Modelling the Development of Breast Cancer Abnormalities

Lead Research Organisation: Aberystwyth University
Department Name: Computer Science

Abstract

Breast cancer is the most occurring cancer in women worldwide, with about 8% of women developing breast cancer in their life. Early detection through screening programmes have shown to be beneficial and computer aided diagnosis (CAD) is starting to play a more significant in this. However, both breast screening experts and CAD miss potential abnormalities, especially at an early stage, and histology is needed to determine malignancy/treatment. This project will have three aspects. In the first instance it will develop a model of the development of various types of breast abnormalities (e.g. using local clustering techniques). For this a very large set of examples will be used, which will be obtained from our clinical collaborators. This will take the morphology of abnormalities (and the associated histology) into account and will order them on their developmental stage. Morphology can be represented by traditional hand-crafted features, by deep learning based features, and/or other novel approaches such as evolutionary algorithms, random projection forests, or graph matching. Secondly, directly linked to the cancer cases used above, we will extend the modelling to include pre-cancer cases, which will be based on previous screening rounds of the abnormalities. This will concentrate on the mammographic morphology. It will be of interest to investigate what mammographic morphology leads to specific mammographic abnormalities. Finally, manifold modelling will be used to develop (using techniques such as learning without forgetting) an overall model of breast cancer development, which is expected to provide pathways from normal tissue to a range of mammographic abnormalities. This will provide a model, which can be used for unseen abnormalities to estimate how these might develop over time, but will also provide a probability for mammographic abnormality development based on normal (pre-cancerous) tissue. The former can be used to contribute to treatment decisions, whilst the latter could inform individual screening intervals.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023992/1 01/04/2019 30/09/2027
2286560 Studentship EP/S023992/1 01/10/2019 30/09/2023 CORY THOMAS