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Title: A biomechanical model for lung fibrosis in proton beam therapy
Author: King, David John Stephen
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2017
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The physics of protons makes them well-suited to conformal radiotherapy due to the well-known Bragg peak effect. From a proton’s inherent stopping power, uncertainty effects can cause a small amount of dose to overflow to an organ at risk (OAR). Previous models for calculating normal tissue complication probabilities (NTCPs) relied on the equivalent uniform dose model (EUD), in which the organ was split into 1/3, 2/3 or whole organ irradiation. However, the problem of dealing with volumes < 1/3 of the total volume renders this EUD based approach no longer applicable. In this work the case for an experimental data-based replacement at low volumes is investigated. Lung fibrosis is investigated as an NTCP effect typically arising from dose overflow from tumour irradiation at the spinal base. Considering a 3D geometrical model of the lungs, irradiations are modelled with variable parameters of dose overflow. To calculate NTCPs without the EUD model, experimental data is used from the quantitative analysis of normal tissue effects in the clinic (QUANTEC) data. Additional side projects are also investigated, introduced and explained at various points. A typical radiotherapy course for the patient of 30×2Gy per fraction is simulated. A range of geometry of the target volume and irradiation types is investigated. Investigations with X-rays found the majority of the data point ratios (ratio of EUD values found from calculation based and data based methods) at ∼20% within unity showing a relatively close agreement. The ratios have not systematically preferred one particular type of predictive method. No Vx metric was found to consistently outperform another. In certain cases there is a good agreement and not in other cases which can be found predicted in the literature. The overall results leads to conclusion that there is no reason to discount the use of the data based predictive method particularly, as a low volume replacement predictive method.
Supervisor: Not available Sponsor: EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available