Evidence map›Paper›PMID 25819722›Full record

Trial reportEuropean urology2015

Precision Medicine in Active Surveillance for Prostate Cancer: Development of the Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator.

Donna P Ankerst, Jing Xia, Ian M Thompson, Josef Hoefler, Lisa F Newcomb, James D Brooks, Peter R Carroll, William J Ellis, Martin E Gleave, Raymond S Lance and 5 more

Abstract readClinical Trial
In one paragraph

Trial report in European urology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Observational
  12. Article
  13. Article
  14. Biomarkers in active surveillance.Translational andrology and urology · 2018
    Review
  15. Active surveillance: a review of risk-based, dynamic monitoring.Translational andrology and urology · 2018
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Donna P AnkerstDepartment of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Life Sciences Mathematics Unit, Technische Universitaet Muenchen, Munich, Germany. Electronic address: ankerst@tum.de.
Jing XiaDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Ian M ThompsonDepartment of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Josef HoeflerDepartment of Urology, University of Washington, Seattle, WA, USA.
Lisa F NewcombDepartment of Urology, University of Washington, Seattle, WA, USA.
James D BrooksDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Peter R CarrollDepartment of Urology, Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA.
William J EllisDepartment of Urology, University of Washington, Seattle, WA, USA.
Martin E GleaveDepartment of Urologic Sciences, The Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada.
Raymond S LanceDepartments of Microbiology and Molecular Cell Biology and Urology, Eastern Virginia Medical School, Norfolk, VA, USA.
Peter S NelsonDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Andrew A WagnerDepartment of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
John T WeiDepartment of Urology, University of Michigan, Ann Arbor, MI, USA.
Ruth EtzioniDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Daniel W LinDepartment of Urology, University of Washington, Seattle, WA, USA.

Funding

TISSUE CULTURE---COREP30CA054174 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Robin Jean Leach · 1991 to 2026
$59.1M
SAN ANTONIO CENTER OF BIOMARKERS OF RISK FOR PROSTATE CAU01CA086402 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI LEACH, ROBIN JEAN, THOMPSON, IAN M. · 2000 to 2018
$33.1M
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
PROMISS - Prostate Modeling to Identify Surveillance StrategiesR01CA183570 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ETZIONI, RUTH D, LIN, DANIEL W. · 2013 to 2017
$2.9M
New Tools for Prostate Cancer Detection and PrognosticationU01CA152737 · NCI · STANFORD UNIVERSITY · PI BROOKS, JAMES D., GAMBHIR, SANJIV S · 2010 to 2015
$2.8M
NCI NIH HHS P30 CA054174NCI NIH HHS R01 CA183570NCI NIH HHS U01 CA086402NCI NIH HHS U01 CA113913NCI NIH HHS U01 CA152737
6 · The paper itself

Abstract

backgroundMen on active surveillance (AS) face repeated biopsies. Most biopsy specimens will not show disease progression or change management. Such biopsies do not contribute to patient management and are potentially morbid and costly.

objectiveTo use a contemporary AS prospective trial to develop a tool to predict AS biopsy outcomes. DESIGN, SETTING, AND

participantsBiopsy samples (median: 2; range: 2-9 per patient) from 859 men participating in the Canary Prostate Active Surveillance Study and with Gleason 6 prostate cancer (median follow-up: 35.8 mo; range: 3.0-148.7 mo) were analyzed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Logistic regression was used to predict progression, defined as an increase in Gleason score from ≤6 to ≥7 or increase in percentage of cores positive for cancer from <34% to ≥34%. Fivefold internal cross-validation was performed to evaluate the area under the receiver operating characteristic curve (AUC). RESULTS AND LIMITATIONS: Statistically significant risk factors for progression on biopsy were prostate-specific antigen (odds ratio [OR]: 1.045; 95% confidence interval [CI], 1.028-1.063), percentage of cores positive for cancer on most recent biopsy (OR: 1.401; 95% CI, 1.301-1.508), and history of at least one prior negative biopsy (OR: 0.524; 95% CI, 0.417-0.659). A multivariable predictive model incorporating these factors plus age and number of months since last biopsy achieved an AUC of 72.4%.

conclusionsA combination of readily available clinical measures can stratify patients considering AS prostate biopsy. Risk of progression or upgrade can be estimated and incorporated into clinical practice. PATIENT SUMMARY: The Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator, an online tool, can be used to guide patient decision making regarding follow-up prostate biopsy.

Indexed as

Early Detection of CancerPrecision MedicineWatchful WaitingAgedAged, 80 and overBiomedical ResearchBiopsyHumansMaleMiddle AgedProspective StudiesProstatic NeoplasmsRisk AssessmentActive surveillanceBiopsyProgressionProstate-specific antigen

Identifiers

PMID25819722
PMCPMC4583313

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Registered trials

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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.