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Elizabeth Burnside
Elizabeth S. Burnside

Associate Professor of Radiology, Breast Imaging Section,
Affiliate appointment in the Department of Biostatistics & Medical Informatics
MD, MPH, Tufts University, 1993
MS, Medical Informatics, Stanford University, 2000

1111 Highland Ave., Room 1319
Madison, WI 53705-2275

Phone: (608) 265-4099
FAX: (608) 265-9840
Research Interests
I am interested in using computational techniques to improve the early detection of breast cancer. My 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. I 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, we are looking at whether inductive logic programming (ILP) and statistical relational learning (SRL) can improve the performance of this system.
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, Trentham-Dietz A, Kelcz F, Collins J. An example of breast cancer regression on imaging. Radiology Case Reports. [Online] 2006;1:27-37. (http://radiology.casereports.net/index.php/rcr/article/view/4/122)

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; 240:666-673.

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.

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; 245:401-410.

Yu B, Burnside ES, Sisney GA, Harter JM, Dhalla AH, Ramanujam N. Feasibility of near-infrared diffuse optical spectroscopy on patients undergoing image-guided core-needle biopsy. Optics Express. 2007; 15:7335-7350.

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.

Zhu C, Burnside ES, Sisney GA, Salkowski LR, Harter JM, Yu B, et al. Fluorescence spectroscopy: an adjunct diagnostic tool to image-guided core needle biopsy of the breast. IEEE Trans Biomed Eng 2009;56(10):2518-28.

Kahn CE, Jr., Langlotz CP, Burnside ES, Carrino JA, Channin DS, Hovsepian DM, et al. Toward best practices in radiology reporting. Radiology 2009;252(3):852-6

Burnside ES, Sickles EA, Bassett LW, et al. The ACR BI-RADS experience: learning from history. J Am Coll Radiol. 2009;6(12):851-60.

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, Sept. 2009. [Epub ahead of print]

Sprague BL, Trentham-Dietz A, Burnside ES. Socioeconomic disparities in the decline in invasive breast cancer incidence. Breast Cancer Res Treat. January, 2010. [Epub ahead of print]

Ayer T, Chhatwal J, Alagoz OA, Kahn CE, Burnside ES. Comparison of Logistic Regression and Artificial Neural Network Models with an Example in Breast Cancer Risk Prediction. Radiographics, November 2009)

Ayer T, Alagoz OA, Kahn CE, Shavlik J, Burnside ES. Artificial Neural Networks for Breast Cancer Risk Prediction: Discrimination and Calibration (In Press: Cancer)

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