- Senior Lead: Prof Jon Emery
- Project Lead: Sibel Saya
- UK team member: Dr Fiona Walter
- Others involved: Dr Jenny Walker, Prof Mark Jenkins, Assistant Prof Daniel Buchanan & Dr James Dowty (all University of Melbourne), Prof Ingrid Winship (Royal Melbourne Hospital)
In most countries, who should be screened for colorectal cancer (CRC) is determined by age and family history of disease. Risk models using genomic and/or lifestyle factors to predict a person’s CRC risk have been developed and could be used to stratify the population into risk categories. Theoretically, if these models were implemented within current population screening programs, more cancers could be detected by screening fewer people, as people at risk are more effectively identified. Behavioural interventions could also be targeted, increasing uptake of screening and reducing risky health behaviours.
This PhD seeks to answer questions of clinical utility, feasibility and acceptability of risk stratified population CRC screening guided by a lifestyle or genomic risk model delivered in general practice.
Study 1: The ability of existing risk prediction models to discriminate cancer events has been well studied (i.e. sensitivity, specificity and area under the receiver operating curve). However, the ability of models to discriminate risk (i.e. whether a small proportion of the population that holds a large amount of future risk can be identified) has not been well examined. This study compared the risk discrimination and clinical impact of two CRC risk predication models using several implementation scenarios in the Australian population.
Study 2: Maximal impact of a genomic CRC risk prediction model could be achieved by offering testing through general practices. However, minimal research about the general public’s and GPs’ impressions of this has been conducted. This study examines the acceptability and feasibility of genomic testing for this purpose in a general practice setting, as well as how patients react to and utilise such personalised risk information.