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Cancer Epidemiol Biomarkers Prev. 2013 Dec;22(12):2241-9. doi: 10.1158/1055-9965.EPI-13-0632. Epub 2013 Oct 2.

Predicting risk of bladder cancer using clinical and demographic information from prostate, lung, colorectal, and ovarian cancer screening trial participants.

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  • 1Authors' Affiliations: Center for Urologic Oncology, Glickman Urological & Kidney Institute, Cleveland Clinic, Cleveland, Ohio; Division of Urologic Surgery, Washington University School of Medicine, St. Louis, Missouri; Divisions of Cancer Epidemiology and Genetics and Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and Division of Urology, Brigham and Women's Hospital, Harvard University Medical School, Boston, Massachusetts.

Abstract

BACKGROUND:

Effective screening and prevention strategies for bladder cancer require accurate risk stratification models. We developed models to predict the risk of bladder cancer based on clinical and sociodemographic data on participants in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial.

METHODS:

Baseline clinical and sociodemographic data were obtained from 149,542 PLCO participants, ages 55 to 74 years, without a prior history of bladder cancer. Cox proportional hazards models were used to predict the risk of all bladder cancers (ABC) and of high-grade bladder cancers (HGBC) from baseline information. We used the HGBC risk model to design a hypothetical bladder cancer mortality prevention trial.

RESULTS:

Over a median follow-up of 12 years, 1,124 men and 259 women developed bladder cancer (including 392 and 72 with HGBC, respectively). The incidence in men and in women was 133.6 and 29.6 cases per 100,000 person-years, respectively. Nomograms constructed for predicting the risk of ABC and HGBC had c-indices of 0.746 and 0.759, respectively. Age, race, education, smoking (intensity and duration), comorbidity, prostatitis, syphilis, and hormone replacement therapy use were statistically significant predictors in the models. We show that our risk model can be used to design a bladder cancer mortality prevention trial half the size of a trial designed without risk stratification.

CONCLUSION:

Models to predict the risk of ABC and HGBC have been developed and validated.

IMPACT:

Using the upper 40th percentile from the HGBC model, a suitable cohort for a screening or chemoprevention trial could be identified, although the size and follow-up of such a trial would be costly.

©2013 AACR.

PMID:
24089460
[PubMed - indexed for MEDLINE]
PMCID:
PMC3876874
[Available on 2014/12/1]
Free PMC Article
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