Quantifying the personalised impact of physical and biological uncertainties in customised radiotherapy treatment planning

The University of Bath is inviting applications for a 4-year fully funded PhD studentship commencing in October 2023.

Overview of the Research:

In the UK, one in two people born after 1960 will develop cancer, with an estimated 375,000 cases per year. Radiotherapy (RT) uses radiation to kill cancer cells and is a major modality in the management of cancer, either alone or in combination with other treatment options. The goal of RT is to deliver a high radiation dose to the tumour whilst sparing healthy tissue, thus aiming to give the highest probability of curing cancer while reducing the risk of side effects.

Modern RT is characterised by the delivery of a highly localised radiation dose to tumour targets combined with a sharp dose reduction to spare normal tissues. The treatment complexity is advancing at a rapid rate with technology constantly evolving to deliver higher precision RT, which comes at the cost of being more susceptible to various treatment delivery (physical) and patient response (biological) uncertainties. The largest biological uncertainty is the individual sensitivity of patients to radiation.  Additionally, these uncertainties are not currently combined. For example, where biological uncertainty exists, a small physical uncertainty, which is normally negligible, could significantly alter treatment outcome. Sensitivity to uncertainties varies by RT type and by treatment site. An accurate understanding of the sources and magnitudes of these uncertainties is therefore essential for producing clinical treatment plans which give the highest chance of delivering the desired treatment outcome. It is currently unclear how to incorporate all elements that contribute to plan uncertainty into a single protocol nor which assessment metrics to use.

This project aims to quantify the personalised impact of physical and biological uncertainties in customising radiotherapy treatment planning. The goal is to develop a future-proof formalism for the evaluation of uncertainty quantification, guidance on combining and incorporating uncertainties into personalised treatment plans to improve outcomes.

The successful applicant will benefit from participating in meetings with both domestic and international collaborators as well as visits to the industrial collaborator NPL (the National Physical Laboratory) based in London (subject to contract). Furthermore, they will be trained in highly transferable modelling. 

Project keywords: radiotherapy treatment planning, uncertainty quantification.

Candidate Requirements:

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent) in a relevant physical science, mathematics, engineering, data science, or other numerate subject. A master’s level qualification would also be advantageous.

The ideal candidate will have a strong interest in problem solving and experience in a scientific programming language such as Python, MATLAB, etc. An interest or familiarity with radiotherapy is an advantage but not a requirement.

Non-UK applicants must meet our English language entry requirement.

Enquiries and Applications:

Informal enquiries are welcomed and should be directed to Dr Tristan Pryer on email address [Email Address Removed].

Formal applications should be made via the University of Bath’s online application for a PhD in Mathematics.

More information about applying for a PhD at Bath may be found on our website.

Equality, Diversity and Inclusion:

We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.