Kamu erişimi zorunlu olan makaleler - Elizabeth Burnside M.D., MPH, M.S.Daha fazla bilgi edinin
Hiçbir yerde sunulmuyor: 2
Breast cancer risk prediction using electronic health records
Y Wu, ES Burnside, J Cox, J Fan, M Yuan, J Yin, P Peissig, A Cobian, ...
2017 IEEE International Conference on Healthcare Informatics (ICHI), 224-228, 2017
Zorunlu olanlar: US National Institutes of Health
Phenol xenoestrogens and mammographic breast density
A Trentham-Dietz, BL Sprague, J Wang, JM Hampton, DSM Buist, ...
Cancer Epidemiology Biomarkers & Prevention 21, 561-562, 2012
Zorunlu olanlar: US National Institutes of Health
Bir yerde sunuluyor: 130
A population-based study of genes previously implicated in breast cancer
C Hu, SN Hart, R Gnanaolivu, H Huang, KY Lee, J Na, C Gao, J Lilyquist, ...
New England Journal of Medicine 384 (5), 440-451, 2021
Zorunlu olanlar: US National Institutes of Health, State of Califonia, Susan G. Komen
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
Zorunlu olanlar: US National Institutes of Health
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
Zorunlu olanlar: US National Institutes of Health
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, 16012, 2016
Zorunlu olanlar: US National Institutes of Health
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
Zorunlu olanlar: US National Institutes of Health
The All of Us Research Program: data quality, utility, and diversity
AH Ramirez, L Sulieman, DJ Schlueter, A Halvorson, J Qian, ...
Patterns 3 (8), 2022
Zorunlu olanlar: US National Institutes of Health
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
Zorunlu olanlar: US National Institutes of Health
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, ...
Journal of the National Cancer Institute 106 (11), dju289, 2014
Zorunlu olanlar: US National Institutes of Health
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, E Morris, E Burnside, ...
Journal of medical imaging 2 (4), 041007-041007, 2015
Zorunlu olanlar: US National Institutes of Health
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
Zorunlu olanlar: US National Institutes of Health
Circulating serum xenoestrogens and mammographic breast density
BL Sprague, A Trentham-Dietz, CJ Hedman, J Wang, JDC Hemming, ...
Breast Cancer Research 15, 1-8, 2013
Zorunlu olanlar: US National Institutes of Health
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
Zorunlu olanlar: US National Institutes of Health
A logistic regression model based on the national mammography database format to aid breast cancer diagnosis
J Chhatwal, O Alagoz, MJ Lindstrom, CE Kahn Jr, KA Shaffer, ...
American Journal of Roentgenology 192 (4), 1117-1127, 2009
Zorunlu olanlar: US National Institutes of Health
Heterogeneity in women’s adherence and its role in optimal breast cancer screening policies
T Ayer, O Alagoz, NK Stout, ES Burnside
Management Science 62 (5), 1339-1362, 2016
Zorunlu olanlar: US National Institutes of Health
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
ES Burnside, K Drukker, H Li, E Bonaccio, M Zuley, M Ganott, JM Net, ...
Cancer 122 (5), 748-757, 2016
Zorunlu olanlar: US National Institutes of Health
The mammographic density of a mass is a significant predictor of breast cancer
RW Woods, GS Sisney, LR Salkowski, K Shinki, Y Lin, ES Burnside
Radiology 258 (2), 417-425, 2011
Zorunlu olanlar: US National Institutes of Health
The effect of budgetary restrictions on breast cancer diagnostic decisions
MUS Ayvaci, O Alagoz, ES Burnside
Manufacturing & Service Operations Management 14 (4), 600-617, 2012
Zorunlu olanlar: US National Institutes of Health
Risk of breast cancer among carriers of pathogenic variants in breast cancer predisposition genes varies by polygenic risk score
C Gao, EC Polley, SN Hart, H Huang, C Hu, R Gnanaolivu, J Lilyquist, ...
Journal of Clinical Oncology 39 (23), 2564-2573, 2021
Zorunlu olanlar: US National Institutes of Health, Cancer Research UK, State of Califonia …
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