Pang, Yaru; (2023) Prescription dose optimization for personalized radiotherapy. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

As one of the most complex tumors, there are over a hundred tumor types of brain tumors. Among all treatment options, radiotherapy (RT) has been shown to greatly enhance the survival and local control rates for brain malignancies, and it is the standard method for brain tumor treatment, with better results than treating with only surgery or chemotherapy. Given the complexity of the biological system in brain tumors, an effective and personalized method for determining doses for radiation prescriptions is essential. Tumor control probability (TCP) and normal tissue complication probability (NTCP) are indicators that can measure how the three-dimensional physical dose distributions are transfered into biological effects. In this thesis, I first investigated the parameter uncertainties in radiobiological models (e.g., TCP and NTCP), and offered a personalized prescription dose prediction method based on an optimized model by considering individual variances in radiobiological parameters and constraints on several organs at risks (OARs). The therapeutic ratio for brain tumors following the proposed principles has been increased, while normal tissues have been protected. Since glioblastoma multiforme (GBM) is one of the most malignant primary brain tumors. Local recurrence after RT is the most common mode of failure. Standard RT practice applies the prescription dose uniformly across tumor volume disregarding radiological tumor heterogeneity. I presented a novel strategy by using diffusion-weighted (DW-) MRI to calculate the cellular density at the voxel level within the gross tumor volume (GTV) in order to facilitate dose escalation to a biological target volume (BTV) to improve tumor control probability. The pre-treatment apparent diffusion coefficient (ADC) maps derived from DW-MRI of ten GBM patients treated with radical chemoradiotherapy were used to calculate the local (per voxel) cellular density. Then, a TCP model was used to calculate voxelated TCP maps from the derived cell density values. The dose was escalated using a simultaneous integrated boost (SIB) to the BTV. By applying a SIB between 3.60Gy and 16.80Gy isotoxically to the BTV, the cohort’s TCP has been increased by a mean of 8.44% (ranging from 7.19% to 16.84%). As a promising alternative treatment, proton radiation therapy can significantly protect normal tissues due to the Bragg curve and thus the dose to tumor can have more headroom to increase, consequently increasing the tumor control rate and the therapeutic ratio. Therefore, proton therapy has been considered as a potential and increasingly popular treatment method. In this thesis, I investigated the uncertainty factors within the proton RT flow, and calculated the proton-related BTV and SIB, leading to an improvement of overall TCP. Comparisons between photon and proton dose optimization methods were also discussed. Exploitation of the proton-related SIB dose with radiosensitivity parameters from in-vitro biological experiments, 4.18Gy to 17.67Gy were provided to BTV, and TCP values were increased by 11.39% to 34.25%. The proton plans had lower doses to all the OARs and the doses to all non-tumor tissue (body minus PTV) was on average 3.31Gy lower than photon treatments, which means the OARs and normal tissues have been better protected.

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