Mantle cell lymphoma (MCL) is a specific tumour of B-lymphocytes which affects approximately 600 new patients in the UK each year. The clinical course is highly variable, with some patients succumbing to aggressive disease and others not requiring therapy for several years. A range of therapies is now available but these are themselves associated with morbidity and mortality and there is a clear need for novel approaches to guide the timing and nature of treatment initiation. While clinical parameters provide a model, the biological basis for the variable disease course is poorly understood. Some biological features, such as variant cellular morphology and cell division rate are encoded in the tissue sections examined by diagnostic pathologists but none are sufficiently reproducible for routine clinical use.
Advances in the technology and availability of digital pathology and the complementary field of machine learning-based computer vision provide new opportunities to develop novel approaches to the identification of micromorphological features associated with disease pathobiology and clinical course and to implement these in clinical practice. Outside of lymphoma, such approaches have proven successful in classifying diseases, identifying the presence of underlying genetic alterations and predicting clinical behaviour, and some algorithms have now gained regulatory approval for clinical use.
Utilising digitised diagnostic pathology tissue sections from the UK-wide MCL Biobank, the student will develop a Machine Learning-based Computer Vision classifier to discriminate patterns of disease behaviour in MCL patients. As part of a broader programme of work through the MCL Biobank, this will be integrated with clinical and genetic data to generate multimodal disease outcome prediction models.
The student will be based primarily in the Scalable Computing research group at Newcastle University in which the first and third supervisors are faculty. Within this group of approximately 20 academic staff and their teams, in association with the Alan Turing Institute, and through personal training, seminars and group meetings, the student will receive deep and broad training in artificial intelligence/machine learning as applied to digital pathology. This project is also supported by the National Cancer Research Institute Lymphoma Science Subgroup which is chaired by the fourth supervisor and is expected to both the Centrepointof a broader multi-institutional MCL project with further clinical, radiological and genetic components, and a springboard for further projects in computational lymphoma pathology. They will spend time in the pathology research / Biobank laboratories in Newcastle with the second supervisor and Liverpool and through the diverse supervisory team will gain a strong knowledge of translational biomedical research.
Benefits of being in the DiMeN DTP:
This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle, York and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.
We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.
Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: https://www.dimen.org.uk/blog
Further information on the programme and how to apply can be found on our website:
https://www.dimen.org.uk/how-to-apply
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