RISK CDT - Mathematical Modelling and Image Analysis in Ocular Melanoma

PLEASE APPLY ONLINE TO THE SCHOOL OF ENGINEERING, PROVIDING THE PROJECT TITLE, NAME OF THE PRIMARY SUPERVISOR AND SELECT THE PROGRAMME CODE "EGPR" (PHD - SCHOOL OF ENGINEERING)

This is a project within the multi-disciplinary EPSRC and ESRC Centre for Doctoral Training (CDT) on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments, within the Institute for Risk and Uncertainty. The studentship is granted for 4 years and includes, in the first year, a Master in Decision Making under Risk & Uncertainty. The project includes extensive collaboration with prime industry to build an optimal basis for employability.

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. About one half of all UM patients will develop metastases, most often to the liver, and this is usually fatal. Routine clinical and histopathological examination of UM has long been used to identify tumour characteristics associated with an increased risk of metastatic spread in this patient population. Clinical features associated with a poor prognosis include: patient age and gender, large tumour size, ciliary body involvement and/or extraocular extension.

Certain histopathological features of UM are also used to predict tumour related mortality. Those morphological parameters associated with a poor prognosis include: epithelioid cell types, high mitotic count, presence of closed connective tissue loops, vascular lakes, and a high density of tumour-infiltrating lymphocytes and tumour-associated macrophages. Finally, non-random gross chromosomal aberrations are also commonly found in UM and have been demonstrated to have strong (poor) prognostication value in these tumours. The most important of these are the complete or partial loss of chromosome 3, chromosome 8 gains and loss of chromosome 1p.

The Liverpool Ocular Oncology Clinic, one of the three referral centres for adult eye tumours in England, has developed a prognostication model to aid stratifying UM patients into groups with low- and high-risk of developing metastases (https://mpcetoolsforhealth.liverpool.ac.uk/matsoap/lumpo3cr_v5.htm). Those UM patients with high risk of metastases have regular scans using Magnetic resonance imaging (MRI), in order to detect the metastases earlier, allowing for either liver surgery or clinical trial recruitment.

One aim of this PhD project is to improve assessment of certain histological features of primary UM using image analysis and mathematical programs, to improve the prognostication model of UM patients, and also to aid prediction of their response to novel therapies. This will be performed using well-defined cohorts of UM with all of the associated clinical and genetic data. Another aim is to undertake analysis of the histological and clinical images (e.g. ultrasound and MRI) of the metastatic UM in the liver, at early and advanced stages, to better understand the biology and progression dynamics of the liver disease. This is important for the development of intrahepatic directed therapies, not only in this disease but in other cancers affecting the liver.

All of the above assessments will be performed under the supervision of Profs K. Chen, S. Coupland and A. Taktak, who have experience in all techniques and applications involved in this PhD project. Prof. Chen heads the Centre of Mathematical Imaging Techniques (CMIT) in the Dept. of Mathematical Sciences at the University of Liverpool, and is the Director of the EPSRC Liverpool Centre for Mathematics in Healthcare (http://tinyurl.com/EPSRC-LCMH), which has an active engagement agenda. His large group of researchers develops a range of image analysis models and novel algorithms (including image segmentation and registration techniques) that are essential in this project while the research environment created by CMIT and LCMH provides excellent networking opportunities and expertise pools to benefit this work. Prof. Coupland is the George Holt Chair of Pathology and Lead of the Liverpool Ocular Oncology Research Group (www.loorg.org) at the University of Liverpool. She is also a Consultant Histopathologist at the Royal Liverpool University Hospital (RLUH), and runs the supraregional eye pathology and molecular pathology service. Prof. Taktak is a Consultant Clinical Scientist/ Honorary Professor in the Dept. of Medical Physics and Clinical Engineering at the RLUH, and has been instrumental in developing and validating the UM prognosticator that is used worldwide.

The combined supervision team has the capacity to lead the project to address an urgent clinical diagnostics problem by developing and selecting highly accurate mathematical image analysis tools. The improved assessment accuracy would enhance the capability of the current prognostication model and accelerate the research impact to clinical applications quickly.