Burnside, Elizabeth S.

Affiliate Faculty
Title: 
Professor of Radiology, Deputy Director of the Institute for Clinical and Translational Research, Associate Dean UW SMPH
Room & Building: 
4234 Health Sciences Learning Center
Email: 

eburnside [at] uwhealth.org

Phone: 
265-4099
Fax: 
Education & Research: 


Research Interests
Dr. Burnside is interested in using computational techniques to improve the early detection of breast cancer. Her work centers on the development of an expert system that can accurately assess the probability of breast cancer using patients’ demographic risk factors and mammography findings. Dr. Burnside and colleagues have developed a Bayesian Network that is designed to assist radiologists in the post-discovery aspects of mammography: interpretation and decision-making. Currently, she is investigating how novel inforamtics algorithms can improve the performance of this system and therefore impact breast cancer care.

Selected Publications
Burnside ES, Rubin DL, Shachter R, Sohlich RE, Sickles, EA. A probabilistic expert system that provides automated mammographic-histologic correlation: Initial experience AJR 2004;182(2):481-8.

Rubin DL, Burnside ES, and Shachter R: "A Bayesian network to assist mammography interpretation" in: Sainfort, F., Brandeau, M.L., and W.P. Pierskalla, Eds., Handbook of Operations Research and Health Care: Methods and Applications, Kluwer Academic Publishers, in press.

Burnside ES, Rubin DL, Shachter RD. Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography” in: Fieschi, M., Coiera, E., and Li, Y.J., Eds., Medinfo 2004, Proceedings of the 11th World Congress on Medical Informatics, Sept. 7-11, 2004, IOS Press, 13-18.

Burnside ES, Rubin DL, Shachter RD. Improving a Bayesian Network’s Ability to Predict the Probability of Malignancy of Microcalcifications on Mammography. Proc Computer Assisted Radiology and Surgery 2004, International Congress Series; 1268: 1021-1026.

Burnside ES, Bayesian networks: computer-assisted diagnosis support in radiology. Acad Radiol 2005;12:422-430.

Burnside ES, Park JM, Fine JP, Sisney GA. The use of batch reading to improve the performance of screening mammography. AJR 2005;185:790-796.

Burnside ES, Ochsner JE, Fowler K, Fine JP, Salkowski L, Rubin DL, Sisney GA. Use of microcalcification descriptors in the BI-RADS 4th Edition to stratify the risk of malignancy. Radiology 2007; 242:388-395.

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; 192:1117-1127.

Burnside ES, Davis J, Chhatwal J, Alagoz O, Lindstrom MJ, Geller BM, Littenberg B, Kahn CE, Jr., Shaffer KA, Page CD. A probabilistic computer model developed from clinical data in the national mammography database format to classify mammographic findings. Radiology 2009; 251:666-673.

Woods RW, Oliphant L, Shinki K, Page CD, Shavlik J, Burnside ES. Validation of results from knowledge discovery techniques: mass density as a predictor of breast cancer, J Digit Imaging. 2010; 23(5):554-561.

Woods RW, Sisney GA, Salkowski LR, Shinki K, Burnside ES. The Mammographic Density of a Mass is a Significant Predictor of Breast Cancer. Radiology. Feb 2011; 258(2):417-425.

Percha B, Nassif H, Lipson J, Burnside E, Rubin D. Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc. Sep 1, 2012; 19(5):913-6.

Liu J, Peissig P, Zhang C, Burnside ES, McCarty C, Page D. High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. JMLR workshop and conference proceedings. 2012; 22:712-721.

Wu Y, Rubin DL, Woods RW, Elezaby M, Burnside ES. Developing a Comprehensive Database Management System for Organization and Evaluation of Mammography Datasets. Cancer Informatics. Oct 2014; 13(S3):53-62.

Wu Y, Liu J, Page D, Peissig P, McCarty C, Onitilo A, Burnside ES. Comparing the value of mammographic features and genetic variants in breast cancer risk prediction. AMIA Annual Symposium proceedings. 2014; 1228-1237.

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 Joint Summits on Translational Science proceedings AMIA Summit on Translational Science. 2015; 107-11

Benndorf M, Kotter E, Langer M, Herda C, Wu Y, Burnside ES. Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon. European Radiology. Jun 2015; 25(6):1768-1775.

Burnside ES, Liu J, Wu Y, Onitilo AA, McCarty C, Page CD, Peissig P, 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. Academic Radiology. Acad Radiol. Jan 2016; 23(1):62-9.

Burnside ES, Sickles EA, Duffy SW. A pragmatic approach to determine components of optimal screening mammography practice. JAMA. 2016; 315(18):1951-3

Bozkurt, S, Gimenez F, Burnside ES, Gulkesen KH, Rubin DL. Using Automatically Extracted Information from Mammography Reports for Decision Support. J Biomedical Inform. Aug 2016; 62:224-31

Fan J, Wu Y, Yuan M, Page CD, Liu J, Ong I, Peissig P, Burnside ES. Structure-leveraged methods in breast cancer risk prediction. Journal of Machine Learning Research. 2016; 17(228)1-15

Search PubMed for other publications by Elizabeth Burnside

Summary: 

Using computational techniques to improve the early detection of breast cancer. Development of an expert system that can accurately assess the probability of breast cancer using patients’ demographic risk factors and mammography findings.