Researchers in Manchester are looking into the development of risk algorithms for a variety of cancers, as well as into the most effective and appropriate way that risk can be communicated to the general public.
A cancer risk prediction algorithm is a series of mathematical steps which takes into account multiple factors in order to calculate an individual’s personalised risk of the disease. This methodology allows for the identification of those who are at high or moderate risk of cancer compared to the general population, which can help to improve its earlier detection for example by increasing screening frequency.
One large scale risk assessment study that has been conducted in Greater Manchester over a number of years is the PROCAS study (Predicting Risk Of Cancer At Screening). The project, led by Professor Gareth Evans, calculated a woman’s risk of breast cancer when they attended their NHS breast screening appointment. Risk was assessed using the validated Tyrer-Cuzick model, which incorporates family history, genetic factors, benign disease, hormonal factors, and lifestyle risk factors into a single statistical model. Mammographic breast density and genetic testing were also calculated alongside the model to increase accuracy of the algorithm for assessing a woman’s risk of breast cancer.
A different type of risk algorithm, instead focusing on modifiable factors affecting an individual’s cancer risk, has been developed in a project led by Professor Kenneth Muir. The REFLECT project aims to predict an individual’s risk of developing 11 different types of cancers. The project also aims to deliver personalised tailored advice on lifestyle factors based on Cancer Research UK’s recommendations, which the individual could change to help lower their personal risk of cancer.
Professor Kenneth Muir
“Cancer prevention and early detection through screening programs represent two important approaches to the management of the cancer burden. Both approaches are currently not optimised to new knowledge and advances in the understanding of risk. Risk based approaches potentially allow for better linkage and centralisation of screening and prevention approaches.
Currently the general population have not taken up important lifestyle changes that would help to reduce cancer occurrence in the population. We are looking at further models that output information on the individual personalised modifiable risk.”