Self funded project open to applicants worldwide. Please confirm your source of funding in the application.
Brain tumours are one of the CR-UK defined cancers of unmet need, where clinical outcomes and survival remains limited. Assessing response to treatment remains challenging in brain cancer. Diagnostic, prognostic and predictive information from standard brain imaging remains limited and a surgical biopsy is required to provide important biomarkers. Biopsy or surgical resection is often limited due to tumour location and the associated risk. Using advanced image processing (radiomics) and deep learning techniques with convolutional neural networks developed by our teams (10.1038/s41598-019-46030-0 1, 10.1007/s11042-020-09661-4) we aim to non-invasively classify brain tumours, extract quantitative imaging features and correlate imaging signatures with known biomarkers. Our hypothesis is that a radiomics enhanced deep learning-based classification model will provide diagnostic, prognostic or predictive biomarkers to improve survival in Glioblastoma Multiforme, the commonest primary brain tumour in adults.
You will have access to a cross-disciplinary network of experts across the Schools of Engineering, Computer Science and Informatics, and the National Health Service through Multi-Disciplinary Research Groups supported by the Wales Cancer Research Centre. As this research is focussed on optimising cancer treatments through imaging, data analytics and artificial intelligence, you will gain invaluable research and work experience through work placements with leading healthcare and data science practitioners.
Outline and timeline of work: Year 1: Training in advanced medical image processing techniques segmentation, texture analysis (radiomics), programming techniques and deep learning, literature review and study of clinical background including data curation. Year 2: Training deep learning convolutional neural network architecture for brain tumour tissue classification enhanced by textural/radiomics data, internal validation and publication of results. Year 3: Correlate quantitative imaging signatures from classifier with known prognostic or predictive biomarkers, external validation of classifier, publish results. Year 4: Thesis writing and submission, further analysis and publications.
Research Environment: This is an exceptional opportunity for you to gain access to training, facilities, academic/professional expertise and data that are not available in one setting alone. We practice a team approach to supervision. You will be embedded in a vibrant research group covering several PhD students and postdocs working in similar domains.
Training and Development: Research skills in medical image analysis, programming, deep learning and AI. Transferable skills in presentation, data handling and analysis, programming, scientific writing. Global mobility opportunities - visiting partner groups across Europe and beyond. Academic career skills – you will participate in teaching, technical demonstrations, scientific publications and will be introduced to the concept of Intellectual Property.
Candidates should hold a good bachelor’s degree (first or upper second-class honours degree) or a MSc degree in an area of Biomedical Engineering, Computer Science, or Medical Physics. Previous knowledge of Medical Imaging would be desirable. Previous experience and MATLAB/Python coding would be advantageous.
Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent)
Applicants should submit an application for postgraduate study via the Cardiff University webpages (http://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/engineering ) including;
· an upload of your CV
· a personal statement/covering letter
· two references (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school)
· Current academic transcripts
Applicants should select Doctor of Philosophy (Engineering), with a start date of 01/10/2021 In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select "I will be applying for a scholarship / grant" and specify that you are applying for advertised funding, reference ES SFP 21
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