A New Comprehensive Colorectal Cancer Risk Prediction Model Incorporating Family History, Personal Characteristics, and Environmental Factors

Abstract Background In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios. Methods We estimated the model’s predicted CRC risk distribution in the Australian population. Predicted CRC risks were categorized into screening recommendations under 3 proposed scenarios to compare with current recommendations: 1) highly tailored, 2) 3 risk categories, and 3) 4 sex-specific risk categories. Under each scenario, for 35- to 74-year-olds, we calculated the number of CRC screens by immunochemical fecal occult blood testing (iFOBT) and colonoscopy and the proportion of predicted CRCs over 10 years in each screening group. Results Currently, 1.1% of 35- to 74-year-olds are recommended screening colonoscopy and 56.2% iFOBT, and 5.7% and 83.2% of CRCs over 10 years were predicted to occur in these groups, respectively. For the scenarios, 1) colonoscopy was recommended to 8.1% and iFOBT to 37.5%, with 36.1% and 50.1% of CRCs in each group; 2) colonoscopy was recommended to 2.4% and iFOBT to 56.0%, with 13.2% and 76.9% of cancers in each group; and 3) colonoscopy was recommended to 5.0% and iFOBT to 54.2%, with 24.5% and 66.5% of cancers in each group. Conclusions A highly tailored CRC screening scenario results in many fewer screens but more cancers in those unscreened. Category-based scenarios may provide a good balance between number of screens and cancers detected and are simpler to implement.

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A new colorectal cancer risk prediction model incorporating family history, personal and environmental factors

10.1101/662106 ◽

2019 ◽

Author(s):

Yingye Zheng ◽

Xinwei Hua ◽

Aung K. Win ◽

Robert J. MacInnis ◽

Steven Gallinger ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Factors ◽

Family History ◽

Prediction Model ◽

Risk Prediction ◽

Prediction Models ◽

Familial Risk ◽

Population Based ◽

Risk Prediction Model ◽

Incidence And Mortality

AbstractPurposeReducing colorectal cancer (CRC) incidence and mortality through early detection would improve efficacy if targeted. A CRC risk-prediction model incorporating personal, family, genetic and environmental risk factors could enhance prediction.MethodsWe developed risk-prediction models using population-based CRC cases (N=4,445) and controls (N=3,967) recruited by the Colon Cancer Family Registry Cohort (CCFRC). A familial risk profile (FRP) was calculated to summarize individuals’ risk based on their CRC family history, family structure, germline mutation probability in major susceptibility genes, and a polygenic component. Using logistic regression, we developed risk models including individuals’ FRP or a binary CRC family-history (FH), and risk factors collected at recruitment. Model validation used follow-up data for population-(N=12,052) and clinic-based (N=5,584) relatives with no cancer history at recruitment, assessing calibration (E/O) and discrimination (AUC).ResultsThe E/O (95% confidence interval [CI]) for FRP models for population-based relatives were 1.04 (0.74-1.45) and 0.86 (0.64-1.20) for men and women, and for clinic-based relatives 1.15 (0.87-1.58) and 1.04 (0.76-1.45). The age-adjusted AUC (95% CI) for FRP models in population-based relatives were 0.69 (0.60-0.78) and 0.70 (0.62-0.77), and for clinic-based relatives 0.77 (0.69-0.84) and 0.68 (0.60-0.76). The incremental values of AUC (95% CI) for FRP over FH models for population-based relatives were 0.08 (0.01-0.15) and 0.10 (0.04-0.16), and for clinic-based relatives 0.11 (0.05-0.17) and 0.11 (0.06-0.17).ConclusionThe FRP-based model and FH-based model calibrate well in both settings. The FRP-based model provided better risk-prediction and discrimination than the FH-based model. A detailed family history may be useful for targeted risk-based screening and clinical management.

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Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists

CHEST Journal ◽

10.1016/j.chest.2019.04.002 ◽

2019 ◽

Vol 156 (1) ◽

pp. 112-119 ◽

Cited By ~ 2

Author(s):

Heber MacMahon ◽

Feng Li ◽

Yulei Jiang ◽

Samuel G. Armato

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Lung Cancer Risk ◽

Risk Prediction Model

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Abstract 881: Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction

10.1158/1538-7445.am2021-881 ◽

2021 ◽

Author(s):

Minta Thomas ◽

Lori C Sakoda ◽

Jeffrey K Lee ◽

Mark A Jenkins ◽

Andrea Burnett-Hartman ◽

...

Keyword(s):

Colorectal Cancer ◽

Cancer Risk ◽

Risk Prediction ◽

Risk Score ◽

Colorectal Cancer Risk ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Genome Wide

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The Cytokinesis-Blocked Micronucleus Assay as a Strong Predictor of Lung Cancer: Extension of a Lung Cancer Risk Prediction Model

Cancer Epidemiology Biomarkers & Prevention ◽

10.1158/1055-9965.epi-14-0462 ◽

2014 ◽

Vol 23 (11) ◽

pp. 2462-2470 ◽

Cited By ~ 21

Author(s):

Randa A. El-Zein ◽

Mirtha S. Lopez ◽

Anthony M. D'Amelio ◽

Mei Liu ◽

Reginald F. Munden ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Lung Cancer Risk ◽

Strong Predictor ◽

Micronucleus Assay ◽

Risk Prediction Model

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Lower Relative Contribution of Positive Family History to Colorectal Cancer Risk with Increasing Age: A Systematic Review and Meta-Analysis of 9.28 Million Individuals

The American Journal of Gastroenterology ◽

10.1038/s41395-018-0075-y ◽

2018 ◽

Vol 113 (12) ◽

pp. 1819-1827 ◽

Cited By ~ 10

Author(s):

Martin C. S. Wong ◽

C. H. Chan ◽

Jiayan Lin ◽

Jason L. W. Huang ◽

Junjie Huang ◽

...

Keyword(s):

Colorectal Cancer ◽

Systematic Review ◽

Family History ◽

Cancer Risk ◽

Meta Analysis ◽

Positive Family History ◽

Colorectal Cancer Risk ◽

Relative Contribution

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The CRISP-P study: feasibility of a self-completed colorectal cancer risk prediction tool in primary care

Family Practice ◽

10.1093/fampra/cmz029 ◽

2019 ◽

Vol 36 (6) ◽

pp. 730-735 ◽

Cited By ~ 1

Author(s):

Elena C Harty ◽

Jennifer G McIntosh ◽

Adrian Bickerstaffe ◽

Nadira Hewabandu ◽

Jon D Emery

Keyword(s):

Colorectal Cancer ◽

Primary Care ◽

Second Language ◽

Cancer Risk ◽

Risk Prediction ◽

Tertiary Education ◽

Colorectal Cancer Risk ◽

Prediction Tool ◽

Recruitment Methods ◽

Increased Risk

Abstract Objective Australia and New Zealand have the highest incidence of colorectal cancer (CRC) globally. Our research team has developed a CRC risk prediction tool for use in primary care to increase targeted screening. This study, Colorectal cancer RISk Prediction tool – patient (‘CRISP-P’), aimed to determine the following to inform a future trial design: (i) the feasibility of self-reporting; (ii) the feasibility of recruitment methods; and (iii) the prevalence of CRC risk. Methods Participants aged between 40 and 75 years were recruited consecutively from three primary care waiting rooms. Participants input data into CRISP on a tablet without receiving clinical advice. Feasibility was evaluated using recruitment rate, timely completion, a self-reported ‘ease-of-use’, score and field notes. Prevalence of CRC risk was calculated using the CRISP model. Results Five hundred sixty-one (90%) patients agreed to use the tool and 424 (84%) rated the tool easy to use. Despite this, 41% of people were unable to complete the questions without assistance. Patients who were older, without tertiary education or with English as their second language were more likely to require assistance (P < 0.001). Thirty-nine percent of patients were low risk, 58% at slightly increased and 2.4% were at moderately increased risk of developing colorectal cancer in the next 5 years. Conclusions The tool was perceived as easy to use, although older, less educated people, and patients with English as their second language needed help. The data support the recruitment methods but not the use of a self-completed tool for an efficacy trial.

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