Risk Prediction Models for Colorectal Cancer: A Review

Win, Aung Ko; MacInnis, Robert J.; Hopper, John L.; Jenkins, Mark A.

doi:10.1158/1055-9965.epi-11-0771

Cited by 88 publications

(73 citation statements)

References 105 publications

(103 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, similar to cardiovascular disease risk prediction models where CRP has been shown to improve risk stratification and reclassification (63)(64)(65)(66), clinical significance of the predictive value of CRP over conventional risk factors in colorectal cancer needs to be assessed by including CRP levels in colorectal cancer risk prediction models. None of the current models use CRP to predict colorectal cancer risk (77).…”

Section: Discussionmentioning

confidence: 99%

C-Reactive Protein and Colorectal Cancer Mortality in U.S. Adults

Goyal¹,

Terry

Jin

et al. 2014

Cancer Epidemiology, Biomarkers &Amp; Prevention

View full text Add to dashboard Cite

Background: Chronic inflammation has been associated with colorectal cancer. Prediagnostic levels of C-reactive protein (CRP), a highly sensitive marker of inflammation, have been weakly associated with increased colorectal cancer incidence, but few data are available examining its relationship with colorectal cancer mortality.Methods: In the Third National Health and Nutrition Examination Survey (NHANES III), 65% of the 15,924 adult participants had CRP levels 0.21 mg/dL. Using this as the reference group, we calculated hazard ratios (HR) for higher CRP categories and colorectal cancer mortality, and compared them with HRs for other mortality causes.Results: Over a median follow-up period of 14.2 years, there were 92 deaths from colorectal cancer. Compared with the reference group, multivariable adjusted HRs for colorectal cancer mortality were 2.66 [95% confidence interval (CI), 1.36-5.20] for CRP levels 0.22-0.50 mg/dL; 3.40 (95% CI, 1.48-7.77) for levels 0.51-1.00 mg/dL; and 3.96 (95% CI, 1.64-9.52) for levels >1.00 mg/dL. Estimates for colorectal cancer mortality did not change appreciably after excluding deaths within the first 3 years or by limiting follow-up to 5 or 10 years.Conclusions: In a large representative study of U.S. adults, we observed strong dose-response associations between CRP levels and colorectal cancer mortality.Impact: Further evaluation of CRP may help identify high-risk groups for colorectal cancer screening and those who might benefit most from prophylactic anti-inflammatory therapy. Cancer Epidemiol Biomarkers Prev; 23(8); 1609-18. Ó2014 AACR.

show abstract

Section: Discussionmentioning

confidence: 99%

C-Reactive Protein and Colorectal Cancer Mortality in U.S. Adults

Goyal¹,

Terry

Jin

et al. 2014

Cancer Epidemiology, Biomarkers &Amp; Prevention

View full text Add to dashboard Cite

show abstract

“…If risk-stratified cancer prevention is to be implemented, it requires risk assessment tools that can be used in primary care to identify those most likely to benefit from this intervention [6]. Of the tools to assess the individualized cancer risk, risk prediction models which are simple and can be applied in a community setting by a trained person are considered as useful [7].…”

Section: Introductionmentioning

confidence: 99%

“…Risk prediction modelling is a mechanism which estimates the probability of an individual having a certain condition based on presence of multiple risk factors [7]. An essential feature of a risk prediction model is that it uses multiple predictors to assess individuals regarding their risk of future occurrence of a specific outcome [8].…”

Section: Introductionmentioning

confidence: 99%

Risk prediction models for colorectal cancer: A Scoping review

Samarakoon¹,

Pathmeswaran

Gunawardena³

2018

J. Postgrad. Inst. Med.

View full text Add to dashboard Cite

Background and objectives Many risk prediction models have been developed globally to identify specific populations at high risk for colorectal cancer in specific settings. Documentation of available evidence from existing studies will serve as a useful information base. We performed a scoping review, to review and analyse published risk prediction models for colorectal cancer the world over. Methods A scoping review was undertaken to address the following question 'what are the existing risk prediction models to identify the risk of developing colorectal cancer among individuals in different countries and settings?' using the framework developed by Arksey and O'Malley for scoping reviews. Forty-one articles were included in this review from database searches and from additional searches. The titles and abstracts were reviewed using predetermined screening criteria. We limited our search to existing literature in English language and included both observational and interventional studies. Results Out of the 58 risk prediction models identified, most were developed for colorectal cancer followed by advanced colorectal cancer. Most of the articles reviewed were cross sectional studies or cohort studies. Statistical methods such as multiple logistic regression was used by a majority, while few have incorporated non-statistical methods such as consensus method and extracting data from published literature. The authors of the 58 risk prediction models have considered 77 different risk factors excluding the genetic variants. Conclusions This comprehensive scoping review demonstrates the capacity of the existing risk models to stratify the general population into risk categories, detailing the studies conducted, location, study design, outcome, overview of the methods, data source and the identified risk predictors. While striving to build on existing knowledge, the review also identifies the research gaps and the need for further improvement.

show abstract

“…Only a few prediction models have been developed for incident colorectal cancer (13), based on case-control data (14), expert opinion (15), solely on men (16), an Asian population where generalizability to non-Asian settings may be in question (17) or confined to colon cancer (18). Our aim was to develop and externally validate a risk 1 score predicting absolute risk of colorectal cancer using data from two large Australian prospective studies, the 45 and Up Study and the Melbourne Collaborative Cohort Study (MCCS).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Risk Score Predicting Risk of Colorectal Cancer

Steffen

MacInnis

Joshy

et al. 2014

Cancer Epidemiology, Biomarkers &Amp; Prevention

Self Cite

View full text Add to dashboard Cite

Background: Quantifying the risk of colorectal cancer for individuals is likely to be useful for health service provision. Our aim was to develop and externally validate a prediction model to predict 5-year colorectal cancer risk.Methods: We used proportional hazards regression to develop the model based on established personal and lifestyle colorectal cancer risk factors using data from 197,874 individuals from the 45 and Up Study, Australia. We subsequently validated the model using 24,233 participants from the Melbourne Collaborative Cohort Study (MCCS).Results: A total of 1,103 and 224 cases of colorectal cancer were diagnosed in the development and validation sample, respectively. Our model, which includes age, sex, BMI, prevalent diabetes, ever having undergone colorectal cancer screening, smoking, and alcohol intake, exhibited a discriminatory accuracy of 0.73 [95% confidence interval (CI), 0.72-0.75] and 0.70 (95% CI, 0.66-0.73) using the development and validation sample, respectively. Calibration was good for both study samples. Stratified models according to colorectal cancer screening history, that additionally included family history, showed discriminatory accuracies of 0.75 (0.73-0.76) and 0.70 (0.67-0.72) for unscreened and screened individuals of the development sample, respectively. In the validation sample, discrimination was 0.68 (0.64-0.73) and 0.72 (0.67-0.76), respectively. Conclusion: Our model exhibited adequate predictive performance that was maintained in the external population.Impact: The model may be useful to design more powerful cancer prevention trials. In the group of unscreened individuals, the model may be useful as a preselection tool for population-based screening programs. Cancer Epidemiol Biomarkers Prev; 23(11); 2543-52. Ó2014 AACR.

show abstract

Risk Prediction Models for Colorectal Cancer: A Review

Cited by 88 publications

References 105 publications

C-Reactive Protein and Colorectal Cancer Mortality in U.S. Adults

C-Reactive Protein and Colorectal Cancer Mortality in U.S. Adults

Risk prediction models for colorectal cancer: A Scoping review

Development and Validation of a Risk Score Predicting Risk of Colorectal Cancer

Contact Info

Product

Resources

About