Literature DB >> 20406957

Empirical estimates of the lead time distribution for prostate cancer based on two independent representative cohorts of men not subject to prostate-specific antigen screening.

Caroline J Savage1, Hans Lilja, Angel M Cronin, David Ulmert, Andrew J Vickers.   

Abstract

BACKGROUND: Lead time, the estimated time by which screening advances the date of diagnosis, is used to calculate the risk of overdiagnosis. We sought to describe empirically the distribution of lead times between an elevated prostate-specific antigen (PSA) and subsequent prostate cancer diagnosis.
METHODS: We linked the Swedish cancer registry to two independent cohorts: 60-year-olds sampled in 1981-1982 and 51- to 56-year-olds sampled in 1982-1985. We used univariate kernel density estimation to characterize the lead time distribution. Linear regression was used to model the lead time as a function of baseline PSA and logistic regression was used to test for an association between lead time and either stage or grade at diagnosis.
RESULTS: Of 1,167 older men, 132 were diagnosed with prostate cancer, of which 57 had PSA>or=3 ng/mL at baseline; 495 of 4,260 younger men were diagnosed with prostate cancer, of which 116 had PSA>or=3 ng/mL at baseline. The median lead time was slightly longer in the younger men (12.8 versus 11.8 years). In both cohorts, wide variation in lead times followed an approximately normal distribution. Longer lead times were significantly associated with a lower risk of high-grade disease in older and younger men [odds ratio, 0.82 (P=0.023) and 0.77 (P<0.001)].
CONCLUSION: Our findings suggest that early changes in the natural history of the disease are associated with high-grade cancer at diagnosis. IMPACT: The distinct differences between the observed distribution of lead times and those used in modeling studies illustrate the need to model overdiagnosis rates using empirical data. Copyright (c) 2010 AACR

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Year:  2010        PMID: 20406957      PMCID: PMC2866147          DOI: 10.1158/1055-9965.EPI-09-1251

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  23 in total

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Authors:  Andrew J Vickers; Hans Lilja
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Journal:  Eur Urol       Date:  2009-11-06       Impact factor: 20.096

Review 9.  Natural history of changes in prostate specific antigen in early stage prostate cancer.

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Journal:  J Urol       Date:  1994-11       Impact factor: 7.450

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Journal:  Br J Cancer       Date:  2009-03-17       Impact factor: 7.640

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  12 in total

1.  Association of prostate cancer risk alleles with unfavourable pathological characteristics in potential candidates for active surveillance.

Authors:  Barry B McGuire; Brian T Helfand; Shilajit Kundu; Qiaoyan Hu; Jessica A Banks; Phillip Cooper; William J Catalona
Journal:  BJU Int       Date:  2011-11-11       Impact factor: 5.588

2.  Sleep disruption among older men and risk of prostate cancer.

Authors:  Lara G Sigurdardottir; Unnur A Valdimarsdottir; Lorelei A Mucci; Katja Fall; Jennifer R Rider; Eva Schernhammer; Charles A Czeisler; Lenore Launer; Tamara Harris; Meir J Stampfer; Vilmundur Gudnason; Steven W Lockley
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-05       Impact factor: 4.254

Review 3.  Overdiagnosis and overtreatment of prostate cancer.

Authors:  Stacy Loeb; Marc A Bjurlin; Joseph Nicholson; Teuvo L Tammela; David F Penson; H Ballentine Carter; Peter Carroll; Ruth Etzioni
Journal:  Eur Urol       Date:  2014-01-09       Impact factor: 20.096

Review 4.  Screening for prostate cancer: early detection or overdetection?

Authors:  Andrew J Vickers; Monique J Roobol; Hans Lilja
Journal:  Annu Rev Med       Date:  2011-11-03       Impact factor: 13.739

5.  18F-Choline PET/mpMRI for Detection of Clinically Significant Prostate Cancer: Part 2. Cost-Effectiveness Analysis.

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6.  Association Between Lead Time and Prostate Cancer Grade: Evidence of Grade Progression from Long-term Follow-up of Large Population-based Cohorts Not Subject to Prostate-specific Antigen Screening.

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Journal:  Eur Urol       Date:  2017-10-21       Impact factor: 20.096

7.  Temporal trends in cause of death among Swedish and US men with prostate cancer.

Authors:  Mara M Epstein; Gustaf Edgren; Jennifer R Rider; Lorelei A Mucci; Hans-Olov Adami
Journal:  J Natl Cancer Inst       Date:  2012-07-25       Impact factor: 13.506

8.  Gleason grade progression is uncommon.

Authors:  Kathryn L Penney; Meir J Stampfer; Jaquelyn L Jahn; Jennifer A Sinnott; Richard Flavin; Jennifer R Rider; Stephen Finn; Edward Giovannucci; Howard D Sesso; Massimo Loda; Lorelei A Mucci; Michelangelo Fiorentino
Journal:  Cancer Res       Date:  2013-08-15       Impact factor: 12.701

9.  Relative value of race, family history and prostate specific antigen as indications for early initiation of prostate cancer screening.

Authors:  Emily A Vertosick; Bing Ying Poon; Andrew J Vickers
Journal:  J Urol       Date:  2014-03-15       Impact factor: 7.450

10.  Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data.

Authors:  Madhusmita Behera; Erin E Fowler; Taofeek K Owonikoko; Walker H Land; William Mayfield; Zhengjia Chen; Fadlo R Khuri; Suresh S Ramalingam; John J Heine
Journal:  Biomed Eng Online       Date:  2011-11-08       Impact factor: 2.819

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