A comparative dosimetric analysis of the effect of heterogeneity corrections used in three treatment planning algorithms

Transcription

1 The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2010 A comparative dosimetric analysis of the effect of heterogeneity corrections used in three treatment planning algorithms Andrea Celeste Herrick Medical University of Ohio Follow this and additional works at: Recommended Citation Herrick, Andrea Celeste, "A comparative dosimetric analysis of the effect of heterogeneity corrections used in three treatment planning algorithms" (2010). Theses and Dissertations This Thesis is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.

2 A Thesis entitled A Comparative Dosimetric Analysis of the Effect of Heterogeneity Corrections Used in Three Treatment Planning Algorithms by Andrea Celeste Herrick Submitted to the Graduate Faculty as partial fulfillment of the requirements for Master of Science in Biomedical Science in Medical Physics E. Ishmael Parsai, Ph.D., Committee Chair David Pearson, Ph.D., Committee Member Diana Shvydka, Ph.D., Committee Member Patricia Komuniecki, Dean College of Graduate Studies The University of Toledo December 2010

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4 An Abstract of A Comparative Dosimetric Analysis of the Effect of Heterogeneity Corrections Used in Three Treatment Planning Algorithms By Andrea Celeste Herrick Submitted to the Graduate Faculty in partial fulfillment of the requirements for the Masters of Science in Biomedical Sciences in Medical Physics The University of Toledo December 2010 Successful treatment in radiation oncology relies on the evaluation of a plan for each individual patient based on delivering the maximum dose to the tumor while sparing the surrounding normal tissue (organs at risk) in the patient. Organs at risk (OAR) typically considered include the heart, the spinal cord, healthy lung tissue and any other organ in vicinity of the target that is not affected by the disease being treated. Depending on the location of the tumor and its proximity to these OARs, several plans may be created and evaluated in order to assess which solution most closely meets all of the specified criteria. In order to successfully review a treatment plan and take the correct course of action, a physician needs to rely on the computer model (treatment planning algorithm) of dose distribution on reconstructed CT scan data to proceed with the plan that best achieve all of the goals. There are many available treatment planning systems from which a Radiation Oncology center can choose from. While the radiation interactions considered are iii

5 identical among clinics, the way the chosen algorithm handles these interactions can vary immensely. The goal of this study was to provide a comparison between two commonly used treatment planning systems (Pinnacle and Eclipse) and their associated dose calculation algorithms. In order to do this, heterogeneity correction models were evaluated via test plans, and the effects of going from heterogeneity uncorrected patient representation to a heterogeneity corrected representation were studied. The results of this study indicate that the actual dose delivered to the patient varies greatly between treatment planning algorithms in areas of low density tissue such as in the lungs. Although treatment planning algorithms are attempting to come to the same result with heterogeneity corrections, the reality is that the results depend strongly on the algorithm used in the situations studied. While the Anisotropic Analytic Method (AAA) and Collapsed Cone Algorithm (CCC) have been shown to be much better than earlier heterogeneity correction algorithms (such as modified Batho), they still fail to agree to a significant degree to ensure agreement between the recommended planning and delivery of better than 2% (Loevinger & Loftus, 1977). For lung plans, average minimum PTV dose was seen to be as much as 285 cgy (4.3% of the prescription dose) less than the same plan calculated with heterogeneity corrections not applied. Average maximum PTV dose was seen to be as much as 680 cgy (10.3% of the prescription dose) higher than the same plan calculated with heterogeneity corrections turned off. When different heterogeneity correction algorithms are compared, average differences of up to 747 cgy in minimum PTV dose (11.3% of the prescription dose) can be seen. These results highlight the importance of careful considerations of the limitations of treatment planning algorithms under certain conditions in clinical use. iv

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7 Contents Abstract Table of Contents List of Figures List of Tables Preface iii v vii ix x 1 Heterogeneities in Treatment Planning KV Imaging in Treatment Planning Deposition of in Matter 3 2 Treatment Planning Algorithms Pencil beam with modified Batho corrections Anisotropic Analytic Algorithm (AAA) Collapsed Cone Convolution (CCC) Importance of Accurate Patient Calculations Algorithm Comparisons 12 3 Methods RTOG 0213 (lung) RROG 0413 (breast) 23 4 Results Lung heterogeneity corrections Pinnacle 25 v

8 4.2 Lung heterogeneity corrections Eclipse Lung plan comparisons between algorithms Breast heterogeneity corrections 43 5 Conclusions 49 References 52 vii

9 List of Figures Figure 2-1 Figure 2-2 Figure 4-1: Figure 4-2: Figure 4-3: Figure 4-4: Figure 4-5: Figure 4-6: Figure 4-7: Table from Fogliata (2007) On the dosimetric behavior of photon dose calculation algorithms in the presence of geometric heterogeneities: Displaying the results of Eclipse AAA (AAA-ECL), Eclipse pencil beam (PBC-ECL), Pinnacle CCC (CC-Pin) and Monte Carlo (MCw). These calculations were done using a 6MV beam and 4cm away from the central axis. Taken from Fogliata (2007) On the dosimetric behavior of photon dose calculation algorithms in the presence of geometric heterogeneities: Displaying the results of Eclipse AAA (AAA-ECL), Eclipse pencil beam (PBC-ECL), Pinnacle CCC (CC-Pin) and Monte Carlo (MCw). These calculations were done using a 15MV beam and 4cm away from the central axis. PTV maximum dose for lung patients under RTOG Plans initially calculated with heterogeneity corrections turned off using Pinnacle CCC. PTV minimum dose for lung patients under RTOG Plans initially calculated with heterogeneity corrections turned off using Pinnacle CCC. PTV mean dose for lung patients under RTOG Plans initially calculated with heterogeneity corrections turned off using Pinnacle CCC. Results of calculations for lung patients PTV maximum dose under RTOG 0213 guidelines. Plans initially calculated with heterogeneity corrections turned off using Eclipse. Results of calculations for lung patients PTV minimum dose under RTOG 0213 guidelines. Plans initially calculated with heterogeneity corrections turned off using Eclipse. Results of calculations for lung patients PTV mean dose under RTOG 0213 guidelines. Plans initially calculated with heterogeneity corrections turned off using Eclipse. PTV minimum dose of six lung patients under RTOG 0213 protocol guidelines. Plans were initially calculated with the Eclipse modified Batho pencil beam algorithm (PB-MB) and then recalculated using the Eclipse vii

10 AAA (AAA) algorithm and Pinnacle CCC (CCC) algorithm, all with heterogeneity corrections turned on. Figure 4-8: Figure 4-9: PTV maximum dose of six lung patients under RTOG 0213 protocol guidelines. Plans were initially calculated with the Eclipse modified Batho pencil beam algorithm (PB-MB) and then recalculated using the Eclipse AAA (AAA) algorithm and Pinnacle CCC (CCC) algorithm, all with heterogeneity corrections turned on. PTV minimum dose of six lung patients under RTOG 0213 protocol guidelines. Plans were initially calculated with the Eclipse modified Batho pencil beam algorithm (PB-MB) and then recalculated using the Eclipse AAA (AAA) algorithm and Pinnacle CCC (CCC) algorithm, all with heterogeneity corrections turned on. Figure 4-10: Calculated maximum patient dose of six breast patients under RTOG 0413 protocol guidelines. Plans were initially calculated with the Pinnacle CCC algorithm with heterogeneity corrections turned off, and then recalculated using same algorithm with heterogeneity corrections turned on. Figure 4-11: Figure 4-12: Figure 4-13: Figure 4-14: Calculated minimum tumor bed dose for six breast patients under RTOG 0413 protocol guidelines. Plans were initially calculated with Pinnacle CCC algorithm with heterogeneity corrections turned off. They were then recalculated using same algorithm with heterogeneity corrections turned on. Calculated chest wall minimum dose, RTOG 0413 protocol. Plans were initially calculated with the Pinnacle CCC algorithm with heterogeneity corrections turned off, and then recalculated using same algorithm with heterogeneity corrections turned on. Calculated chest wall maximum dose, RTOG 0413 protocol. Plans were initially calculated with the Pinnacle CCC algorithm with heterogeneity corrections turned off, and then recalculated using same algorithm with heterogeneity corrections turned on. Calculated chest wall mean dose, RTOG 0413 protocol. Plans were initially calculated with the Pinnacle CCC algorithm with heterogeneity corrections turned off, and then recalculated using same algorithm with heterogeneity corrections turned on. viii

11 List of Tables Table 4-1: Table 4-2: Table 4-3: Table 4-4: Table 4-5: Table 4-6. Table 4-7: Results of computations on six lung patients under RTOG 0213 plan guidelines. Plans were initially calculated with heterogeneity corrections turned off using Pinnacle collapsed cone convolution, and then recalculated with heterogeneity corrections turned on. Summary of the dose change (in cgy) and the percentage change (from the homogenous plan) for each patient. Results of calculations for lung patients under RTOG Plans were initially calculated with heterogeneity corrections turned off using Eclipse. Summary of the dose change (in cgy) and the percentage change (from the homogenous plan) for each patient, for both of the two planning algorithms (AAA and MB-PB). Results of computations on six lung patients under RTOG 0213 plan guidelines. Plans were initially calculated with the Eclipse modified Batho pencil beam algorithm and then recalculated using the Eclipse AAA algorithm and Pinnacle CCC algorithm. Summary of the dose change (in cgy) and the percentage change (from the original pencil beam with modified Batho plan) for each patient, for both of the two planning algorithms (AAA and CCC). Results of computations on six breast patients under RTOG 0413 protocol guidelines. Plans were initially calculated with the Pinnacle CCC algorithm with heterogeneity corrections turned off, and then recalculated using same algorithm with heterogeneity corrections turned on. ix

12 Preface One of the primary concerns when analyzing a plan is to ensure that the target receives an adequate dose for tumor control while sparing the organs at risk surrounding the target. Radiation interactions in matter can be very complex however, and every treatment planning system deals with these calculations in a slightly different manner. In order to appreciate and understand the differences in algorithms, it is important to understand what they are calculating and why. Treatment deliveries and proper dose distributions have been determined by carefully analyzing outcomes and enrollment of patients in radiation therapy protocols. One group has been designing many of these protocols under the Radiation Therapy Oncology Group (RTOG). An RTOG protocol specifies the amount of dose to deliver, the way it should be delivered (modality), and suggests appropriate radiation doses to the organs at risk surrounding the tumor. One problem that has occurred started with the use of heterogeneity corrections in treatment planning systems. Because of the variety of ways different treatment planning systems handle the corrections, dose in areas of high heterogeneity (such as in the lung and chest wall) can lead to discrepancies in the actual doses delivered to the patient in contrast to what was planned, even with similar looking plans in the planning system. To combat this, and to ensure the doses delivered between sites was consistent, the RTOG, in several protocols, recommended using a heterogeneity x

13 uncorrected model (RTOG 0117, 2001). This approach helped in the analysis of outcomes, and to ensure that all of the patients sent in to the protocol from sites that used different planning systems delivered equivalent plans to enrolled patients. xi

14 Chapter 1 Heterogeneities in Treatment Planning 1.1 The Tissue Inhomogeniety in Megavoltage Therapy Prior to advent of fast computers and complex treatment planning algorithms, the issue of errors resulted from tissue inhomogenieties was not fully considered. Most published data even for national protocols, almost exclusively relied on homogeneous dose calculations in treatment planning. Advances in treatment planning systems have made it possible the incorporation of complex algorithms to model dose distribution in complex tissues with variable densities. The human body consists of numerous heterogeneous tissue type compositions and cavities with widely differing radiologic properties. Tissues such as lungs, bones, teeth, sinuses, nasal and oral cavities present complexities when predicting dose distribution. Optimization of radiotherapeutic dose requires correct accounting for these heterogeneities so that absorbed dose may be accurately determined in all irradiated tissues. In their review of tissue inhomogeneity corrections, Task Group 65 (TG-65) of the American Association of Physicists in Medicine (AAPM) recognized that properly accounting for tissue heterogeneity is an essential component of dose optimization and the objective analysis of clinical results, especially with the advent of 3D precision 1

15 conformal radiotherapy and the extension of intensity-modulated radiation therapy (IMRT) treatments to structures that have not been irradiated before. With regard to the goal of achieving optimum radiotherapy outcomes, TG-65 concluded that the general principle of 3% accuracy in dose delivery with the corresponding need for better than 2% accuracy in correcting for inhomogeneities is a reasonable, albeit challenging, goal. (Papanikolaou et, al., 2004). As early as three decades ago most of the treatment planning data was acquired through films using kilovoltage (kv) x-rays for a simple two dimensional plane of a patient to be displayed and field borders defined based on approximate localization of the target tissue. A patients external outline was determined by a contour (wire) of a single slice. This allowed determination of the thickness of the patient to allow for basic calculations of dose. Since the advent of computed tomography (CT) in the early 1970s, the ability to more accurately define a patient s internal anatomy has led to growth in dose calculations in radiation therapy for cancer. A CT determines the attenuation of the kv x-rays through the patient by scanning the patient in a helical manner. This patient model is a vast improvement as it is a 3D representation. The data obtained can be reconstructed to give an image of the patient showing the Hounsfield Unit of the various tissues that make up the patient. The Hounsfield unit is simply a way to compare the attenuation of the x-rays through a material as compared with water, and can be expressed as: HU μm μ = 1000 μ water water 2

16 where µ m represents the linear attenuation coefficient of the material other than water and µ water represents the linear attenuation coefficient of water. These numbers can be converted to electron density for megavoltage (MV) radiation, and can therefore be used as a way to calculate dose in a patient for each voxel of the CT. Although this technology was adopted in the early 1970s, the field of radiation oncology continued doing dose calculations on a homogenous patient representation for many years. The CT data was used to determine the delineation of the tumor and critical structures, but the dose calculation was done on a homogenous phantom. The reasoning behind this is complex, however it centers around the fact that calculating radiation dose in a heterogeneous material requires a large amount of computational time, and the development of sufficiently accurate algorithms took time. As a result of these problems, treatment planning was generally limited to manipulation of standard isodose lines and relied on the experience of the dosimetrist to determine beam weightings and monitor units to be used. More sophisticated calculations could be determined based on tissue air ratios (TAR) and effective percent depth dose shifts to account for different patient external contours, however internal heterogeneities were generally ignored. 1.2 Deposition of in Matter Isodose Distributions The two-dimensional treatment planning algorithms accounted for primary and scatter components of the treatment megavoltage beam and added those two components 3

17 to determine dose to a point or to compute a planar dose. The isodose distribution was often used to display an approximate representation of deposited dose in central plane. To understand the complexities dealt with in treatment planning algorithms, a brief review of interaction of radiation with matter is presented below. When x-ray radiation interacts with matter, it does not directly lead to damage on the cellular level. A photon (x-ray) must first interact with the individual atoms to create charged particles which then further interact to deposit dose. There are five main types of interactions that occur within materials when a high energy photon enters. These five interactions are: photo disintegration, coherent (Rayleigh scattering), photoelectric interactions, Compton scattering and Pair Production. Of these three, only the photoelectric effect, Compton scattering, and pair production are relevant in the energy range of radiation therapy machines. Photo disintegration occurs only in high energy photon interactions and in materials with a high atomic number (Z). This makes it of concern primarily in shielding calculations, where x-rays are interacting with metal and other high atomic number materials. The result of this interaction is the emission of a nucleon (usually a neutron) which then have the ability to travel a long distance before they deposit any dose. This reaction, therefore, is not responsible for a significant portion of the absorbed dose to the patient and is not an important consideration in clinical energy ranges. Coherent (Rayleigh) scattering results from the wave nature of the x-rays. As they pass near an electron, it will begin to oscillate. This oscillation re-radiates energy at the same frequency as the original radiation beam, resulting simply in scatter at small angles. Because no energy is transferred (only scattered) it has no affect on patient dose. 4

18 At diagnostic energy levels (kv imaging such as CT scans) and low therapy energy levels, the photoelectric effect is of primary concern. This interaction involves a photon interacting with a tightly bound orbital electron. All of the energy of the photon is initially absorbed by the orbital electron. The emitted electron (photoelectron) has an energy equal to the initial energy of the photon minus the binding energy of the electron. This effect can cause a small cascade effect, where a high energy orbital electron falls into the now vacant hole cause by the emission of the photoelectron. This fall in energy leads to the emission of what is known as a characteristic x ray, which can in turn be internally absorbed in the atom leading to the ejection of Auger electrons (monoenergetic electrons that are produced when the characteristic x rays are internally absorbed by the atom). For the most part, the attenuation coefficient due to this affect is directly proportional to the inverse of the incident energy cubed and directly proportional to the cube of the atomic number of the material: τ ρ Z E 3 3 In clinical situations, this scatter is emitted primarily in the forward direction (direction of the primary beam path). The Compton effect mainly occurs at higher photon energies, because it is necessary for the incident photon to have an incident energy much greater than the binding energy of the electron. In this situation, not all of the incident energy of the photon is transferred to the electron. Due to this, the end result is a scattered electron and a scattered photon, both with a fraction of the incident energy of the original photon. Depending on how the photon hits the electron, the angle and energy of the emitted 5

19 particles will vary. The electron may acquire any energy between zero and E max (the maximum energy), the equation for which is given by: 2α hυ0 E max = hυ0 where α = (Khan, 2003) α m c 0 In contrast with the photoelectric effect, therefore, the Compton effect occurs for photon energies much higher than the binding energy of the electron where the photoelectric effect occurs for photon energies approximately equal to the binding energy of the electron. Above a certain energy, however, the probability of a Compton interaction again decreases and pair production (discussed next) begins to dominate. The Compton effect, unlike the photoelectric effect and pair production, is essentially independent of atomic number Z. It does however depend on the number of electrons per gram (electron density) which is material dependent. The electron density decreases slowly with atomic number, but most materials other than hydrogen (for radiation therapy purposes) can be considered as having the approximate same number of electrons per gram, leaving the attenuation coefficient for the Compton Effect essentially independent of material. It is important, however, to keep in mind that the attenuation coefficient is density dependent, so the equivalent amount of material traversed does depend on the material. Pair production has a threshold energy of 1.02 MeV (the rest energy of the sum of the two particles that are produced). The incident photon gives up all of its energy to the atomic nucleus, leading to the emission of a positive electron (positron) and a negative electron scattered in the mostly forward direction. As the positron travels, it eventually 6

20 slows and interacts with a free electron in its vicinity leading to emission of annihilation photons of 0.51 MeV energy each. Conservation of momentum leads to these photons being ejected in opposite directions. The distance the positron will travel depends on the incident energy of the photon, the material through which it traverses, and the proportion of the incident photon energy given to the positron. Pair production, unlike the Compton effect, is highly dependent upon atomic number (Z). The incidence of pair production increases very quickly as the atomic number of the material increases. In fact, the attenuation coefficient for this effect varies with the square of the atomic number Z above the threshold energy of 1.02 MeV. Therefore, in materials with a large atomic number, this effect occurs at a lower energy and has more of a contribution to patient dose. It can be seen from the description of these algorithms that the dose deposited in a material and what direction the scattered particles leave at is directly related to the energy of the incident beam and the material being studied. Therefore, modeling deposited dose in a patient who is composed of materials of a variety of atomic numbers is a complex problem. 7

21 Chapter 2 Treatment Planning Algorithms Treatment planning has increased in complexity as advances have allowed 3D imaging of patient anatomy. The complex interactions that occur inside a patient have delayed radiation therapy dosimetry algorithms from taking advantage of the new information CT scanning provided. Treatment planning in radiation therapy is a compromise between what you want to do (model ever interaction as it occurs within the tissue and track each particle) and what you can do (what is computationally possible). Current algorithms all rely on approximations of some sort that effect their accuracy in modeling dose in a patient. The following descriptions are only of the treatment planning systems utilized in this study. 2.1 Pencil beam with modified Batho corrections In 1977 Sontag and Cunningham described a method of tissue inhomogeneity correction, modified Batho, which improved upon previous algorithms. This algorithm was of a generation known as a correction based algorithm. The initial dose was still computed in a homogenous phantom; however dose could be scaled based on attenuation 8

22 to account for heterogeneities. The preceding algorithms (such as the original Batho model) could determine dose only to a water equivalent tissue. The calculations could be done to determine dose beyond a different density tissue, however the dose to a point could only be determined to water equivalent tissue. The modified Batho correction algorithm is combined with a pencil beam algorithm. This method describes a user s individual beam configuration by a vast array of tables of measurements obtained by the medical physicist. These tables are used to compose small beams that travel through the patient. The dose these beams contribute is first computed in a homogenous (water) phantom. Corrections to each pencil are obtained by a scaling factor to account for differences in attenuation. The dose from adjacent pencil beams is not considered in each calculation, which can lead to errors in determination of dose in tissues that are within areas of large heterogeneity. The effect is a heterogeneity correction only in the longitudinal (beam path) direction. (Sontag & Cunningham, 1977) This algorithm is available for use within the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, Ca). 2.2 Anisotropic Analytic Algorithm (AAA) An improvement of the pencil beam with modified Batho corrections algorithm has recently been introduced by Eclipse (Varian Medical Systems, Palo Alto, Ca), known as the Anisotropic Analytic Algorithm (AAA). This algorithm is still a pencil beam model. Instead of the pencil beams being derived from measurements, the pencil beams are derived from Monte Carlo simulations. These pencil beams are essentially pre- 9

23 convolved point spread doses, and the algorithm is therefore in the class of a superposition convolution algorithm with pre-convolved pencil beams. The anisotropic analytic algorithm is an improvement over the previous models in that it also calculates a lateral scatter component, so the density of adjacent tissues is taken into consideration. The heterogeneity corrections are applied as scaling factors. is then calculated as a superposition of two photon sources (primary and secondary) and an electron contamination source. The photon component is composed of pre-calculated Monte-Carlo scatter kernels, which are then scaled for the patient. Heterogeneities in the patient are computed by scaling the beamlet attenuation (primary radiation) by an equivalent depth parameter, similar to the modified Batho correction technique. Heterogeneity corrections are not considered until the superposition step, where they are scaled as mentioned. The primary Monte Carlo based pencil beams are still calculated on a homogenous water phantom and then corrections are applied as mentioned to account for heterogeneities. (Van Esch, Tillikainen, Pyykkonen, Tenhunen & Helminen, 2006). 2.3 Collapsed Cone Convolution (CCC) A third algorithm in clinical use, known as Collapsed Cone Convolution (CCC) was first described by Ahnesjo in It is currently being used by the Pinnacle treatment planning system (Philips medical Systems, Milpitas, CA). This algorithm uses Monte Carlo derived dose deposition kernels to determine dose. This algorithm is also known as a superposition convolution model. This can be explained in the following way: 10

24 deposition in a patient is not a direct result of the photon beam entering the patient. The photon beam must first impart its energy to charged particles which can then deposit dose in a patient. Therefore, there is a difference between the Total Energy Released in a Material (TERMA) and the Kinetic Energy Released to Charged Particles (KERMA). The TERMA values are determined for every point within the patient CT based on the density obtained from the CT. This data is then convolved with the Monte Carlo derived point dose kernels and calculated for the entire patient volume. is calculated in a spherical coordinate system from the point of interaction. These cones are then collapsed into the Cartesian coordinate system of the CT to produce a dose distribution in the patient that inherently includes heterogeneity corrections. See TG-65 (Papanikolaou et al., 2004) and (Ahnesjo, 1988). 2.4 Importance of Accurate Patient Calculations While all three algorithms take scatter into account, only Collapsed Cone Convolution and the Anisotropic Analytic Algorithm computer scatter effects in both longitudinal (along the beam path) and lateral directions. Pencil beam calculations with corrections such as the modified Batho correction consider heterogeneities only along the pencil beam path (longitudinally) via a correction factor such as effective depth. None of the mentioned algorithms model secondary electron transport in the patient. The American Association of Physicists in Medicine Task Group 65 has set a recommendation that dose be delivered to patients with an error of less than 5% (2004). This recommendation was based upon clinical observations that a change in dose of 7% 11

25 can be physically observed. A 7% increase in dose can manifest itself clinically as increased normal tissue reactions and a 7% decrease in dose can manifest as a reduction in tumor response. Therefore, each step along the way from setup to planning to delivery should have a margin of error far less than this. One analysis recommended that the dose calculation portion of this should have an accuracy around 2% in order to meet this criteria (Loevinger & Loftus, 1977). These numbers should be kept in mind while reviewing comparison data between planning algorithms and systems. In the case where Monte Carlo comparisons are not available and true dose delivered is not known, the algorithms compared should agree to much less than 2% in order to ensure all of the algorithms compared would be within 2% of the correct value. (ICRU Report 44, 1989). 2.5 Algorithm Comparisons Several studies have been conducted to attempt to analyze the mentioned treatment planning algorithms. In order to asses the accuracy of an algorithm, the gold standard has been to use a Monte Carlo simulation. Monte Carlo simulations follow individual particles through their interactions in matter. Each step along the way through the material is calculated and all of the scatter and interaction particles are followed as well. These computations tend to be very time consuming, which necessitates the need for the algorithms mentioned in every day clinical use. These algorithms, however, rely on approximations that have been shown to be inaccurate under certain circumstances. Monte Carlo simulations do not suffer from these types of approximations, so they are 12

26 considered to be a benchmark for analytic calculations, and a means to assess their accuracy (Papanikolaou et al., 2004). Hasenbalg investigated the accuracy of the AAA algorithm and the CCC algorithm against Monte Carlo simulations (Hasenbalg, Neuenschwander, Mini & Born, 2007). Their conclusion was that, for lung and breast cases, CCC provided the closest match in the DVH and isodose distribution as compared with Monte Carlo. They noted especially that there were significant differences in low dose regions as modeled by AAA. They noted also differences in the lung-chest wall interface which indicated superior results with the CCC algorithm. It has been shown that errors greater than 3% could result when using AAA to calculate dose beyond large air gaps. This result was shown to be similar when using the Eclipse pencil beam algorithms to account for heterogeneities simulated by a water tank and water equivalent plastic and measured with an ion chamber (Gray, Oliver, & Johnston, 2009). Comparisons with AAA and Monte Carlo calculations have shown errors can be much larger when a low density region of greater than 10cm (such as in lung) is traversed. (Robinson, 2008). This research also provides evidence that the AAA algorithm tends to overestimate dose beyond low density heterogeneities. This overestimation is shown to be, on average, 3% at distances less than 10cm and up to 7% at distances greater than 10cm. This error is far beyond the TG-65 benchmark of no greater than a 2% deviation in the dose calculation step of treatment (Papanikolaou et al., 2004). Van Esch confirmed these results in a further study where errors of up to 6% were demonstrated in these situations (Van Esch, 2006). 13

27 A further study by Fogliata (Fogliata, Eugenio, Albers, Brink, & Clivio, 2007) demonstrates that in areas of heterogeneities, CCC provides the closest estimation of the actual dose (determined by a Monte Carlo calculation). This study also evaluated the accuracy of a pencil beam with modified Batho correction calculation in comparison with Monte Carlo. In water, it was shown, all of the treatment planning algorithms produced results in close agreement to Monte Carlo. Therefore, homogenous calculations using any of the mentioned algorithms should be in agreement. The results are summarized in the tables, taken from the original paper, seen below. 14

28 Figure 2-1 Table from Fogliata (2007) On the dosimetric behavior of photon dose calculation algorithms in the presence of geometric heterogeneities: Displaying the results of Eclipse AAA (AAA-ECL), Eclipse pencil beam (PBC-ECL), Pinnacle CCC (CC-Pin) and Monte Carlo (MCw). These calculations were done using a 6MV beam and 4cm away from the central axis. 15

29 As indicated by these figure 2-1, for 6 MV photon beams, in areas of higher density lung tissue both AAA and CCC follow closely with what is predicted by Monte Carlo calculations. In lower density lung materials, however, there is a large deviation from what AAA predicts to what CCC and Monte Carlo predict. Keeping this in mind, it can be seen that in situations where there are differences in predicted dose, Pinnacle s CCC algorithm is more likely to be consistent with Monte Carlo calculations. The analysis seen in Figure 1 was conducted in a relatively simple phantom consisting of square layers of uniform thickness of the various density materials. What occurs in an actual patient is more complex, and can be compared in the results of this study. For a higher energy, (15MV) the agreement between AAA and Monte Carlo is much worse, even in normal lung tissue. The results of comparison between Pinnacle and Monte Carlo, however, are still seen to follow closely with each other. 16

30 Figure 2-2 Taken from Fogliata (2007) On the dosimetric behavior of photon dose calculation algorithms in the presence of geometric heterogeneities: Displaying the results of Eclipse AAA (AAA-ECL), Eclipse pencil beam (PBC-ECL), Pinnacle CCC (CC-Pin) and Monte Carlo (MCw). These calculations were done using a 15MV beam and 4cm away from the central axis. 17

31 The authors of this paper concluded that the results using the AAA algorithm consistently fell in between results for Monte Carlo and CCC (which they grouped together) and a simple pencil beam calculation with a modified Batho correction. This indicates that, while AAA is a significant improvement over the pencil beam, it still predicts a dose different from what CCC and Monte Carlo predict. 18

32 Chapter 3 Methods As mentioned previously, many RTOG protocols, including some that are still accepting patients, require heterogeneity corrections to be turned off for patients treated under protocols for lung and breast cancer. This requirement has been explained as being due to the discrepancies in the way various treatment planning algorithms correct for heterogeneities. Consistency of results between different clinics throughout the world allows for an easier evaluation of various treatment options. Because of this, physicians may have elected to treat non-protocol patients in the same manner for consistency and ease of comparison with proven RTOG protocol results. With the introduction of more advanced treatment planning algorithms, this requirement is changing for the newer protocols to allow heterogeneity corrections to be used. These protocols typically specify which algorithms would be acceptable to utilize. Physicians that may be accustomed to evaluating and approving plans based on a nonheterogeneity corrected model are now shifting to reviewing plans that look different with heterogeneity corrections applied. They are further expected to translate their experience and knowledge of past trials conducted under homogonous conditions into the doses they see on a heterogeneous patient CT. 19

33 For physicians who travel between sites which could potentially have different algorithms, it would therefore be beneficial to have a means by which to compare plans done without heterogeneity corrections to plans with those corrections applied. A further comparison between the various heterogeneity correction models (AAA, CCC, and modified Batho) would aid in evaluating plans produced under these different circumstances. These evaluations would also aid a physician who has grown accustomed to seeing doses represented by a heterogeneity uncorrected model in determining what is clinically appropriate on a case by case basis. The planning systems used were Phillips Pinnacle version 8.0M (Collapsed Cone Convolution) and Varian Eclipse version 8.2 (Anisotropic Analytic Algorithm and Modified Batho), operating at two different sites. In order to compare algorithms across treatment planning systems, the same accelerator model has to be used in each case. The algorithms studied already had the accelerator model for their individual site modeled, but an accelerator model that could be shared between them had to be created for cross algorithm comparisons. For comparisons between CCC, AAA, and Modified Batho, the linear accelerator used to create these plans in the Eclipse treatment planning system was commissioned in the Pinnacle treatment planning system. The data used to commission this accelerator in Pinnacle was taken from the same data used to originally commission the Eclipse workstation. The first step in this process involved locating golden beam data for the accelerator to be modeled. This simply provided a starting point for the physical components of the accelerator such as the distances between primary collimators, multi 20

34 leaf collimators, flattening filters and other treatment head components. These numbers had to be made equivalent between algorithms. The second step in the process was to input measured profile and depth dose data for various field sizes and beam energies. This data had to be verified to be equivalent between the two systems by running test plans on a homogenous phantom. To prove the equivalency of these models, percent depth doses and beam profiles were obtained from each planning system and compared. This data was put into the PINNACLE treatment planning system and was manipulated to match the data for the accelerator modeled in the Eclipse treatment planning system. 3.1 RTOG 0213 (lung) RTOG protocol 0213 (2003) was a trial to evaluate the use of the COX-2 inhibitor Celebrex with limited field radiation in the treatment of non-small cell lung cancer. It was activated on July 30, 2002 and closed on June 30, For radiation treatment, it required heterogeneity corrections to be turned off. The setup of the radiation fields was left open to the discretion of the physician, dosimetrist and physicist. It specified that the radiation dose should be given in 2 Gy fractions for 33 fractions, to a total dose of 66 Gy. The reference point was to be located at the center of the PTV. In order to meet protocol guidelines, 90% of the prescription dose (59.4 Gy) was required to cover the entire PTV. The maximum dose was not to exceed 115% of the prescription dose (75.9 Gy). The PTV was indicated to include a minimum of a 10mm margin around the physician indicated GTV. 21

35 In this protocol, dose to organs at risk was also specified. The dose to the esophagus was not to exceed 55 Gy to 28% of the esophagus, and a mean dose of less than 32 Gy. The lungs-ptv dose limitation was <32% of the volume receiving 20 Gy or more. The spinal cord maximum dose was limited to 45 Gy, and the heart dose maximum was 40 Gy (RTOG 0213, 2003). Plans were created for six different patients in the Pinnacle treatment planning system (CCC) and six patients in the Eclipse treatment planning system (AAA) to meet these guidelines. As stated in the protocol, heterogeneity corrections were turned off for the entire optimization process. All plans were normalized to the 100% isodose line for consistency in evaluation organ at risk and tumor dose. When optimization was finished, the plan was copied and heterogeneity corrections were turned on. For the Eclipse station, a calculation was done for the Pencil Beam Algorithm and the AAA algorithm to highlight their differences. As shown previously, a heterogeneity corrected model more closely describes the actual dose received to the patient. Therefore, this comparison was to provide an indication of what the actual dose distribution delivered would have looked like. To meet this goal, monitor units and beam configurations were kept the same. Normal tissue doses and PTV doses were obtained from the DVH in each planning system. Although the original protocol specified optimization in a homogenous phantom, this does not allow a comparison between plans computed using different algorithms with heterogeneity corrections turned on. To explore these differences, identifying data was stripped from the original CT data set, and it the original CT was then imported into the Eclipse treatment planning system for six patients. Plans were created using the RTOG 22

36 protocol guidelines using the Modified Batho pencil beam algorithm, with heterogeneity corrections turned on. These plans were then recomputed with the same beam configuration and monitor units using the AAA algorithm. Although the AAA algorithm fits more closely with Monte Carlo data, it has been shown that Pinnacle s CCC agrees with Monte Carlo calculations even better, specifically in low density regions such as in the lung. Therefore, the beam configuration was exported into Pinnacle and recomputed using the CCC algorithm. The same linear accelerator model was used for this comparison. Normal tissue and PTV doses were again obtained from the DVH of each plan and evaluated for adherence to protocol guidelines. 3.2 RTOG 0413 (breast) RTOG protocol 0413 (2005) is a trial to evaluate the effectiveness of partial breast irradiation versus whole breast irradiation. It was activated on March 21, 2005 and as of October 2010 is still accepting patients. For the purposes of this study, the whole breast irradiation guidelines, which require heterogeneity corrections to be turned off, are followed. Intensity Modulated Radiation Therapy (IMRT) is specifically banned. Field in field techniques are allowed, provided that greater than 50% of the monitor units are given through an open field at each gantry angle. Gantry angles are required to provide tangential beams according to guidelines indicated in the protocol, sparing dose to the ipsilateral lung. The radiation dose should be given in 1.8 Gy fractions to a total dose of 50.4 Gy. The reference point location is also indicated in the protocol. In order to meet protocol guidelines, 90% of the prescription dose (45.36 Gy) is required to cover the physician drawn lumpectomy cavity. The maximum dose is not to 23

37 exceed 115% of the prescription dose (57.96 Gy). Further normal tissue constraints are not supplied by the protocol. However, because chest wall coverage is an important consideration to physicians, a chest wall contour was created and evaluated in the plans computed using the Pinnacle Treatment planning software. This contour was created by expanding the ipsilateral lung anterior, posterior and lateral by 1cm. Two open opposed fields were computed in a homogenous phantom. The chest wall contour was created to include the lung expansion that was within the 95% isodose line. Plans were created for six different patients in the Pinnacle treatment planning system (CCC) to meet these guidelines. As stated in the protocol, heterogeneity corrections were turned off for the entire optimization process. All plans were normalized to the 100% isodose line for consistency in evaluation of dose. When optimization was finished, the plan was copied and heterogeneity corrections were turned on. As shown previously, a heterogeneity corrected model more closely describes the actual dose received to the patient. Therefore, this comparison was to provide an indication of what the actual dose distribution delivered would have looked like. To meet this goal, monitor units and beam configurations were kept the same. Normal tissue doses and PTV doses were obtained from the DVH in each planning system. (RTOG 0413, 2005). 24

38 Chapter 4 Results 4.1 Lung heterogeneity corrections - Pinnacle For these cases, lung cancer treatment plans were created based upon the guidelines of RTOG 0213 (2003). The tumor volume used was the original tumor volume drawn by the physician. Beam angles were kept similar to what was approved and used to treat the patient. The plans were optimized with heterogeneity corrections turned off. From the DVH, values for the PTV minimum dose, PTV maximum dose, PTV mean dose, point of maximum dose (max point), point of maximum spinal cord dose (cord max), heart mean dose and percent of lungs-ptv volume receiving greater than 20 Gy of dose. When optimization was completed, the plans were recalculated with heterogeneity corrections turned on. All computations were done using the Pinnacle CCC algorithm, and all of the plans met the RTOG 0213 criteria when optimized with heterogeneity corrections turned off. When the heterogeneity corrections were turned on, 5 out of the 6 calculated plans failed the criteria from the RTOG protocol which stated that the PTV was to be covered by a minimum of 90% of the prescription dose. The average PTV minimum dose in the 25

39 original optimization without heterogeneity corrections was 92.1% of the prescription dose (60.79 Gy). The average PTV minimum dose when the heterogeneity corrections were turned on was 88.7% of the prescription dose (58.56 Gy). So, on average, the minimum PTV dose coverage dropped 5% of the original prescription dose or an average of cgy over the course of the treatment. Considering the per fraction dose of 180 cgy, this essentially means that the minimum PTV dose delivered was the equivalent of missing 1.9 total fractions. In contrast to the PTV minimum dose which decreased, the PTV maximum dose increased in all cases when heterogeneity corrections were applied. Only one of the six plans failed to meet the criteria of the protocol, which was that the maximum dose delivered should not exceed 115% of the prescription dose. The average PTV maximum dose after the original optimization with heterogeneity corrections turned off was 104.5% of the prescription dose (68.95 Gy). When the heterogeneity corrections were turned on, the average PTV maximum dose was increased to an average of 110.5% of the original prescription dose (72.93 Gy). This represents an increase of an average of cgy, the equivalent of more than two additional fractions. The RTOG protocol specified a maximum dose of 115% of the prescription dose. Because this point occurred outside of the PTV for all patients, five out of six patients studied also failed this part of the criteria. The average maximum point dose in the plans increased from an average of 109.4% of the prescription dose (72.17 Gy) to an average of 117% of the prescription dose (77.31 Gy). This translates into an average increase of cgy (7%). 26

40 These results show that when a lung patient is planned under homogenous circumstances, the dose uniformity delivered to the tumor varies by more than what is indicated by the treatment planning system. This could present a significant problem if patient outcomes are based on a given dose, which is assumed to be essentially uniform throughout the tumor. These results highlight the importance of using heterogeneity corrections in these cases. One further PTV analysis of the mean dose revealed that when the heterogeneity corrections were turned applied, the mean PTV dose increased across the board by an average of 6% (169 cgy). Therefore, the prescription specified to a patient when planned under homogenous conditions would give less dose (an average of slightly less than one fraction) to the tumor than a prescription specified to a patient planned under heterogeneous conditions. This is an important consideration when using RTOG protocol results to determine patient dose under different calculation conditions. Maximum cord dose, lungs-ptv (20 Gy) dose, and mean heart dose was roughly the same between the two trials. An exception occurred with patient 5, where the maximum cord dose was seen to increase by a predicted 628 cgy with the CCC algorithm turned on. In this case, the cord was right outside the field edge of one of the beams. This is an indication of the inappropriate scatter calculation without heterogeneity corrections. Despite this, the majority of the results show that the main change in dose occurs in the tumor area. While this is good news in assuring patients don t undergo enhanced normal tumor damage when switching between models, it still leaves much to be considered in regards to the tumor dose. The results of this study are summarized in the Tables 4-1 and 4-2 and figures 4-1 through 4-3 seen below. 27