Colorectal cancer risk prediction using a simple multivariable model - Gillian S. Dite; Chi Kuen Wong; Aviv Gafni; Erika Spaeth (PLoS ONE, 2025)
Accurate population stratification of colorectal cancer risk enables identification of individuals who would benefit from screening and risk-reducing interventions. We conducted a population-based cohort study using almost 400,000 unaffected UK Biobank participants who were aged 40–69 years at their baseline assessment and who had genetically determined UK ancestry. For women and men separately, we developed (i) a multivariable risk prediction model using family history, a polygenic risk score (PRS) and clinical risk factors, and (ii) a simple model comprising family history and a PRS. We then compared their performance to that of existing models. The models were developed using Cox regression with age as the time axis in a 70% training dataset. The performance of the 10-year risk of colorectal cancer was assessed in a 30% testing dataset using Cox regression to estimate the hazard ratio per standard deviation of risk, Harrell’s C-index to assess discrimination and logistic regression to assess calibration. There were 214,183 women and 181,889 men in the dataset with 1,913 women and 2,598 men diagnosed with colorectal cancer during the follow-up period. The mean age at diagnosis was 66.4 years (standard deviation = 7.3 years) for women and 67.3 years (standard deviation = 6.7 years) for men. In the 30% testing dataset, the new multivariable models discriminated better (Harrell’s C-index = 0.690, 95% CI = 0.669 to 0.712 for women; 0.699, 95% CI = 0.681 to 0.717 for men) than the new family history and PRS models (Harrell’s C-index = 0.683, 95% CI = 0.663 to 0.704 for women; 0.692, 95% CI = 0.673 to 0.710 for men; change in discrimination P = 0.02 for women and P = 0.01 for men). Our models identify individuals who are at increased risk of colorectal cancer and who would benefit from personalised screening and risk-reduction options.