Development and validation of lifestyle-based prediction models for the prevention of the most common preventable cancers

Talk Code: 
Juliet Usher-Smith
Stephen Sharp, Robert Luben, Simon Griffin
Author institutions: 
Department of Public Health and Primary Care University of Cambridge, MRC Epidemiology Unit University of Cambridge


Providing individualised estimates of risk of cancer in primary care settings, alongside demonstration of the impact of lifestyle change on that risk, may help motivate change among individuals and complement wider collective approaches to shifting population distributions of behaviour and risk factors. Most risk models for cancer are either specific to individual cancers or include complex or predominantly non-modifiable risk factors. The aim of this study was to develop and validate lifestyle-based prediction models for the five most common preventable cancers in men and women in the UK.


We developed and validated a risk prediction model in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines. For each of the included cancers (lung, colorectal, bladder, kidney and oesophageal for men and breast, lung, colorectal, endometrial and kidney for women), we selected lifestyle risk factors from the European Code against Cancer and obtained estimates of relative risks from meta-analyses of observational studies. We used mean values for risk factors from 25,621 participants from the EPIC-Norfolk cohort and mean 10-year estimated absolute risks calculated using the current probability method and data from the Office for National Statistics. We assessed the performance of the models in 21,880 participants in the EPIC-Norfolk cohort who were aged 40 or over at baseline, had 10-year follow-up, data for all risk factors, and no history of the five selected cancers at baseline.


In men the combined risk model showed good discrimination (AUC: 0.74, 95% CI 0.72-0.76) and calibration. Discrimination was lower in women (AUC: 0.61 95% CI 0.59-0.63) but calibration was good. In both sexes the individual models for lung cancer had the highest AUCs (0.83, 95%CI 0.80-0.85 for men and 0.83, 95% CI 0.77-0.88 for women). The lowest AUCs were for breast and endometrial cancer in women.


We have developed and validated models in men and women for prediction of the individual or combined absolute risk of the five most common preventable cancers, which can also be used to present relative risks compared with either an average or a recommended lifestyle. These models could be used to identify those most likely to benefit from lifestyle interventions and to demonstrate the impact of change to individuals to contribute to decisions to change behaviour. This risk assessment could be conducted within existing healthcare and prevention services or made available online. Further research is now needed to develop a user-friendly interface in which these models can be incorporated into clinical practice and implementation studies quantifying the potential benefits and harms of providing such information.

Submitted by: 
Juliet Usher-Smith
Funding acknowledgement: 
We would like to acknowledge the contribution of the staff and participants of the EPIC-Norfolk Study. This research was funded by a grant from the NIHR School for Primary Care Research (reference number 342). JUS is funded by a Cancer Research UK Prevention Fellowship (C55650/A21464). SJS is supported by the Medical Research Council (unit programme no MC_ UU_12015/1). The University of Cambridge has received salary support in respect of SJG from the NHS in the East of England through the Clinical Academic Reserve. EPIC-Norfolk is supported by the Medical Research Council programme grants (G0401527,G1000143) and Cancer Research UK programme grants (C864/A8257, C864/A14136). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. All researchers were independent of the funding body and the funder had no role in data collection, analysis and interpretation of data; in the writing of the report; or decision to submit the article for publication.