Personalized estimates of breast cancer risk in clinical practice and public health

Abstract

This paper defines absolute risk and some of its properties, and presents applications in breast cancer counseling and prevention. For counseling, estimates of absolute risk give useful perspective and can be used in management decisions that require weighing risks and benefits, such as whether or not to take tamoxifen to prevent breast cancer. Absolute risk models are also useful in designing intervention trials to prevent breast cancer and in assessing the potential reductions in absolute risk of disease that might result from reducing exposures that are associated with breast cancer. In these applications, it is important that the risk model be well calibrated, namely that it accurately predicts the numbers of women who will develop breast cancer in various subsets of the population. Absolute risk models are also needed to implement a 'high risk' prevention strategy that identifies a high-risk subset of the population and focuses intervention efforts on that subset. The limitations of the high-risk strategy are discussed, including the need for risk models with high discriminatory accuracy, and the need for less toxic interventions that can reduce the threshold of risk above which the intervention provides a net benefit. I also discuss the potential use of risk models in allocating prevention resources under cost constraints. High discriminatory accuracy of the risk model, in addition to good calibration, is desirable in this application, and the risk assessment should not be expensive in comparison with the intervention.

Figure 1

Plots of the probability that risk exceeds a threshold t in cases versus the probability that the risk exceeds that threshold in the general population as t varies. The area under this curve (AUC) is a measure of discriminatory accuracy and is shown for the equiangular line, for three risk models, and for a hypothetical risk model with AUC=0.75. Curves were calculated assuming risks had beta distributions with the AUC values shown. The AUC values are independent of the mean risk, which was 0.02 for each of the beta distributions.