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Dr Sue Astley

Sue Astley leads work on the development of imaging biomarkers (breast density and texture) for breast cancer risk, and the science underpinning stratified screening. Her research encompasses a range of technologies from Computer Aided Detection (CAD) and Digital Breast Tomosynthesis (DBT) to the use of a computer game input device to aid selection of breast implants.

Reader

Centre for Imaging Sciences
The University of Manchester
Stopford Building
Oxford Road
Manchester
M13 9PT

Email: sue.astley@manchester.ac.uk
Tel: +44 (0)161 275 5162

Websites

Institute of Population Health

I am a Reader in the Centre for Imaging Science in the Institute of Population Health. A mathematician and physicist by training, I worked as an astronomer and cosmic ray physicist before developing an interest in medical imaging.

Breast cancer risk feedback to women in the UK NHS breast screening population.

Evans DG, Donnelly LS, Harkness EF, Astley SM, Stavrinos P, Dawe S, Watterson D, Fox L, Sergeant JC, Ingham S, Harvie MN, Wilson M, Beetles U, Buchan I, Brentnall AR, French DP, Cuzick J, Howell A.

British Journal of Cancer. 2016 Apr 26;114(9):1045-52


Increased peri-ductal collagen micro-organization may contribute to raised mammographic density.

McConnell JC, O’Connell OV, Brennan K, Weiping L, Howe M, Joseph L, Knight D, O’Cualain R, Lim Y, Leek A, Waddington R,  Rogan J, Astley SM, Gandhi A, Kirwan CC, Sherratt MJ*, Streuli CH*.

Breast Cancer Research. 2016 Jan 8;18(1):5 *Joint senior/contributing authors.


Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort.

Adam R Brentnall, Elaine F Harkness, Susan M Astley, Louise S Donnelly, Paula Stavrinos, Sarah Dawe, Lynne Fox,  Jamie C Sergeant, Michelle N Harvie, Mary Wilson, Ursula Beetles, Anthony Howell, Jack Cuzick, D. Gareth R Evans.

Breast Cancer Res. 2015 Dec 1;17(1):147


The TOMMY trial: a comparison of TOMosynthesis with digital mammographY in the UK NHS Breast Screening Programme - a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone.

Gilbert FJ, Tucker L, Gillan MG, Willsher P, Cooke J, Duncan KA, Michell MJ, Dobson HM, Lim YY, Purushothaman H, Strudley C, Astley SM, Morrish O, Young KC, Duffy SW.

Health Technology Assessment, Winchester, 2015;19(4):1-136.


A Novel Framework for Fat, Glandular Tissue, Pectoral Muscle and Nipple Segmentation in Full Field Digital Mammograms.

Xin Chen, Emmanouil Moschidis, Chris Taylor, Susan Astley. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes on Computer Science 8539: International Workshop on Breast Imaging; Gifu, Japan. Switzerland:Springer International; 2014. p. 201-208.


Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms.

Xin Chen, Emmanouil Moschidis, Chris Taylor, Susan Astley. In: Golland, Polina ; Hata , Nobuhiko ; Barillot, Christian ; Hornegger, Joachim ; Howe, Robert .

MICCAI 2014: Lecture Notes in Computer Science 8673: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014; 14 Sep 2014-18 Sep 2014; Boston, USA. Springer International; 2014. p. 536-543.


Breast Volume Measurement Using a Games Console Input Device.

Stefanie T. L. Pöhlmann, Jeremy Hewes, Andrew I. Williamson, Jamie C. Sergeant, Alan Hufton, Ashu Gandhi, Christopher J. Taylor, Susan M. Astley. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture notes in Computer Science 8539: International Workshop on Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 666-673.


Breast cancer risk in young women in the National Breast Screening Programme: implications for applying NICE guidelines for additional screening and chemoprevention.

D. G Evans, A. Brentnall, M Harvie, S Dawe, J Sergeant, P Stavrinos, S Astley, M Wilson, J Ainsworth, J Cuzick, I Buchan, L Donnelly, A Howell.

Cancer Prevention Research. 2014;7(10):993-1001.


Comparison of Calcification Cluster Detection by CAD and Human Observers at Different Image Quality Levels.

Padraig T. Looney, Lucy M. Warren, Susan M. Astley, Kenneth C. Young. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes in Computer Science 8539: International Workshop on Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 643-649.


Factors Affecting Agreement Between Breast Density Assessment using Volumetric Methods and Visual Analogue Scales.

Beattie L, Harkness E, Bydder M, Sergeant J, Maxwell A, Barr N, Beetles U, Boggis C, Bundred S, Gadde S, Hurley E, Jain A, Lord E, Reece V, Wilson M, Stavrinos P, Evans DG, Howell T, Astley S. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes in Computer Science 8539: International Workshop on Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 80-87.


Mammographic density and breast cancer characteristics.

Ren K, Harkness E, Boggis C, Gadde S, Wilson M. Lim Y, Sergeant J, Whiteside S, Morris J, Astley SM. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes in Computer Science 8539: International Workshop on Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 290-297.


Texture-Based Breast Cancer Prediction in Full-Field Digital Mammograms Using the Dual-Tree Complex Wavelet Transform and Random Forest Classification.

Emmanouil Moschidis, Xin Chen, Chris Taylor, Sue Astley. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes in Computer Science 8539: International Workshop on Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 209-216.


The Impact of Introducing Full Field Digital Mammography into a Screening Programme.

Fyall T, Boggis C, Sergeant J, Harkness E, Whiteside S, Morris J, Wilson M, Astley SM. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes on Computer Science 8539: International Workshop on Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 56-63.


The relationship of Volumetric Breast Density to Socio-Economic Status in a Screening Population.

Louisa Samuels, Harkness E, Astley S, Maxwell A, Sergeant J, Morris J, Wilson M, Stavrinos P, Evans DG, Howell T, Bydder M. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes in Computer Science 8539: Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 273-281.


Use of Volumetric Breast Density Measures for the Prediction of Weight and Body Mass.

O Donovan E, Sergeant J, Harkness E, Morris J, Wilson M, Lim Y, Stavrinos P, Howell A, Evans DG, Boggis C, Astley S. In: Fujita, Hiroshi; Hara, T; Muramatsu, C.

Breast Imaging: Lecture Notes in Computer Science 8539: Breast Imaging; Gifu, Japan. Switzerland: Springer International; 2014. p. 282-289.


Correcting for rater bias in scores on a continuous scale, with application to breast density.

Matthew Sperrina, Lawrence Bardwell, Jamie C. Sergeant, Susan Astley and Iain Buchan.

Statistics in medicine 32, no. 26 (2013): 4666-4678.


Assessing individual breast cancer risk within the UK National Health Service Breast Screening Programme: A new paradigm for cancer prevention.

D G Evans, J Warwick, S M Astley, P Stavrinos, S Sahin, S Ingham, H McBurney, B Eckersley, M Harvie, m Wilson, U Beetles, R Warren, A Hufton, J Sergeant, W Newman, I Buchan, J Cuzick, A Howell

Cancer Prevention Research. 2012;5(7):943-951.


Assessment of change in breast density: reader performance using synthetic mammographic images.

S Astley, C Swayamprakasam, M Berks, J Sergeant, J Morris, M Wilson, N Barr, C Boggis. In: Abbey, Craig; Mello-Thoms, Claudia.

SPIE Medical Imaging 2012 Volume 8318 : Image Perception, Observer Performance and Technology Assessment: SPIE Medical Imaging; 06 Feb 2012-10 Feb 2012; San Diego, California, USA. USA: SPIE; 2012.


Prevention of breast cancer in the context of a national breast screening programme.

Howell A, Astley S, Warwick J, Stavrinos P, Sahin S, Ingham S, McBurney H, Eckersley B, Harvie M, Wilson M, Beetles U, Wareen R, Hufton A, Sergeant J, Newman W, Buchan I, Cuzick J and Evans DG.

Journal of Internal Medicine. 2012;271:321-330.

Imaging Biomarkers for Breast Cancer Risk

Mammography is widely used for screening the asymptomatic population for early signs of cancer, and the advent of digital imaging has opened the door to the development of automated techniques, both for detecting cancer and identifying women at increased risk. We are focussing on two measures: mammographic density, which describes the quantity of radiodense and fatty tissues in a woman’s breasts; and mammographic texture, which describes the organisation and distribution of the tissues. Both are related to risk of developing cancer, and it is likely that both will also be related to the efficacy of mammography as a screening tool.

Stratification for Screening

Screening programmes are generally based on a one-size-fits-all model, with only limited differentiation for women in the highest risk groups. As we are better able to predict risk using imaging biomarkers, we can also use machine learning techniques to develop new models for stratification of women in the wider screening population.

Computer Aided Detection (CAD)

Computer based methods can be used to detect potential abnormalities and present the locations as prompts to attract radiologists’ attention. In order for CAD to be successful both the sensitivity and specificity of the prompting algorithms must be high. The situation is complex, particularly when multiple algorithms are used, and this remains an active area of interest. We have conducted experiments based on synthetic images to further our understanding of the situation, and evaluated four commercially available prompting systems and conducted both retrospective and prospective clinical trials to compare single reading with CAD and the current standard clinical practice of double reading.

Improving the Outcome of Reconstructive Surgery

We have developed a method which uses a commercially available games console input device (the Microsoft Kinect) to measure the volume of the breast. We are currently undertaking a clinical trial to see whether the method can be used to predict the most appropriate implant size to achieve a symmetric outcome after breast reconstruction.

Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is a new X-Ray imaging modality which provides depth information at high resolution and low dose. We have investigated the use of DBT in the Tommy Trial, evaluated microcalcification CAD for DBT and begun to investigate the way in which images are interpreted. We are currently developing algorithms to automatically extract information from DBT images.

Detecting and classifying linear structures in mammograms using random forests.

Berks M, Chen Z, Tresadern P, Astley S, Taylor C.

Information Processing in Medical Imaging 22: Information Processing in Medical Imaging; 2011. p. 510-524


Single Reading with Computer-Aided Detection for Screening Mammography.

FJ Gilbert, SM Astley, MC Gillan, OF Agbaje, MG Wallis, J James, CRM Boggis, SW Duffy.

New England Journal of Medicine. 2008;359(16):1675-1684.