Background: Growing awareness of the potential to predict a person's future risk of cancer has resulted in the development of numerous algorithms. Such algorithms aim to improve the ability of policy makers, doctors and patients to make rational decisions about behaviour modification or surveillance, with the expectation that this activity will lead to overall benefit. There remains debate however, about whether accurate risk prediction is achievable for most cancers.
Methods: We conducted a brief narrative review of the literature regarding the history and challenges of risk prediction, highlighting our own recent experiences in developing tools for oesophageal adenocarcinoma.
Results and conclusions: While tools for predicting future risk of cardiovascular outcomes have been translated successfully to clinical practice, the experience with cancer risk prediction has been mixed. Models have now been developed and validated for predicting risk of melanoma and cancers of the breast, colo-rectum, lung, liver, oesophagus and prostate, and while several of these have adequate performance at the population-level, none to date have adequate discrimination for predicting risk in individual patients. Challenges of individual risk prediction for cancer are many, and include long latency, multiple risk factors of mostly small effect, and incomplete knowledge of the causal pathways.
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