Introduction: The treatment option for poorly differentiated stage I anal cancer includes photon radiation therapy with volumetric-modulated arc therapy (VMAT). Intensity-modulated proton therapy (IMPT) has a clinical benefit of reduced normal tissue toxicity over traditional photon therapy. The aim of this study was to evaluate on potential benefits and drawbacks of robust optimized IMPT compared to VMAT plans for anal cancer treatment.
Methods: Data from 20 patients treated with VMAT for anal cancer was used for retrospective IMPT treatment planning. One IMPT plan was created with 2 beam directions, while another was created with 4 beam directions. The composite minimax robust optimization (CMRO) was used for IMPT plan optimization. The quality of the treatment plans was evaluated in terms of target dose coverage and organ at risk (OAR) dose constraints.
Results: Robust target dose coverage was achieved in both IMPT plans in all patients. In general, the dose to OARs was significantly lower in the IMPT plans compared to the VMAT plans: the dose constraint ”Femoral Heads D50 < 30 Gy” was 0.3 Gy in the IMPT plans, while this was 24.1 Gy in the VMAT plans, and the dose constraint ”Bone marrow D30 < 40 Gy” was 8.1 - 9.0 Gy in the IMPT plans, while this was 31.2 Gy in the VMAT plans.
Conclusions: IMPT achieves robust target dose coverage and reduces dose to OARs compared to VMAT in anal cancer treatment, even with only two beams. Because of large dosimetric benefits, IMPT might be considered for anal cancer treatment in the clinic without full model-based decision-making.

During pre-clinical research on proton therapy, validating the dosimetry is important for accurate and representable results. To ensure that research outcomes can eventually be implemented in clinical practices, it is equally as important to also take biological effects into account. The aim of this thesis was, therefore, to dosimetrically and biologically commission a pre-clinical proton therapy research line. A phantom was designed, 3D-printed and irradiated to check accurate positioning in the beamline. Radiochromic film was used as a dosimeter at different positions within the phantom. The blackening of this radiochromic film after proton irradiations was quantified to obtain information on the dosimetry. It seemed that the phantom was correctly positioned at different places in the spread-out Bragg peak. However, accurate and precise dosimetry appeared to be difficult with radiochromic film. The added compensator successfully gave more uniform profiles throughout the whole depth of the phantom. Furthermore, the dose response of spheroids made from FaDu tumour cells was characterised and
compared to the dose response of cells grown as a monolayer. Spheroid growth and viability, based on the cellular ATP levels, were used as biological endpoints. A different dose response of FaDu spheroids was found when compared to that of cells, however, no clear explanation was found and more research remains to be done. Overall, with some further research, this 3D-printed phantom, along with the tumour spheroids, can
be implemented to validate a pre-clinical proton therapy research line.

With FLASH proton radiotherapy, healthy tissue is spared more compared to conventional proton therapy. The FLASH effect is present at high fractional doses of more than 8 Gy, at ultra-high mean dose rates of more than 40 Gy per second and at dose delivery times of less than 200 milliseconds. However, particle accelerator intrinsic uncertainties can influence the FLASH effect negatively. FLASH proton therapy is affected by pencil beam positioning errors and by proton beam current fluctuations. In this work, these two types of uncertainties were added to a Gaussian two-dimensional analytical dose model. The impact of pencil beam placement errors was evaluated on dose deposition and related to clinically used metrics. These are the V95, V107, D2 and D98. The constraint on the V95 was violated for 5\% of patients first. For all evaluated FLASH dose fields, the allowed Gaussian pencil beam placement error is less than 1.0-mm in standard deviation. A trade-off between target coverage and the level of FLASH effect was found. Proton beam current fluctuations were coupled to the pencil beam scanning dose rate (PBSDR98), the minimum dose rate for 98\% of the target volume. For all evaluated FLASH fields the permitted beam current fluctuation was 6.8\%. This is considered more achievable than the limit on pencil beam placement errors. Proton FLASH radiotherapy is feasible, but proton beam characterisation is required to quantify for which patients this is the case.

The PBSDR is sensitive to added uncertainties and contradicts our current understanding of the FLASH effect. In this thesis, a more robust metric is developed which depends on the radiolytic oxygen depletion hypothesis for the FLASH effect. This metric is called the FER_eff. The oxygen concentration over time in a cell is modelled with an ordinary differential equation (ODE) involving an oxygen depletion and re-oxygenation term. The FER_eff can be calculated with the oxygen concentration over time for a treatment plan. Currently, The onset of the FLASH effect lies between a prescribed dose of 8 Gy and 16 Gy. This is in line with the FLASH dose threshold. The shortcomings of the PBSDR are dealt with.

A three-dimensional uncertainty analysis can be done with a semi-analytical dose engine from the TUD. With this engine, changes in dose deposition are coupled to Hounsfield unit (HU) perturbations without the necessity of a full dose re-computation. To study the machine uncertainties, an EMC FLASH treatment plan formed with iCycle was recreated with the TUD dose engine. The iCycle optimisation results contain all the required information for a dose calculation. The gamma pass rates between the iCycle dose and engine dose were satisfactory for the target but not good enough for an organ-at-risk (OAR) yet. Similarity can be improved by adjusting the dose recreation procedure. When the iCycle dose and engine dose are comparable, a trustworthy clinical uncertainty analysis can be done. The idea is to simulate field and individual pencil beam placement errors by shifting the original patient CT to a virtual CT. First, it should be studied if the computed dose response is valid for a combination of CT shifts and HU perturbations. In a later stage, pencil beam placement errors should be coupled to the clinically used ICRU metrics and constraints. The impact of beam current fluctuations can also be studied in the future.

Pencil beam scanning is a technique used in the proton therapy. This technique makes use of a thin beam which can irradiate each spot at millimetre precision. It is a very promising technique, especially for more radiation sensitive areas, e.g. lung carcinogenesis, as it can spare more healthy surrounding tissue compared to conventional radiation therapy due to proton’s physical property of the Bragg peak: this is the explosively release of the particles’ energy at a certain point in the beam path; a minimal energy loss is obtained before this peak and no energy is deposited after this Bragg peak. In the PBS, interplay is a concern. The interplay effect is defined as the interference of the moving beam and breathing induced tumour motion. Hence an inhomogeneous dose distribution occurs which differs from the planned dose. In the interplay effect different parameters are involved, which can be divided into beam and patient parameters.

The HollandPTC developed a Quality Assurance tool for assessing the interplay effect in lung treatment plans in which a first order sinusoidal function is utilized. However, a sine function is not reflecting the daily variation in target motion and also beam delivery fluctuates over time. By implementing variations into our QA tool, a more realistic interplay assessment could be obtained. A previously done literature search provided us insufficient information regarding beam and patient parameters describing variations over time, hence we did our own research. In this thesis, an investigation was done to analyse the variation in beam and patient specific parameters.

For the machine parameters, the variation in Energy Layer Switching Time (ELST) and Beam On Time (BOT) was determined for the VARIAN ProBeam 4.0. Three clinical lung plans which contained in total nine beams, were irradiated on an array of ionization chambers multiple times over multiple days. The output files were analysed regarding the ELST and BOT. For each beam 15 measurements were done, a total of 135 measurements were executed over a period of two weeks. We found variations in ELST between energy layers, but also within an energy layer variations in ELST was observed. The within day and day-to-day variations were comparable. Regarding the BOT, this variable strongly depends on the planned dose. Looking at each individual beam: variations between energies are found; no variations within an energy layer was found. By dividing the measured dose of all output files within an energy layer by the corresponding BOT, the dose rate for each energy was obtained. The dose rate was low at low energies and gradually increased towards the higher energies.

Patient parameters were also investigated regarding the tumour motion pattern: the variation in amplitude, period and degree of asymmetry was investigated. In three lung patients, the breathing signal was recorded for 1.5 minutes utilizing the Anzai belt. Five measurements were done: two measurements for two patients and one measurement for one patient. The obtained signals were analysed in terms of amplitude, period and degree of asymmetry for each breathing cycle. Each cycle was fitted into the Lujan’s model, hence a value for each of the three variables were observed. For our group the amplitude variated between 6 and 12mm and the period variated between 2.5 and 4.5 seconds. The degree of asymmetry was most likely to be 1. We found significant interpatient variation, mainly for the amplitude and period.

In this analysis, we found that the ELST mainly depends on the step size of the degrader and slit movement. Unfortunately, no hard conclusion regarding the variation within an energy layer could be stated, probably the data transfer systems of the beam line are involved in this variation. Developing a prediction tool for assessing the ELST seems appealing, however more data in different energy ranges should be obtained. Regarding the BOT: this time was more or less dependent on the planned dose. Variations measured for this parameter were caused by inaccuracies of our measurement tool itself.

Regarding the patient parameters, significant differences are found between patients’ respiratory signal e.g. the tumour motion strongly depends on tumour characteristics. Hence, we advise to analyse the tumour motion pattern patient specifically.

In this thesis report the first insights towards the fluctuations in the relevant parameters were determined. The retrieved information can be used to optimize the interplay QA tool, to get a more realistic interplay assessment in the plans.

The aim of this thesis is to study the dosimetric feasibility of FLASH proton therapy for early-stage breast cancer patients. The biological effect of FLASH is seen under ultra-high dose-rates conditions and is beneficial in the damage to healthy tissue. The FLASH effect could enable the clinical feasibility of definitive radiotherapy for low-risk early-stage breast cancer patients. Some of these patients can not receive surgery for medical reasons and definitive conventional radiotherapy carries a high risk of toxicities. FLASH proton therapy could be a viable option for these patients. The biological FLASH effect enables to reduce the risk of toxicities by sparing healthy tissue and proton therapy enables to deliver dose to the cancer cells effectively.
This thesis proposes a pencil beam scanning (PBS) ridge filter simulation set-up to achieve ultra-high dose-rates. The ridge filter generates spread-out Bragg peak (SOBP) beams that cover a broader depth of the tumor, avoiding the time-consuming energy switching of conventional PBS. An optimization is performed with intensity-modulated ridge filtered beams. SOBP beams with a width of 3 cm are implemented in the in-house treatment planning software Erasmus-iCycle to generate ridge filter treatment plans for two patients.
In this study, the dosimetric feasibility is determined by comparing the ridge filter treatment plans to conventional intensity-modulated proton therapy (IMPT) treatment plans. The clinical acceptability of the treatment plans is assessed by dose uniformity and dose coverage. Furthermore, toxicity-model endpoints for fat necrosis and fibrosis are used to evaluate the treatment plans. The generated ridge filter treatment plans outperform the generated IMPT treatment plans for a FLASH enhancement ratio between 1.19-1.39 for one of the patients. The other patient required a FER in the order of magnitude of 2.0.
The results of the study suggest that it is feasible to generate ridge filter treatment plans with the proposed set-up. However, the dosimetric feasibility comes with limitations in the tumor characteristics (size and position) and needs further investigation. More knowledge is needed of the optimal ridge filter, the optimization of SOBP beams, and the FLASH effect before clinically acceptable FLASH-compatible ridge filter treatment plans can be achieved.

FLASH proton therapy is a growing field of research, especially due to its biological benefits in radiation oncology: sparing healthy tissue while delivering the treatment within a millisecond. However, instead of sparing healthy tissue, the conventional FLASH approach, using transmission beams, damages the tissue behind the distal edge of a tumour. Therefore, this approach is less attractive in some clinical applications of FLASH proton therapy. To solve this problem, the use of a ridge filter and patient-specific range compensator, to shift the spread-out Bragg peak (SOBP) of the proton beam to the tumour, is proposed. In this research, the clinical feasibility and acceptability of FLASH-compatible treatment plans, optimized with multiple, Monte Carlo-simulated ridge filter beams, is analysed. An SOBP-database is generated using energy spectrum approximations and interpolations of energy spectra retrieved from Monte Carlo simulations in TOPAS. To obtain optimized FLASH-compatible treatment plans for neuro-oncological targets, this database is implemented in the in-house treatment planning software of the Erasmus Medical Center, iCycle.  The resulting treatment plans show that it is possible to generate FLASH-compatible treatment plans using a ridge filter. A FLASH enhancement ratio between 1.4 and 2.1 would potentially give clinically acceptable plans for the three patients considered. In some optimized plans, the homogeneity of the tumour dose is also increased. A limitation of this research is that configuration of a stable ridge filter beam treatment plan optimizer appears to be challenging. Besides this, the FLASH enhancement ratio and the dose rate are not taken into account to find the regions in the patient where the FLASH conditions (dose > 8 Gy, dose rate > 40 Gy/s and treatment time < 0.1 s) are met.  Recommendations for future research include: implementing the FLASH enhancement ratio and the dose rate optimization in treatment plan optimization; investigating the influence of fractionation ofa FLASH treatment plan on the tumour control and the healthy tissue irradiated; study the relative biological effectiveness (RBE) and the biological character of FLASH radiotherapy, and investigate the clinical potential of a combination of FLASH and non-FLASH treatment.

Radiotherapy treatment planning is a complex and time consuming process prone to differences as result of choices of individual planners. Autoplanning systems have been introduced to both reduce the time consumption and to counteract the influence of individual planning choices. Although autoplanning generally increases performance of the treatment plans, the plans still need to be checked manually to ensure plan accuracy. Increasing plan consistency with knowledge based planning (KBP) or automatic plan evaluation would be clear ways to improve upon this problem. For both improvements, deep learning can be an important tool to produce accurate and fast 3D dose distributions. In this study such a deep learning tool is developed in two parts to accommodate this.
In the first part a structure based deep learning model, using a U-Net architecture is developed, used for dose distribution prediction for prostate patients treated with volumetric modulated arc therapy (VMAT) plans, generated by autoplanning. Different loss functions have been tested to see which achieve the best results. In the second part physics information is included in the neural network prediction by using a hybrid physics-data approach. For this, an approximate dose distribution, the segment dose, is used as extra input for the dose prediction network. This segment dose is created by predicting multi leaf collimator (MLC) positions and reconstructing the corresponding dose with a simple dose engine. The predictions are evaluated using several dose characteristics, dose volume histogram (DVH) curves and other clinically relevant metrics. The U-Net architecture proved able to accurately predict the dose of patients in the test set. The best performing loss function for accurate dose characteristics prediction was found to be the weighted mean squared error (WMSE) loss, which predicted several PTV DVH statistics within a 1.5 % error and the rectum statistics within 3.5 % error. Furthermore, the model predicts the DVH points of the PTV within 0.84 ± 0.40 Gy average absolute dose difference and the rectum in 0.91 ± 0.72 Gy average dose difference. The hybrid physics-data model did not improve the prediction accuracy of the dose prediction model in terms of DVH statistics and DVH prediction, as the MLC positions could not be predicted accurately enough. The performance of the structure based prediction is similar to the performance of state-of-the-art prediction models. Although the hybrid model with the predicted segment doses did not improve the prediction accuracy, a significant effect could be seen from using the correct MLC positions instead. The bottleneck of the prediction is therefore identified to be the segment prediction. As such, it would be interesting to focus on segment prediction in follow up research.

Radiotherapy is an important treatment type for patients with cancer. An advantage of state of the art proton therapy with respect to traditional photon therapy is the spatial energy deposition of protons, which is characterized by the Bragg peak. Due to this particular course of energy deposition, the tumor can be irradiated more precisely and as a consequence more healthy tissue can be spared. A drawback is that proton therapy is more sensitive to uncertainties like misalignment of the patient, which is referred to as a set-up error. Robust optimization is the current way to account for such uncertainties during which a few discrete error scenarios are included in the planning. Due to the nature of the uncertainties, probabilistic optimization, on the other hand, is more promising, since it can handle many more scenarios with their occurrence probability taken into account as well. To investigate probabilistic planning, Polynomial Chaos methods were used in this research. Polynomial Chaos can approximate a stochastic response, depending on for example the set-up error, by a series expansion in terms of polynomials. The advantage is that typically the Polynomial Chaos Expansion (PCE) can be evaluated much faster than the stochastic response itself. In this research an investigation is done how PCE can be used with probabilistic treatment planning for proton therapy. First, an overview is given on how PCE can be used to describe probabilistic objective functions. It has been found that in general it is possible to describe random set-up errors with PCE without the need of considerably additional computational power. Moreover, polynomials in dose are especially suitable when fractionation is taken into account. Second, a simple one-dimensional geometry consisting of a tumor with a surrounding organ has been considered. The effects of fractionation on the dose distribution has been calculated with PCE and the results were in accordance with earlier research by Unkelbach et al. (2018). Third, a simplified three-dimensional geometry which mimics a spinal cord with surrounding tumor has been optimized probabilistically with PCE. The expected value of the sum of the quadratic differences between the dose and the prescribed dose has been minimized. The effects of probabilistic optimization became clear from both the nominal and the expected dose distribution. This objective function (i.e. the expected value of a quadratic function of the dose) turned out to be extremely suitable for probabilistic optimization with PCE, however its clinical relevance is less clear.
Last, a real skull base meningioma patient has been considered. As objective function, a percentile of the dose volume parameter has been used with PCE. This function demonstrated a bumpy dependence on the beam intensity and it has been proven that it cannot have a negative gradient. Consequently, a high step size is recommended to approximate the gradient and, if needed, the Hessian. Probabilistic treatment plans were obtained with a single probabilistic objective, with two probabilistic objectives and with nominal constraints and objectives on OARs. The results have been compared with a robust treatment plan according to the recipes of Ter Haar et al. (2018). It has been found that the probabilistic treatment plan shows a more conformal dose distribution, since it does not exhibit any preferred direction, contrary to the robust treatment plan. As a consequence, the automatic extension of the tumor is smaller. A clear benefit has been demonstrated in favor of the probabilistic treatment plan. Besides that, a major advantage of probabilistic optimization with respect to robust optimization is that it makes it possible to directly steer to a probabilistic goal independent of the patient and the treatment site. The computation time of probabilistic planning (in the order of 1-3 weeks) is unfortunately still too high for clinical practice.