Image-guided adaptive radiotherapy for cervical cancer
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Date
2024-08-23ICR Author
Author
Lalondrelle S
Wang, L
Lalondrelle, S
Type
Thesis or Dissertation
Metadata
Show full item recordAbstract
Due to interfraction motion of the cervical cancer low-risk clinical target volume
(CTVLR), large non-adaptive PTVs are required to ensure coverage, increasing normal tissue toxicity. Adaptive radiotherapy enables targeting of the dose to CTVLR position on daily imaging, but there are barriers to implementation. The aim of this MD(Res) was to investigate several solutions.
First, I reviewed the literature to consolidate evidence of dosimetric benefit of plan-of-the-day and identify strategies for implementation. Varying patient characteristics and metrics made comparison of published PTV generation methods difficult, so I recreated them on an independent dataset to find strategies which had high target coverage with small PTV sizes.
Due to the resource cost of adaptive radiotherapy, it may be desirable to select patients who have more interfraction motion. I identified items of pre-treatment imaging and patient-related data which predicted the degree of motion. I developed two multivariate models which outperformed a published patient selection method.
Plan selection based on cone beam computerised tomography (CBCT) can be challenging due to image quality. I conducted a prospective clinical study which showed that addition of ultrasound improved uterocervix visualisation, but this did not translate into improved plan selection accuracy.
I provided 51 matched CT-CBCT pairs with manual contours for a commercial partner to develop an CBCT autosegmentation algorithm via synthetic CT creation. I evaluated the similarity of autocontours with ground-truth manual contours on an unseen test; autocontours were less accurate than contours deformably mapped using commercially-available deformable image registration algorithms.
The work in this thesis offers solutions for centres implementing plan-of-the-day, while highlighting the challenges of daily online replanning on CBCT.
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Research team
Gynaecological Cancer
Language
eng
License start date
2024-08-23
Citation
2024
Publisher
Institute of Cancer Research (University Of London)