Ursula Beetles

823 total citations
17 papers, 531 citations indexed

About

Ursula Beetles is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Ursula Beetles has authored 17 papers receiving a total of 531 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Oncology, 11 papers in Pulmonary and Respiratory Medicine and 10 papers in Artificial Intelligence. Recurrent topics in Ursula Beetles's work include Global Cancer Incidence and Screening (11 papers), Digital Radiography and Breast Imaging (11 papers) and AI in cancer detection (10 papers). Ursula Beetles is often cited by papers focused on Global Cancer Incidence and Screening (11 papers), Digital Radiography and Breast Imaging (11 papers) and AI in cancer detection (10 papers). Ursula Beetles collaborates with scholars based in United Kingdom, Canada and Netherlands. Ursula Beetles's co-authors include Susan Astley, Mary Wilson, Jack Cuzick, Jamie C. Sergeant, D. Gareth Evans, Anthony Howell, Paula Stavrinos, Michelle Harvie, Adam R. Brentnall and Elaine F. Harkness and has published in prestigious journals such as Cancer Research, British Journal of Cancer and Journal of Internal Medicine.

In The Last Decade

Ursula Beetles

16 papers receiving 517 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ursula Beetles United Kingdom 8 391 242 231 183 99 17 531
Daniëlle van der Waal Netherlands 13 357 0.9× 106 0.4× 128 0.6× 135 0.7× 47 0.5× 23 476
Sarah Sampson United Kingdom 9 303 0.8× 107 0.4× 90 0.4× 241 1.3× 56 0.6× 10 472
Paula Stavrinos United Kingdom 13 610 1.6× 218 0.9× 206 0.9× 431 2.4× 95 1.0× 24 844
Michael C. S. Bissell United States 10 255 0.7× 102 0.4× 114 0.5× 66 0.4× 63 0.6× 15 364
Soujanya Gadde United Kingdom 7 216 0.6× 167 0.7× 148 0.6× 78 0.4× 87 0.9× 12 337
Ellen Paap Netherlands 11 482 1.2× 120 0.5× 147 0.6× 56 0.3× 92 0.9× 14 633
Lynne Fox United Kingdom 3 210 0.5× 92 0.4× 82 0.4× 117 0.6× 41 0.4× 4 269
Sara Bundred United Kingdom 6 202 0.5× 161 0.7× 142 0.6× 73 0.4× 73 0.7× 9 295
Valerie Reece United Kingdom 4 184 0.5× 141 0.6× 121 0.5× 73 0.4× 65 0.7× 6 279
Shivaani Mariapun Malaysia 9 154 0.4× 91 0.4× 103 0.4× 99 0.5× 37 0.4× 18 313

Countries citing papers authored by Ursula Beetles

Since Specialization
Citations

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

Fields of papers citing papers by Ursula Beetles

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ursula Beetles. 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 Ursula Beetles. The network helps show where Ursula Beetles may publish in the future.

Co-authorship network of co-authors of Ursula Beetles

This figure shows the co-authorship network connecting the top 25 collaborators of Ursula Beetles. A scholar is included among the top collaborators of Ursula Beetles 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 Ursula Beetles. Ursula Beetles is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Astley, Susan, Elaine F. Harkness, Jamie C. Sergeant, et al.. (2018). A comparison of five methods of measuring mammographic density: a case-control study. Breast Cancer Research. 20(1). 10–10. 80 indexed citations
2.
Harkness, Elaine F., Philip Foden, Mary Wilson, et al.. (2018). Reader performance in visual assessment of breast density using visual analogue scales: are some readers more predictive of breast cancer?. Research Explorer (The University of Manchester). 8673. 31–31. 2 indexed citations
3.
Evans, D. Gareth, Louise S. Donnelly, Elaine F. Harkness, et al.. (2016). Breast cancer risk feedback to women in the UK NHS breast screening population. British Journal of Cancer. 114(9). 1045–1052. 71 indexed citations
4.
Brentnall, Adam R., Elaine F. Harkness, Susan Astley, et al.. (2015). Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Research. 17(1). 147–147. 175 indexed citations
5.
Lim, Yit Yoong, Anthony Maxwell, Ursula Beetles, et al.. (2015). Digital breast tomosynthesis at screening assessment: are two views always necessary?. British Journal of Radiology. 88(1055). 20150353–20150353. 9 indexed citations
6.
Howell, Anthony, Sue Astley, Elaine F. Harkness, et al.. (2015). Abstract P5-12-01: Predicting the effect of tamoxifen on the breast: Change in measures of breast density, serum markers and SNPs. Cancer Research. 75(9_Supplement). P5–12. 1 indexed citations
7.
Wilson, Mary, Ursula Beetles, Caroline Boggis, et al.. (2013). PB.17: Inter-observer agreement in visual analogue scale assessment of percentage breast density. Breast Cancer Research. 15(S1). 3 indexed citations
8.
Sergeant, Jamie C., Mary Wilson, Ursula Beetles, et al.. (2013). Same task, same observers, different values: the problem with visual assessment of breast density. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8673. 86730T–86730T. 11 indexed citations
9.
Astley, Susan, Yit Yoong Lim, Catriona Tate, et al.. (2013). A comparison of image interpretation times in full field digital mammography and digital breast tomosynthesis. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8673. 86730S–86730S. 6 indexed citations
10.
Maxwell, Anthony, Ursula Beetles, Sara Bundred, et al.. (2013). PB.18: Factors affecting breast density assessment. Breast Cancer Research. 15(S1). 1 indexed citations
11.
Evans, D. Gareth, Jane Warwick, Susan Astley, et al.. (2012). Assessing Individual Breast Cancer Risk within the U.K. National Health Service Breast Screening Program: A New Paradigm for Cancer Prevention. Cancer Prevention Research. 5(7). 943–951. 93 indexed citations
12.
Howell, Anthony, Susan Astley, Jane Warwick, et al.. (2012). Prevention of breast cancer in the context of a national breast screening programme. Journal of Internal Medicine. 271(4). 321–330. 26 indexed citations
13.
Tate, Catriona, Julie Morris, Sigrid Whiteside, et al.. (2012). A comparison of reading times in full-field digital mammography and digital breast tomosynthesis. Breast Cancer Research. 14(S1). 4 indexed citations
14.
15.
Duffy, Stephen W., Irıs D. Nagtegaal, Susan Astley, et al.. (2008). Visually assessed breast density, breast cancer risk and the importance of the craniocaudal view. Breast Cancer Research. 10(4). R64–R64. 37 indexed citations
16.
Gilbert, Fiona J., Susan Astley, Caroline Boggis, et al.. (2008). Variable size computer-aided detection prompts and mammography film reader decisions. Breast Cancer Research. 10(4). R72–R72. 7 indexed citations
17.
Gilbert, Fiona J., Heather Deans, Karen A. Duncan, et al.. (2004). Design of a retrospective study of computer-aided detection in mammographic screening: Computer Aided Detection Evaluation Trial. Breast Cancer Research. 6(S1).

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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