Elizabeth S. Burnside

8.3k total citations · 2 hit papers
176 papers, 4.9k citations indexed

About

Elizabeth S. Burnside is a scholar working on Oncology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Elizabeth S. Burnside has authored 176 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Oncology, 70 papers in Artificial Intelligence and 39 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Elizabeth S. Burnside's work include Global Cancer Incidence and Screening (64 papers), AI in cancer detection (57 papers) and Colorectal Cancer Screening and Detection (29 papers). Elizabeth S. Burnside is often cited by papers focused on Global Cancer Incidence and Screening (64 papers), AI in cancer detection (57 papers) and Colorectal Cancer Screening and Detection (29 papers). Elizabeth S. Burnside collaborates with scholars based in United States, United Kingdom and Portugal. Elizabeth S. Burnside's co-authors include Oğuzhan Alagöz, Daniel L. Rubin, Jagpreet Chhatwal, Gale A. Sisney, Charles E. Kahn, Edward A. Sickles, Jason P. Fine, Turgay Ayer, Elizabeth A. Morris and Gary J. Whitman and has published in prestigious journals such as Nature Medicine, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Elizabeth S. Burnside

167 papers receiving 4.7k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2016 373 citations Hui Li, Elizabeth S. Burnside et al. Radiology profile →
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

2016 287 citations Hui Li, Elizabeth S. Burnside et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Elizabeth S. Burnside United States	36	2.2k	1.6k	1.2k	746	586	176	4.9k
Marilyn A. Roubidoux United States	35	1.8k 0.8×	1.4k 0.9×	965 0.8×	1.2k 1.7×	718 1.2×	132	3.9k
Linn Abraham United States	27	1.1k 0.5×	1.3k 0.8×	1.8k 1.5×	1.1k 1.5×	569 1.0×	57	4.1k
Jeffrey D. Blume United States	33	1.7k 0.8×	358 0.2×	804 0.7×	916 1.2×	505 0.9×	100	4.4k
Marcus R. Makowski Germany	38	3.1k 1.4×	1.2k 0.7×	445 0.4×	1.4k 1.9×	252 0.4×	362	6.7k
Alicia Y. Toledano United States	32	1.2k 0.6×	648 0.4×	825 0.7×	1.2k 1.7×	207 0.4×	62	4.5k
Patrick Brennan Australia	31	2.5k 1.1×	1.2k 0.7×	1.1k 0.9×	1.4k 1.9×	194 0.3×	352	4.4k
Mireille J. M. Broeders Netherlands	40	1.5k 0.7×	1.6k 1.0×	3.9k 3.3×	1.6k 2.1×	1.2k 2.0×	192	6.0k
Kun‐Hsing Yu United States	28	1.3k 0.6×	1.2k 0.8×	518 0.4×	560 0.8×	305 0.5×	58	4.2k
Gina Lockwood Canada	48	1.4k 0.6×	866 0.5×	2.8k 2.4×	3.5k 4.7×	829 1.4×	136	8.3k
Berta M. Geller United States	45	1.8k 0.8×	2.5k 1.6×	4.3k 3.6×	2.0k 2.7×	1.4k 2.5×	143	7.6k

Countries citing papers authored by Elizabeth S. Burnside

Since Specialization

Citations

This map shows the geographic impact of Elizabeth S. Burnside's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Elizabeth S. Burnside with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elizabeth S. Burnside more than expected).

Fields of papers citing papers by Elizabeth S. Burnside

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Elizabeth S. Burnside. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Elizabeth S. Burnside. The network helps show where Elizabeth S. Burnside may publish in the future.

Co-authorship network of co-authors of Elizabeth S. Burnside

This figure shows the co-authorship network connecting the top 25 collaborators of Elizabeth S. Burnside. A scholar is included among the top collaborators of Elizabeth S. Burnside based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Elizabeth S. Burnside. Elizabeth S. Burnside is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Woods, Ryan W., et al.. (2025). Racial and Ethnic Disparities in Screening Mammography During COVID-19 in the Upper Midwest. Journal of the American College of Radiology. 22(3). 315–323. 2 indexed citations

Afshar, Majid, Cara Joyce, Dmitriy Dligach, et al.. (2025). Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults. Nature Medicine. 31(6). 1863–1872. 1 indexed citations

Burnside, Elizabeth S., et al.. (2023). Management of Women at High Risk for Breast Cancer. The Journal of the American Board of Family Medicine. 36(6). 1029–1032. 5 indexed citations

Afshar, Majid, Marlon P. Mundt, Randall Brown, et al.. (2023). Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults. JMIR Medical Informatics. 11. e44977–e44977. 14 indexed citations

Brasier, Allan R., et al.. (2023). Temporal development of high-performance translational teams. Journal of Clinical and Translational Science. 7(1). e117–e117. 2 indexed citations

Burnside, Elizabeth S., Sarina Schrager, Lori L. DuBenske, et al.. (2022). Team Science Principles Enhance Cancer Care Delivery Quality Improvement: Interdisciplinary Implementation of Breast Cancer Screening Shared Decision Making. JCO Oncology Practice. 19(1). e1–e7. 3 indexed citations

Schrager, Sarina, et al.. (2021). Patient and Clinician Characteristics That Predict Breast Cancer Screening Behavior in 40–49-Year-Old Women. SHILAP Revista de lepidopterología. 8(4). 331–335. 1 indexed citations

Burnside, Elizabeth S., Lucy M. Warren, Jonathan P. Myles, et al.. (2021). Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case–control study. British Journal of Cancer. 125(6). 884–892. 6 indexed citations

Rolland, Betsy, Elizabeth S. Burnside, Corrine I. Voils, Manish N. Shah, & Allan R. Brasier. (2020). Enhancing reproducibility using interprofessional team best practices. SHILAP Revista de lepidopterología. 5(1). e20–e20. 16 indexed citations

10.

Shah, Dhavan V., et al.. (2020). Framing the Clinical Encounter: Shared Decision-Making, Mammography Screening, and Decision Satisfaction. Journal of Health Communication. 25(9). 681–691. 5 indexed citations

11.

Berg, Wendie A., Jeremy M Berg, Edward A. Sickles, et al.. (2020). Cancer Yield and Patterns of Follow-up for BI-RADS Category 3 after Screening Mammography Recall in the National Mammography Database. Radiology. 296(1). 32–41. 48 indexed citations

12.

Fuentes‐Guerra, Francisco Javier Giménez, et al.. (2019). A Probabilistic Model to Support Radiologists’ Classification Decisions in Mammography Practice. Medical Decision Making. 39(3). 208–216. 4 indexed citations

13.

Schrager, Sarina & Elizabeth S. Burnside. (2018). Breast Cancer Screening in Primary Care: A Call for Development and Validation of Patient-Oriented Shared Decision-Making Tools. Journal of Women s Health. 28(2). 114–116. 13 indexed citations

14.

Burnside, Elizabeth S., Daniel Vulkan, R G Blanks, & Stephen W. Duffy. (2018). Association between Screening Mammography Recall Rate and Interval Cancers in the UK Breast Cancer Service Screening Program: A Cohort Study. Radiology. 288(1). 47–54. 21 indexed citations

15.

Sutton, Elizabeth J., Erich P. Huang, Karen Drukker, et al.. (2017). Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. European Radiology Experimental. 1(1). 22–22. 27 indexed citations

16.

Bozkurt, Selen, Francisco Javier Giménez Fuentes‐Guerra, Elizabeth S. Burnside, Kemal Hakan Gülkesen, & Daniel L. Rubin. (2016). Using automatically extracted information from mammography reports for decision-support. Journal of Biomedical Informatics. 62. 224–231. 42 indexed citations

17.

Burnside, Elizabeth S., Jie Liu, Yirong Wu, et al.. (2015). Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Academic Radiology. 23(1). 62–69. 10 indexed citations

18.

Ayer, Turgay, Oğuzhan Alagöz, Jagpreet Chhatwal, et al.. (2010). Breast cancer risk estimation with artificial neural networks revisited. Cancer. 116(14). 3310–3321. 90 indexed citations

19.

Davis, Jesse, Irene M. Ong, Jan Struyf, et al.. (2007). Change of representation for statistical relational learning. International Joint Conference on Artificial Intelligence. 16(2). 2719–2726. 20 indexed citations

20.

Davis, Jesse, Elizabeth S. Burnside, Inês Dutra, et al.. (2005). View learning for statistical relational learning: with an application to mammography. International Joint Conference on Artificial Intelligence. 677–683. 27 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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