Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719315
Title: Optimising adaptive radiotherapy for head and neck cancer
Author: Beasley, William
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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Abstract:
Anatomic changes occur throughout head and neck radiotherapy, and a new treatment plan is often required to mitigate the resulting changes in delivered dose to key structures. This process is known as adaptive radiotherapy (ART), and can be labour-intensive. The aim of this thesis is to optimise ART, addressing some of the technical and clinical challenges facing its routine clinical implementation. Optimising the frequency and timing of adaptive replanning is important, and it has been shown here that intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) are equally robust to weight loss during head and neck radiotherapy. Plan adaptation strategies that have previously been developed for IMRT are therefore applicable to VMAT.Contour propagation is an important component of ART, and it is essential to ensure that propagated contours are accurate. A method for assessing the suitability of a metric for measuring automatic segmentation accuracy has been developed and applied to the head and neck. For the parotids and larynx, metrics based on surface agreement were better than the commonly used Dice similarity coefficient. By establishing a consensus on which metrics should be used to assess segmentation accuracy, comparison of different algorithms is more objective and should lead to more accurate automatic segmentation. A novel method of assessing contour propagation accuracy on a patient-specific basis has also been developed. This was demonstrated on a cohort of head and neck patients and shows potential as a tool for identifying propagated contours that are subject to a high degree of uncertainty. This is a novel tool that will increase the efficiency of automatic segmentation and, therefore, ART.Optimum ART requires consideration of different radiotherapy-related toxicities, and image-based data mining is a powerful technique for spatially localising dose-response relationships. Correction for multiple comparisons through permutation testing is essential, but has so far only been applied to categorical data. A novel method has been developed for performing permutation testing and image-based data mining with a continuously variable clinical endpoint. Application to trismus for head and neck radiotherapy identified a region with a dose-response relationship in the ipsilateral masseter. Sparing this structure during radiotherapy should reduce the severity of radiation-induced trismus. ART mitigates the dosimetric effects of anatomic changes, and this thesis has addressed technical and clinical challenges that have so far limited its clinical implementation. Detailed knowledge of dose-response relationships will enable selection of patients for ART based on potential clinical benefit, and accurate contour propagation will make ART more efficient, facilitating its routine implementation.
Supervisor: Van Herk, Marcel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.719315  DOI: Not available
Keywords: Adaptive radiotherapy ; Deformable image registration ; Automatic segmentation ; Head and neck cancer
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