Prediction models for cancer outcomes can provide direct clinical value and are becoming increasingly important to individualise treatment. This has been underpinned by recent developments in machine learning & artificial intelligence. To create such models, access to large healthcare databases is needed, however some types of cancer are so rare that it can be hard to get sufficient data. In addition, ethical considerations and data protection regulations often limit sharing of data between hospitals and thus make data sharing across institutions and countries more challenging. New data analysis methods, called distributed learning, have opened up for models to be created across several institutions, without data leaving the individual institution.
The project will focus on the radiotherapy-based treatment of anal cancer. However, the methodology around outcome modelling, use of routine clinical data, and distributed learning can also be applied to other cancer sites. Anal cancer can be effectively treated with a combination of radiotherapy and chemotherapy, but some patients still get cancer recurrence, and many experience side effects after treatment. Anal cancer is a rare cancer, which makes single institution datasets too small to be of much use on their own. Additionally, radiotherapy data are complex and multidimensional, making modelling using traditional regression methods challenging. If better models can be created and validated, these can guide optimization of future radiotherapy, potentially offering better cure with fewer side effects.
We will connect three centres with anal cancer patient data. As a first step, we will create basic outcome models using distributed learning. Secondly, we will develop methodology to incorporate complex radiotherapy data into the modelling. Alongside these two projects, the student will work directly with the data managers in the radiotherapy department in Leeds to optimize and streamline local collection of routine clinical data. A final part of the project will depend on the interests of the candidate, but could involve deep learning using imaging data, creation of decision support tools, or incorporation of patient-reported quality of life outcomes into the framework. Candidates with prior experience in creating deep models, including Generative Adversarial Networks and encoder-decoder architectures for cross-modality image synthesis, are particularly encouraged to apply.
The Leeds Radiotherapy Research Group is a multi-disciplinary group encompassing clinical oncologists, medical physicists, radiographers and statisticians. The group is led by Prof David Sebag-Montefiore and sits within the Leeds Institute of Medical Research at St James’s (LIMR), School of Medicine, University of Leeds.
The project will be conducted in close collaboration with Dr Marianne Guren and Prof Eirik Malinen at Oslo University Hospital in Oslo, Norway, and with Prof Andre Dekker and Dr Leonard Wee at the MAASTRO Clinic in Maastricht, Netherlands.
You should hold a first degree equivalent to at least a UK upper second class honours degree in a relevant subject. This project would suit a candidate with a strong background in either computer science, biostatistics, biomedical engineering, (medical) physics, Machine learning or equivalent. Although not compulsory, the ideal candidate will, in addition to general computer science and biostatistics skills, have prior experience working with health care data or deep learning models.
The project will involve collaborative work, between multiple disciplines, between clinical and academic departments, and across international borders. The candidate will work directly with colleagues in the Leeds Cancer Centre at St James’ University Hospital. They should be willing to undertake one or more stays abroad, including a longer visit to the MAASTRO clinic in The Netherlands, learning the setup and the theoretical background of the distributed learning approach.
The Faculty minimum requirements for candidates whose first language is not English are:
• British Council IELTS - score of 6.5 overall, with no element less than 6.0
• TOEFL iBT - overall score of 92 with the listening and reading element no less than 21, writing element no less than 22 and the speaking element no less than 23.
How to Apply
To apply for this scholarship applicants should complete a Faculty Scholarship Application form using the link below https://medicinehealth.leeds.ac.uk/download/downloads/id/283/application_form_20182019.docx
and send this alongside a full academic CV, degree certificates and transcripts (or marks so far if still studying) to the Faculty Graduate School [email protected]
We also require 2 academic references to support your application. Please ask your referees to send these references on your behalf, directly to [email protected]
by no later than Monday 2 September 2019.
Any queries regarding the application process should be directed to [email protected]