There is information in a mammogram that does more than reveal likely existing tumours – and that information predicts future tumours (breast cancer), and/or tumours likely to be missed at screening (masking). It has been referred to as mammographic density and breast density. The challenge is how to get the best predictors (of inherent risk, of interval cancers and of masking) and how to translate this into clinical and population health practice so as to lower the impact of breast cancer. Digital mammography and other screening modalities open new opportunities, especially when combined with sophisticated computer algorithms. Just as blood pressure studies made a major impact on ameliorating the impact of cardiovascular and heart disease by being a measurable biomarker with predictive value, the same could be done for breast cancer. I will highlight some of the major issues and recent findings. First, what is mammographic density, and what is breast density, and how do they differ? Second, can we obtain measures of risk at a young age that open the door for early life interventions and better screening protocols? Third, what are the best measures of risk and masking based on screening images? Fourth, how can these findings be used to advantage in terms of: screening, prevention, biological research, and genetics and other omics research.