Send to

Choose Destination
Br J Cancer. 2015 Dec 22;113(12):1645-50. doi: 10.1038/bjc.2015.409. Epub 2015 Dec 3.

Risk prediction tools for cancer in primary care.

Author information

The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
Faculty of Medicine, Dentistry and Health Sciences, Department of General Practice, Melbourne Medical School, The University of Melbourne, 200 Berkeley Street, Carlton, VIC 3053, Australia.
College House, University of Exeter Medical School, St Luke's Campus, Exeter EX1 2LU, UK.


Numerous risk tools are now available, which predict either current or future risk of a cancer diagnosis. In theory, these tools have the potential to improve patient outcomes through enhancing the consistency and quality of clinical decision-making, facilitating equitable and cost-effective distribution of finite resources such as screening tests or preventive interventions, and encouraging behaviour change. These potential uses have been recognised by the National Cancer Institute as an 'area of extraordinary opportunity' and an increasing number of risk prediction models continue to be developed. The data on predictive utility (discrimination and calibration) of these models suggest that some have potential for clinical application; however, the focus on implementation and impact is much more recent and there remains considerable uncertainty about their clinical utility and how to implement them in order to maximise benefits and minimise harms such as over-medicalisation, anxiety and false reassurance. If the potential benefits of risk prediction models are to be realised in clinical practice, further validation of the underlying risk models and research to assess the acceptability, clinical impact and economic implications of incorporating them in practice are needed.

[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center