Literature DB >> 26611771

The use of multiphase nonlinear mixed models to define and quantify long-term changes in serum prostate-specific antigen: data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Azza Shoaibi1, Gowtham A Rao2, Bo Cai3, John Rawl4, James R Hébert5.   

Abstract

PURPOSE: To test the hypothesis that the pattern of prostate-specific antigen (PSA) change in men diagnosed with high-risk prostate cancer (PrCA) differs from the pattern evident in men diagnosed with low-risk PrCA or those with no evidence of PrCA.
METHODS: A retrospective cohort study from which PSA measures were taken before PrCA diagnosis from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Data were fitted using a nonlinear regression model to estimate the adjusted absolute and relative (%) change of PSA.
RESULTS: Data on 20,888 men with an average age of 61.61 years were included in the analysis. Of these, the 324 (1.55%) diagnosed with high-risk PrCA had a steeper and earlier transition into an exponential pattern of PSA change than the 1368 men diagnosed with low-risk cancer. At 1 year before diagnosis and/or exit, the average absolute PSA rates were 0.05 ng/mL/year (0.05-0.05), 0.59 (0.52-0.66), and 2.60 (2.11-3.09) for men with no evidence of PrCA, men with low-risk PrCA and those with high-risk PrCA, respectively.
CONCLUSIONS: The pattern of PSA change with time was significantly different for men who develop high-risk PrCA from those diagnosed with low-risk PrCA. Further research is required to validate this method and its utilization in PrCA screening.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  High-risk prostate cancer; Mixed-effect model; PSA change rate; PSA growth curves; PSA kinetics; PSA velocity; Prostate cancer detection

Mesh:

Substances:

Year:  2015        PMID: 26611771      PMCID: PMC4688139          DOI: 10.1016/j.annepidem.2015.10.003

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


  22 in total

1.  PSA velocity for the diagnosis of early prostate cancer. A new concept.

Authors:  H B Carter; J D Pearson
Journal:  Urol Clin North Am       Date:  1993-11       Impact factor: 2.241

2.  [Factors influencing variability of PSA blood concentration in patients with prostate benign hyperplasia].

Authors:  S Sanz Chinesta; J M Martínez Jabaloyas; F Boronat Tormo; M Martínez Sarmiento; J F Jiménez Cruz
Journal:  Actas Urol Esp       Date:  1996 Nov-Dec       Impact factor: 0.994

3.  Associations of lifestyle factors and anthropometric measures with repeat PSA levels during active surveillance/monitoring.

Authors:  Anya J Burton; Richard M Martin; Jenny L Donovan; J Athene Lane; Michael Davis; Freddie C Hamdy; David E Neal; Kate Tilling
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2012-08-02       Impact factor: 4.254

4.  Prostate cancer: why is PSA velocity such a sticky concept?

Authors:  Andrew J Vickers
Journal:  Nat Rev Urol       Date:  2013-03-12       Impact factor: 14.432

5.  An empirical evaluation of guidelines on prostate-specific antigen velocity in prostate cancer detection.

Authors:  Andrew J Vickers; Cathee Till; Catherine M Tangen; Hans Lilja; Ian M Thompson
Journal:  J Natl Cancer Inst       Date:  2011-02-24       Impact factor: 13.506

6.  The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial of the National Cancer Institute: history, organization, and status.

Authors:  J K Gohagan; P C Prorok; R B Hayes; B S Kramer
Journal:  Control Clin Trials       Date:  2000-12

Review 7.  A commentary on PSA velocity and doubling time for clinical decisions in prostate cancer.

Authors:  Andrew J Vickers; Ian M Thompson; Eric Klein; Peter R Carroll; Peter T Scardino
Journal:  Urology       Date:  2014-03       Impact factor: 2.649

8.  Combining longitudinal studies of PSA.

Authors:  Lurdes Y T Inoue; Ruth Etzioni; Elizabeth H Slate; Christopher Morrell; David F Penson
Journal:  Biostatistics       Date:  2004-07       Impact factor: 5.899

9.  Long-term prostate-specific antigen velocity in improved classification of prostate cancer risk and mortality.

Authors:  David D Ørsted; Stig E Bojesen; Pia R Kamstrup; Børge G Nordestgaard
Journal:  Eur Urol       Date:  2013-02-04       Impact factor: 20.096

10.  Mixed-effects regression models for studying the natural history of prostate disease.

Authors:  J D Pearson; C H Morrell; P K Landis; H B Carter; L J Brant
Journal:  Stat Med       Date:  1994 Mar 15-Apr 15       Impact factor: 2.373

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  3 in total

1.  Diet-related inflammation and risk of prostate cancer in the California Men's Health Study.

Authors:  Daria M McMahon; James B Burch; James R Hébert; James W Hardin; Jiajia Zhang; Michael D Wirth; Shawn D Youngstedt; Nitin Shivappa; Steven J Jacobsen; Bette Caan; Stephen K Van Den Eeden
Journal:  Ann Epidemiol       Date:  2018-11-02       Impact factor: 3.797

2.  Commentary: Building an Evidence Base for Promoting Informed Prostate Cancer Screening Decisions: An Overview of a Cancer Prevention and Control Program.

Authors:  Otis L Owens; Daniela B Friedman; James Hébert
Journal:  Ethn Dis       Date:  2017-01-19       Impact factor: 1.847

3.  Prostate-Specific Antigen Trends Predict the Probability of Prostate Cancer in a Very Large U.S. Veterans Affairs Cohort.

Authors:  R Jeffrey Karnes; F Roy MacKintosh; Christopher H Morrell; Lori Rawson; Preston C Sprenkle; Michael W Kattan; Michele Colicchia; Thomas B Neville
Journal:  Front Oncol       Date:  2018-08-06       Impact factor: 6.244

  3 in total

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