Elizabeth J. Sutton

3.8k total citations · 2 hit papers
97 papers, 2.8k citations indexed

About

Elizabeth J. Sutton is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research and Surgery. According to data from OpenAlex, Elizabeth J. Sutton has authored 97 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Cancer Research and 17 papers in Surgery. Recurrent topics in Elizabeth J. Sutton's work include Radiomics and Machine Learning in Medical Imaging (39 papers), MRI in cancer diagnosis (35 papers) and Breast Cancer Treatment Studies (16 papers). Elizabeth J. Sutton is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (39 papers), MRI in cancer diagnosis (35 papers) and Breast Cancer Treatment Studies (16 papers). Elizabeth J. Sutton collaborates with scholars based in United States, Germany and United Kingdom. Elizabeth J. Sutton's co-authors include Elizabeth A. Morris, Heike E. Daldrup‐Link, Tobias D. Henning, Sunitha B. Thakur, Gary J. Whitman, Maryellen L. Giger, Elizabeth S. Burnside, Jose Net, Karen Drukker and Hui Li and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Cancer.

In The Last Decade

Elizabeth J. Sutton

90 papers receiving 2.7k 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, Yitan Zhu 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, Yitan Zhu 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 J. Sutton United States	28	1.8k	557	460	378	332	97	2.8k
Maxine S. Jochelson United States	41	2.7k 1.5×	719 1.3×	1.1k 2.5×	431 1.1×	964 2.9×	142	4.5k
Jurgen Peerlings Netherlands	8	3.6k 2.0×	520 0.9×	149 0.3×	942 2.5×	766 2.3×	9	4.2k
Lee B. Jordan United Kingdom	29	1.3k 0.7×	215 0.4×	1.0k 2.2×	889 2.4×	1.3k 3.8×	79	3.7k
Stephanie Tanadini‐Lang Switzerland	36	3.0k 1.6×	317 0.6×	123 0.3×	812 2.1×	599 1.8×	163	4.7k
Eren D. Yeh United States	23	1.0k 0.6×	128 0.2×	740 1.6×	197 0.5×	460 1.4×	57	1.9k
Mary K. Hayes United States	18	623 0.3×	553 1.0×	522 1.1×	165 0.4×	704 2.1×	49	2.4k
Zhi Yang China	32	1.4k 0.8×	62 0.1×	446 1.0×	496 1.3×	1.3k 3.9×	244	3.5k
Clemens C. Cyran Germany	28	922 0.5×	68 0.1×	150 0.3×	260 0.7×	432 1.3×	126	2.6k
Cornelis A. Hoefnagel Netherlands	39	1.1k 0.6×	93 0.2×	1.0k 2.2×	190 0.5×	1.4k 4.1×	119	4.0k
Radu A. Manoliu Netherlands	14	784 0.4×	122 0.2×	448 1.0×	125 0.3×	409 1.2×	29	1.9k

Countries citing papers authored by Elizabeth J. Sutton

Since Specialization

Citations

This map shows the geographic impact of Elizabeth J. Sutton'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 J. Sutton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elizabeth J. Sutton more than expected).

Fields of papers citing papers by Elizabeth J. Sutton

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Elizabeth J. Sutton. 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 J. Sutton. The network helps show where Elizabeth J. Sutton may publish in the future.

Co-authorship network of co-authors of Elizabeth J. Sutton

This figure shows the co-authorship network connecting the top 25 collaborators of Elizabeth J. Sutton. A scholar is included among the top collaborators of Elizabeth J. Sutton 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 J. Sutton. Elizabeth J. Sutton 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

Sutton, Elizabeth J., Yu Huang, Berman Kayis, et al.. (2025). High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI. Radiology Artificial Intelligence. 7(5). e240550–e240550. 1 indexed citations

Eskreis‐Winkler, Sarah, Arka Bhowmik, Lindsey Kelly, et al.. (2025). Adaptive Breast MRI Scanning Using AI. Radiology. 315(3). e242924–e242924.

Huang, Yu, Roberto Lo Gullo, Mary Hughes, et al.. (2024). Cross-site Validation of AI Segmentation and Harmonization in Breast MRI. Journal of Imaging Informatics in Medicine. 38(3). 1642–1652.

Omisore, Adeleye Dorcas, Elizabeth J. Sutton, Gavin Tansley, et al.. (2023). Population-Level Access to Breast Cancer Early Detection and Diagnosis in Nigeria. JCO Global Oncology. 9(9). e2300093–e2300093. 3 indexed citations

Eskreis‐Winkler, Sarah, Janice S. Sung, Sunitha B. Thakur, et al.. (2023). High–Temporal/High–Spatial Resolution Breast Magnetic Resonance Imaging Improves Diagnostic Accuracy Compared With Standard Breast Magnetic Resonance Imaging in Patients With High Background Parenchymal Enhancement. Journal of Clinical Oncology. 41(30). 4747–4755. 2 indexed citations

Lynch, Kathleen, Thomas M. Atkinson, Adeleye Dorcas Omisore, et al.. (2022). Developing a Technology Acceptability and Usage Survey (TAUS) for mHealth Intervention Planning and Evaluation in Nigeria: Pilot Study. JMIR Formative Research. 6(4). e34035–e34035. 3 indexed citations

Sutton, Elizabeth J., et al.. (2022). Immunohistochemistry Use in Mohs Micrographic Surgery: A Survey of the American College of Mohs Surgery. Dermatologic Surgery. 48(8). 893–894. 4 indexed citations

Eskreis‐Winkler, Sarah, Katherine Simon, Melissa Reichman, et al.. (2021). Multispectral Imaging for Metallic Biopsy Marker Detection During MRI-Guided Breast Biopsy: A Feasibility Study for Clinical Translation. Frontiers in Oncology. 11. 605014–605014.

Leithner, Doris, Blanca Bernard‐Davila, Danny F. Martinez, et al.. (2019). Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes. Molecular Imaging and Biology. 22(2). 453–461. 59 indexed citations

10.

Dashevsky, Brittany Z., Jung Hun Oh, Aditya Apte, et al.. (2018). MRI features predictive of negative surgical margins in patients with HER2 overexpressing breast cancer undergoing breast conservation. Scientific Reports. 8(1). 315–315. 7 indexed citations

11.

Creasy, John M., Abhishek Midya, Jayasree Chakraborty, et al.. (2018). Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases. European Radiology. 29(1). 458–467. 12 indexed citations

12.

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

13.

Sutton, Elizabeth J., Emily C. Zabor, Zhigang Zhang, et al.. (2017). Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients. European Journal of Radiology Open. 4. 101–107. 31 indexed citations

14.

Tyagi, Neelam, Elizabeth J. Sutton, Margie Hunt, et al.. (2016). Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions. International Journal of Radiation Oncology*Biology*Physics. 97(2). 411–419. 6 indexed citations

15.

Li, Hui, Yitan Zhu, Elizabeth S. Burnside, et al.. (2016). 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. Radiology. 281(2). 382–391. 373 indexed citations breakdown →

16.

Burnside, Elizabeth S., Karen Drukker, Hui Li, et al.. (2015). Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer. 122(5). 748–757. 54 indexed citations

17.

Boddington, Sophie, Elizabeth J. Sutton, Tobias D. Henning, et al.. (2010). Labeling Human Mesenchymal Stem Cells with Fluorescent Contrast Agents: the Biological Impact. Molecular Imaging and Biology. 13(1). 3–9. 25 indexed citations

18.

Meier, Reinhard, Daniel Golovko, Sophie Boddington, et al.. (2010). Indocyanine green–enhanced imaging of antigen‐induced arthritis with an integrated optical imaging/radiography system. Arthritis & Rheumatism. 62(8). 2322–2327. 42 indexed citations

19.

Henning, Tobias D., Elizabeth J. Sutton, Anne Kim, et al.. (2009). The influence of ferucarbotran on the chondrogenesis of human mesenchymal stem cells. Contrast Media & Molecular Imaging. 4(4). 165–173. 62 indexed citations

20.

Boddington, Sophie, Tobias D. Henning, Elizabeth J. Sutton, & Heike E. Daldrup‐Link. (2008). Labeling Stem Cells with Fluorescent Dyes for non-invasive Detection with Optical Imaging. Journal of Visualized Experiments. 29 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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