Sue Astley

568 total citations
29 papers, 313 citations indexed

About

Sue Astley is a scholar working on Artificial Intelligence, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sue Astley has authored 29 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 10 papers in Oncology and 8 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sue Astley's work include AI in cancer detection (18 papers), Global Cancer Incidence and Screening (9 papers) and Digital Radiography and Breast Imaging (8 papers). Sue Astley is often cited by papers focused on AI in cancer detection (18 papers), Global Cancer Incidence and Screening (9 papers) and Digital Radiography and Breast Imaging (8 papers). Sue Astley collaborates with scholars based in United Kingdom, United States and Netherlands. Sue Astley's co-authors include Peter I. Miller, Chris Taylor, Kevin W. Bowyer, D. Gareth Evans, Ewan Gray, Nico Karssemeijer, Katherine Payne, Carla H. van Gils, Michael Berks and Zezhi Chen and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and European Journal of Cancer.

In The Last Decade

Sue Astley

28 papers receiving 285 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sue Astley United Kingdom	9	189	106	99	90	68	29	313
Radhika Sivaramakrishna United States	8	185 1.0×	188 1.8×	130 1.3×	87 1.0×	46 0.7×	14	369
Brandon Ginley United States	8	195 1.0×	86 0.8×	102 1.0×	74 0.8×	52 0.8×	27	354
Brendon Lutnick United States	7	159 0.8×	78 0.7×	85 0.9×	59 0.7×	43 0.6×	18	306
Nick Weiss Germany	10	176 0.9×	151 1.4×	104 1.1×	45 0.5×	49 0.7×	16	353
Lídia Tortajada Spain	9	131 0.7×	120 1.1×	62 0.6×	41 0.5×	48 0.7×	14	271
Andrea M. Olofson United States	7	186 1.0×	182 1.7×	47 0.5×	41 0.5×	124 1.8×	13	324
Ronnachai Jaroensri United States	5	200 1.1×	151 1.4×	61 0.6×	25 0.3×	57 0.8×	7	328
Nasrin Ahmadinejad Iran	11	147 0.8×	215 2.0×	67 0.7×	34 0.4×	28 0.4×	47	367
Khushboo Munir Italy	5	190 1.0×	171 1.6×	73 0.7×	34 0.4×	62 0.9×	6	396
W. Roger Williams United States	2	201 1.1×	156 1.5×	65 0.7×	31 0.3×	52 0.8×	2	360

Countries citing papers authored by Sue Astley

Since Specialization

Citations

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

Fields of papers citing papers by Sue Astley

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sue Astley

This figure shows the co-authorship network connecting the top 25 collaborators of Sue Astley. A scholar is included among the top collaborators of Sue Astley 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 Sue Astley. Sue Astley 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

Ntentas, Georgios, David J. Cutter, Richard Cowan, et al.. (2025). Radiation exposure of breast tissue in lymphoma radiotherapy: a systematic review of breast dose metrics published since 2000. Acta Oncologica. 64. 1117–1124.

Howell, Sacha J., et al.. (2024). Maintaining high resolution information in AI-based breast cancer risk prediction. Research Explorer (The University of Manchester). 67–67. 1 indexed citations

Howell, Sacha J., et al.. (2023). Artificial intelligence for image-based breast cancer risk prediction using attention. 76–76. 1 indexed citations

Howell, Sacha J., et al.. (2023). Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention. Tomography. 9(6). 2103–2115. 8 indexed citations

Fergie, Martin, Catharine West, Kim Linton, et al.. (2018). Quantifying cell-type interactions and their spatial patterns as prognostic biomarkers in follicular lymphoma. Research Explorer (The University of Manchester). 22. 15–15. 2 indexed citations

Gray, Ewan, Nico Karssemeijer, Carla H. van Gils, et al.. (2017). Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis. Value in Health. 20(8). 1100–1109. 46 indexed citations

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

Astley, Sue, Michael Berks, Jamie C. Sergeant, et al.. (2012). Assessment of change in breast density: reader performance using synthetic mammographic images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8318. 831810–831810. 1 indexed citations

Berks, Michael, Zezhi Chen, Sue Astley, & Chris Taylor. (2011). Detecting and Classifying Linear Structures in Mammograms Using Random Forests. Lecture notes in computer science. 22. 510–524. 14 indexed citations

10.

Evans, D. Gareth, Sue Astley, Paula Stavrinos, et al.. (2011). P4-11-07: Feasibility and Acceptability of Offering Breast Cancer Risk Estimation in the Context of the UK National Health Service Breast Cancer Screening Programme: A New Paradigm for Cancer Prevention.. Cancer Research. 71(24_Supplement). P4–11. 5 indexed citations

11.

Berks, Michael, et al.. (2010). Evaluating the realism of synthetically generated mammographic lesions: an observer study. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7627. 762704–762704. 6 indexed citations

12.

Evans, D. Gareth, Anthony Howell, Jack Cuzick, et al.. (2009). Breast cancer risk assessment and validation in The National Breast Screening Programme: PRoCAS – Predicting Risk of Cancer At Screening study. European Journal of Surgical Oncology. 35(11). 1207–1207. 1 indexed citations

13.

Astley, Sue, et al.. (2006). Digital Mammography, 8th International Workshop, IWDM 2006. 4 indexed citations

14.

Astley, Sue. (2004). Computer-based detection and prompting of mammographic abnormalities. British Journal of Radiology. 77(suppl_2). S194–S200. 26 indexed citations

15.

Bowyer, Kevin W. & Sue Astley. (1994). State of the Art in Digital Mammographic Image Analysis. 20 indexed citations

16.

Astley, Sue, et al.. (1994). Computer Aided Detection of Abnormalities in Mammograms. 34.1–34.10. 1 indexed citations

17.

Procter, Rob, et al.. (1994). User interface design and data management for digital mass mammographic screening. Research Explorer (The University of Manchester). 415–422. 1 indexed citations

18.

Miller, Peter I. & Sue Astley. (1993). AUTOMATED DETECTION OF MAMMOGRAPHIC ASYMMETRY USING ANATOMICAL FEATURES. International Journal of Pattern Recognition and Artificial Intelligence. 7(6). 1461–1476. 19 indexed citations

19.

Astley, Sue, et al.. (1993). Automated detection of Breast Asymmetries. 52.1–52.10. 7 indexed citations

20.

Astley, Sue & Chris Taylor. (1990). Combining cues for mammographic abnormalities. 45.1–45.6. 10 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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