Application Deadline:18th December
4-year PhD studentship funded by NPL, Mount Vernon Cancer Centre and the i4health CDT at UCL
Variability and reproducibility in delineating anatomy is arguably the biggest source of uncertainty in radiotherapy and inaccuracies impact on both tumour control and normal tissue toxicity. More accurate delineations would improve outcomes for patients both in survival and reduced toxicity, with the greatest impact for proton therapy where delineation variability can create large dosimetric uncertainties. Typically, delineation is performed manually by a clinician following national and local guidelines, which can take a significant amount of time and result in considerable variability between patients, clinicians, and clinical sites. Recently, several commercial and research AI solutions to delineate tissues have become available, with more on the horizon, which have the potential to both save time and reduce variability compared to manual outlining. These solutions are now starting to be adopted clinically in some hospitals, and the next few years are likely to see a considerable increase in the use of AI-generated delineations in clinical trials and for routine clinical practice. It is therefore imperative that objective and efficient methods for fairly assessing and comparing manual and AI-generated delineations are developed. This will help improve the efficiency and effectiveness of clinical trials and facilitate the safe adoption of AI solutions into clinical practice.
The objective of this projects is to develop state-of-the-art machine learning methods for measuring and parameterising variability in delineations over many patients, being suitable for both manual and AI-generated delineations. These methods will be used for objective and balanced comparisons between manual and AI-generated delineations for different cohorts of patients. This will be implemented to critically evaluate delineations for specific patients and determine if they are within the range of variability seen in the wider population, facilitating fair and efficient QA of delineations for clinical trials and routine clinical use.
For further details regarding this PhD studentship can be found here:
https://www.ucl.ac.uk/intelligent-imaging-healthcare/case-studies/2022/nov/measuring-variability-ai-and-manual-delineations-radiotherapy-using-machine
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