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Validation of a Colorectal Cancer Risk Prediction Model in US Black Women
Jessica L Petrick1, Nelsy Castro-Webb1, Gary R Zirpoli1
1Slone Epidemiology Center at Boston University , Boston, Massachusetts.
Cancer Prevention Research (Philadelphia, Pa.)
|May 5, 2026
Summary
The National Cancer Institute
Area of Science:
- Oncology
- Epidemiology
- Biostatistics
Background:
- The National Cancer Institute's Colorectal Cancer Risk Assessment Tool (NCI-CCRAT) was developed using data from US White populations.
- Black Americans have the highest incidence of colorectal cancer (CRC), yet the NCI-CCRAT's performance in this demographic is unknown.
- External validation is crucial to ensure risk assessment tool equity and accuracy across diverse populations.
Purpose of the Study:
- To externally validate the NCI-CCRAT for predicting 5-year CRC risk in Black women.
- To assess the calibration and discriminatory accuracy of the NCI-CCRAT in a Black population.
- To identify potential reasons for discrepancies in risk prediction.
Main Methods:
- Utilized data from 53,324 Black Women's Health Study (BWHS) participants (aged 40-84).
- Predicted 5-year absolute CRC risks for three periods: 2001-2005, 2006-2010, and 2011-2015.
- Calculated calibration (Expected/Observed ratio, E/O) and discriminatory accuracy (Area Under the Curve, AUC).
Main Results:
- The NCI-CCRAT significantly overestimated CRC cases in BWHS participants (E/O=1.34).
- Calibration was good for 2001-2005 (E/O=1.10) but overestimated risk in later periods.
- Age-adjusted AUC was 0.62 (95% CI: 0.57-0.66) for 2001-2005, indicating moderate discriminatory accuracy.
Conclusions:
- The NCI-CCRAT overestimates CRC risk in Black women, likely due to population-specific differences in risk factor associations.
- Current risk factors (physical activity, diet, BMI, aspirin use) showed weaker associations in BWHS compared to the NCI-CCRAT development cohort.
- Further research is needed to identify key CRC predictors in Black Americans and develop a tailored risk prediction model.
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