Elizabeth Burnside M.D., MPH, M.S.
Elizabeth Burnside M.D., MPH, M.S.
Professor of Radiology, University of Wisconsin School of Medicine and Public Health
Zweryfikowany adres z uwhealth.org - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
ACR BI-RADS atlas: breast imaging reporting and data system; mammography, ultrasound, magnetic resonance imaging, follow-up and outcome monitoring, data dictionary
American College of Radiology, CJ D'Orsi
ACR, American College of Radiology, 2013
1833*2013
Introduction to statistical relational learning
D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ...
MIT press, 2007
18032007
Acr bi-rads® mammography
EA Sickles, CJ d’Orsi, LW Bassett, CM Appleton, WA Berg, ES Burnside
ACR BI-RADS® atlas, breast imaging reporting and data system 5, 2013, 2013
5842013
Differentiating Benign from Malignant Solid Breast Masses with US Strain Imaging
ES Burnside, TJ Hall, AM Sommer, GK Hesley, GA Sisney, WE Svensson, ...
Radiology 245 (2), 401-410, 2007
3652007
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
H Li, Y Zhu, ES Burnside, K Drukker, KA Hoadley, C Fan, SD Conzen, ...
Radiology 281 (2), 382-391, 2016
3282016
The ACR BI-RADS® experience: learning from history
ES Burnside, EA Sickles, LW Bassett, DL Rubin, CH Lee, DM Ikeda, ...
Journal of the American College of Radiology 6 (12), 851-860, 2009
2812009
Use of Microcalcification Descriptors in BI-RADS 4th Edition to Stratify Risk of Malignancy
ES Burnside, JE Ochsner, KJ Fowler, JP Fine, LR Salkowski, DL Rubin, ...
Radiology 242 (2), 388-395, 2007
2192007
Toward Best Practices in Radiology Reporting
CE Kahn Jr, CP Langlotz, ES Burnside, JA Carrino, DS Channin, ...
Radiology 252 (3), 852-856, 2009
2112009
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
H Li, Y Zhu, ES Burnside, E Huang, K Drukker, KA Hoadley, C Fan, ...
NPJ breast cancer 2 (1), 1-10, 2016
2062016
Comparison of Logistic Regression and Artificial Neural Network Models in Breast Cancer Risk Estimation
T Ayer, J Chhatwal, O Alagoz, CE Kahn Jr, RW Woods, ES Burnside
Radiographics 30 (1), 13-22, 2010
1722010
Patient, faculty, and self-assessment of radiology resident performance: A 360-degree method of measuring professionalism and interpersonal/communication skills
J Wood, J Collins, ES Burnside, MA Albanese, PA Propeck, F Kelcz, ...
Academic radiology 11 (8), 931-939, 2004
1382004
Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration
T Ayer, O Alagoz, J Chhatwal, JW Shavlik, CE Kahn Jr, ES Burnside
Cancer 116 (14), 3310-3321, 2010
1262010
Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality
D Munoz, AM Near, NT Van Ravesteyn, SJ Lee, CB Schechter, O Alagoz, ...
JNCI: Journal of the National Cancer Institute 106 (11), 2014
1242014
Optimal breast biopsy decision-making based on mammographic features and demographic factors
J Chhatwal, O Alagoz, ES Burnside
Operations research 58 (6), 1577-1591, 2010
1222010
Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience
ES Burnside, DL Rubin, JP Fine, RD Shachter, GA Sisney, WK Leung
Radiology 240 (3), 666-673, 2006
1112006
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
W Guo, H Li, Y Zhu, L Lan, S Yang, K Drukker, EA Morris, ES Burnside, ...
Journal of medical imaging 2 (4), 041007, 2015
1102015
A Bayesian network for mammography.
E Burnside, D Rubin, R Shachter
Proceedings of the AMIA Symposium, 106, 2000
1002000
Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings
ES Burnside, J Davis, J Chhatwal, O Alagoz, MJ Lindstrom, BM Geller, ...
Radiology 251 (3), 663-672, 2009
962009
Circulating serum xenoestrogens and mammographic breast density
BL Sprague, A Trentham-Dietz, CJ Hedman, J Wang, JDC Hemming, ...
Breast Cancer Research 15 (3), 1-8, 2013
862013
An integrated approach to learning Bayesian networks of rules
J Davis, E Burnside, I de Castro Dutra, D Page, VS Costa
European Conference on Machine Learning, 84-95, 2005
85*2005
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