|Title:||Validation of existing risk prediction models for colorectal cancer using UK Biobank|
|Project Description:||The aim of this study was to assess the performance of a range of existing risk scores developed to identify individuals at high risk of developing colorectal cancer in a large UK cohort in order to inform future risk stratified screening approaches in the UK. To do this we externally validated fourteen risk models identified from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries.There were 1719 (0.46%) cases of incident colorectal cancer within the cohort. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver, Wells and Ma all had moderate-to-good discrimination with an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals.
We concluded that several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.
|Start date:||1st October 2015|
|End date:||30th June 2017|
|Contact person:||Dr Juliet Usher-Smith|
|Contact Details:||Telephone: (01223) 748693
|Collaborative:||Professor Simon Griffin
Dr Fiona Walter
Professor Jon Emery
|Funding Organisation:||School for Primary Care Research|
|Further Information, References and Publications|
|Usher-Smith JA, Harshfield A, Saunders CL, Sharp SJ, Emery J, Walter FM, Muir K, Griffin SJ. External validation of risk prediction models for incident colorectal cancer using UK Biobank. British Journal of Cancer doi: 10.1038/bjc.2017.463|