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132.Baro J, et al. PENELOPE: an algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter. Nucl Instrum Methods B 1995;100:31–46.

133.Ma C-M, et al. DOSXYZ users manual. Ottawa: NRCC. 1995.

134.Neuenschwander H, Mackie TR, Reckwerdt PJ. MC–a highperformance Monte Carlo code for electron beam treatment planning. Phys Med Biol 1995;40(4):543–574.

135.Rogers DW, et al. BEAM: a Monte Carlo code to simulate radiotherapy treatment units. Med Phys 1995;22(5):503–524.

136.Briesmeister JF. MCNP-A general Monte Carlo N-Particle transport code, version 4B. 1997; Los Alamos National Laboratory report LA-12625-M.

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138.VMCþþ, electron and photon Monte Carlo calculations optimized for Radiation Treatment Planning, in Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications: Proceedings of the Monte Carlo 2000. In: Meeting Kling A, et al.editors. Berlin; Lisbon: Springer,2001;p 229–236.

139.Hartmann Siantar CL, et al. Description and dosimetric verification of the PEREGRINE Monte Carlo dose calculation system for photon beams incident on a water phantom. Med Phys 2001;28(7):1322–1337.

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141.van der Zee W, Hogenbirk A, van der Marck SC. ORANGE: a Monte Carlo dose engine for radiotherapy. Phys Med Biol 2005;50(4):625–641.

142.Guber W, et al. A geometric description technique suitable for computer analysis of both the nuclear and conventional vulnerability of armored military vehicles. Washington (DC): 1967.

143.Berger M. Monte Carlo calculation of the penetration and diffusion of fast charged particles, in Methods in Computational Physics. In: Alder B, Fernbach S, Rotenberg M, editors. New York: Academic; 1963. p 135–215.

144.Papanikolaou N, et al. Tissue inhomogeneity corrections for megavoltage photon beams. Medical Physics Publishing; 2004.

145.Fraass BA, Smathers J, Deye J. Summary and recommendations of a National Cancer Institute workshop on issues limiting the clinical use of Monte Carlo dose calculation algorithms for megavoltage external beam radiation therapy. Med Phys 2003;30(12):3206–3216.

146.Ahnesjo A. Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media. Med Phys 1989;16(4):577–592.

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148.Ekstrand KE, Barnes WH. Pitfalls in the use of high energy X-ray to treat tumors in the lung. Int J Radiat Oncol Biol Phys 1990;18(1):249–252.

149.White PJ, Zwicker RD, Huang DT. Comparison of dose homogeneity effects due to electron equilibrium loss in lung for 6 MV and 18 MV photons. Int J Radiat Oncol Biol Phys 1996;34(5):1141–1146.

150.Yorke E, et al. Dosimetric considerations in radiation therapy of coin lesions of the lung. Int J Radiat Oncol Biol Phys 1996;34(2):481–487.

151.Klein EE, et al. A volumetric study of measurements and calculations of lung density corrections for 6 and 18 MV photons. Int J Radiat Oncol Biol Phys 1997;37(5):1163–1170.

152.Miften M, et al. Comparison of RTP dose distributions in heterogeneous phantoms with the BEAM Monte Carlo simulation system. J Appl Clin Med Phys 2001;2(1):21–31.

153.Wang L, et al. Dosimetric advantage of using 6 MV over 15 MV photons in conformal therapy of lung cancer: Monte Carlo studies in patient geometries. J Appl Clin Med Phys 2002;3(1):51–59.

154.Osei EK, et al. EGSNRC Monte Carlo study of the effect of photon energy and field margin in phantoms simulating small lung lesions. Med Phys 2003;30(10):2706–2714.

155.Chetty I, et al. The influence of beam model differences in the comparison of dose calculation algorithms for lung cancer treatment planning. Phys Med Biol 2005;50:801–815.

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See also RADIATION DOSE PLANNING, COMPUTER-AIDED; RADIOTHERAPY TREATMENT PLANNING, OPTIMIZATION OF; STATISTICAL METHODS.

RADIATION THERAPY, QUALITY ASSURANCE IN

GLEN GEJERMAN

JOSEPH HANLEY

Hackensack University Medical

Hackensack, New Jersey

INTRODUCTION

The curative goal in radiation therapy is to deliver sufficient doses of tumoricidal radiation to a target volume while protecting the contiguous normal tissues. As radiation dose and accuracy of treatment delivery correlate with improved disease-free survival and avoidance of toxicity, quality control must be maintained throughout the planning and delivery of radiotherapy. The International Commission on Radiation Units and Measurements (ICRU) has recommended that treatment should be delivered to within 5% of the prescribed dose. Treatment planning and delivery is a multistep process that includes clinical decision making, patient immobilization, simulation, delineation of target and avoidance structures, determination of beam number and orientation, dose calculation, dosimetric scrutiny, patient set up, and treatment administration. In order to meet the ICRU’s stringent recommendation, each of these steps must achieve better than 3% accuracy, and yet several studies have shown that geometric uncertainty

and dosimetric inaccuracy impact the clinical reality of radiotherapy. Patient misidentification, set-up variability, organ motion, block or multileaf collimation placement errors, and dosimetric miscalculation can lead to underdosing or overdosing the target volume and unintentional irradiation of normal surrounding tissue. Studies have shown that these types of errors occur at various stages in the treatment planning and delivery process, are often due to inadequate communication and mistakes in data transfer, and quality assurance procedures facilitate early detection of and reduction in their occurrence (1,2). A recent review of radiation therapy errors over a 5 year period at a major tertiary cancer center found that 44% of errors were due to field deviations, 38% due to incorrect use of beam modifiers, and 18% due to deviations from the prescribed dose. Once a quality improvement intervention that addressed several technological issues such as electronic charting was initiated, a significant impact in error reduction was noted (3). The numerous important quality assurance duties coupled with the increasing sophistication of radiation treatment planning and delivery systems, calls for an integrated comprehensive program that validates, verifies, and maintains accuracy throughout the entire process of radiation therapy delivery (4).

Treatment inaccuracies can be divided into systematic or treatment preparation variations (which include positioning errors, organ motion during treatment planning simulation, contouring errors, field shaping errors, and machine calibration errors) and random or execution variations (which include day-to-day patient misalignment and organ motion during treatment). A comprehensive quality assurance program must encompass both systematic and random uncertainty in treatment planning and delivery in order to minimize their occurrence. Although both types of errors lead to geometrical deviations of the target volume, they have different effects on the delivered dose. Systematic errors cause a displacement of the dose distribution away from the intended target and random errors lead to blurring of the dose distribution. The impact of systematic errors on target dose and the tumor control probability is therefore much greater than the impact of random execution variations (5,6). Identifying and correcting planning preparation errors early in the treatment process is critical in order to mitigate their impact on the treatment outcome.

To minimize treatment inaccuracies, it is essential that each radiation oncology department establish a ‘‘quality system’’ or quality assurance program to provide the organizational structure, responsibilities, procedures, processes, and resources for assuring the quality of patient management. A series of checklists to monitor compliance with the objectives of the program should be developed and applied. Due to the ever-changing nature of radiation oncology, this quality assurance program should be reviewed annually.

SIMULATION QA

The foundation of treatment planning is the simulation process. After the radiation prescription has been filled out to include the intended target, the organs to avoid, and the

total and fractional doses, the patient undergoes treatment planning simulation, during which anatomical data is acquired, patient topography is measured, and the target volume and avoidance structures are delineated. Radiation therapy simulators use fluoroscopic, X-ray and computed tomography (CT) techniques to visualize internal anatomy in relation to external landmarks and can be divided into two broad categories: conventional or fluoroscopic simulators and CT simulators. These simulators can replicate the treatment machine geometry either physically, in the case of a conventional simulator, or virtually on a computer for CT simulation. The quality assurance program for the physical simulators must be parallel to that of the treatment machines, so that the geometric relationship between the treatment unit and the target volume can be accurately and consistently reproduced. To ensure the same accuracy in the case of virtual simulation, the treatment unit must be precisely modeled in the simulation computer.

To minimize intratreatment movement, and to ensure accurate daily positioning, patients are simulated (conventional or CT) in the treatment position with the use of special immobilization devices. These devices extend beyond the treatment site and rigidly immobilize the patient while providing them with support to enhance relaxation and minimize movement. Studies comparing set-up variations in immobilized versus free set up of patients note a significant reduction in positioning errors. In patients without custom immobilization, the percentage of fractions with set-up errors greater than 5 mm ranged from 17–57% and errors greater than 10 mm occurred in 15% of fractions (7,8). A randomized trial analyzing patients in the prone position receiving pelvic radiotherapy found a statistically significant benefit when using rigid immobilization. In the group treated without immobilization, 31% of port films had isocenter deviations greater than 10 mm compared with 11% in the immobilized patients. Average set-up deviations in the anteroposterior, right-left, and superior-inferior directions were 5.2 mm, 3.2 mm, and 4.3 mm in the patients treated without immobilization versus 2.9 mm, 2.1 mm, and 3.9 mm in those treated with rigid immobilization, respectively (9). Patientrelated uncertainties also include organ motion. The patient’s treatment position can impact the extent of both interand intrafractional movement. A randomized trial analyzing organ motion during treatment demonstrated less prostate motion in the supine treatment position. The mean anterior-posterior organ motion was 0.1 mm for patients treated in the supine position as opposed to 0.7 mm in those treated prone (10). These data demonstrate why proper immobilization is such a vital part of the quality assurance program. It should be noted that the integrity of immobilization devices should be checked on a daily basis during the treatment course.

Quality assurance of the conventional simulator is necessary to avoid inaccuracies that could lead to targetbeam misalignment. After installation, and prior to clinical use, a detailed customer acceptance procedure is often performed and can act as a baseline for ongoing testing. A complete QA program for a simulator should follow the guidelines detailed in the American Association of Medical Physicists (AAPM) Task Group 40 (TG40) report (4) and be

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augmented by any city, state, or federal guidelines. Table 3 of TG40 specifies the tests to be performed and at what frequency and to what tolerance to perform them. As a minimum, the lasers and the optical distance indicator should be checked daily. On a monthly basis, evaluation of mechanical uncertainties includes a check of field size settings and rotational settings, light-field radiation field congruence, treatment couch movement, laser positioning, and cross-hair centering. An example of a monthly QA checklist is shown in Fig. 1. A more thorough series of mechanical checks should be performed on an annual basis. These checks include determination of true axes of rotation of the simulator and the coincidence of these axes. Annual tests of the X-ray generator are essential to ensure that the exposure to the patient is minimized. It is important to check the imaging chain as image quality can directly affect patient care. Specialized phantoms can be used to determine the spatial and contrast resolution. Conventional simulators are equipped with X-ray and fluoroscopic modes, and both modes should be tested.

During conventional simulation, fluoroscopy is used to determine the treatment portals to cover the appropriate

Hackensack University Medical Center

Department of Radiation Oncology

Notes:

References:

(1)Recommended by AAPM TG-40 (4).

(2)Adopted from manufacturer's specification and/or clinical considerations.

(3)Taken from Report No. 13, Physical Aspects of Quality Assurance in Radiotherapy. American Association of Physicists in Medicince, May, 1984, New York.

Figure 1. Monthly checklist for simulator machine QA.

target volume. When using fluoroscopic simulation, one must be certain that the field size and shape adequately encompasses the target volume. Without detailed knowledge of the true extent of the tumor volume, fluoroscopically determined treatment portals may result in inadequate coverage. In an analysis of patients with cervical cancer, reconstruction of CT-defined tumor volumes on the simulation films demonstrated inadequate anterior and posterior borders in a significant proportion of patients (11). When the simulation and target localization is completed, the position of the treatment field’s isocenter is marked on the patient’s skin or immobilization apparatus. In order to be able to consistently reproduce the treatment set up established at the simulation, three or more laser beams are used to establish fiducial marks often called triangulation points on the patient. A detailed recording of these simulation parameters, including gantry and collimator settings and the source-skin distance (SSD) for each treatment field, in the treatment chart will allow for accurate repositioning in the treatment room (12). The data acquired at the time of simulation can also be directly captured into a Record & Verify system (R&V), obviating the need for manual entry with the potential of transcription error. At completion of the simulation, a set of simulation films that show the field size, field isocenter, and projected anatomy from the chosen beam direction and distance is obtained. These films are used as the standard by which the future port films in the treatment room will be measured for set-up accuracy and to assess patient movement.

Over the past several years, CT simulators have been replacing or used in conjunction with conventional simulators. The CT simulator acquires CT images of a patient and sends them to a computer workstation on which a virtual simulation can be performed. The patient’s anatomy can be reconstructed in 3D allowing for a display in a beam’s eye view (BEV) perspective of the target and its relation to the normal surrounding tissues in different treatment angles. The BEV is used to create beam apertures that geometrically conform to the projections of the target and normal anatomy through different treatment angles. The computer software can be used to define the treatment isocenter, and a CT-generated virtual image of the patient is then used to complete the simulation with a digitally reconstructed radiograph (DRR) or a digitally composited radiograph (DCR). DRRs are computed radiographs that use the CT simulation data to provide planar reference images with the target volume, organs at risk, isocenter, and field edges shown. The DCR is created by computer enhancement or suppression of the CT numbers that allow for better visualization of the targeted organs. Although the principles of patient immobilization, treatment field delineation, and patient coordinate marking are similar to those of conventional simulation, the digital nature of CT simulation requires additional quality controls. During the acceptance testing, a CT dataset of a humanoid phantom is used for treatment planning to assess contouring capabilities, isocenter calculation, target localization, and DRR reconstruction and data transfer from the CT to the treatment planning system. The phantom is then used to test field size accuracy, virtual

gantry and collimator rotation, and the ability to accurately shift the isocenter. In addition to standard QA procedures for conventional CT scanners, CT simulators require interval testing of the laser system and of the data link to the virtual simulation computer system that allows tumor contouring, isocenter and field size definition, transfer of coordinates to the patient’s skin, as well as construction of the DRR (13). Upon completion of virtual simulation, the patient’s images, contours, and treatment beams are electronically sent to the treatment planning system.

TREATMENT PLANNING QA

Modern treatment planning systems consist of complex software run on sophisticated platforms with multiple peripheral devices. Recognizing the challenge of ensuring proper maintenance and use of these increasingly complicated systems, the AAPM Task group 53 (TG53) published a comprehensive set of quality assurance guidelines that can be applied to clinical radiotherapy planning (14). Acceptance testing and commissioning of a treatment planning system provides the benchmark by which the system will be evaluated during the periodic quality assurance testing. Acceptance testing is performed after installation but prior to clinical use of the system. The process entails testing that the system’s hardware, software, and peripheral devices function according to manufacturer’s specifications. These tests ensure that the system can properly acquire patient data, process anatomical contouring, orient beam direction, perform dose calculation, display the resultant isodose plots, and print hard copies of the approved treatment plan’s parameters. The ability to properly transfer imaging data is confirmed by scanning phantoms of known geometry with internal markers and transferring the imaging data to the treatment planning system. The transferred data is then compared with film images to validate orientation, measurement, and fiducial positioning. System commissioning involves extensive testing of the dosimetric algorithms for a variety of clinical scenarios. The physical properties of each treatment unit have to be entered into the system and checked for consistency with manufacturer’s specifications. Data such as percent depth-dose tables, off-axis profiles, and output factors are acquired using a computer-controlled water phantom for each treatment beam on each treatment unit to be used in the planning system. Phantoms with known geometric target volumes are used to simulate common clinical scenarios and treatment plans are evaluated to verify calculated dose distributions. Although anthropomorphic phantoms (that are shaped like the human body) are well-suited to test clinical treatment techniques, geometric phantoms (that are cylindrical or cubic) have more reliable ionization chamber positioning (15). If dosimetric calculations that account for inhomogeneities within the patient are to be performed, a CT number to electron density calibration curve must be established, which is performed for each CT acquisition unit that sends images to the treatment planning system. A phantom containing plugs of known electron density is scanned on the CT and

the corresponding CT number is determined. These numbers are plotted versus electron density to derive the calibration curve for that scanner. As an incorrect conversion of CT number to electron density can lead to significant dosimetric miscalculations, the American College of Radiology (ACR) recommends testing this calibration curve monthly. Although the most accurate form of dose calculations are Monte-Carlo-based, these calculations are computationally intensive and cannot currently be used for routine planning. All other dose calculation algorithms used in treatment planning systems have limitations, and it is essential to understand where these limitations manifest, for example, in areas where electronic equilibrium does not exist, such as lung-tissue interfaces. Routine periodic quality assurance testing of the planning system consists of daily, monthly, and annual tests. Daily tests validate the performance of input devices such as point digitizers and the accuracy of output devices such as printers. Monthly tests can involve calculating computer checksums for the treatment planning software executables and machine data, to ensure the program and data has not been modified. Annual tests are more involved and should include a subset of standard treatment plans that cover a wide range of clinical scenarios ranging from pointdose calculations, 2D, 3D conformal radiation therapy (3DCRT), and intensity-modulated radiation therapy (IMRT) plans. This set of standard plans are used for testing whenever software upgrades, either patches or version changes, are applied.

Once the imaging data has been transferred to the treatment planning system, the target volume and avoidance structures must be delineated if not already defined at the CT simulation. This delineation can be the major contributor to overall uncertainty in the treatment planning chain, as many factors exist that contribute to this uncertainty. It is imperative that the treating physician and the radiation treatment planner share a common vocabulary regarding the tumor volume and the additional margins necessary to account for organ motion and set-up inaccuracies. Prescribing and designing a treatment plan to a target without correcting for geometric uncertainties will result in a substantially different delivered dose than the intended one. In order to address these issues, the ICRU Report 50 (16) recommended using specific definitions regarding margins and volumes. The gross tumor volume (GTV) represents the visible tumor. The clinical target volume (CTV) denotes the GTV with an additional region encompassing suspected microscopic spread. The planning target volume (PTV) contains the CTV with margins added to account for geometric uncertainties. These margins are determined based on the extent of uncertainty caused by patient and tumor movement as well as the inaccuracies in beam and patient setup. Several margin recipes based on geometrical uncertainties and coverage probabilities have been published; however, their clinical impact remains to be proven (17). The organs at risk (OAR) are the normal tissues that are contiguous with the CTV (such as small bowel, rectum, and spinal cord) whose radiation tolerance can affect the maximum deliverable dose and treatment technique. The ICRU report 62 (18) refined the definition of the PTV with the concepts of

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internal margin and set-up margin. Internal margin uncertainty that is caused by physiological changes such as respiratory movement cannot be easily modified without using respiratory gating techniques. In contrast, set-up margin uncertainty can be more readily minimized by proper immobilization and improved machine accuracy. The report also addressed the issue of OAR mobility by introducing the planning organ at risk volume (PRV) in which additional margins are added to account for the geometric uncertainty of these organs. In order to avoid significant radiation toxicity and to maintain post treatment quality of life, the planning physician must be vigilant when considering avoidance structures. In a Radiation Therapy and Oncology Group (RTOG) analysis of the impact of dose escalation in prostate cancer, a lack of physician awareness leading to unnecessary exposure of the penile bulb to high radiation doses lead to treatmentinduced impotence (19).

Even with a common terminology and attention to detail when delineating the anatomical structures, several uncertainties exist that are related to the imaging modality used for data acquisition. Proper acquisition of CT data is challenging in that numerous factors, including slice thickness, slice spacing, CT number scale, and organ motion, can affect this information resulting in dosimetric and anatomic inaccuracies. A CT image artifact known as partial volume averaging occurs when two structures of different tissue density occupy the same voxel resulting in an averaging of their CT numbers. Unless the appropriate CT slice thickness is used, accurate target delineation can be compromised, as details of contiguous anatomic structures may not be appreciated. CT imaging of a moving organ can lead to significant distortions, particularly when the organ is small compared with the extent of its displacement. When the scan time is protracted, the artifact can be significant enough to render the reconstructed images unrecognizable in relation to its stationary counterpart (20). TG 53 recommends the use of imaging protocols that standardize scan parameters such as patient position and immobilization, CT slice spacing and thickness, the extent of the patient’s anatomy to be scanned, breathing techniques for patients with abdominal or thoracic tumors, and the use of contrast agents (14). Some anatomical structures are better visualized using alternate imaging modalities such as Magnetic Resonance Imaging (MRI) or functional imaging such as Positron Emission Tomography (PET) scans. For example, to improve the accuracy of thoracic GTV recognition, PET scans have been used in conjunction with CT-based simulation. Although in some circumstances, the ability to distinguish between thoracic tumor and atelectasis can result in a smaller GTV (21); at other times subclinical mediastinal adenopathy appreciated on PET will require enlarging the treatment field to encompass all active disease (22). When multiple images sets, acquired with different imaging modalities, are used in the planning process, the images must be accurately correlated in a common frame of reference. Typically, the images sets are ‘‘fused’’ onto the CT frame of reference. In visual fusion, the independent images are studied side by side and are visually fused using data from both to outline the GTV. In software fusion, the independent studies are geometrically

registered with each other using an overlay of anatomic reference locations. A recent review found that software fusion reduced intraand interobserver variability and resulted in a more consistent delineation of tumor volume when compared with visual fusion (23). It is imperative to perform QA on the fusion software. Acquiring datasets of a phantom with known geometrical landmarks on all modalities to be tested and performing the fusion process can accomplish this goal. PET/CT scanners that obtain both images simultaneously allowing for self-registration are becoming more widely available and will further facilitate accuracy in contouring.

In addition to uncertainties associated with various imaging modalities, it is well documented that interand intraobserver reproducibility exists in GTV delineation, and significant differences in the size of the GTV are noted depending on the imaging modality used (24,25). When contouring CT images, the correct window level settings must be used to appreciate the extent of the tumor shape and its relation to contiguous organs at risk. The treatment planning CT must be carefully reviewed to assess for positional or anatomic anomalies. For example, data acquired in the thoracic or abdominal region should be carefully examined for any sharp discontinuities in the outer contour that might indicate a change in breathing pattern or physical shift of the patient due to coughing, for example. A retrospective review of prostate cancer patients treated with conformal radiotherapy found an association between rectal distension on the planning CT and decreased probability of biochemical cure. Planning with a distended rectum can result in a systematic error in prostate location and was found to have a greater impact on outcome than disease risk group (26).

Once all the relevant organs have been contoured and the target dose and dose constraints have been unambiguously communicated to the dosimetry team, the appropriate combination of beam number, beam direction, energy, and intensity is determined. These parameters are optimized to deliver maximum dose to the CTV and minimum dose to the OAR. Conventional dosimetric calculations known as forward planning involves an experienced planner choosing multiple beams aimed at the isocenter and altering beam orientation and weighting to achieve an acceptable plan. The dose delivered with the chosen beam arrangement will be affected by the interaction of the radiation beam with the patient’s tissue density and is calculated by the planning computer. 3D conformal radiotherapy planning uses CT data to generate tumor and normal organ 3D images and displays them from the perspective of different angles using a BEV technique. Optimization of the treatment plan is performed by iteratively adjusting the beam number and direction, selectively adjusting the field aperture, and applying compensators such as wedges. In contrast, IMRT uses inverse planning to deliver a desired dose to the GTV and PTV with constraints to the OAR. Instead of choosing beam directions and then evaluating the resultant dosimetry, the desired dose distribution is stipulated using dose-volume constraints to the PTV and OAR and then the computer algorithm alters the various beam intensities in an attempt to achieve these planning goals.

After the dosimetry team completes their calculations, the proposed treatment plan must be carefully evaluated to confirm the prescription fractional and total doses and to determine whether it satisfies the prescription goal. For conventional treatments, this determination is performed by inspecting 2D isodose displays through one or more cross sections of the anatomy. For 3DCRT and IMRT, BEV data and dose volume histogram (DVH) analysis is used in addition to the isodose displays to evaluate dose minima, maxima, and means of both target and avoidance structures. The DVH that graphically depicts the percentage of a volume of interest that receives a particular dose does not give spatial information regarding dose distribution. If the DVH indicates underdosing, only by reviewing the plan’s isodose display can one locate the area of inadequate coverage. Mathematical models that use DVH statistics to estimate the normal tissue complication probability (NTCP) have been developed. These NTCP models have been found to more accurately predict the likelihood of radiation-induced toxicity than point-dose radiation tolerance data. An important task in a quality assurance program is to calculate the fractional and total doses to the OAR in order to estimate the risk of radiation injury. These doses can be described in terms of minimum, maximum, and mean doses to an entire organ or as the volume of an organ receiving greater than a particular dose. In situations where the PTV anatomically overlaps the OAR, clinical judgment must be used to assign a priority to each goal. The location and volume of dosimetric inhomogeneity (both hot spots and cold spots) must be evaluated, which is particularly true for IMRT where dose homogeneity is often sacrificed for dose conformality.

When the plan has been approved by the dosimetrist and the physician, all documented parameters including patient setup, beam configuration, beam intensity, and monitor units are sent to a R&V system either manually or, preferably, electronically. All the data from the plan, printouts, treatment chart, and R&V undergoes an independent review by a qualified medical physicist. This second check entails review of the prescription, the plan’s calculation algorithm, wedge placement, dose distribution, DVH, and beam apertures. Hand calculations of a point dose in each field are analyzed to verify the dosimetry. The patient then undergoes a verification simulation to confirm the accuracy and reproducibility of the proposed plan. During this confirmatory simulation, the isocenter position is radiographically confirmed, block geometry is checked, and measurements such as SSD are validated. Finally, a pretreatment port film verification is obtained on the treatment machine to verify reproducibility of set up and to confirm measurements such as the SSD and distance to tabletop.

The verification of patient-specific dose distributions with water phantoms, ionization chambers, diodes, or film dosimetry is an essential component of the QA process. The standard method of evaluation consists of overlaying hardcopy plots of measured and calculated isodose distributions and qualitatively assessing concordance. As a result of the nonuniform intensity inherent in IMRT and the resulting steep dose gradients throughout the treatment field, IMRT plan verification is more challenging. Radiographic film

dosimetry can be used to verify the IMRT leaf sequences and monitor units; however, as film sensitivity varies with beam energy, field size, film positioning, and film processing, great care must be taken to normalize the calculated and measured dose. Computer-assisted registration techniques are now available to determine the relative difference between the planned and delivered individual beam fluence or combined dose distributions on a pixel-by-pixel basis in order to score the plan using a predetermined criterion of acceptability (27).

LINEAR ACCELERATOR QA

The quality assurance protocol for linear accelerators is designed to monitor and correct performance of the equipment so that the physical and dosimetric parameters established during commissioning and acceptance testing can be maintained. TG40 (4) described a thorough QA program for linear accelerators with recommended test frequency. The daily tests include checking the safety features such as the door interlock and audiovisual intercom systems. Mechanical performance such as the localizing lasers and the machine’s optical distance indicator and dosimetric output such as the X-ray and electron constancy is also checked daily. Monthly checks of the linear accelerator’s mechanical accuracy include light-field coincidence; cross hair centering; gantry, collimator, field size, and couch position indicators; latching of the electron cone, wedge, and blocking trays; electron cone interlocks; and the emergency off switch. An example of a monthly mechanical and safety checklist for a linear accelerator is shown in Fig. 2. Monthly checks of the dosimetric accuracy include constancy of the X-ray and electron output, central axis parameters, and X-ray and electron beam flatness. Annual mechanical tests include checks of the safety locks; the tabletop sag; vertical travel of the treatment couch; the collimator’s, gantry’s, and couch’s rotation isocenter; the coincidence of the radiation and mechanical isocenter; and the coincidence of the collimator gantry and couch axes with the isocenter. The annual dosimetry tests check for monitor chamber linearity; wedge transmission factor constancy; off-axis factor constancy; and X-ray and electron output and off-axis constancy dependence on gantry angle. In addition, a subset of the depth-dose and offaxis profile scans acquired at commissioning are performed and compared with the baseline.

The use of multileaf collimators (MLC) in 3D conformal and intensity-modulated radiotherapy requires additional QA measures. When using MLC for 3DCRT, leaf position inaccuracies will have an effect on the resultant dosimetry; however, because of the PTV margins, the effect is minimal. In contrast, when using MLCs for IMRT, a miscalibration of 0.5 mm causing a 1 mm error in radiation portal size can cause a 10% dose error when delivering IMRT with an average field size of 1 cm (15). As MLC function is critical to dosimetric accuracy, rigorous QA protocols are required. The accuracy of the multileaf collimator (MLC) is verified by using radiographic film to measure radiation dose patterns and by checking for a gap between the leaves when they are programmed to be in the closed position.

548 RADIATION THERAPY, QUALITY ASSURANCE IN

Hackensack University Medical Center

Department of Radiation Oncology

Figure 2. Monthly mechanical and safety checklist for a linear accelerator.

TREATMENT DELIVERY QA

After the linear accelerator’s function has been verified with a thorough QA program, it is important to confirm that the treatment parameters established during treatment planning are accurately transferred to the treatment machine and that the daily set up and treatment are accurately executed. The R&V system is an important tool that confirms that proper field size, beam arrangement, multileaf collimator settings, collimator angle, gantry angle, beam energy, wedges, and monitor units are used each day. As an R&V system will give a daily validation of the entered parameters, it is essential to verify the set-up data with an independent check on the first day of treatment. The quality assurance mechanism for daily radiation treatments includes laser alignment of the fiducial tattoos and validation of SSD and tabletop measurements as well as weekly port films. These port films are obtained prior to treatment initiation as well as weekly and are compared with the initial simulation film or with the DRR to evaluate isocenter location and block or MLC position. A recent

advance in set-up verification and error detection is the development of electronic portal imaging devices (EPID). As a replacement to port films, the EPID provides faster acquisition time, tools that allow digital image enhancement, and tools that can measure the distance of anatomic landmarks to the isocenter or field edge (28). The use of EPID has allowed for the potential of adaptive radiotherapy that collects geometrical uncertain information during the first few treatment fractions and sends it back to the treatment planning system for further optimization. Once the set-up variations have been characterized, the treatment is adapted to either adjust the field size and treatment couch position or adjust the fluence profiles to correct for the set-up error.

SUMMARY

An overview of the major components of quality assurance for external beam radiation therapy has been given. The need to establish a quality assurance program, which provides the organizational structure, responsibilities, procedures, processes, and resources for assuring the quality of patient management, has been demonstrated. The various components that contribute to treatment inaccuracies have been identified as systematic or random variations, and it was established that the impact of systematic errors on target dose and the tumor control probability is much greater than the impact of random variations. The simulation was described as the foundation of treatment planning process and intraand interfraction patient motion was addressed with patient immobilization. QA programs for simulators, linear accelerators, and CT scanners should follow the guidelines provided in AAPM TG40 (4). QA for treatment planning systems is outlined in AAPM TG53 (14). The many factors that contribute to target delineation uncertainty, namely, acquisition parameters, organ motion, imaging modality, image fusion, and intra-observer variability, should all be examined closely for their contributions to treatment uncertainties.

As a result of the ever-changing nature of radiation oncology, this quality assurance program should be reviewed annually. Special attention should be given to new devices and treatment protocols. As adaptive radiation therapy evolves, a whole new component of the quality assurance program will need to be developed.

BIBLIOGRAPHY

1.Yeung TK, et al. Quality assurance in radiotherapy: Evaluation of errors and incidents recorded over a 10 year period. Radiother Oncol 2005;74:283–291.

2.Valli MC, et al. Evaluation of most frequent errors in daily compilation and use of a radiation treatment chart. Radiother Oncol 1994;32:87–89.

3.Huang G, et al. Error in the delivery of radiation therapy: Results of a quality assurance review. Int J Radiation Biol Phys 2005;61:1590–1595.

4.Kutcher GJ, et al. Comprehensive QA for radiation oncology: Report of AAPM radiation therapy committee task group 40. Med Phys 1994;21:581–618.