRISTUS Medical Center Hospital, San Antonio, TX, USA.
Andrew A WagnerDivision of Urology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Daniel W LinCancer Prevention Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Urology, University of Washington, Seattle, WA, USA.
Yingye ZhengBiostatistics Program, Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Funding

The Early Detection Research Network: Data Management and Coordinating CenterU24CA086368 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI RUTH D ETZIONI, Ziding Feng · 2010 to 2026
$75.7M
EDRN--DATA MANAGEMENT AND COORDINATING CENTERU01CA086368 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI FENG, ZIDING · 2000 to 2010
$16.9M
Prostate Cancer Biomarker and Imaging Validation Alliance: Emory University, University of Alabama Birmingham, and University of Texas SouthwesternU01CA113913 · NCI · EMORY UNIVERSITY · PI Martin G. Sanda · 2005 to 2026
$16.4M
Statistical Methods for Prospective Evaluation of BiomarkersR01GM085047 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ZHENG, YINGYE · 2009 to 2017
$2.6M
NCI NIH HHS U01 CA086368NCI NIH HHS U01 CA113913NCI NIH HHS U24 CA086368NIGMS NIH HHS R01 GM085047
6 · The paper itself

Abstract

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

objectiveTo develop prediction methods for utilizing serial PSA and evaluate frequency of collection. DESIGN, SETTING, AND

participantsData 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.

conclusionsPSAk 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.

Indexed as

Decision Support TechniquesWatchful WaitingAgedBiopsyClinical Decision-MakingDisease ProgressionHumansKallikreinsKineticsMaleMiddle AgedNeoplasm GradingNorth AmericaPredictive Value of TestsProspective StudiesProstate-Specific AntigenKallikreinsKLK3 protein, humanProstate-Specific AntigenActive surveillanceKineticsOutcomesProstate cancerProstate-specific antigen

Identifiers

PMID29433975
PMCPMC6263168

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.