Predictive models in cancer management: A guide for clinicians.

BACKGROUND Predictive tools in cancer management are used to predict different outcomes including survival probability or risk of recurrence. The uptake of these tools by clinicians involved in cancer management has not been as common as other clinical tools, which may be due to the complexity of some of these tools or a lack of understanding of how they can aid decision-making in particular clinical situations. AIMS The aim of this article is to improve clinicians' knowledge and understanding of predictive tools used in cancer management, including how they are built, how they can be applied to medical practice, and what their limitations may be. METHODS Literature review was conducted to investigate the role of predictive tools in cancer management. RESULTS All predictive models share similar characteristics, but depending on the type of the tool its ability to predict an outcome will differ. Each type has its own pros and cons, and its generalisability will depend on the cohort used to build the tool. These factors will affect the clinician's decision whether to apply the model to their cohort or not. CONCLUSIONS Before a model is used in clinical practice, it is important to appreciate how the model is constructed, what its use may add over and above traditional decision-making tools, and what problems or limitations may be associated with it. Understanding all the above is an important step for any clinician who wants to decide whether or not use predictive tools in their practice.

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