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Elizabeth S. Burnside, MD, MPH, MS

AIMBE College of Fellows Class of 2018
For outstanding contributions to the development of evidence-based algorithms to improve breast cancer detection and diagnosis.

Quantitative Analysis Improves Breast Screening: Research Highlights from RSNA

Via PR Newswire | November 27, 2018

Quantitative analysis improves breast cancer screening, according to four abstracts at the 104th Annual Radiological Society of North America (RSNA) meeting, November 25–30, 2018. The investigators all used Volpara Solutions’ breast imaging analysis tools, including assessment of volumetric density and compression pressure, in their research.

In the study “Using Quantitative Breast Density Analysis to Predict Interval Cancers and Node Positive Cancers in Pursuit of Improved Screening Protocols,” (SSE01-03, Monday, November 26, 3:20–3:30 PM, Room: E451B), Elizabeth Burnside, MD, and colleagues investigated whether quantitative breast density can predict interval cancers and node-positive, screen-detected cancers in order to serve as a biomarker to consider more aggressive screening to improve early detection. The study involved 599 cases of screen-detected cancers and interval cancers and 605 controls from the UK NHS Breast Screening Programme. For each case, breast density was assessed by a radiologist using a visual analog scale (VAS) and BI-RADS 5th Edition density categories and using fully automated Volpara®Density™ software to calculate fibroglandular volume (FGV) and Volpara Density Grade (VDG®)… Continue reading.

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Dr. Elizabeth Burnside Inducted into Medical and Biological Engineering Elite

Via AIMBE | April 10, 2018

WASHINGTON, D.C.—The American Institute for Medical and Biological Engineering (AIMBE) has announced the induction of Elizabeth S. Burnside, MD, MPH, MS, Professor, Department of Radiology; Associate Dean, School of Medicine and Public Health; Deputy Director, Institute for Clinical and Translational Research, University of Wisconsin-Madison, to its College of Fellows. Dr. Burnside was nominated, reviewed, and elected by peers and members of the College of Fellows for outstanding contributions to the development of evidence-based algorithms to improve breast cancer detection and diagnosis.

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