Elizabeth S. Burnside, MD, MPH, MS Print Friendly PageFaculty, University of Wisconsin School of Medicine and Public Health
In medical school, Dr. Burnside got her MD degree combined with master’s in Public Health. In addition, between residency and fellowship, Dr. Burnside got a master’s degree in Medical Informatics from Stanford University. As a result her research has involved the use of artificial intelligence methods to improve decision-making in the domain of breast imaging. This multidisciplinary research is facilitated by affiliate appointments in the Departments of Biostatistics and Medical Informatics and Population Health Science at UW. Dr. Burnside received the General Electric Radiology Research Fellowship from 2003-2005 to investigate the utility of a probabilistic computer model to improve decision-making in mammography. She has published 23 peer review articles and has served on an NIH Study Section (DMG). Dr. Burnside is a subspecialty trained breast imager and provides all imaging procedures for the early diagnosis of breast cancer. She was recently elected a Fellow in the Society of Breast Imaging and is a member of the The American College of Radiology Comission on Breast Imaging Education. Dr. Burnside is a member of the UW Health Breast Center.
UW Health Clinics
|Medical interpreters are available to help patients communicate with hospital and clinic staff. For more information, please contact interpreter services at (608) 262-9000.|
UW School of Medicine and Public Health
|Department of Radiology|
Professional Certifications and Education
University of California San Francisco, San Francisco, CA, CA
University of California San Francisco, San Francisco, CA, CA
Tufts University School of Medicine, Boston, MA, 1993
Our doctors provide a wide range of services. The following list represents some, but not all, of the procedures offered by this physician.
Wu Y Alagoz O Ayvaci MU Munoz Del Rio A Vanness DJ Woods R Burnside ES .
A Comprehensive Methodology for Determining the Most Informative Mammographic Features. J Digit Imaging. 2013 Mar 16;
[PubMed ID: 23503987]
Pooler BD Kim DH Hassan C Rinaldi A Burnside ES Pickhardt PJ .
Variation in Diagnostic Performance among Radiologists at Screening CT Colonography. Radiology. 2013 Feb 28;
[PubMed ID: 23449954]
Caretta-Weyer H Sisney GA Beckman C Burnside ES Salkowsi LR Strigel RM Wilke LG Neuman HB .
Impact of axillary ultrasound and core needle biopsy on the utility of intraoperative frozen section analysis and treatment decision making in women with invasive breast cancer. Am J Surg. 2012 Sep;204(3):308-14
[PubMed ID: 22483606]
Nassif H Wu Y Page D Burnside E .
Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. AMIA Annu Symp Proc. 2012;2012:1330-9
[PubMed ID: 23304412]
Liu J Peissig P Zhang C Burnside E McCarty C Page D .
High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. JMLR Workshop Conf Proc. 2012;22:712-721
[PubMed ID: 23606924]
Burnside ES Chhatwal J Alagoz O .
What is the optimal threshold at which to recommend breast biopsy? PLoS One. 2012;7(11):e48820
[PubMed ID: 23144986]
Woods RW Sisney GS Salkowski LR Shinki K Lin Y Burnside ES .
The mammographic density of a mass is a significant predictor of breast cancer. Radiology. 2011 Feb;258(2):417-25
[PubMed ID: 21177388]
Salkowski LR Fowler AM Burnside ES Sisney GA .
Utility of 6-month follow-up imaging after a concordant benign breast biopsy result. Radiology. 2011 Feb;258(2):380-7
[PubMed ID: 21079199]
Xu H Rao M Varghese T Sommer A Baker S Hall TJ Sisney GA Burnside ES .
Axial-shear strain imaging for differentiating benign and malignant breast masses. Ultrasound Med Biol. 2010 Nov;36(11):1813-24
[PubMed ID: 20800948]
Woods RW Oliphant L Shinki K Page D Shavlik J Burnside E .
Validation of results from knowledge discovery: mass density as a predictor of breast cancer. J Digit Imaging. 2010 Oct;23(5):554-61
[PubMed ID: 19760292]
Sprague BL Trentham-Dietz A Burnside ES .
Socioeconomic disparities in the decline in invasive breast cancer incidence. Breast Cancer Res Treat. 2010 Aug;122(3):873-8
[PubMed ID: 20087648]
Ayer T Alagoz O Chhatwal J Shavlik JW Kahn CE Jr Burnside ES .
Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration. Cancer. 2010 Jul 15;116(14):3310-21
[PubMed ID: 20564067]
Ayer T Ayvaci MU Liu ZX Alagoz O Burnside ES .
Computer-aided diagnostic models in breast cancer screening. Imaging Med. 2010 Jun 1;2(3):313-323
[PubMed ID: 20835372]
Ayer T Chhatwal J Alagoz O Kahn CE Jr Woods RW Burnside ES .
Informatics in radiology: comparison of logistic regression and artificial neural network models in breast cancer risk estimation. Radiographics. 2010 Jan;30(1):13-22
[PubMed ID: 19901087]
Burnside ES Sickles EA Bassett LW Rubin DL Lee CH Ikeda DM Mendelson EB Wilcox PA Butler PF D'Orsi CJ .
The ACR BI-RADS experience: learning from history. J Am Coll Radiol. 2009 Dec;6(12):851-60
[PubMed ID: 19945040]
Zhu C Burnside ES Sisney GA Salkowski LR Harter JM Yu B Ramanujam N .
Fluorescence spectroscopy: an adjunct diagnostic tool to image-guided core needle biopsy of the breast. IEEE Trans Biomed Eng. 2009 Oct;56(10):2518-28
[PubMed ID: 19272976]
Burnside ES Davis J Chhatwal J Alagoz O Lindstrom MJ Geller BM Littenberg B Shaffer KA Kahn CE Jr Page CD .
Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings. Radiology. 2009 Jun;251(3):663-72
[PubMed ID: 19366902]
Chhatwal J Alagoz O Lindstrom MJ Kahn CE Jr Shaffer KA Burnside ES .
A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. AJR Am J Roentgenol. 2009 Apr;192(4):1117-27
[PubMed ID: 19304723]
Burnside ES Hall TJ Sommer AM Hesley GK Sisney GA Svensson WE Fine JP Jiang J Hangiandreou NJ .
Differentiating benign from malignant solid breast masses with US strain imaging. Radiology. 2007 Nov;245(2):401-10
[PubMed ID: 17940302]
Burnside ES Ochsner JE Fowler KJ Fine JP Salkowski LR Rubin DL Sisney GA .
Use of microcalcification descriptors in BI-RADS 4th edition to stratify risk of malignancy. Radiology. 2007 Feb;242(2):388-95
[PubMed ID: 17255409]
Burnside ES Rubin DL Fine JP Shachter RD Sisney GA Leung WK .
Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology. 2006 Sep;240(3):666-73
[PubMed ID: 16926323]