Title: Moving from population based treatment margins to personalised treatment margins by quantifying and accounting for delineation uncertainties in radiotherapy treatments.
Funder: Health Education England

Abstract: Modern radiotherapy techniques are characterised by steep dose gradients at the boundaries between the target and organs at risk (OAR). Complex radiotherapy treatments are produced based on target and OAR outlines drawn by clinicians on a CT scan of the patient, therefore accurate knowledge of target and OAR location is crucial since small variations can lead to unacceptably high levels of toxicity, or insufficient tumour control. There are many sources of uncertainty in the location of target and organ boundaries. These uncertainties permeate through the whole radiotherapy process, with outlining, the process by which clinicians draw the target and OAR boundaries on the CT scan, by clinicians being one of the largest sources of uncertainty. It is vital to account for these uncertainties in radiotherapy treatment in order to avoid serious side-effects and ineffective treatments. This is currently done by adding safety margins to each target or critical OARs. Treatments are then produced to deliver sufficient dose to the target plus the safety margin, whilst sufficiently sparing organs at risk with their safety margin. The result of this is that healthy tissue receives a therapeutic dose to ensure that the target receives sufficient dose, and conversely, the target my not receive the desired dose to ensure that critical OARs do not receive too much dose. These applied margins are based on population data and assumes that a patient closely matches that of the population. Similarly, the margins ignore local variations in the uncertainty within a patient, for example those arising from a poorly differentiated OAR or tumour boundary. The aim of this research is to develop a set of software tools which allow treatments to be developed which minimise the impact of outlining uncertainty on a radiotherapy treatment whilst also removing the reliance on population based estimations of this source of uncertainty. This will be achieved by developing a tool which will allow clinicians to record their estimation of the uncertainty on every part of the tumour or OAR outline they draw on the CT. A second tool will then be developed to show how robust a developed treatment is to these identified uncertainties, therefore allowing the user to produce treatments which are more robust to these uncertainties, therefore potentially benefiting the patient. The initial part of the research will identify how best to create the outlining tool, including working closely with clinicians to develop a specification, so that it is easy and efficient for them to use. The tool will then be utilised by clinicians on a set of brain and gynaecological cancer patients previously treated at our institution. A second tool will be developed which will use the data from the outlining tool to show how, for a given treatment, these uncertainties could affect the patients' outcome. This will allow an investigation into how to modify the treatment so that the impact of the identified uncertainties can be minimised, which will constitute the final phase of the research. This research would benefit radiotherapy patients as it'll allow safer and more effective treatments to be created since treatments will be less dependent on population data and can account for each patient's unique anatomy and sources of uncertainty. Ultimately it will allow the radiotherapy team to factor the individuals' uncertainties into the treatment they develop to minimise their impact on that individual patient, hopefully leading to improve tumour control rates and lower rates of toxicity. In addition, this can be achieved using existing hardware as the tools are software based, therefore these benefits will be brought at a minimal expense to the NHS.

Lay abstract: The radiotherapy process involves creating treatments based on outlines of tumours and organs on CT scans. Uncertainties in the position of these outlines are accounted for by adding population based safety margins to the tumour or organ. The aim of this project is to develop software tools which allow uncertainties to be accounted for, based on each individual patient rather than using population data. This will be achieved by allowing clinicians to digitally record uncertainties in each part of each outline and then showing how these uncertainties impact on the treatment, so that uncertainty robust treatment can be created.

Research activity codes: 5.5 RADIOTHERAPY AND OTHER NON-INVASIVE THERAPIES
Health categories: CANCER AND NEOPLASMS