Medicine: UKRI CDT PhD Scholarship at Swansea: therapeutics for ovarian cancer
- Full cost of tuition fees, plus a stipend
- 12 February 2021
Medicine: UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing: Understanding and optimising therapeutics for ovarian cancer through an AI and advanced computing twin
This scholarship is funded by UK Research and Innovation (UKRI).
Start date: October 2021
The UK Research and Innovation (UKRI) Centre for Doctoral Training (CDT) in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
Our doctoral training programme is constructed around three research themes:
- T1: data from large science facilities (particle physics, astronomy, cosmology)
- T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
- T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)
- First supervisor: Professor R. S. Conlan (Medical School)
- Second supervisor: Professor P. Nithiarasu (Engineering)
- Third supervisor: Professor D. Gonzalez (Medical School)
- Fourth supervisor: Dr L.W. Francis (Medical School)
- Fifth supervisor (external): Dr L. McKnight (Consultant Radiologist, Swansea Bay UHB)
Department/Institution: Medical School, College of Engineering and Swansea Bay UHB
- T2: biological, health and clinical sciences
- T3: novel mathematical, physical and computer science approaches
Ovarian cancer (OC) is the seventh leading cause of cancer-related death in women worldwide. It causes around 4,100 deaths annually in the UK, where recurrence rates are up to 75% and the five-year survival rate is only 46%. High vascular permeability and compromised lymphatic drainage result in ascites, the accumulation of fluid, in the peritoneum that is associated with advanced OC.
We will develop a new paradigm for effective drug delivery/treatment based around microparticle drug delivery that will restrict particles (and therefore drugs carried within them) to the peritoneal cavity. Furthermore, particles will be geometrically-designed to preferentially accumulate at cancer sites (i.e. the walls of the cavity). Together this will result in increased efficacy, and reduced cytotoxicity and loss of therapeutic agents from the peritoneal cavity.
The project will exploit available computed tomography (CT) images of OC patients to generate virtual models (digital twins) of the ascites containing peritoneal cavity and surrounding structures. From this complex fluid flow modelling will be developed to predict microparticle movement within the digital twin and determine the particle geometry needed to ‘target’ cancer sites.
This proposal builds on and integrates academic research in the fields of patient imaging, computational modelling, AI and cancer biology, and will deliver an optimised pathway for computerised tomography (CT) scan simulation for drug delivery. The AI outputs will be immediately usable by other researcher/clinical teams, with their application not limited to OC or peritoneal disease but extensible to any CT scan.
The project will involve interdisciplinary research between Engineering, Medicine and the NHS. Successful completion will lead to novel drug delivery pathways for OC therapeutics through utilisation of imaging data (20-40 of CT images); an exemplar for how patient data and samples can be used to drive healthcare developments.
Digital twin of peritoneal cavity and computational fluid dynamics to optimize therapeutics. This project uses patient data including CT scans to reconstruct a digital twin of the peritoneal cavity for us in modelling natural - or induced- ascites (fluid) flow microparticle geometry-driven movement. A combination of existing source codes including our own open-source (swansim.org), other open source codes such VMTK platform and commercial codes will be used to develop a very accurate representation of a digital twin. The subject-specific digital twin models will then be used in studying the effectiveness of proposed microparticle drug-delivery vehicles via a computational fluid dynamics (CFD) study.
The typical academic requirement is a minimum of a 2:1 undergraduate degree in biological and health sciences; mathematics and computer science; physics and astronomy or a relevant discipline.
Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).
This scholarship is open to UK and international candidates (including EU and EEA).
This scholarship covers the full cost of tuition fees and an annual UKRI standard stipend (currently £15,285 for 2020/21).
Additional funding is available for training, research and conference expenses.
Please visit our website for more information.
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