Susan Astley

5.5k total citations · 1 hit paper
94 papers, 3.2k citations indexed

About

Susan Astley is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine and Oncology. According to data from OpenAlex, Susan Astley has authored 94 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Artificial Intelligence, 53 papers in Pulmonary and Respiratory Medicine and 51 papers in Oncology. Recurrent topics in Susan Astley's work include AI in cancer detection (61 papers), Digital Radiography and Breast Imaging (53 papers) and Global Cancer Incidence and Screening (47 papers). Susan Astley is often cited by papers focused on AI in cancer detection (61 papers), Digital Radiography and Breast Imaging (53 papers) and Global Cancer Incidence and Screening (47 papers). Susan Astley collaborates with scholars based in United Kingdom, Saudi Arabia and United States. Susan Astley's co-authors include Caroline Boggis, Elaine F. Harkness, Fiona J. Gilbert, D. Gareth Evans, I. Hutt, Dibendu Betal, Emmanuel A. Stamatakis, PM Taylor, James Parker and John Suckling and has published in prestigious journals such as New England Journal of Medicine, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Susan Astley

89 papers receiving 3.0k citations

Hit Papers

align trajectories

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.

The Mammographic Image Analysis Society digital mammogram database

1994 848 citations Susan Astley, Caroline Boggis et al. profile →

Peers

Susan Astley

ONCOL

RNMI

PRM

CVPR

John Tomaszewski United States

Despina Kontos United States

Jason Hipp United States

John Heine United States

Arvind Rao United States

Shu Wang China

Albert Gubern‐Mérida Netherlands

Erika Denton United Kingdom

Lin Ma United States

Cleo‐Aron Weis Germany

John Tomaszewski United States

Susan Astley

2.1k ×1.1

379 ×0.3

ONCOL

1.5k ×1.5

RNMI

628 ×0.6

PRM

1.2k ×1.6

CVPR

Citations per year, relative to Susan Astley Susan Astley (= 1×) peers John Tomaszewski

Countries citing papers authored by Susan Astley

Since Specialization

Citations

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

Fields of papers citing papers by Susan Astley

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Susan Astley

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

Harvie, Michelle, et al.. (2025). Mammographic density assessed using deep learning in women at high risk of developing breast cancer: the effect of weight change on density. Biomedical Physics & Engineering Express. 11(5). 57001–57001.

Harkness, Elaine F., et al.. (2024). Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality. Biomedical Physics & Engineering Express. 10(4). 45021–45021. 1 indexed citations

Evans, D. Gareth, Elke M. van Veen, Elaine F. Harkness, et al.. (2022). Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel. Genetics in Medicine. 24(7). 1485–1494. 30 indexed citations

Evans, D. Gareth, Elke M. van Veen, Elaine F. Harkness, et al.. (2021). Breast Cancer Risk Stratification in Women of Screening Age: Incremental Effects of Adding Mammographic Density, Polygenic Risk and a Gene Panel. SSRN Electronic Journal. 1 indexed citations

Rossi, Paolo Giorgi, Olivera Djurić, Susan Astley, et al.. (2021). Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk. Scientific Reports. 11(1). 19884–19884. 3 indexed citations

Evans, D. Gareth, Elaine F. Harkness, Adam R. Brentnall, et al.. (2019). Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants. Breast Cancer Research and Treatment. 176(1). 141–148. 49 indexed citations

Wang, Christopher, Adam R. Brentnall, Jack Cuzick, et al.. (2018). Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds. Breast Cancer Research. 20(1). 49–49. 8 indexed citations

Maxwell, Anthony, Michael Michell, Yit Yoong Lim, et al.. (2017). A randomised trial of screening with digital breast tomosynthesis plus conventional digital 2D mammography versus 2D mammography alone in younger higher risk women. European Journal of Radiology. 94. 133–139. 11 indexed citations

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

10.

Taylor, Chris, et al.. (2015). Appearance and Understanding of Digital Breast Tomosynthesis Images.. 177–182. 1 indexed citations

11.

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

12.

Evans, D. Gareth, Adam R. Brentnall, Michelle Harvie, et al.. (2014). Breast Cancer Risk in Young Women in the National Breast Screening Programme: Implications for Applying NICE Guidelines for Additional Screening and Chemoprevention. Cancer Prevention Research. 7(10). 993–1001. 32 indexed citations

13.

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

14.

Gilbert, Fiona J., et al.. (2010). Digital Breast Tomosynthesis. Research Explorer (The University of Manchester). 8 indexed citations

15.

Gilbert, Fiona J., Susan Astley, Maureen Gc Gillan, et al.. (2008). Single Reading with Computer-Aided Detection for Screening Mammography. New England Journal of Medicine. 359(16). 1675–1684. 195 indexed citations

16.

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

17.

Astley, Susan. (2006). Digital mammography : 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006 : proceedings. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 2 indexed citations

18.

Gilbert, Fiona J., Susan Astley, Magnus A. McGee, et al.. (2006). Single Reading with Computer-aided Detection and Double Reading of Screening Mammograms in the United Kingdom National Breast Screening Program. Radiology. 241(1). 47–53. 71 indexed citations

19.

Zwiggelaar, Reyer, Susan Astley, Caroline Boggis, & Chris Taylor. (2004). Linear Structures in Mammographic Images: Detection and Classification. IEEE Transactions on Medical Imaging. 23(9). 1077–1086. 94 indexed citations

20.

Davies, J D, et al.. (1997). 3-D HISTO-RADIOGRAPHIC COMPARISON OF SURGICAL CLEARANCES OF MICROCALCIFIED LESIONS IN BREAST LOCALIZATION BIOPSIES. The Journal of Pathology. 182(1). 45–53. 3 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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