Childhood cancer treatment has become relatively successful and nowadays 75% of the patients survive for 10 years or more. Radiotherapy (RT) had a fundamental role in improving outcomes, but it may cause side effects in later life. The growing tissues and organs of developing children are particularly sensitive to radiation. Some harmful effects are debilitating but non-life-threatening, such as infertility and growth problems; others can be fatal later in life, such as new (second) cancers.
Nowadays the RT dose used to treat patients is recorded with inaccuracies and is one of the reasons why current predictive models of incidence of side-effects are still crude. The RT dose as exported from commercial treatment planning systems differs from the dose actually delivered. Several factors can contribute to deviations in dose, such as uncertainties in the dose calculation algorithms, variations in patient anatomy and setup, uncertainties in the attenuation properties of young tissue, which all vary depending on the choice of RT modality. The impact of these uncertainties also depend on the distance to the primary target – i.e., high dose, fall-off and out-of-field regions. Furthermore, the dose delivered in image guidance protocols is often disregarded from dose considerations. Paediatrics are a particularly challenging cohort due to the variability in anatomy between sexes, age, height, weight and internal growth of organs. In general, the RT dose delivered has not yet been well characterised for childhood cancer patients.
The goal of this project is to develop accurate 3D models of the dose delivered during the course of RT. The outputs can then be used to develop predictive models of the harmful long term effects of treatment. The candidate will make use of medical imaging data (patient and phantoms) and explore different techniques, such as image processing, analytical models and general-open source Monte Carlo software, to simulate the delivery of real treatments and setup imaging of children. We expect the prediction of the incidence of toxic effects to be used to inform how we plan and deliver radiotherapy in future patients, leading to better long term patient outcomes.
Applicants are expected to have a first degree in Physics or Biomedical Engineering or relevant Physical Sciences based subject passed at 2:1 level (UK system or equivalent) or above. A Masters degree or equivalent in a relevant subject area is desirable. Good working knowledge of C++ and/or Python and/or MATLAB is desirable. Some experience with medical image analysis, Monte Carlo simulations or radiotherapy is also desirable. The candidate must be committed to deliver excellence in research, and will also be expected to provide regular research progress and present at international conferences.
This 4-year PhD studentship is available in the UCL Centre for Medical Image Computing (CMIC). The funding covers an annual tax free stipend (approximately £17,000) and tuition fees. The successful candidate will join the UCL CDT in Medical Imaging cohort and benefit from the activities and events organised by the centre.
To make an application for this project please send a CV and cover letter detailing why you want to apply for this studentship, why you believe you are suitable for the studentship, your long-term research and professional goals, and any particular expertise you have that you feel may be applicable in this work to Dr Catarina Veiga at [email protected]