A New Comprehensive Colorectal Cancer Risk Prediction Model Incorporating Family History, Personal Characteristics, and Environmental Factors
AuthorZheng, Y; Hua, X; Win, AK; MacInnis, RJ; Gallinger, S; Le Marchand, L; Lindor, NM; Baron, JA; Hopper, JL; Dowty, JG; ...
Source TitleCancer Epidemiology, Biomarkers and Prevention
PublisherAMER ASSOC CANCER RESEARCH
AffiliationMelbourne School of Population and Global Health
Document TypeJournal Article
CitationsZheng, Y., Hua, X., Win, A. K., MacInnis, R. J., Gallinger, S., Le Marchand, L., Lindor, N. M., Baron, J. A., Hopper, J. L., Dowty, J. G., Antoniou, A. C., Zheng, J., Jenkins, M. A. & Newcomb, P. A. (2020). A New Comprehensive Colorectal Cancer Risk Prediction Model Incorporating Family History, Personal Characteristics, and Environmental Factors. CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION, 29 (3), pp.549-557. https://doi.org/10.1158/1055-9965.EPI-19-0929.
Access StatusAccess this item via the Open Access location
Open Access URLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060114
PURPOSE: Reducing colorectal cancer incidence and mortality through early detection would improve efficacy if targeted. We developed a colorectal cancer risk prediction model incorporating personal, family, genetic, and environmental risk factors to enhance prevention. METHODS: A familial risk profile (FRP) was calculated to summarize individuals' risk based on detailed cancer family history (FH), family structure, probabilities of mutation in major colorectal cancer susceptibility genes, and a polygenic component. We developed risk models, including individuals' FRP or binary colorectal cancer FH, and colorectal cancer risk factors collected at enrollment using population-based colorectal cancer cases (N = 4,445) and controls (N = 3,967) recruited by the Colon Cancer Family Registry Cohort (CCFRC). Model validation used CCFRC follow-up data for population-based (N = 12,052) and clinic-based (N = 5,584) relatives with no cancer history at recruitment to assess model calibration [expected/observed rate ratio (E/O)] and discrimination [area under the receiver-operating-characteristic curve (AUC)]. RESULTS: The E/O [95% confidence interval (CI)] for FRP models for population-based relatives were 1.04 (0.74-1.45) for men and 0.86 (0.64-1.20) for women, and for clinic-based relatives were 1.15 (0.87-1.58) for men and 1.04 (0.76-1.45) for women. The age-adjusted AUCs (95% CI) for FRP models for population-based relatives were 0.69 (0.60-0.78) for men and 0.70 (0.62-0.77) for women, and for clinic-based relatives were 0.77 (0.69-0.84) for men and 0.68 (0.60-0.76) for women. The incremental values of AUC for FRP over FH models for population-based relatives were 0.08 (0.01-0.15) for men and 0.10 (0.04-0.16) for women, and for clinic-based relatives were 0.11 (0.05-0.17) for men and 0.11 (0.06-0.17) for women. CONCLUSIONS: Both models calibrated well. The FRP-based model provided better risk stratification and risk discrimination than the FH-based model. IMPACT: Our findings suggest detailed FH may be useful for targeted risk-based screening and clinical management.
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