Originally published as JCO Early Release 10.1200/JCO.2008.17.4797 on December 29 2008

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Colorectal Cancer Risk Prediction Tool for White Men and Women Without Known Susceptibility

From the Division of Cancer Control and Population Sciences; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda; Information Management Systems, Rockville, MD; School of Medicine, University of Utah, Salt Lake City, UT; and the Division of Research, Kaiser Permanente Medical Group, Oakland, CA.

Corresponding author: Andrew N. Freedman, PhD, National Cancer Institute, National Institutes of Health, EPN 4005 MSC 7344, 6130 Executive Blvd, Bethesda, MD 20892-7344; e-mail: Andrew_Freedman{at}nih.gov.

Purpose Given the high incidence of colorectal cancer (CRC), and the availability of procedures that can detect disease and remove precancerous lesions, there is a need for a model that estimates the probability of developing CRC across various age intervals and risk factor profiles.

Methods The development of separate CRC absolute risk models for men and women included estimating relative risks and attributable risk parameters from population-based case-control data separately for proximal, distal, and rectal cancer and combining these estimates with baseline age-specific cancer hazard rates based on Surveillance, Epidemiology, and End Results (SEER) incidence rates and competing mortality risks.

Results For men, the model included a cancer-negative sigmoidoscopy/colonoscopy in the last 10 years, polyp history in the last 10 years, history of CRC in first-degree relatives, aspirin and nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, body mass index (BMI), current leisure-time vigorous activity, and vegetable consumption. For women, the model included sigmoidoscopy/colonoscopy, polyp history, history of CRC in first-degree relatives, aspirin and NSAID use, BMI, leisure-time vigorous activity, vegetable consumption, hormone-replacement therapy (HRT), and estrogen exposure on the basis of menopausal status. For men and women, relative risks differed slightly by tumor site. A validation study in independent data indicates that the models for men and women are well calibrated.

Conclusion We developed absolute risk prediction models for CRC from population-based data, and a simple questionnaire suitable for self-administration. This model is potentially useful for counseling, for designing research intervention studies, and for other applications.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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