Radiotherapy uses high energy x-rays or protons to target cancer and is used as part of the treatment of >50% of cancer patients. Radiotherapy plans are unique to each patient, aiming to deliver a curative dose to the cancer while minimising dose to critical healthy tissues. It is not possible to eliminate all healthy tissue dose, which can induce toxicities that impact an individual’s quality-of-life. There is an unmet need to better understand how toxicity risk depends on the interaction of this bespoke radiotherapy dose and patient factors, to better optimise each radiotherapy plan.
Current predictive models estimate the risk of toxicity from a given 3-dimensional radiotherapy dose across each organ, accounting for patient-specific factors. However, these models only tell us the association between outcomes and predictive factors, i.e. that a patient is at risk of developing toxicities. These models cannot tell us how to change that patient’s treatment to improve their outcome, as they cannot infer what is causing the predicted risk. Causal predictive modelling is a novel statistical methodology that allows ‘what if’ queries regarding hypothetical treatments to be posed at the individual patient level.
In this project, we will focus on patients treated for lung or head and neck cancer. We will develop a novel causal predictive modelling methodology that will integrate clinical factors with the planned 3-dimensional radiotherapy dose at the image voxel level (every anatomical location). We have access to routine patient data of >1,000s of patients, with radiotherapy plan, clinical factors, and toxicity outcomes available. We also have access to a national prospective study dataset for external validation. Together, these rich data sets will allow us to investigate our hypothesis: A novel causal framework for prediction of normal tissue toxicity under hypothetical treatments can identify the optimal radiotherapy plan for every individual.
Eligibility
Candidates must hold, or be about to obtain, a minimum upper second class undergraduate degree, or the equivalent qualifications gained outside the UK, in a relevant subject. A related master's degree would be an advantage.
How to Apply
To be considered for this project you MUST submit a formal online application form. Details of how to apply are available here (https://www.bmh.manchester.ac.uk/study/research/funded-programmes/mcrc-training-scheme/apply/). For Visa requirements, international candidates must select the full-time study option.
General enquiries can be directed to [Email Address Removed].
Interviews: Friday 13 January 2023
Commencement: October 2023
Equality, Diversity and Inclusion
Equality, diversity and inclusion is fundamental to the success of The University of Manchester and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/
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