Intensity-Modulated Proton Therapy for prostate cancer:

Transcription

1 Delft University of Technology Intensity-Modulated Proton Therapy for prostate cancer: Evaluating strategies to account for interfraction organ motion Christiana Balta

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3 Intensity-Modulated Proton Therapy for prostate cancer: Evaluating strategies to account for interfraction organ motion By Christiana Balta in partial fulfilment of the requirements for the degree of Master of Science in Biomedical Engineering at the Delft University of Technology, to be defended publicly on December 17th at 14:00 PM. Supervisors: Dr. M. S Hoogeman Erasmus MC Cancer Institute Supervisors: Ir. S. van de Water Erasmus MC Cancer Institute Supervisors: Dr. ir. D. Schaart Delft University of Technology 3

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5 Acknowledgments I am using this opportunity to express my gratitude to everyone who supported me throughout the course of this MSc. project.. I would like to thank Dr. Mischa Hoogeman for giving me the opportunity to work on this project in Erasmus MC-Cancer Institute. I am thankful for sharing some of his knowledge and expertise and for the invaluable constructive criticism during the project work. I would also like to thank my daily supervisor, PhD candidate, Steven van de Water for his tireless guidance throughout my thesis, whilst allowing me the room to work in my own way. Without his comments, suggestions and positive attitude this work would have been a lot more difficult. In addition, I would like to thank my TU Delft supervisor Dr. Dennis Schaart for all of his assistance from our first discussions about graduation projects till the final stages of this preparation. At this point, I would like to thank the two other members of my graduation committee Dr. Martijn Engelsman and Dr. Frans Vos, for showing interest in my project and for their willingness to aid my graduation. I would like to thank the people from the Physics department of Erasmus MC, for making the lunch breaks into an energy-boosting time with chats and funny stories and contributing to a gezellig working atmosphere during all these months. I want also to thank all my friends and fellow master students who stood by me and made these two years in Delft really fun. Special thanks to my friends who are in Greece, UK or any other place in the world; with their calls I had cosy moments -like in the old daysand with their visits I had relaxing days, which I really needed sometimes. To wrap it up, I want to thank my parents, Georgios and Zoe, my brother Andreas and my grandfather Chris, for their inspiring support all this time and for being there for me every time I needed them. Without your contribution, this journey would not even begin... 5

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7 Abstract Background/purpose: Intensity-modulated proton therapy (IMPT) is a promising new form of radiotherapy with unique physical dose deposition characteristics. In prostate cancer patients, the use of IMPT might result in a reduction of the dose received by organs-at-risk (OARs), such as the rectum and bladder. Prostate treatments are delivered in up to 38 fractions and the patient anatomy can vary considerably in between fractions. In conventional photon therapy, this internal organ motion is typically accounted for by applying a margin around the target volume. These margins are generally considered inadequate for IMPT, as they do not account for the additional error in proton range. Socalled robust planning techniques, in which treatment errors are explicitly included in the plan optimization, are increasingly used for IMPT treatment planning. The currently used robust planning techniques can only account for setup errors and range errors, and not for anatomical changes, which are often observed during the treatment of prostate cancer. The aim of this study is to investigate which margins and robustness settings are needed to account for interfraction anatomical changes in IMPT treatments for prostate cancer. Materials and Methods: In this study, in total 1602 treatment plans have been generated for ten final stage prostate cancer patients. Four patient groups were defined according to the target volume(s) and the prescribed dose. For group 1, only the prostate was the target volume. In groups 2 and 3, the target volume consisted of the prostate and its accessory glands -the seminal vesicles (SV). Finally, group 4 involved the prostate, SV and also the pelvic lymph nodes (LN). Treatment plans were generated for each patient while varying the setup robustness from 0 to 8 mm and the range robustness from 0% to 4%. For each combination of setup and range robustness, the margins on each of the target structures (the prostate, SV, and LN according to the patient group) were increased from 0 to 20 mm. We evaluated these treatment plans by recalculating them on 8-10 repeat CT-scans. Treatment plans were considered acceptable when the target volume receiving 95% of the 7

8 prescription dose (CTV 95%, coverage) was higher than 98% in all repeat CT scans except one (in which CTV 95% > 97%) and the CTV 107% (target volume receiving 107% of the prescription dose) below a reasonable level (ideally) smaller than 2%. For each setup robustness setting, we selected for every patient the plan that fulfilled the constraints with the smallest margin, and consequently with the lowest OAR doses. Next to that, we determined for each setup robustness setting the margin -in every target volume- that would result in an acceptable treatment in 90% of the patients. We finally compared the resulting OAR doses between different setup robustness settings and between the patientspecific and population-based approaches. Results: In some cases, large margins were needed to ensure adequate target coverage in all of the repeat CTs. Among all target structures, dose coverage of the prostate was more robust thus requiring smaller margins (up to 8 mm) compared to the SV (up to 12 mm) and the LN (up to 20 mm). In a patient-specific margin approach, lowest OAR doses were achieved when using 2 mm of setup robustness and margins ranging from 0-6 mm for the prostate. For the SV and the LN setup robustness did not reduce the OAR dose and margins of 4-12 mm and of 2-20 mm were needed respectively. Conversely, for the lowest OAR doses, the population-margin approach (90% of the patients) required setup robustness of 4 mm and margin of 2 mm for the prostate, and 6 mm for the SV. For the LN, setup robustness did not have a clear benefit and population-based margins were ranging from 2-12 mm. Conclusions: Margins and setup robustness were to a large extent interchangeable and a combination of both resulted in the lowest OAR doses while preserving target coverage. A population-based planning strategy caused OAR doses to be considerably higher in most patients, suggesting that the use of online adaptive treatment planning could be beneficial for IMPT in prostate cancer patients. 8

9 Contents Acknowledgments... 5 Abstract... 7 Introduction Prostate cancer Radiotherapy Protons in Radiotherapy Uncertainties in proton therapy Range uncertainties Setup uncertainties Anatomical uncertainties Dealing with treatment uncertainties In photons In protons Problem definition and study objective Materials & Methods Treatment planning system Patients and dose prescription Treatment plan generation & dose recalculation Plan evaluation & decision rules for each patient Selecting the optimal margins for each setup robustness Towards population-based margin recipe Results The effect of range robustness Patient-Group Patient-Group Patient-Group Patient-Group Discussion Summary & Conclusions Appendix Group Group Group Group

10 CT images from patients Bibliography

11 1 Introduction 1.1. Prostate cancer The prostate is a walnut-sized gland located between the bladder and the penis and just in front of the rectum as illustrated in Figure The normal prostate size for an adult male is 15cc to 30cc. Men whose prostate gland is larger than 30 cc are more likely to be diagnosed with either benign hyperplasia (enlargement) or prostate cancer than those whose prostate is in the normal range (Uro14). The urethra runs through the center of the prostate, from the bladder to the penis, letting urine flow out of the body. Prostate cancer occurs when some of the cells of the prostate reproduce far more rapidly and uncontrolled than in a normal prostate, causing a swelling or tumor. Prostate cancer cells eventually break out of the prostate to the seminal vesicles (SV) and the pelvic lymph nodes (LN), or invade distant parts of the body, particularly the bones producing distant tumors, a process known as metastasis. Prostate cancer is one of the most common types of cancer amongst men in Europe and the USA (Nat141) (Ferlaya J, 2013). It is usually found in men above 45 years old and it can be diagnosed in various stages. There are 4 clinical categories to describe the extent of a prostate tumor, ranging from stage 1 to 4. The first two stages 11

12 describe a localized disease within the prostatic volume. In stage 3, the disease is locally advanced, having spread beyond the prostate to the SV. In stage 4, cancer has spread beyond the SV to the LN or healthy nearby tissues, or has metastasized to the bones (or other organs) though the blood stream (Heidenreich A, 2013). Depending on the case, several treatment modalities are available. Radiotherapy is one of the main treatment modalities for prostate cancer, along with surgery, hormone therapy and chemotherapy. In fact, more than 60% of the prostate cancer patients are treated with radiotherapy (Joiner M, 2009). Figure 1.1.1: The prostate is located immediately below the bladder and just in front of the rectum (Nat141) Radiotherapy In radiotherapy an external beam of ionizing radiation is carefully directed towards the patient s body. A high radiation dose expressed in Gy [=Joule/kg] needs to 12

13 be delivered to the tumor, while avoiding healthy tissues. The organs that are in close proximity to the tumor are the so-called organs at risk (OARs). The radiation doses in the OARs should be as low as possible in order to avoid radiation induced complications. In case of prostate cancer radiotherapy, the OARs are the bladder, urethra, rectum, bowel cavity and the femoral heads. The rectum is the most important OAR that needs to be spared. To give an example, if the rectum receives more than Gy then sphincter control is affected and other severe symptoms might occur including rectal bleeding and proctitis (=inflammation of the rectum and anus) (Gulliford, 2010). Similarly, if the bladder receives high doses the main complication that will be noticed is urinary incontinence. Before delivering the radiation dose, a treatment plan needs to be generated. The aim in treatment planning is to find the machine control settings to deliver a sufficiently high dose to the target volume while sparing the OARs. At first, a planning computed tomography (CT) scan is acquired. Then, the radiation oncologist outlines the tumor and the OARs in the CT scan. The plan generation is subsequently performed according to the dose prescriptions as defined by the radiation oncologist, describing the dose to be delivered at the tumor and dose limits for the OARs. The full dose of radiation is usually delivered into a number of smaller doses called fractions. This allows healthy cells to recover between the fractions. Additionally, radiation given in fractions is able to irradiate the tumor cells during different stages of cell growth, possibly causing more damage. In fractionated radiotherapy, the prescribed dose is typically delivered in fractions of 2 Gy Protons in Radiotherapy Most commonly, photons have been used in radiotherapy. Technological advances have improved the photon dose delivery. The development of the multileaf collimator 13

14 (MLC) and inverse planning software led to the clinical introduction of intensitymodulated radiation therapy (IMRT) in the mid-1990s. The leaves of the MLC are usually made of tungsten and are able to open or close in order to let the beam pass or to block the beam. IMRT treatment fields are delivered from multiple directions and the intensity of each radiation field is modulated to create an optimal beam profile for the desired final dose distribution. A new method has recently become available, the volumetric modulated arc therapy (VMAT). VMAT is the delivery of IMRT while the beam source rotates around the patient (Hardcastle N, 2011). A highly promising form of radiotherapy is the irradiation of patients using proton beams, so-called proton therapy. Protons lose their energy in matter in a different way compared to photons. If they enter the patient they have interactions (transfer of energy) in the form of collisions with a relatively small energy loss per interaction. The amount of energy transferred increases with decreasing residual energy of the proton. This results in a steep increase in the dose deposition near the end of proton range, after which the energy depositions rapidly fall off. This is denoted as the Bragg Peak and is illustrated in Figure

15 Figure 1.3.1: Comparison of dose deposition as a function of depth for 10-MV x-rays and 160-MeV protons. The modulated proton beam is the Spread-out Bragg peak that enables the covering of the tumor volume (Shipley WU, 1979 ). The clinical potential of protons was first suggested in 1946 by Robert Wilson. Compared to photons, protons have some favorable dose-characteristics that enable them to be used in cancer therapy. After the Bragg peak, protons do not deposit any dose. So, they can better spare the healthy tissue and critical structures just distal to the target volume. Also, the integral dose they deliver is lower by a factor of 2 5 compared to photons (Pedroni E, 1995). Finally, even without the need of many treatment fields, protons can give a high dose to the tumor, to kill the tumor cells and to enable tumor control (Niemerko A, 1992). However, pristine Bragg peaks are not sufficiently large to cover the whole target volumes. The target volume can be fully covered by applying multiple Bragg peaks resulting in the spread-out Bragg peak (SOBP) in order to treat the entire tumor in depth. Proton beams also have to be spread out in lateral directions. Conventionally, this was performed by spreading the beam using a scattering foil. In the most modern technique of proton therapy this is done by magnetically steering the protons using 15

16 sweeper magnets, the so-called spot-scanning approach. Spot-scanning was first explicitly described by the Swiss National Paul Scherrer Institute (Pedroni E, 1995). Since protons are heavy charged particles with positive charge, they can be magnetically steered in both lateral directions using sweeper magnets (Safai S, 2012). Typically, several hundreds or thousands of narrow pencil beams denoted as spots are used for a single irradiation field, each with its own energy (range in tissue) and lateral position to cover the tumor with the desired dose. The total amount of dose delivered in each spot can also be optimized by modulating the weights, expressed in GigaProtons, of the individual pencil beams. This is the main principle of the intensitymodulated proton therapy (IMPT); the optimization of the intensity of each spot individually. IMPT has gained interest to be implemented in clinical practice. It can be used for tumors that that are difficult to be treated with photons; particularly big, odd-shaped and of complex geometry. In a clinical environment, pencil beam proton therapy has been delivered only in a few therapy centers around the world Uncertainties in proton therapy The radiotherapy treatment is delivered in a series of daily treatment fractions spanning a period up to eight weeks. But the treatment plan is generated based on a single snapshot of the patient anatomy acquired pretreatment. Differences between the planned and delivered dose distributions can occur as there will inevitably be uncertainties during the treatment process. This is true for radiotherapy in general and possibly even more for proton therapy due to the presence of the steep dose gradient in the depth direction. The uncertainty in the depth of a proton beam may lead to overshoots or undershoots of the proton beams. Any shift of the Bragg peak in depth could potentially increase the dose to the organ at risk or lower the dose to the tumor. An example is 16

17 illustrated in Figure There are three typical types of treatment uncertainties: the range uncertainties, the setup uncertainties and the anatomical uncertainties. Range uncertainties The range of a charged particle, like the proton, is the distance it travels before it is coming to rest. The exact location of the Bragg peak is affected by the material involved along the beam path. The planning CT scan gives information about the composition and nature of the tissues. The density of the structure imaged is measured in Hounsfield units (HU). Unfortunately, there are a number of different sources affecting the accuracy of the calculation of range in the patient. These are: uncertainties in the conversion of HU to proton stopping power, CT image artifacts due to implants, metal artifacts (Unkelbach J, 2009). If these are found in a CT scan they result in wrongly calculated densities and subsequent stopping powers. If such a CT scan is used for the treatment plan, then the result is dose calculation inaccuracies. (Lomax AJ, 2008). These range uncertainties should be carefully considered during the process of planning. They can influence the delivery of Bragg peaks resulting in range errors. Figure 1.4.1: The impact of uncertainties in protons and photons. The planned beams are represented by: dark blue for photons, red for protons. The uncertain beams are: light blue for photons, orange for protons. 17

18 Setup uncertainties A position is typically defined in 3-D space. Setup uncertainties refer to the possible patient s shifts in all three directions with respect to the treatment beam (Chen W, 2012). Both translation and rotational displacements might happen and the setup uncertainty is denoted as setup error. Sources of setup errors are: patient s shift during treatment, patient misalignment and lack of mechanical precision in the delivery system. A shift of the patient may lead to a shift of the delivered dose. In protons, misalignment of the patient can generate new range errors (Liu W, 2013). The misalignment of patient can change the radiological density of the pencil beam path. If for instance a high density structure such as metal implant moves into the path of the pencil beam, the dose deposition will be altered (Unkelbach J, 2009). Even small positioning errors might cause target underdosage and/or healthy tissue overdosage (Pflugfelder D, 2008). Anatomical uncertainties Even under a correct patient positioning, the internal anatomy of the patient is varying during a course of treatment or from one treatment fraction to another. This cannot be captured by the planning CT scan which is a static snapshot of a certain anatomy. For example, after a number of treatment sessions the differences that can be noticed either visually or by a repeat CT scan are: extreme weight loss or tumor shrinkage, changes in air cavities. Furthermore, internal target/organ motion occurs. In general, organ motion may be divided into two general categories. The first category is the interfraction motion. Such motion involves fraction-to-fraction changes in the position of tumor and/or organs. Interfraction motion has been quantified using: fiducial markers, multiple CT scans and ultrasound-based systems. The second organ movement category is intrafraction 18

19 motion. It refers to the internal organ motion that happens during the actual treatment time. Compared to interfraction motion, intrafraction is much smaller (Huang E, 2002). Intrafraction organ motion is not taken into account in this study. The magnitude of the internal organ motion depends on its location in the patient. In case of prostate cancer, the tumor is very close to deformable organs. Depending on the filling stage of the rectum and bladder the prostate will change position and shape. The prostate can move with respect to both of them but not in a predictable fashion (Stenmark MH, 2012). Moreover, in higher stages of prostate cancer the target volume encompasses the SV. The deformations of the SV are even more profound. The residual motion is depicted in the repeat CT scans is illustrated in Figure The treatment beams are aligned using intra-prostatic fiducial markers. The beam is intended to pass through a path of a certain radiological density. But since the prostate moves with respect to the bony anatomy, the beams might meet a path of different radiological density. They might meet bones or air cavities that were not present in the planning phase. This results in changes in the range of the proton beam. Internal organ motion in IMPT affects the range of protons. Figure 1.4.2: Example of interfraction organ motion. Left: axial (up) and sagittal (down) CT slices of the prostate. Right: axial (up) and sagittal (down) CT slices of the prostate with dashed lines indicating the residual motion and deformation of the SV caused by changes in the filling of rectum and bladder (Stenmark MH, 2012). 19

20 1.5. Dealing with treatment uncertainties In photons To deal with uncertainties, a common method is to apply margins, expanding the clinical target volume (CTV) to the Planning Target Volume (PTV). The PTV is a geometric concept that is designed to allow for geometrical uncertainties in the shapes of the CTV and variations in its location relative to the radiation beams (Antolak JA, 1999). More precisely, it includes: organ delineation, setup errors, and organ motion and deformation that occur throughout the planning and treatment process (Meijer GJ, 2008). For photons, PTV-margins are commonly used to account for positioning and motion uncertainties to ensure that the CTV receives the prescribed dose. (Zhang X, 2007). The margins have been established after dose population statistics. A widely used margin recipe has been described by van Herk et al., as it generates CTV to PTV expansions ( ) such that the 90% of the patients receive at least 95% of the nominal dose in the entire CTV. (1.5.1) Where is the standard deviation of all systematic errors and is the standard deviation of random errors (van Herk M, 2002). In protons A quite modern solution to reduce the impact of uncertainties in the treatment plans is robust optimization. The main idea is to explicitly account for uncertainties a-priori. This is achieved by incorporating in the optimization process of factors that cause uncertainty in dose distributions. In robust treatment planning different error scenarios i.e. setup errors, range errors, are taken into account in the optimization (Engelsman M, 2013). 20

21 Therefore, the robustness of a plan is realized when it displays little or no quality degradation between planning and delivery. There are different robust optimization methods. To begin with, there is the worst-case dose optimization method. Multiple scenarios of range and setup uncertainty are considered. Examples of these scenarios are: ±ξx, ±ξy, ±ξz shifts, ±λr for range uncertainty and nominal. One dose distribution is computed for each uncertainty scenario. This method selects the worst case dose in each voxel (minimum in target voxel and maximum in normal tissue voxel) (Pflugfelder D, 2008). Another approach of a less conservative method is the minimax optimization which uses physically realizable uncertainty scenarios. It minimizes the possible loss of the worst case scenario (Fredriksson A, 2011). The traditional PTV-concept is a common practice in photon radiotherapy, and it has been employed in some proton radiotherapy studies. PTV has been successful in case of photon irradiation because the dose distribution is not substantially altered if the geometry or anatomy of the patient is changed. But this is not the case in protons and the PTV-concept ignores the proton range uncertainty (Liu W, 2013). Therefore, the PTV is not completely suitable for IMPT and it has been suggested that it has a limited usefulness in IMPT (Chen W, 2012). On the contrary, the robust optimization methods, discussed above, enable robust treatment planning against both range and setup errors (Unkelbach J, 2009) Problem definition and study objective As discussed in Section 1.2, the dose is delivered in multiple fractions based on a single treatment plan. In fractionated radiotherapy for prostate cancer, the patient anatomy will differ from fraction to fraction. The target structures in prostate cancer patients (i.e. prostate, seminal vesicles and lymph nodes) can show considerable interfraction organ motion and deformations. We can perform robust planning, but this accounts only for 21

22 setup and range errors and theoretically not for anatomical errors. And regarding margins, these are typically considered inadequate for IMPT because by their definition they do not account for range uncertainties. Thus, currently it is unclear how anatomical uncertainties, such as internal organ motion and deformation, should be accounted for in IMPT. In addition, at the moment we generate the treatment plan based on the planning CT, we are not able to know in advance how the anatomy of a certain patient is going to be altered in the following fractions. Consequently, the exact margins and robustness that would be needed for a patient are considered unknown at the moment we generate the treatment plan. Beyond that, it is also anticipated that every patient displays a different anatomy. We investigate whether there would be a treatment plan approach that could be applied not for a single patient but for a population of patients. Similarly to the idea of the margin recipes, the approach we generate should be acceptable for at least 90% of the patients. Questions investigated in this study were: Can robust treatment planning and/or margins be used to account for interfraction organ motion? Which of these strategies (or a combination of robustness and margins) is most effective? What would be the optimal approach to treat a population of patients? The aim of this study is to determine the best strategy can be used to account for interfraction organ motion and deformation prostate with IMPT. Different amounts of margins and robustness were used. Their effect on target coverage and OAR was evaluated. The most effective patient-specific combinations against interfraction organ motion were determined. Also, we tested margins that in combination with setup robustness can treat at least 90% of the patients with adequate target dose coverage.and finally, we evaluated the population-based margin recipe in terms of OAR sparing. 22

23 This thesis is submitted in partial fulfillment for the degree of Master of Science from Delft University of Technology. This project has been conducted in the radiation Physics department of Erasmus MC-Cancer Institute, in Rotterdam. The research is a part of wider research and development that is conducted for the Holland Particle Therapy Center (Holland PTC), the first proton clinic in the Netherlands. 23

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25 2 Materials & Methods 2.1. Treatment planning system Treatment plans were generated using the in-house treatment planning system the Erasmus-iCycle (Breedveld S, 2012). Plan generation with Erasmus-iCycle is based on a wish-list, containing hard constraints and objectives. Constrains should be strictly obeyed and the objectives are prioritized. The system optimizes objectives one-by-one according to their priorities and the optimization stops when the objectives (set in the wish-list) cannot be optimized further. The treatment plan is a solution to this optimization problem. Typically, a single wish-list can be used for every patient within a certain group, thus allowing for automated treatment planning (Voet PWJ, 2013). This also improves the objectivity when comparing different treatment plans, as the userrelated variability is not present. Erasmus-iCycle was originally developed for IMRT but it has been extended with IMPT. The proton dose calculation algorithm used in ErasmusiCycle was developed in Massachusetts General Hospital and Harvard Medical School (Kooy, 2010). We used a dose grid resolution of mm 3, where the 2 mm in z- 25

26 direction corresponds to the CT slice thickness. Available energies ranged from 70 to 230 MeV. The corresponding beam width (sigma, in air at the isocenter) varied from 7 mm (at 70 MeV) to 3 mm (at 230 MeV). To select and optimize the weight of spots, we used the resampling method as implemented in Erasmus-iCycle (van de Water S, 2013). The principle of this method is iterative, performing in each iteration: 1) random selection of candidate spots from a very fine grid, 2) inverse optimization and 3) elimination of low contributing spots. The iterations are terminated when the addition of new spots does not further improve the plan quality. The number of candidate spots per iteration is user-defined. We used a sample size of 5000 candidate spots per iteration. Spots were randomly selected from a grid with lateral spot spacing of 1 mm. The spacing between energy layers was set to the longitudinal width of the Bragg peak (at 80% of the peak height) and therefore varies with energy (small spacing at low energy, larger spacing at high energy). The robust optimization technique used by Erasmus-iCycle is the minimax worstcase optimization (Fredriksson A, 2011), as discussed in Section 1.5. It optimizes the worst value (for example the lowest dose for the tumor and the maximum dose for OARs) over all error scenarios that are included in the optimization. With robust optimization, setup errors and range errors are explicitly taken into account in the treatment plan. Setup errors were modeled by shifting the treatment isocenter in the x, y or z direction, while range errors were modeled by adjusting the proton energy. In total, we included 9 error scenarios in the robust optimization: setup errors of ξ mm in positive and negative direction along three axes (6 scenarios) range errors of λ% overshoot or undershoot (2 scenarios) the nominal (unperturbed) scenario Throughout this report, robustness against setup errors will be denoted as setup robustness and robustness against range errors will be denoted as range robustness. 26

27 2.2. Patients and dose prescription CT data of 10 patients with locally advanced prostate cancer previously treated with intensity-modulated RT (IMRT) were used in this study. For each patient, we had an image set consisting of a planning CT as well as 8 10 additional CT-scans acquired evenly distributed throughout their treatment course. All CT scans were obtained with the patients in supine position, while using the fixation devices for the planning CT and the repeat CT-scans (Thörnqvist S, 2013). The voxel size was mm 3 (Thörnqvist S, 2010). For each CT scan, an experienced radiation oncologist contoured the three clinical target volumes (CTV); the prostate (CTV-p), the seminal vesicles (CTV-sv), the pelvic lymph nodes (CTV-ln). The rectum, bladder, and the femur heads were also contoured. During the course of treatment no bladder or rectum preparation protocol was followed. Each patient had three fiducial gold markers implanted in the prostate which were used for image guidance. Usually, the treatment plan is generated based on one CT scan that was obtained before the start of the treatment course. The planning CT was not used in this study because it was acquired with contrast agent. As this would have resulted in an error in the proton range, plan generation was performed using the 1 st repeat CT scan for all patients instead. For the 1 st repeat CT scan the volumes of each organ are summarized in Table

28 Table 2.2.1: The volumes (in ml) of the three target volumes and organs of the 10 patients used in this study; measured in the 1 st repeat CT. Green colored characters indicate the minimum value and red the maximum. Patient Prostate SV LN Rectum Bladder (ml) (ml) (ml) (ml) (ml) Median Min Max The dataset contained CT-scans of high-stage prostate cancer patients. We used the CT data to generate treatment plans for four patient groups. The patient groups differ in target structures and prescription doses: Group 1: 78 Gy to prostate Group 2: 78 Gy to prostate (CTV high ), 72 Gy to SV (CTV low ) Group 3: 78 Gy to prostate and SV Group 4: 70 Gy to prostate and SV (CTV high ), 51.8 Gy to LN (CTV low ) The treatment plan s beam configuration consisted of 2 lateral opposed beams ( gantry angles). The wish-list of groups 1, 2, 3 and 4 are presented in, Table 28

29 2.2.2, Table 2.2.3, Table and Table respectively. Each wish-list was tuned by iterative planning, plan evaluation and wish-list adjustments for a small number of patients before the wish-lists were finalized. The minimum CTV doses were constrained, while the maximum CTV dose, OAR doses, high-dose conformality, and low-dose conformality were optimized as objectives. The dose constrains and objectives on the target volumes were chosen such that 98% of the target volume should receive 95% of the prescribed dose (V 95% 98%). Similarly, no more than 2% of the target volume should receive 107% of the prescribed dose (V 107% 2%). The wish-list indicates whether or not an objective is robustly optimized. If not, then the objective is evaluated only in the nominal scenario. We are using robust optimization for the target structures and also for the OARs (rectum, bladder, and femur heads). In groups 2 and 4 there are two different prescription doses at each CTV (highdose CTV vs. low-dose CTV). To achieve a steep dose fall-off for a high-dose region to a low-dose region, we divided the low-dose CTV into a transition zone (CTV-intermediate) and then the remaining low-dose CTV (denoted as CTV-low without the CTVintermediate) (van de Water S, 2013). The conformality rings were only optimized for the nominal scenario, because they are used to improve dose conformality towards the target volumes and do not necessarily have to be limited at a certain dose value. 29

30 Table 2.2.2: The wish-list for Group 1 patients as it was used in our study. Constrains Prostate Cases Structure Type Limit Robust CTV Maximize Gy Yes minimum Objectives Priority Structure Type Goal Robust 1 CTV Minimize Gy Yes maximum 2 CTV-ring (high dose Minimize Gy No conformality) maximum 3 CTV-ring (high dose Minimize Gy No conformality) maximum 4 Femur heads Minimize 45 Gy Yes maximum 5 Rectum Minimize mean 1 Gy Yes 6 Bladder Minimize mean 1 Gy Yes 7 Bowel cavity Minimize mean 1 Gy Yes 8 Femur heads Minimize mean 1 Gy Yes 9 CTV-rings (low dose Minimize 1 Gy No conformality) maximum 9 CTV-rings (low dose Minimize mean 1 Gy No conformality) 10 Giga-protons Minimize maximum 1 No 30

31 Table 2.2.3: The wish-list for Group 2 patients as it was used in our study. Constrains Prostate Cases Structure Type Limit Robust CTV-high Maximize Gy yes minimum CTV-intermediate Maximize Gy yes minimum CTV-low Maximize minimum Gy yes Objectives Priority Structure Type Goal Robust 1 CTV-high Minimize Gy yes maximum 1 CTV-intermediate Minimize Gy yes maximum 1 CTV-low Minimize Gy yes maximum 2 CTV-rings (high dose Minimize Gy no conformality) maximum 2 CTV-rings (high dose Minimize Gy no conformality) maximum 2 CTV-rings (high dose Minimize Gy no conformality) maximum 3 Femur heads Minimize 45 Gy yes maximum 4 Rectum Minimize mean 1 Gy yes 5 Bladder Minimize mean 1 Gy yes 6 Bowel cavity Minimize mean 1 Gy yes 8 Femur heads Minimize mean 1 Gy yes 9 CTV-rings (low dose Minimize 1 Gy no conformality) maximum 9 CTV-rings (low dose Minimize mean 1 Gy no conformality) 10 Giga-protons Minimize maximum 1 no 31

32 Table 2.2.4: The wish-list for Group 3 patients as it was used in our study. Constrains Prostate Cases Structure Type Limit Robust CTV Maximize Gy Yes minimum Objectives Priority Structure Type Goal Robust 1 CTV Minimize Gy Yes maximum 2 CTV-ring (high Minimize Gy No dose conformality) maximum 3 CTV-ring (high Minimize Gy No dose conformality) maximum 4 Femur heads Minimize 45 Gy Yes maximum 5 Rectum Minimize mean 1 Gy Yes 6 Bladder Minimize mean 1 Gy Yes 7 Bowel cavity Minimize mean 1 Gy Yes 8 Femur heads Minimize mean 1 Gy Yes 9 CTV-rings (low Minimize 1 Gy No dose conformality) maximum 9 CTV-rings (low Minimize mean 1 Gy No dose conformality) 10 Giga-protons Minimize maximum 1 No 32

33 Table 2.2.5: The wish-list for Group 4 patients as it was used in our study. Constrains Prostate Cases Structure Type Limit Robust CTV-high Maximize Gy yes minimum CTV-intermediate Maximize Gy yes minimum CTV-low Maximize minimum Gy yes Objectives Priority Structure Type Goal Robust 1 CTV-high Minimize Gy yes maximum 1 CTV-intermediate Minimize Gy yes maximum 1 CTV-low Minimize Gy yes maximum 2 CTV-rings (high Minimize Gy no dose conformality) maximum 2 CTV-rings (high Minimize Gy no dose conformality) maximum 2 CTV-rings (high Minimize Gy no dose conformality) maximum 3 Femur heads Minimize 50 Gy yes maximum 4 Rectum Minimize mean 1 Gy yes 5 Bladder Minimize mean 1 Gy yes 6 Bowel cavity Minimize mean 1 Gy yes 8 Femur heads Minimize mean 1 Gy yes 9 CTV-rings (low Minimize 1 Gy no dose conformality) maximum 9 CTV-rings (low Minimize mean 1 Gy no dose conformality) 10 Giga-protons Minimize maximum 1 no 33

34 2.3. Treatment plan generation & dose recalculation We generated treatment plans while varying 1) the setup robustness, 2) the range robustness and 3) the margins around the prostate (M-p), around the seminal vesicles (Msv) and around the lymph nodes (M-ln). The magnitude of these parameters was varied in a systematic fashion as follows: Margins: 0-10 mm in steps of 2 mm. Setup robustness: 0-8 mm in steps of 2 mm. Range robustness: 0-4% in steps of 2%. To determine whether a treatment plan is robust against interfraction organ motion, we performed dose recalculations on the 8-10 repeat CTs for each patient. Dose recalculation was performed only for the nominal scenario. The repeat CTs were aligned based on the fiducial markers implanted into the prostate. We used a routine to systematically test different combinations of margins and robustness settings, as shown in Table For certain setup and range robustness settings, the routine started with patient-group 1 and stepwise increased the prostate margin (M-p). When the program finds the margin that ensured CTV 95% value greater than 98% in all of the repeat CTs, the routine stopped increasing M-p. This M-p was then fixed and used as prostate margin also for the next patient-groups. In groups 2 and 3, the M-sv was stepwise increased, until the CTV 95% criterion was met in all repeat CT scans. The M-sv found in group 3 was used in group 4, where the M-ln is increased until the CTV 95% value is greater than 98% for all of the repeat CTs. This procedure was repeated for different settings of setup robustness and range robustness, which allowed us to investigate whether the use of different robustness settings would lead to different margins. Each of the numerous combinations of margins and/or robustness settings represents a single treatment plan. To quantify the dose received by OARs for a certain treatment plan, we used the dose parameter averaged over the repeat CT scans. 34

35 For the rectum, the mean dose (Rectum-D mean ) and the volume percentages that receive more than 45 Gy (Rectum-V 45 Gy ), more than 60 Gy (Rectum-V 60 Gy ) and more than 75 Gy (Rectum-V 75 Gy ), were evaluated. The Rectum-V 60 Gy and Rectum-V 75 Gy are associated with rectum toxicity and rectal bleeding (Voet PWJ, 2014). For the bladder, the mean dose (Bladder-D mean ) and volume percentages that receive more than 45 Gy (Bladder-V 45 Gy ) and more than 65 Gy (Bladder-V 65 Gy ) were calculated. Eventually, the routine resulted for each patient in a clinically acceptable treatment plan for each combination of setup and range robustness. Treatment planning was performed on a 16-core computer using MATLAB (The MathWorks, Inc.). 35

36 Table 2.3.1: The method used in the study described by pseudocode. Simulation algorithm of margins and robustness Import Patient CT dataset Loop over range-robustness settings Loop over setup-robustness settings Loop over patient-groups Loop over margins if current group = 1 Increase M-p else if current group = 2 Increase M-sv else if current group = 3 Increase M-sv else if current group = 4 Increase M-ln end Generate treatment plan Recalculate treatment plan on the repeat CTs Calculate the dose parameters CTV and OARs if CTV 95% > 98% in all repeat CTs if current group = 1 keep M-p fixed (for the next patient-groups) else if current group = 3 keep M-sv fixed (for the next patient-group) end Exit margin loop (Move to next patient-group) Else Increase margin value End End End End End 36

37 2.4. Plan evaluation & decision rules for each patient Selecting the optimal margins for each setup robustness The optimal margins -for each setup robustness-, needed to be selected from a set of clinically acceptable treatment plans. Ideally, in terms of target coverage we would like to have treatments plans that lead to CTV 95% 98% and CTV 107% < 2% in all repeat CTs. But requiring such dose thresholds for all repeat CTs might be too strict for certain robustness settings. On top of that, while preserving target coverage, we needed to spare the OARs as much as possible. For this reason, after the treatment plans acquisition, the dose evaluation constrains on the CTVs were slightly relaxed. We defined a treatment plan to be clinically acceptable when: Minimum CTV 95% of all the repeat CTs should be more than 98% with the exception of one repeat CT that we allow a CTV 95% to be more than 97% Maximum CTV 107% below a reasonable level The CTV 95% target coverage is mainly determined by the margins and the setup robustness settings. The CTV 107% is primarily determined by the range robustness. For this reason, we first evaluated the effect of range robustness on the CTV 107% and additionally on the Rectum-V 60Gy. In this process, we focused only on the plans were the above target dose coverage criteria were met. We evaluated for all patients the range robustness settings. Then, the range robustness setting resulting in the lowest CTV 107% (and Rectum-V 60Gy ) was selected. This selected range robustness settings was fixed. And for this range robustness we evaluated the different margins and setup robustness settings. Even with fixed range robustness, in some cases, there were multiple acceptable plans for certain setup robustness. Therefore, for each setup robustness setting we selected one plan -for each patient- that delivered the lowest Rectum-V 60Gy and the smallest margin, while meeting the target coverage criteria. If margins or setup robustness that led to deliverable plans found to be of 0 mm, we assumed that they were not needed in that case. 37

38 Finally, we compared the OAR doses between the different setup robustness settings. Statistical analysis of the results was performed using Wilcoxon signed-rank test. P-values lower than 0.05 were considered to be statistically significant 2.5. Towards population-based margin recipe The evaluation process was again performed for every patient-group. In group 1, we attempt to select the M-p that would be needed to adequately treat 90% of the patients. This procedure is visualized schematically in Figure The population-based M-p was selected and used as an input for treatment planning. Ten treatment plans (as the number of patients), for every setup robustness were generated (in total 50 treatment plans per patient group). Each plan was recalculated in the repeat CTs and evaluated in terms of target coverage. Figure 2.5.1: Flow chart with the selection process of the population-based margins for patient-group 1. In patient-groups 2 and 3, the target volumes are both the prostate and the SV. Both of the target volumes should be covered with enough dose to consider a margin combination to be successful. For instance, if in group 2, 90% of the prostates (i.e. the CTV high ) is treated with M-p = x mm, and 90% of the SV (or the CTV low ) with M-sv = y mm. We are going to expand the margin on the structure that is underdosed, depending on the extend of underdosage. At this target structure that is the least underdosed, coverage 38

39 would be more easily achievable, with smaller margin expansions (and consequently less dose increase at the OARs). The procedure used for patient groups 2 and 3 is schematically visualized in Figure For instance if the prostate is underdosed to a lesser extend compared to the SV, we are going to increase the M-p because target coverage would be achieved with less steps of margin increase. We expand the M-p by adding 2 mm until coverage is achieved. The reason why 2 mm have been chosen is because this is the step of margin expansion used in the simulation of margins and robustness routine. In the end, in this example we expect to achieve adequate prostate coverage for all patients, and only one patient exhibiting underdosage in the SV. Figure 2.5.2: Flow chart with the selection process of the population-based margins for patient-groups 2 and 3. In patient-group 4 we are going to act similarly as in groups 2 and 3. Besides, in this group the target volumes were three; the prostate, SV and the LN. We are going to expand the margin on the structure that caused the underdosage. If there are more than one underdosed structures, we are going to increase the margin around the structure that 39

40 could more easily be covered with sufficient dose for all patients. As a schematic example, the procedure followed for patient group 4 is visualized in Figure Figure 2.5.3: Flow chart with the selection process of the population-based margins for patient-group 4. 40

41 3 Results In this chapter, the results of the study are presented. First, in Section 3.1. the effect of range robustness in the generation of the treatment plans is evaluated. The evaluation is performed in terms of target coverage and doses at the OARs. Then, we present for every patient-group the patient-specific and the population-based margins. Also, we show the corresponding OAR doses found in the acceptable treatment plans for each patient individually. These OAR doses are categorized by the different setup robustness settings. Patient group 1 is reported in Section 3.2, and patient-groups 2, 3 and 4 are discussed in Sections 3.3, 3.4 and 3.5 respectively The effect of range robustness We generated 1452 treatment plans in total with range robustness of 0%, 2% and 4% and also with different margin and setup robustness settings. The plans that did not fulfill the clinical constraints were removed from the dataset. We are primarily interested to evaluate the effect of range robustness overall for all patient groups in acceptable treatment plans obtained with different margins and setup robustness. And eventually, to 41

42 select the range robustness magnitude that would be the optimal. For that purpose, we categorized the dataset into different range robustness settings. We visualize the effect of range robustness on CTV 107% for acceptable treatment plans in Figure Treatment plans with range robustness of 2% ensured adequate dose received by the target structures in all repeat CTs of each patient. On the contrary, we notice that when range robustness of 4% is used the treatment plans receive the highest amounts of CTV 107% and this is something should be avoided. In the same figure we demonstrate the effect of range robustness on Rectum-V 60Gy. It is observed that when no range robustness (0%) is used the plans display the maximum Rectum-V 60Gy. On the other hand, range robustness of 4% gives lower Rectum-V 60Gy doses, but unnecessarily high CTV 107%. Overall, it is observed that range robustness of 2% acts beneficially in terms of reducing the amount of CTV 107% below a certain level and also keeps the rectum doses as low as possible. We calculated the min, max, mean and median values of the CTV 107% and Rectum-V 60Gy range robustness, which are presented in Table In general, we notice the beneficial impact of range robustness of 2%. Range robustness of 2% performs best, particularly for CTV 107%. We therefore only report results of treatment plans generated using range robustness of 2%. CTV 107% [%] Rectum-V 60Gy [%] RR [%] RR=0 RR=2 RR=4 RR=0 RR=2 RR=4 min max mean median Table 3.1.1: Data from all ten patients with adequate target coverage in all repeat CTs. Values of CTV 107% and Re-V60Gy for the different range robustness (RR) used in treatment planning. 42