About the Project
Soft-tissue sarcoma (STS) is a rare form of cancer that develops in connective tissues. Approximately 3,300 new cases are diagnosed every year and only 50% of patients are expected to live for five years or more. STS tumours often consist of different components including fatty, cystic, cellular and haemorrhagic tissues; these tumours are considered highly ‘heterogeneous’. In patients undergoing external-beam radiotherapy, conventional imaging methods for assessing treatment response are limited as tumours may not change in size, or may even appear grow following effective therapy. Better methods for assessing the success of radiotherapy in STS, and which also account for the high degree of tumour heterogeneity, are thus highly desired.
In this project, the student will investigate artificial intelligence (AI) techniques with deep-learning to provide methods for automatic response assessment of soft-tissue sarcoma using multi-parametric magnetic resonance imaging (MRI). MRI is widely used for non-invasive imaging of STS, due to its excellent soft-tissue contrast and the lack of ionising radiation associated with other imaging techniques such as computed tomography (CT) and positron emission tomography (PET). Quantitative MRI (qMRI) techniques provide information about the biological properties throughout the entire tumour volume, including (i) the cell density, (ii) the dynamics of blood vessels, and (iii) fat content within the targeted cancer. The student will uncover new ways to combine these quantitative metrics for response assessment of heterogeneous changes in STS. In addition, the best approach for combining such imaging metrics with other blood- or tissue-based measurements will be explored, to provide clinicians with new tools that improve the patient pathway for STS.
The successful candidate will join a recently established Computational Imaging Team, within the division of Radiotherapy and Imaging, whose core aim is to develop new computational approaches for radiotherapy response assessment and prediction. Leveraging the close partnership with the imaging team at the Royal Marsden Hospital (RMH), the student will have access to state-of-the-art imaging and radiotherapy equipment, and also the most up-to-date computing architecture.
Download a PDF of the complete project proposal: https://d1ijoxngr27nfi.cloudfront.net/docs/default-source/studying-at-the-icr/9_blackledge_huang_messiou_robinson_oelfke_icr-studentship.pdf?sfvrsn=e5eb5e69_2
Candidate profile
Candidates must have a first class or upper second class honours BSc Honours/MSc or equivalent in Physics, mathematics or computational science. In interest in medical application of these subjects is essential.
How to apply
Full details about these studentship projects, and the online application form, are available on our website, at: www.icr.ac.uk/phds Applications for all projects should be made online. Please ensure that you read and follow the application instructions very carefully.
Closing date: Monday 19th November 2018
Applicants should be available for interview 28h and 29th January 2019.
Please apply via the ICR vacancies web portal
https://apply.icr.ac.uk/
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