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(MRC DTP) Quantifying change in mammographic appearance using Artificial Intelligence

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  • Full or part time
    Dr S Astley
    Dr Yit Lim
    Dr S Howell
    Dr Martin Fergie
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Breast cancer is the most common cancer among women, with over 54,000 new cases diagnosed each year in the UK. The NHS screening programme offers mammography every three years with the aim of detecting cancers at an early stage, however breast cancer risk varies between individuals, and some women have dense breasts in which mammography is less effective. Measurement of breast density is thus important for personalising breast screening. Women at high risk of developing the disease and those with dense breasts may benefit from more frequent screening and alternative or supplemental screening with different imaging techniques. Change in breast density is also a useful indication of whether a preventive intervention is succeeding in lowering an individual’s breast cancer risk [Cuzick 2011].
We have shown that expert radiologists are better than many computer-based algorithms at assessing whether a mammographic density pattern corresponds to high risk of cancer [Astley 2018]. We have also shown that this can be replicated by training a neural network on radiologists’ judgements, with the AI-based approach outperforming more conventional breast density methods [Ionescu 2019]. In this project we will focus on learning how breast appearance changes over time, using machine learning to determine whether we can distinguish between normal change and change due to developing abnormality. We will also apply the methods developed to assess change in response to preventive interventions. The work involves a number of technical challenges, the solutions of which are likely to be useful for a wide range of applications.
The student will work at the intersection of advanced image analysis, radiology, oncology and epidemiology. They will benefit from a multidisciplinary supervisory team, with expertise in breast radiology, oncology and cancer prevention, deep learning and imaging science. They will be actively involved in national and international collaborations involving density assessment, and will be encouraged to seek further opportunities to exploit and evaluate their methodology in a range of settings.

https://www.research.manchester.ac.uk/portal/sue.astley.html

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the MRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the MRC DTP website www.manchester.ac.uk/mrcdtpstudentships

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Cuzick, Jack, Jane Warwick, Elizabeth Pinney, Stephen W. Duffy, Simon Cawthorn, Anthony Howell, John F. Forbes, and Ruth ML Warren. "Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case–control study." Journal of the National Cancer Institute 103, no. 9 (2011): 744-752.



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