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
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
|Medical interpreters are available to help patients communicate with hospital and clinic staff. For more information, please contact interpreter services at (608) 262-9000.|
Our doctors provide a wide range of services. The following list represents some, but not all, of the procedures offered by this physician.
Bozkurt S Gimenez F Burnside ES Gulkesen KH Rubin DL .
Using Automatically Extracted Information from Mammography Reports for Decision- Support. J Biomed Inform. 2016 Jul 4;
[PubMed ID: 27388877]
Burnside ES Sickles EA Duffy SW .
A Pragmatic Approach to Determine Components of Optimal Screening Mammography Practice. JAMA. 2016 May 10;315(18):1951-3
[PubMed ID: 27163983]
Li H Zhu Y Burnside ES Drukker K Hoadley KA Fan C Conzen SD Whitman GJ Sutton EJ Net JM Ganott M Huang E Morris EA Perou CM Ji Y Giger ML .
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology. 2016 May 5;:152110
[PubMed ID: 27144536]
Lee CS Bhargavan-Chatfield M Burnside ES Nagy P Sickles EA .
The National Mammography Database: Preliminary Data. AJR Am J Roentgenol. 2016 Apr;206(4):883-90
[PubMed ID: 26866649]
Burnside ES Drukker K Li H Bonaccio E Zuley M Ganott M Net JM Sutton EJ Brandt KR Whitman GJ Conzen SD Lan L Ji Y Zhu Y Jaffe CC Huang EP Freymann JB Kirby JS Morris EA Giger ML .
Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer. 2016 Mar 1;122(5):748-57
[PubMed ID: 26619259]
Benndorf M Wu Y Burnside ES .
A history of breast cancer and older age allow risk stratification of mammographic BI-RADS 3 ratings in the diagnostic setting. Clin Imaging. 2016 Mar-Apr;40(2):200-4
[PubMed ID: 26995570]
Abbey CK Wu Y Burnside ES Wunderlich A Samuelson FW Boone JM .
A Utility/Cost Analysis of Breast Cancer Risk Prediction Algorithms. Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9787
[PubMed ID: 27335532]
Wu Y Abbey CK Liu J Ong I Peissig P Onitilo AA Fan J Yuan M Burnside ES .
Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9787
[PubMed ID: 27279675]
Burnside ES Liu J Wu Y Onitilo AA McCarty CA Page CD Peissig PL Trentham-Dietz A Kitchner T Fan J Yuan M .
Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Acad Radiol. 2016 Jan;23(1):62-9
[PubMed ID: 26514439]
Guo W Li H Zhu Y Lan L Yang S Drukker K Morris E Burnside E Whitman G Giger ML Ji Y Tcga Breast Phenotype Research Group .
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging (Bellingham). 2015 Oct;2(4):041007
[PubMed ID: 26835491]
Wu Y Abbey CK Chen X Liu J Page DC Alagoz O Peissig P Onitilo AA Burnside ES .
Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation. J Med Imaging (Bellingham). 2015 Oct;2(4):041005
[PubMed ID: 26835489]
Benndorf M Burnside ES Herda C Langer M Kotter E .
External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets. Med Phys. 2015 Aug;42(8):4987-96
[PubMed ID: 26233224]
Wu Y Liu J Del Rio AM Page DC Alagoz O Peissig P Onitilo AA Burnside ES .
Developing a clinical utility framework to evaluate prediction models in radiogenomics. Proc SPIE Int Soc Opt Eng. 2015 Feb 21;9416
[PubMed ID: 27095854]
Liu J Wu Y Ong I Page D Peissig P McCarty C Onitilo AA Burnside E .
Leveraging Interaction between Genetic Variants and Mammographic Findings for Personalized Breast Cancer Diagnosis. AMIA Jt Summits Transl Sci Proc. 2015;2015:107-11
[PubMed ID: 26306250]
Kuusisto F Dutra I Elezaby M Mendonça EA Shavlik J Burnside ES .
Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems. AMIA Jt Summits Transl Sci Proc. 2015;2015:87-91
[PubMed ID: 26306246]
Liu J Zhang C Burnside E Page D .
Multiple Testing under Dependence via Semiparametric Graphical Models. Proc Int Conf Mach Learn. 2014 Dec 31;2014:955-963
[PubMed ID: 25309970]
Obadina ET Dubenske LL McDowell HE Atwood AK Mayer DK Woods RW Gustafson DH Burnside ES .
Online support: Impact on anxiety in women who experience an abnormal screening mammogram. Breast. 2014 Dec;23(6):743-8
[PubMed ID: 25193424]
Almeida E Ferreira P Vinhoza T Dutra I Li J Wu Y Burnside E .
ExpertBayes: Automatically refining manually built Bayesian networks. Proc Int Conf Mach Learn Appl. 2014 Dec;2014:362-366
[PubMed ID: 27066596]
Munoz D Near AM van Ravesteyn NT Lee SJ Schechter CB Alagoz O Berry DA Burnside ES Chang Y Chisholm G de Koning HJ Ali Ergun M Heijnsdijk EA Huang H Stout NK Sprague BL Trentham-Dietz A Mandelblatt JS Plevritis SK .
Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality. J Natl Cancer Inst. 2014 Nov;106(11)
[PubMed ID: 25255803]
Ayvaci MU Alagoz O Chhatwal J Munoz del Rio A Sickles EA Nassif H Kerlikowske K Burnside ES .
Predicting invasive breast cancer versus DCIS in different age groups. BMC Cancer. 2014 Aug 11;14:584
[PubMed ID: 25112586]