Risk Prediction Models for Colorectal Cancer: A Review

2011 ◽  
Vol 21 (3) ◽  
pp. 398-410 ◽  
Author(s):  
Aung Ko Win ◽  
Robert J. MacInnis ◽  
John L. Hopper ◽  
Mark A. Jenkins
BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.


2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Krasimira Aleksandrova ◽  
Robin Reichmann ◽  
Mazda Jenab ◽  
Sabina Rinaldi ◽  
Rudolf Kaaks ◽  
...  

Abstract Background Colorectal cancer represents a major public health concern and there is a worrying tendency of increasing incidence rates among younger people in the last decades. Risk stratification of high-risk individuals may aid targeted disease prevention. We therefore aimed to evaluate the predictive value of a wide range of lifestyle and biomarker variables using data within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods A range of lifestyle, anthropometric and dietary variables in 329,885 participants in the EPIC cohort were evaluated as potential predictors for risk of colorectal cancer over 10 years. Biomarker measurements of 41 parameters were available for 1,320 CRC cases and 1,320 controls selected using incidence density matching. Best sets of predictors were selected using elastic net regularization with bootstrapping. Random survival forest was applied as a novel technique to validate the set of selected predictors taking variable interactions into account. Results The results suggested a set of lifestyle factors including age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary that showed good discrimination (Harrell's C-index: 0.710) and excellent calibration. The analyses further revealed a set of biomarkers that increased the predictive performance beyond age, sex and lifestyle factors. Conclusions Risk prediction models based on lifestyle and biomarker data may prove useful in the identification of individuals at high risk for colorectal cancer. Key messages Risk prediction models incorporating lifestyle and biomarker data could contribute to developing strategies for targeted colorectal cancer prevention.


2015 ◽  
Vol 9 (1) ◽  
pp. 13-26 ◽  
Author(s):  
Juliet A. Usher-Smith ◽  
Fiona M. Walter ◽  
Jon D. Emery ◽  
Aung K. Win ◽  
Simon J. Griffin

2018 ◽  
Vol 4 (2) ◽  
pp. 56
Author(s):  
Yasara Manori Samarakoon ◽  
Arunasalam Pathmeswaran ◽  
Nalika Sepali Gunawardena

2020 ◽  
Vol 122 (10) ◽  
pp. 1572-1575
Author(s):  
J. A. Usher-Smith ◽  
A. Harshfield ◽  
C. L. Saunders ◽  
S. J. Sharp ◽  
J. Emery ◽  
...  

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Tom G. S. Williams ◽  
Joaquín Cubiella ◽  
Simon J. Griffin ◽  
Fiona M. Walter ◽  
Juliet A. Usher-Smith

2020 ◽  
Vol 11 (8) ◽  
pp. 1217-1228 ◽  
Author(s):  
Esteban T.D. Souwer ◽  
Esther Bastiaannet ◽  
Ewout W. Steyerberg ◽  
Jan-Willem T. Dekker ◽  
Frederiek van den Bos ◽  
...  

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