The treatment of cancer using radiotherapy has been in use for several decades using high energy linear accelerator and is used in over 50% of patients cured of the condition. Advances in technology has aided the delivery of the treatment enabling the therapist to avoid healthy tissue and increase the dose to the target area.
A common method of achieving this is Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). The planning of these treatments is a complex process, traditionally performed manually by specialist dosimetrists. Manual techniques can be time consuming and dependent on dosimetrists’ experience. Automated planning is a relatively new approach to the planning of advanced treatment techniques in which the plans for IMRT/VMAT patients are automatically optimised to produce, ideally, fully optimised plans based on the individual patient anatomy. It has the potential to increase efficiency, plan quality and enable more extensive adaptive planning processes to be implemented for a wide range of patients.
A key challenge in automated planning is incorporating the dosimetrists’ or oncologists’ clinical experience and decision-making within the autonomous process. A number of different methods have been employed: knowledge based planning utilises databases of previous clinical plans to correlate the relationship between patient geometry and the resultant radiotherapy dose, which then informs the optimisation of new patients; lexicographic ordering techniques optimise plans based on a list of clinically prioritised goals; and other site-specific solutions manage the optimisation process using hard coded algorithms.
This research aims at delivering step changes in service efficiency with a novel approach to advanced automation techniques in the field of radiotherapy treatment planning. It builds on a successful 18-month research project, funded by Health and Care Research Wales (HCRW), which developed and evaluated specialist algorithms for automated treatment planning, and will be carried out in the context of a fully funded programme of research in the field at Velindre Cancer Centre. The project will build and improve on our current automated algorithms though utilising artificial intelligence and machine learning techniques which will learn from clinician preferences and delivers fully personalised treatment plans. The research will lead to improved plan quality and an expansion in the number of clinical sites where automated planning can be successfully applied.
The successful candidate will develop the automated planning techniques building on existing research at the University’s School of Engineering and Velindre Cabcer Centre, and will become skilled in the following areas: (1) advanced radiotherapy treatment planning, (2) treatment plan optimisation using the Pareto principle, (3) Radiotherapy data management, (4) Developing databases, (5) Informatics and statistical analyses.
This project is best suited for students with a strong interest in Medical Physics and Engineering, Data Analysis, Machine Learning and Software Development. Informal enquiries are welcome and should be addressed to Dr.Emiliano Spezi ([email protected]
). Further details about School of Engineering and Velindre Cancer Centre can be found at the following links: http://research.engineering.cf.ac.uk
Applicants for a studentship must hold, at least, a 2.1 degree or Master’s degree in in a relevant subject such as:
• Physics/Medical Physics
• Medical/Clinical Engineering
• Computer Science
Applicants whose first language is not English will be required to demonstrate proficiency in the English language (minimum IELTS 6.5 or equivalent)
The studentship is open to Home, EU and Overseas candidates, and provides full UK/EU tuition fees, as well as a Doctoral Stipend (£14,553 p.a. for 2017/18). However, it should be noted that overseas candidates will be required to pay the difference between the home and overseas fee.
Applicants should submit an application for postgraduate study via the Cardiff University webpages
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 January, April, July or October 2018.
In the research proposal section of your application, please specify the project title and supervisor 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-ENG-Velindre2