A new automated measure of breast cancer risk from digital and film mammography (Cirrus)
|Professor John Hopperfirstname.lastname@example.org||+ 61 3 834 40697||Personal web page|
- Develop an automated algorithm for the prediction of breast cancer risk directly from film and digital presentation mammograms (Cirrus).
- Validation of the Cirrus algorithm.
- Deploy a proof-of-principle pilot implementation of Cirrus at BreastScreen services.
Mammographic density is one of the strongest predictors of breast cancer risk but its impractical measurement prevents its use in a clinical setting. An automated procedure that determines which aspects of a mammogram best predict cancer would allow screening programs to identify and target women at higher risk of breast cancer, which in turn could lead to earlier diagnoses and better breast cancer outcomes. A key to this will be the actual implementation of the automated measure into real-time practice.
We will develop and validate an automated measurement, maximized by its ability to predict breast cancer risk that is directly applicable to digital presentation mammograms from a wide range of vendors. This will primarily be done by utilising the information in a digital mammogram that is unaffected by the types of processing mammography machines typically perform on their unprocessed mammograms to produce their presentation mammograms. A presentation mammogram is the image seen by radiologists during screening, and is the only mammogram routinely stored by screening services. It is therefore crucial for successful translation that an automated method should work directly on these presentation mammograms. We have already made substantial steps in this direction through an NHMRC grant (2011-2012) using analog mammograms. We now have access to large samples of digital mammograms which are essential for our method to have clinical relevance, given that digital mammography has been rolled out across BreastScreen Australia.
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