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Journal of Theoretical Medicine
Volume 4, Issue 1, Pages 3-20

Breast Cancer: Modelling and Detection

1Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, UK
2Department of Engineering Science, Parks Road, Oxford OX1 3PJ, UK
3OXIVA, Oxford Centre For Innovation, Mill Street, Oxford OX2 OJX, UK
4Centre for Mathematical Biology, Mathematical Institute, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK

Received 7 October 2000; Accepted 23 April 2001

Copyright © 2002 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection.