DEEP-BREAST

Breast cancer is the most frequently diagnosed cancer in women worldwide. A malignancy in the breast is often associated with local stiffness changes that occasionally can be detected by manual palpation. However, what the doctor feels during palpation does not always correspond to what the radiologist sees in mammography images of the breast. This hampers reliable detection, localisation and characterisation of suspected lesions seen in mammography and often requires extensive and expensive additional diagnostic procedures like Ultrasound and MRI to reach a final diagnosis.

In DEEP-BREAST it is proposed to measure mechanical parameters of the breast during a mammographic examination. This allows establishing a direct link between what is seen in a 3D mammography (tomosynthesis) image of the breast and corresponding quantitative measurement of local stiffness at the same location. These mechanical parameters are expected to yield information that has the potential to increase detectability and characterisation of breast lesions.

To estimate mechanical parameters of the breast two approaches are proposed: 1) analysing data acquired with the Sigma Paddle, a special compression paddle developed by Sigmascreening that enables recording mechanical and geometrical parameters (breast thickness, force, pressure, contact area) during breast compression. 2) measuring local strain in the breast obtained from two tomosynthesis images at different compression levels. These parameters will be used for the development and training of a machine learning application (deep learning) to establish the relationship between mechanical information and breast pathology.

It is envisioned that, after successful completion of the project, knowledge will be available to develop a deep learning application that allows improvement of breast cancer detection and characterization based on mechanical parameters. This application will direct the application of the Sigma Paddle and associated analysis software towards a product that enables improved and cost effective breast diagnostics in a single modality.