Project Funding Details

We have discovered two new measures based on different aspects of mammographic images, which, when combined, are better than all known genetic risk factors in predicting breast cancer on a population basis. These measures are: (i) the amount of brighter areas on a mammogram (Altocumulus and Cirrocumulus), which have a stronger risk association than the conventional mammographic density measure (Cumulus) and (ii) mammographic features not related to brightness (Cirrus) found using machine learning. Digital mammography is now used worldwide. VOLPARA globally markets automated Cumulus-equivalent and has the potential to do this for Altocumulus, Cirrocumulus and Cirrus. Aims: (i) study the genetic, epigenetic, constitutional and lifestyle-related causes of our new mammography-based measures; (ii) address the problem of dense breasts hiding existing tumours that have poor outcomes by developing a better risk predictor for women at risk of interval cancer; (iii) translate our ideas into global clinical practice through VOLPARA. Research plan: (i) collect digital mammograms for >4,000 women in national breast cancer twin and family studies for whom there are genetic and epigenetic data, complete genotyping, and conduct statistical analyses to find determinants of the measures; (ii) use the measures to produce better predictors of interval cancers using two population-based studies; and (iii) calibrate the measures against VOLPARA’s automated measure based on their density maps. Innovation and creativity: we are the only group in the world who can make these new measures. Significance: having new and better automated risk information could make screening more cost-effective and bring a new understanding of breast cancer aetiology, with global implications. Feasibility: we build on decades of experience in mammography-based research and family studies, existing epidemiological, genetic and epigenetic resources, and expertise in analysing omic and family data.

We recently found two new breast cancer risk factors that are based on the brightness and texture of mammograms. We now wish to use these discoveries to learn more about the causes of breast cancer and improve the management of women with dense breasts, an issue of increasing concern for the breast cancer community, health professionals and researchers, and do so using digital mammography. We believe that we can make breast screening more cost-effective and lessen the anxiety and invasive procedures that occur when women are called back for further tests. ‍ We have data to support our ideas, but most of this has been from studying traditional film mammograms. Mammographic screening now is digital across Australia and globally, which makes it possible for women to be told about their breast cancer risk at the time of their screening. ‍ We believe that when, and how often, to have screening could be based on a woman’s risk of breast cancer, not just her age. But for this to happen, our new breast cancer risk factors need to be incorporated into clinical practice. We aim to do this by collecting digital mammograms for more than 5,000 women in our long-term studies for whom we already have a large amount of questionnaire, family history, genetic and other data. We will also conduct a new study of women having digital mammograms at a specialist clinic. We will conduct statistical analyses that make it possible for our new measures to be included in a commercial automated mammographic measurement tool, VOLPARA, that is being used across the world. Our work therefore has the potential to improve breast screening globally.