Automated mammographic measures that predict breast cancer risk [ 2011 - 2013 ]

Research Grant

Researchers: Prof John Hopper (Principal investigator) ,  Dr Carmel Apicella ,  Dr Daniel Schmidt ,  Dr Enes Makalic ,  Dr Jennifer Stone
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Brief description Mammographic density (MD) is one of the strongest predictors of breast cancer risk but its impractical measurement prevents its use in a clinical setting. An automated measure of MD would allow screening programs to identify and target women at higher risk of breast cancer which could lead to earlier diagnoses and better breast cancer outcomes. We aim to develop an automated measurement, maximized by its ability to predict breast cancer risk, and applicable to both film and digital mammograms.

Funding Amount $AUD 406,260.97

Funding Scheme NHMRC Project Grants

Notes Standard Project Grant