Modelling and Accounting for Delineation Uncertainties and Anatomical Changes in Proton Beam Therapy using Machine Learning

1) Brief description of the context of the research including potential impact

The project will utilise state-of-the-art machine learning methods to model the uncertainties in the organ at risk (OAR) delineations and day to day anatomical changes to patients receiving proton therapy for head and neck cancer treatments. Different models will be developed for these two sources of uncertainty. Deep-learning based approaches will be used to simultaneously generate high-quality automatic delineations of organs at risk and to estimate and quantify the different types of uncertainty in the delineations (uncertainties in the training data, e.g. variability in the manual delineations, uncertainty in the model parameters). Statistical shape/deformation models and deep-learning based approaches will be investigated to model the variability in the day to day changes in OAR using routine imaging from patients acquired during the course of treatment. These predictive models can then be used to forecast uncertainty in shape and position of OAR for a new patient over the course of proton therapy and ultimately guide robust proton treatment planning to take account of these changes.

2) Aims and Objectives

The project aims to use state-of-the-art machine learning methods to model and estimate the uncertainties in OAR delineations and anatomical changes that affect proton therapy treatments, and to develop proton therapy treatment plans that are robust to these uncertainties. This is split into the following objectives:
1. Produce images and delineations from CBCT scans that are suitable for dose calculations and analysis
2. Model variability in delineations and anatomical changes across the population of patients and use models to estimate uncertainty in delineations and anatomical changes for new individuals
3. Produce images and contours suitable for assessing the robustness of proton therapy plans to the estimated uncertainties for a specific individual
4. Update estimates of uncertainty for an individual as new CBCT images become available for that individual
Use updated estimates of uncertainty to predict if/when a re-plan will be required and to produce images and contours suitable for use in generating the re-plan

3) Novelty of Research Methodology

This work will utilise state-of-the-art deep-learning based approaches to contour propagation, synthetic CT generation, and uncertainty estimation that have been developed for different applications, and will adapt and combine these approaches in new and novel ways for the application of CBCT dose calculations. New and novel approaches will then be developed for modelling the variability and estimating the uncertainty in the delineations and anatomical changes, and for producing proton therapy plans that are robust to the estimated uncertainties.

4) Alignment to EPSRC's strategies and research areas

This project is aligned with the following EPSRC Healthcare Technologies Grand Challenges: Frontiers of Physical Intervention; and Optimising Treatment. It also will build the following Cross-cutting research capabilities: Novel computational and mathematical sciences; Novel Imaging Technologies

5) Any companies or collaborators involved

This project is a collaboration with the Proton Therapy team at UCLH. No industrial collaborators are involved

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.