Background: Breast cancer screening based on mammography (MG) has had a major impact on reducing mortality at affordable cost. However, overdiagnosis, overtreatment, and the lack of sensitivity of mammography, especially in women with dense breasts, need to be addressed to improve screening. More effective screening cannot be achieved with the generalised use of MG for all the population; personalisation is required where the density of a woman’s breasts or increased cancer risk is assessed prior to determining whether or not additional imaging (i.e. magnetic resonance imaging (MRI), 3D ultrasound (ABUS), or tomosynthesis (DBT)) is necessary. Hence, the question is no longer if the screening regimens will be personalised, but mainly how this can be efficiently implemented. Although these complementary modalities are able to increase the sensibility compared to MG alone in specific patient groups, they still present several drawbacks that limit their effectiveness in personalised screening scenarios. Those are mainly related to technological issues linked to their novelty and to diagnostic accuracy, increased costs and reading times.
Hypothesis: image analysis techniques can be used for the development of Intelligent Diagnostic Enhancement tools based on automated detection and density estimation in ABUS, DBT and MRI. These tools will impact in improving reading times and diagnostic accuracy of these modalities in order to be used in personalised breast screening scenarios.
Main objective: to develop and evaluate novel imaging tools that can be integrated early into the screening workflow to steer image acquisition and guide the selection of appropriate personalised screening protocols; and to process imaging data in an intelligent way to minimise interpretation time. Tools will be based on breast density estimation algorithms and automated breast cancer detection algorithms applied to DBT, ABUS and MRI.
