(BRC) Machine learning image analysis for refined risk prediction and personalised screening for second cancers in lymphoma survivors (Clinical)

   Faculty of Biology, Medicine and Health

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  Dr Marianne Aznar, Dr K Linton, Dr Eliana Vasquez Osorio  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

Radiotherapy – the therapeutic use of radiation such as x-rays and protons – is integral to curing many cancers. However, radiation increases the risk of developing second cancers. These are new cancers that are different to a first cancer and typically develop many years after first cancer treatment. People diagnosed with Hodgkin lymphoma (HL) in childhood or young adulthood are at significantly increased risk of developing second cancers of the breast and lung following radiotherapy to the chest. 

This Manchester BRC PhD project is a themed project within the Cancer Cluster Living With and Beyond Cancer and Advanced Radiotherapy themes. Aims are to develop methods to better understand the development of second cancers after treatment for HL. Data will be analysed from The Christie NHS Foundation trust and images curated from the national Breast Screening after Radiotherapy Dataset (BARD), a public database hosted in Manchester in partnership with the National Cancer Registration and Analysis Service (NCRAS) that directs individual breast screening appointments. BARD includes data from approximately 4,500 women across England at risk of breast cancer as a result of previous radiotherapy (+/- chemotherapy). The BARD imaging dataset will be completed in 2024.

The project will involve processing and analysing longitudinal medical images (CT, radiographs, mammograms) from a BARD imaging dataset, combining imaging features with other risk factors (radiation dose, breast volume, smoking, etc) and developing novel machine-learning approaches to further our understanding of late effects. Key learning outcomes will include quantitative skills in statistics, computation, data analytics and informatics, machine learning and artificial Intelligence, as applied to radiotherapy health records and medical images (mammography, CT). The student will also acquire interdisciplinary skills at the interface of physics, computer science, epidemiology, oncology and bio-statistical modelling. They will benefit from a highly multidisciplinary supervisory team within the Manchester BRC BARD research group, including medical physicists, computer scientists, and oncologists. They will be actively involved in collaborations at the national and international level in the field of cancer survivorship / childhood and young adult cancers. The study could ultimately lead to personalised guidelines and national policy for second cancer screening, driving improved quality of life and outcomes for cancer survivors. 


Applicants must have obtained or be about to obtain a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant discipline or in Physics, computer science, engineering.

Before you Apply 

Applicants must make direct contact with the primary supervisor before applying to discuss their interest in the project. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.  

How to Apply 

To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the BRC website https://www.bmh.manchester.ac.uk/study/research/funded-programmes/manchester-brc-phd-studentships/ 

Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. If you have any queries regarding making an application please contact our admissions team.

Equality, Diversity and Inclusion  

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/  

Computer Science (8)

Funding Notes

This studentship covers tuition fees and stipend for home candidates only.


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