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Updated: Jun 26, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Colorectal cancer risk prediction tool for white men and women without known susceptibility
Andrew N Freedman1, Martha L Slattery, Rachel Ballard-Barbash
1Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, EPN 4005 MSC 7344, Bethesda, MD 20892-7344, USA. Andrew_Freedman@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.
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