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while minimizing treatment-related side effects. Conventionally, RT tries to accom- plish this goal by delivering a high uniform dose of radiation to the tumour while minimizing the dose received by surrounding healthy tissues. However, there are several key challenges to this approach which support the case for developing person- alized adaptive RT strategies. Four challenges are broadly described below and this thesis will address two of these challenges.

1)Determination of ideal dose prescriptions

The relationship between the radiation dose fractionation (particularly SBRT) and the resulting tumour control, as well as the associated treatment side-effects may not always be well-understood. Therefore, in some cases, it can be unclear about the trade-offs between tumour control and normal tissue toxicities. This equivocation is particularly challenging for cancers with poor outcomes or for late-stage disease where patients are declining in health with no curative intent treatment options available. In cases where the disease is localized, RT may be able to substantially assist in survival and improve quality of life. Liver cancer provides an instructive example where there is a risk of radiation induced liver disease (RILD) [18]. The risk of RILD is related to the size of the tumour relative to the rest of the healthy liver. On the other hand, sufficient dose must be given to control the tumour. Therefore, the resulting cost- benefit analysis between tumour control and RILD is patient-specific. Determining the radiation doses required to effectively control liver tumours is one of the focuses of this thesis.

2)Motion management during treatment

Voluntary and involuntary patient motion during treatment can cause radiation to be deposited where it is not intended, decreasing the dose received by the tumour and increasing the dose received by normal tissues. Dose can be delivered to larger regions which encompass the tumour motion, however this also increases the dose received by normal tissues. This in turn may decrease the overall radiation dose that can be prescribed to the tumour. Lung and liver tumours provide examples where motion is a challenge. Lung and liver tumours are influenced by respiratory motion and in two small studies have been shown to move an average of 12 mm±2 mm and 9 mm ± 5 mm in the superior-inferior directions respectively [20, 21]. This motion is patient-specific, pseudo-periodic, and may or may not be consistent throughout treatment [20, 21].

A variety of techniques have been developed to manage breathing-related tumour motion. Keall et al. [22] provide a comprehensive review of current clinical motion management strategies. Some of the most common techniques include immobilization, breath-hold, active breathing control, and gating. Adapting radiation delivery to inter and intra-fraction patient motion is a key aspect of current clinical practice and is typically referred to as image-guided RT (IGRT).

3)Geometric adaptation

Tumours may change size and patients may gain or lose weight during treat- ment. For example, one study found that head and neck tumours shrink by a median of 69.5% in volume during RT [23]. As a result of these intra-treatment changes, the planning CT may no longer accurately represent the anatomy of the patient. Therefore the RT plan, which is based on the pre-treatment planning CT, may be- come sub-optimal leading to patient-specific changes in the dose received by the tu-

mour and surrounding normal tissues. Adaptive re-planning has been implemented whereby patients undergo one or more planning CT scans throughout treatment and the treatment plan is updated according to large tumour and anatomical changes [24]. Adaptive re-planning based on volumetric tumour and anatomical changes has been investigated for head and neck, lung, bladder, and prostate cancers in the clinic [24–30].

4)Functional adaptation

Finally, as discussed in §1.2, cancer is a fundamentally heterogeneous and adapt- able disease. Significant genetic variation exists between different tumour types, be- tween tumours of the same type, and even between different cells within a single tumour [31]. Currently, different treatment regimens are applied to different tumour types, addressing a portion of this variability. However, standardized treatment pro- tocols are applied to tumours of the same type and therefore inter-patient variability is not addressed. Moreover, tumours are usually targeted with a uniform dose of radiation which cannot address intra-tumour variability. Different sub-regions within a tumour can be more or less sensitive to radiation depending on factors like cycling hypoxia and local oxygen concentration [32]. These factors can also change through- out the course of treatment. Therefore the use of standardized uniform doses may not maximize tumour control for individual patients. While adaptation due to volumetric changes in tumours is being practiced in the clinic, adaptation to intra-tumour het- erogeneity has not yet entered the clinic. This thesis investigates and develops tools to support this goal.

In summary, key challenges in conventional RT relate to the application of the same treatment plan throughout the fractionated treatment course. Cancer represents a heterogeneous and adaptable disease which may necessitate personalized adaptive treatment strategies in order to maximize tumour control and minimize treatment- related side-effects. RT is particularly well suited towards adaptive implementations since treatment fractionation provides multiple opportunities to evaluate and adapt during the treatment course. RT is already an image-guided localized treatment and so has the potential to adapt to local variability. However, personalized adaptive RT requires additional resources and conventional standardized RT already provides favourable outcomes for many cancers. Therefore investigation of adaptive strategies may be best suited to disease contexts with relatively poor outcomes such as short survival or high toxicities.

This thesis focuses on personalized dose-prescription (the first challenge) and using functional imaging to assess tumour response to guide RT adaptation (the fourth challenge). Current and emerging adaptive RT strategies related to these challenges are described in the next section.