Tailoring Colorectal Cancer Risk Assessments by Comparing Model Performance and Custom Thresholds in a Predominantly...

Colorectal cancer remains a significant public health concern, particularly among underserved populations. This study evaluated colorectal cancer risk factors and compared risk assessment models in a predominantly Hispanic cohort residing along the US-Mexico border. We analyzed data from 4,202 colorectal cancer screening participants, mostly women (78.3%), with a mean age of 57.3 years [standard deviation (SD) = 5.54]. Most were born in Mexico (86.2%) and had resided in the United States for an average of 25 years (SD, 16.5). Risk was assessed using four models: Freedman, Wells, Kaminski, and Yeoh. We examined model concordance, risk distributions, and gender-specific trends. Freedman's model showed that 41.1% of participants had a lifetime colorectal cancer risk above the national average. Men had a higher lifetime risk (51.3%) than women (38.3%), whereas more women had an elevated 10-year risk (26.3% vs. 21.1%). Freedman and Wells models demonstrated a strong concordance correlation coefficient (0.76), with an optimal Freedman cutoff of 0.97 (AUC = 87%). Kaminski and Yeoh identified more participants at above-average risk (27.8% and 35%) compared with Freedman (20.6%) and Wells (14.9%). Key contributors to elevated risk included smoking pack-years, obesity, and family history, with gender-specific variations. Freedman's model, offering both lifetime and 10-year risk estimates, emerged as the most practical tool in this cohort. Custom thresholds (0.89 for women and 1.41 for men) improved risk stratification, aligning with validated polyp detection rates. These findings support the use of tailored colorectal cancer risk models for Hispanic populations to enhance targeted screening and prevention efforts.

By showing that standard risk models underestimate colorectal cancer risk in Hispanic adults and identifying more accurate, population-specific cutoffs, this study supports targeted screening approaches that can improve early detection and reduce disparities.