Breast screening services at Leeds Teaching Hospitals NHS Trust have become part of an exciting UK first that will lead to improved breast cancer screening, diagnosis and outcomes for women in Leeds.
As part of a transatlantic research partnership, the breast unit is pioneering the use of world-leading software that analyses screening and diagnostic images and helps identify women who may be at higher risk of developing cancer.
The software also helps assess screening and diagnostic image quality to provide data that can help develop professional skills, ultimately leading to better diagnosis and patient outcomes.
The Leeds team have signed an agreement with Mohamed Abdolell, CEO, Densitas and Associate Professor of Diagnostic Radiology, Dalhousie University, Canada. The deal is backed by research experts at the Trust and Director of Breast Screening, Dr Nisha Sharma.
Dr Sharma said: “This is a really exciting development for us because it has the potential to make a real difference to the quality of our screening programme and ultimately to the health outcomes of women in Leeds. I’m very pleased that the Leeds Breast Screening unit and research team have the opportunity to collaborate with Densitas. This is the foundation for future developments that will improve breast care, so it’s excellent news for women.”
Breast tissue density is one of the factors that can mean a higher risk of breast cancer for women and this is currently assessed subjectively. The Densitas software will provide an automated way of recording the breast density, which will be readily available with the mammographic images. This will allow them to be able to record the breast density for all women who attend for a mammogram. This is a timely development as there is a lot of interest in stratifying breast screening and density plays an important role. This will allow the Leeds Teaching Hospital to collaborate with other centres to perform innovative research going forward.
The Densitas software is unique in that it can assess breast density using processed DICOM images and therefore supports prospective and retrospective research.
