Incorporating non-genetic risk factors and behavioural modifications into risk prediction models for colorectal cancer

2013 ◽  
Vol 37 (3) ◽  
pp. 324-329 ◽  
Author(s):  
Jane M. Yarnall ◽  
Daniel J.M. Crouch ◽  
Cathryn M. Lewis
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Prediction Models ◽  
Genetic Risk Factors ◽  
Risk Prediction Models

scholarly journals Integrating genome-wide polygenic risk scores and non-genetic risk factors to develop and validate risk prediction models for colorectal cancer

2021 ◽  
Author(s):  
Sarah EW Briggs ◽  
Philip Law ◽  
James E East ◽  
Sarah Wordsworth ◽  
Malcolm Dunlop ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Prediction Models ◽  
Fold Increase ◽  
Risk Scores ◽  
Risk Prediction Models ◽  
Polygenic Risk ◽  
Genome Wide

Objective While population screening programs for cancer colorectal (CRC) have proven benefit, risk-stratified approaches may improve screening outcomes further. To date, genome-wide polygenic risk scores (PRS) for CRC have not been integrated with non-genetic risk factors. We aimed to evaluate several genome-wide approaches, and the benefit of adding PRS to the QCancer-10 (colorectal cancer) non-genetic risk model, to identify those at highest risk of CRC. Design Using UK Biobank we developed and compared six different PRS for CRC. The top-performing genome-wide and GWAS-significant PRS were then combined with QCancer-10 and performance compared to QCancer-10 alone. Results PRS derived using LDpred2 software performed best, with an odds-ratio per standard deviation of 1.58, and top age- and sex-adjusted C-statistic of 0.733 in logistic regression and 0.724 in Cox regression models in the Geographic Validation Cohort. Integrated QCancer-10+PRS models out-performed QCancer-10, with C-statistics of 0.730 and 0.693, and explained variation of 28.1% and 21.0% from QCancer-10+LDpred2 and QCancer-10 respectively in men; performance improvements in women were similar. Men in the top 20% of risk accounted for 47.6% of cases, and women 42.5% using QCancer-10+LDpred2 models, with a 3.49-fold increase in risk in men and 2.75-fold increase in women in the top 5% of risk, compared to average risk. Decision curve analysis showed that adding PRS to QCancer-10 improved net-benefit and interventions avoided across most probability thresholds. Conclusion Integrated QCancer-10+PRS models out-perform existing CRC risk prediction models. Evaluation of risk stratified screening using this approach in a bowel screening population could be warranted.

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

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.

Machine Learning and Network-Based Models to Identify Genetic Risk Factors to the Progression and Survival of Colorectal Cancer

2021 ◽  
pp. 104539
Author(s):  
Md. Jakir Hossain ◽  
Utpala Nanda Chowdhury ◽  
M. Babul Islam ◽  
Shahadat Uddin ◽  
Mohammad Boshir Ahmed ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Genetic Risk ◽  
Genetic Risk Factors

scholarly journals Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

Stroke ◽  
2020 ◽  
Vol 51 (7) ◽  
pp. 2095-2102
Author(s):  
Eugene Y.H. Tang ◽  
Christopher I. Price ◽  
Louise Robinson ◽  
Catherine Exley ◽  
David W. Desmond ◽  
...  
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Risk Prediction ◽  
Prediction Models ◽  
Disease Risk ◽  
Risk Index ◽  
Area Under The Curve ◽  
Risk Prediction Models ◽  
C Statistic ◽  
Dementia Risk

Background and Purpose: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. Methods: Data were harmonized from 4 stroke studies (follow-up range, ≈12–18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. Results: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). Conclusions: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

scholarly journals A Review on Automatic Mammographic Density and Parenchymal Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-31 ◽  
Author(s):  
Wenda He ◽  
Arne Juette ◽  
Erika R. E. Denton ◽  
Arnau Oliver ◽  
Robert Martí ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Prevention Measures ◽  
Clinical Environment ◽  
Tissue Segmentation ◽  
Risk Prediction Models ◽  
The Impact

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

Sign in / Sign up

Export Citation Format

Share Document