Evidence map›Paper›PMID 32852532›Full record

ArticleJAMA oncology2020

Tailoring Intensity of Active Surveillance for Low-Risk Prostate Cancer Based on Individualized Prediction of Risk Stability.

Matthew R Cooperberg, Yingye Zheng, Anna V Faino, Lisa F Newcomb, Kehao Zhu, Janet E Cowan, James D Brooks, Atreya Dash, Martin E Gleave, Frances Martin and 6 more

Abstract readMulticenter Study
In one paragraph

Article in JAMA oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

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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

16 authors.

Matthew R CooperbergHelen Diller Family Comprehensive Cancer Center, Department of Urology, University of California, San Francisco.
Yingye ZhengFred Hutchinson Cancer Research Center, Biostatistics Program, Public Health Sciences, Seattle, Washington.
Anna V FainoFred Hutchinson Cancer Research Center, Biostatistics Program, Public Health Sciences, Seattle, Washington.
Lisa F NewcombFred Hutchinson Cancer Research Center, Cancer Prevention Program, Public Health Sciences, Seattle, Washington.
Kehao ZhuFred Hutchinson Cancer Research Center, Biostatistics Program, Public Health Sciences, Seattle, Washington.
Janet E CowanHelen Diller Family Comprehensive Cancer Center, Department of Urology, University of California, San Francisco.
James D BrooksDepartment of Urology, Stanford University, Stanford, California.
Atreya DashDepartment of Urology, Veterans Affairs Puget Sound Health Care System, Seattle, Washington.
Martin E GleaveDepartment of Urologic Sciences, University of British Columbia, Vancouver, Canada.
Frances MartinDepartment of Urology, Eastern Virginia Medical School, Virginia Beach.
Todd M MorganDepartment of Urology, University of Michigan, Ann Arbor.
Peter S NelsonDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Ian M ThompsonCHRISTUS Medical Center Hospital, San Antonio, Texas.
Andrew A WagnerDivision of Urology, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Peter R CarrollHelen Diller Family Comprehensive Cancer Center, Department of Urology, University of California, San Francisco.
Daniel W LinFred Hutchinson Cancer Research Center, Cancer Prevention Program, Public Health Sciences, Seattle, Washington.

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
Prostate cancer Active Surveillance Study (PASS) Cohort: Infrastructure Support for Cancer ResearchU01CA224255 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI LIN, DANIEL W. · 2019 to 2023
$6.7M
Statistical Methods for Prospective Evaluation of BiomarkersR01CA236558 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ZHENG, YINGYE · 2019 to 2024
$1.9M
NCI NIH HHS R01 CA236558NCI NIH HHS U01 CA086368NCI NIH HHS U01 CA113913NCI NIH HHS U01 CA224255
6 · The paper itself

Abstract

Importance: Active surveillance is increasingly recognized as the preferred standard of care for men with low-risk prostate cancer. However, active surveillance requires repeated assessments, including prostate-specific antigen tests and biopsies that may increase anxiety, risk of complications, and cost. Objective: To identify and validate clinical parameters that can identify men who can safely defer follow-up prostate cancer assessments. Design, Setting, and Participants: The Canary Prostate Active Surveillance Study (PASS) is a multicenter, prospective active surveillance cohort study initiated in July 2008, with ongoing accrual and a median follow-up period of 4.1 years. Men with prostate cancer managed with active surveillance from 9 North American academic medical centers were enrolled. Blood tests and biopsies were conducted on a defined schedule for least 5 years after enrollment. Model validation was performed among men at the University of California, San Francisco (UCSF) who did not enroll in PASS. Men with Gleason grade group 1 prostate cancer diagnosed since 2003 and enrolled in PASS before 2017 with at least 1 confirmatory biopsy after diagnosis were included. A total of 850 men met these criteria and had adequate follow-up. For the UCSF validation study, 533 active surveillance patients meeting the same criteria were identified. Exclusion criteria were treatment within 6 months of diagnosis, diagnosis before 2003, Gleason grade score of at least 2 at diagnosis or first surveillance biopsy, no surveillance biopsy, or missing data. Exposures: Active surveillance for prostate cancer. Main Outcomes and Measures: Time from confirmatory biopsy to reclassification, defined as Gleason grade group 2 or higher on subsequent biopsy. Results: A total of 850 men (median [interquartile range] age, 64 [58-68] years; 774 [91%] White) were included in the PASS cohort. A total of 533 men (median [interquartile range] age, 61 [57-65] years; 422 [79%] White) were included in the UCSF cohort. Parameters predictive of reclassification on multivariable analysis included maximum percent positive cores (hazard ratio [HR], 1.30 [95% CI, 1.09-1.56]; P = .004), history of any negative biopsy after diagnosis (1 vs 0: HR, 0.52 [95% CI, 0.38-0.71]; P < .001 and ≥2 vs 0: HR, 0.18 [95% CI, 0.08-0.4]; P < .001), time since diagnosis (HR, 1.62 [95% CI, 1.28-2.05]; P < .001), body mass index (HR, 1.08 [95% CI, 1.05-1.12]; P < .001), prostate size (HR, 0.40 [95% CI, 0.25-0.62]; P < .001), prostate-specific antigen at diagnosis (HR, 1.51 [95% CI, 1.15-1.98]; P = .003), and prostate-specific antigen kinetics (HR, 1.46 [95% CI, 1.23-1.73]; P < .001). For prediction of nonreclassification at 4 years, the area under the receiver operating curve was 0.70 for the PASS cohort and 0.70 for the UCSF validation cohort. This 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. Conclusions and Relevance: In this study, among men with low-risk prostate cancer, heterogeneity prevailed in risk of subsequent disease reclassification. These findings suggest that active surveillance intensity can be modulated based on an individual's risk parameters and that many men may be safely monitored with a substantially less intensive surveillance regimen.

Indexed as

AdultAgedAged, 80 and overBiopsyHumansMaleMiddle AgedNeoplasm GradingPredictive Value of TestsProspective StudiesProstate-Specific AntigenProstatic NeoplasmsRiskProstate-Specific Antigen

Identifiers

PMID32852532
PMCPMC7453344

What OpenQuestion holds

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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.