Elizabeth J. Sutton
- Radiology, Nuclear Medicine and Imaging top 0.5%
- Artificial Intelligence top 2%
- Cancer Research top 5%
- Biomedical Engineering top 10%
- Oncology top 10%
- Co-authors
- Elizabeth A. MorrisHeike E. Daldrup‐LinkTobias D. HenningSunitha B. ThakurElizabeth S. BurnsideJose NetGary J. WhitmanMaryellen L. Giger
- Topics
- Radiomics and Machine Learning in Medical Imaging (39 papers)MRI in cancer diagnosis (35 papers)Breast Cancer Treatment Studies (16 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Elizabeth J. Sutton
90 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 135
- Radiology, Nuclear Medicine and Imaging 1.8k
- Artificial Intelligence 557
- Cancer Research 460
- Biomedical Engineering 378
- Oncology 332
Countries citing papers authored by Elizabeth J. Sutton
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 59 | |
| 8 | 7 | |
| 9 | 12 | |
| 10 | 27 | |
| 11 | 6 | |
| 12 | MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assaysbreakdown → | 373 |
| 13 | Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data setbreakdown → | 287 |
| 14 | 41 | |
| 15 | 40 | |
| 16 | 25 | |
| 17 | 42 | |
| 18 | 62 | |
| 19 | 17 | |
| 20 | 21 |
About Elizabeth J. Sutton
Elizabeth J. Sutton is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research and Pathology and Forensic Medicine, having authored 97 papers that have together received 2.8k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (39 papers), MRI in cancer diagnosis (35 papers) and Breast Cancer Treatment Studies (16 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.8k citations), Health Informatics (54 citations) and Cancer Research (460 citations). Elizabeth J. Sutton has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Elizabeth A. Morris, Heike E. Daldrup‐Link, Tobias D. Henning, Sunitha B. Thakur, Elizabeth S. Burnside, Jose Net, Gary J. Whitman, Maryellen L. Giger, Karen Drukker and Hui Li. Their work appears in journals such as Journal of Clinical Oncology, PLoS ONE and Cancer.
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.