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Enhancing breast cancer detection using a novel research and educational platform: BREAST II


Developing and implementing BREAST II as an interactive research and educational platform to transform breast cancer detection.


Professor Patrick Brennan.

Research location

Medical Imaging and Radiation Sciences Research Group

Program type



Early diagnosis of breast cancer results in a 97% survival rate. However, to achieve this survival rate and even more importantly to achieve zero deaths from breast cancer by 2030, we must significantly reduce the 30-40% of breast cancers that fail to be diagnosed. Through BREAST, a world-first research and educational interactive infrastructure that uses the latest technological innovations, over the last 4-5 years, we, with local and international experts have identified reasons for mis-diagnoses and presented exciting translational solutions. To date the work has been shown to improve radiologists' performance by a mean value of 34%, an improvement unparalleled by any other innovation in recent years. Its unprecedented success has led to engagement by 80% of breast-reading clinicians across all states in Australia and research agreements with world-leading imaging scientists across Australia, North and South America, Asia and Europe. It has contributed to 20 PhD projects and 70 publications.

We have a clear plan for consolidating our achievements with BREAST so that early breast cancer diagnosis within screening and symptomatic facilities continues to be transformed. It also provides a comprehensive work schedule enabling the introduction of 5 highly exciting innovations reflecting recent technological advancements, social responsibilities and educational needs. With regard to these innovations, BREAST II will specifically incorporate:

• Digital breast tomosynthesis (DBT) image sets: DBT is rapidly transforming breast cancer imaging in Australia and elsewhere, however currently there is no on-line training and performance monitoring system anywhere;
• Data files focusing on underserved populations: women of different cultures have different breast densities thus effectiveness of detecting cancer may vary. We will focus on indigenous populations in Australia and women in China and Southeast Asia;
• Educational image sets of high difficulty: particularly those with architectural distortion and lesion speculation so that readers can focus learning in these areas;
• Data sets designed for registrar radiologists.
• Pathology image files for pathologists: pathology is considered to be the diagnostic truth, however agreement between pathologists can be as low as 48%.

Each of the above items can be a PhD project.

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Opportunity ID

The opportunity ID for this research opportunity is 2195

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