09_01
Intensity-Modulated Radiation Treatment Techniques and Clinical Applications
K. S. Clifford Chao
Radhe Mohan
Nancy A. Lee
Gregory Chronowski
Daniel Low
Qiuwen Wu
Lei Dong
Since its introduction into clinical use (21,76,77), intensity-modulated radiation therapy (IMRT) has generated widespread interest. IMRT optimally assigns nonuniform intensities (i.e., weights) to tiny subdivisions of beams, which have been called rays or “beamlets.” The ability to optimally manipulate the intensities of individual rays within each beam permits greatly increased control over the radiation fluence, enabling custom design of optimum dose distributions. These improved dose distributions potentially may lead to improved tumor control and reduced normal tissue toxicity. IMRT requires the settings of the relative intensities of tens of thousands of rays comprising an intensity-modulated treatment plan. This task cannot be accomplished manually and requires the use of specialized computer-aided optimization methods.
The optimum beamlet intensities are determined using a systematic iterative process during which the computer sequentially generates intensity-modulated plans one by one, evaluates each of them according to user-selected criteria (“desired objectives”), and makes incremental changes in the ray intensities based on the deviation from the desired objectives. The quality of an intensity-modulated treatment plan produced in this manner depends on a number of factors. These include the mathematical function and its parameters used by the optimization process to evaluate and compare competing treatment plans; the mathematics and algorithms of optimization; the number, orientation, and energy of radiation beams; margins assigned to the planning target volume (PTV) and to normal structures; dose-calculation algorithms; and so on. We will discuss many of these in detail in this chapter.
The term IMRT is used to mean much more than its literal meaning might suggest. Strictly speaking, the use of wedges and conventional compensators to compensate for surface curvature is also intensity modulation. In this chapter, IMRT is a form of three-dimensional (3D) conformal radiation therapy (CRT) in which a computer-aided optimization process is used to determine customized nonuniform fluence distributions to attain certain specified dosimetric and clinical objectives.
IMRT Rationale
IMRT has many potential advantages. It can be used to produce dose distributions that are far more conformal than those possible with standard 3D conformal radiation therapy (3DCRT). Dose distributions within the PTV, in theory, can be made more homogeneous and, if so desired, a sharper fall-off of dose at the PTV boundary can be achieved. Experience with current IMRT systems has led to an impression among many that IMRT inherently produces inhomogeneous dose distribution within the target volume. Inhomogeneity commonly observed is the result of the overriding need to partially or wholly protect one or more critical organs. In other words, the dose distributions tend to be more heterogeneous because the homogeneity criterion is made less important than the normal structure avoidance criterion. If all things were equal, the IMRT plan always should produce more homogeneous dose distribution than a plan made with uniform beams. A sharper fall-off of dose at the PTV boundary, In turn, means that the volume of normal tissues exposed to high doses may be reduced significantly. These factors may allow escalation of tumor dose, reduction of normal tissue dose, or both, hopefully leading to an improved outcome. A lower rate of complications also may mean lower cost of patient care following the treatment. In addition, IMRT has the potential to be more efficient with regard to treatment planning and delivery than standard 3DCRT, although gains in this direction are being realized rather slowly. The treatment design process is relatively insensitive to the choice of planning parameters, such as beam directions. There are no secondary field-shaping devices other than the computer-controlled multileaf collimator (MLC). Furthermore, large fields and boosts can be integrated into a single treatment plan, and, in many cases, electrons can be dispensed with, permitting the use of the same integrated boost plan for the entire course of treatment. An integrated boost treatment may offer an additional radiobiologic advantage in terms of lower dose per fraction to normal tissues while delivering higher dose per fraction to the target volume. Higher dose per fraction also reduces the number of fractions and hence lowers the cost of a treatment course.
IMRT Limitations and Risks
We should recognize, however, that IMRT has limitations. There are many dose distributions (or dose-volume combinations) that are simply not physically achievable. Furthermore, our knowledge about what is clinically optimal and achievable and how best to define clinical and dosimetric objectives of IMRT is limited. Moreover, the best solution may elude us because of the limitations of the mathematical formalism used or because of the practical limits of computer speed and the time required for finding it.
Uncertainties of various types (e.g., those related to daily, or interfraction, positioning; displacement and distortions of internal anatomy; intrafraction motion; and changes in physical and radiobiologic characteristics of tumors and normal tissues during the course of treatment) may limit the applicability and efficacy of IMRT. Dosimetry characteristics of a delivery device, such as radiation scattering and transmission through the MLC leaves, introduce some limitations in the accuracy and deliverability of IMRT fluence distributions. In addition, the limited spatial and temporal coverage and overall accuracy of current IMRT dosimetric verification systems (based principally on radiographic film) diminish the confidence in the delivered dose. Furthermore, most current dose-calculation models are limited in their accuracy, especially for the small, complex shapes required for IMRT. It is quite conceivable that inaccuracies in dose calculations may yield a solution different from the one derived if dose calculations were accurate. Perhaps the most important factor that may limit the immediate success of IMRT is the inadequacy of imaging technology to define the true extent of the tumor, its extensions, and radiobiologic characteristics as well as geometric, dose-response, and functional characteristics of normal tissues.
P.240
We also should be aware of the risks of IMRT. The effect of large fraction sizes used in integral boost IMRT on tissues embedded within the gross tumor volume (GTV) is uncertain and may present an increased risk of injury (97). There also may be an increased risk that improper use of spatial margins, coupled with the high degree of conformation with IMRT, may lead to geographic misses of the disease and recurrences, especially for disease sites where positioning and motion uncertainties play a large role or where there are significant changes in anatomy and radiobiology during the course of radiotherapy. Similarly, high doses in close proximity to normal critical structures may pose a greater risk of normal tissue injury. In addition, IMRT dose distributions are unusual and highly complex and existing experience is too limited to interpret them properly and evaluate their efficacy. This may lead to unforeseen sequelae.
These limitations and risks point to the need for continued investigations to improve the method and to minimize the uncertainties. Such investigations are essential to exploit the full power of IMRT.
IMRT—An Unconventional Paradigm
The application, process, and dose distributions of IMRT are significantly different from those of conventional two-dimensional (2D) CRT or 3DCRT. This means the traditional methods of specification and fractionation of treatments, evaluation of treatment plans, and reporting of results are limited and new methods need to be introduced.
The traditional 3DCRT process involves “forward planning,” in which beam parameters (directions, apertures and their margins, beam weights, beam modifiers) are specified and dose distributions are computed. The treatment plan is evaluated by a human being, and, if necessary, beam parameters are modified to achieve a satisfactory dose distribution. In IMRT, an inverse process (“inverse planning”) is used in which the desired dosimetric and clinical objectives are stated mathematically (in the form of an “objective function”). The term inverse planning sometimes is confused with matrix inversion of a given dose distribution. In the present context it is used to distinguish it from forward planning for conventional 3DCRT. The IMRT optimization software iteratively adjusts beam parameters with the aim of obtaining the best possible approximation of the desired dose distribution. In each optimization iteration, the optimization software computes the value of the objective function (i.e., the IMRT plan score) to judge the overall quality of each of a large number of plans to choose the optimum one.
IMRT is most conformal and most efficient if all target volumes (gross disease, subclinical extensions, and electively treated nodes) are treated simultaneously using different fraction sizes. Such a treatment strategy has been called the simultaneous integrated boost (SIB) (97). This is in contrast to conventional radiotherapy in which the same fraction size (typically 1.8 or 2 Gy) is used for all target volumes with successive reductions in field sizes to protect critical normal structures and to limit the dose to electively treated and subclinical disease regions.
Alternative IMRT Approaches
During the past 15 years, a variety of techniques have been explored for designing and delivering optimized IMRT (5,8,9,10,11,13,14,15,16,20,21,27,30,34,35,53,60,76,77,86,91,92,93,96,97,115,116,117,118,119,120,128,131,132,133,134,135,136,147,148). Many of these are implemented in commercial IMRT systems. The most significant differences among the various approaches are in terms of the mechanisms they use for the delivery of nonuniform fluences. Although the merits of each often are speculated, the superiority of any of the approaches is difficult to assess because there have been no systematic comparisons of clinical treatment plans.
Of the various approaches proposed, two dominant but significantly different methods have emerged. Mackie et al. (85,86) proposed an approach called tomotherapy in which intensity-modulated photon therapy is delivered using a rotating slit beam. A temporally modulated slit MLC is used to rapidly move leaves in or out of the slit. Like a computed tomography (CT) unit, the radiation source and the collimator continuously revolve around the patient. Either the patient is translated between successive rotations (serial tomotherapy) or during rotation (helical tomotherapy). For helical tomotherapy, the system looks like a conventional CT scanner and includes a megavoltage portal detector to provide for the tomographic reconstruction of the delivered dose distribution. The first clinical tomotherapy machine is in the process of being implemented.
A commercial slit collimator (called MIMiC) of the type proposed by Mackie et al. (85,86) has been designed and built by the NOMOS Corporation (North American Scientific Chatsworth, CA). It has been incorporated into their serial tomotherapy system, known as Peacock (Fig. 9.1), for planning and rotational delivery of intensity-modulated treatments (21).
In the second approach, implemented first into clinical use at Memorial Sloan-Kettering Cancer Center (76,77,91,92,96,98,131,132), a standard MLC is used to deliver the optimized fluence distribution in either dynamic mode (defined as the leaves moving while the radiation is on) or static mode, i.e., “step-and-shoot” mode (defined as sequential delivery of radiation subportals that combine to deliver the desired fluence distribution), to deliver a set of intensity-modulated fields incident from fixed-gantry angles. These techniques are gaining wide acceptance rapidly. Every major commercial treatment-planning system manufacturer has implemented one or both of these approaches (Fig. 9.2).
A third approach, called intensity-modulated arc therapy or IMAT, developed by Yu (147), uses a combination of dynamic multileaf collimation and arc therapy. The shape of the field formed by the MLC changes continuously during gantry rotation. Multiple superimposing arcs are used, and the field shape for a specific gantry angle changes from one arc to the next appropriately so that the cumulative fluence distribution of all arcs is equal to the desired distribution.
In addition to these approaches, the University of Michigan has used the so-called multisegment approach in which each of a number of beams is divided into multiple segments. One segment for each beam frames the entire target while the others spare one or more normal structures. Each segment is uniform in intensity. The weights of segments of all beams are optimized to produce the desired treatment plan. The treatments are delivered as a sequence of multiple uniform field segments. A similar approach previously was proposed by Mohan et al. (94). In almost all of these significantly different treatment-delivery approaches, the underlying principles of optimization are similar, although the specifics may be quite different.
The IMRT Process Overview
As mentioned previously, there are significant differences in 3DCRT and IMRT concepts and processes. However, there are also many similarities. In particular, IMRT relies on many of the same imaging, dose calculations, plan evaluation, quality assurance (QA), and delivery tools as 3DCRT.
The IMRT planning, QA, and delivery phases of the dynamic or static MLC process are shown in Figure 9.3. Figures 9.4, 9.5 and 9.6 show the steps in each phase of the IMRT process. The tomotherapy process is similar, except that the fixed-beam angle selection is replaced by selection of the slice thickness and, for serial tomotherapy, the gantry rotation angles.
P.241
In the preparatory phase of the IMRT process, volumes of interest (such as tumors and normal organs) are delineated on 3D CT images, often with assistance from other coregistered imaging modalities. Also, the desired objectives in the form of an objective function, its parameter values, and the IMRT fractionation strategy are specified, and beam configuration is defined.
In the treatment-plan optimization phase, an iterative process is used to adjust and set the intensities of rays of each beam (or portion of the arc) so that the resulting intensity distributions yield the best approximation of the desired objectives. The IMRT plan then is evaluated to ensure that the trade-offs made by the optimization system are acceptable. If further improvement is deemed necessary and possible, the objective function parameters are modified and the optimization process is repeated until a satisfactory treatment plan is achieved.
In the leaf sequence-generation phase, the intensity distributions are converted into sequences of leaf positions. It is conceivable that certain dose distributions cannot be delivered as a result of the leakage characteristics of the delivery devices. Therefore, in most treatment-planning systems, the leaf sequences are used in a reverse process to calculate the dose distributions they are expected to deliver. These dose distributions, called the deliverable dose distributions, are evaluated for clinical adequacy. If necessary, objective function parameters are further adjusted to produce an intensity distribution that leads to a deliverable dose distribution that meets the desired objectives. This is the practice in most systems. However, in some systems, the leaf sequence-generation process is incorporated into the IMRT plan optimization loop so that the optimized and deliverable dose distributions are identical. More details on this are given later in this chapter.
The leaf sequences then are transmitted to the treatment machine and used to verify that the dose distribution that will be delivered to the patient is correct and accurate. The patient then is set up in the usual fashion and treated. In general, the
P.242
entire treatment is delivered remotely without the need to re-enter the treatment room in between fields.
Preparatory and IMRT Planning Phases
This section discusses each of the steps of the preparatory and IMRT plan design phases. For reasons of clarity, the order in which these steps are discussed is not the same as the order in which they occur as shown in Figs. 9.4, 9.5 and 9.6.
Imaging and Volumes of Interest
As with 3DCRT, the treatment-planning process begins with the delineation of the outlines of the GTV, clinical target volume (CTV), and the critical normal structures considered to be at risk on a sequence of CT image sections. In an important contrast with standard 3DCRT, regions of subclinical extension (volume typically enclosed by a 1- to 2-cm margin around the gross tumor) and potential subclinical disease (e.g., electively treated lymph–node-bearing regions) also need to be outlined. The optimization process considers explicitly and simultaneously both the gross disease and the larger volumes of occult or microscopic disease to design an IMRT plan. As will be explained later, this strategy has some distinct advantages. A supplementary margin is added to allow for uncertainties related to the movement of the tumor volume from one day to the next and for intrafraction motion to obtain the PTVs. The number of normal structures that need to be drawn also increases. In conventional radiotherapy, in which the use of large uniform fields is typical and the treatment plans are evaluated manually, a clinician can make a reasonable estimate of dose received by a volume of interest even if it is not explicitly drawn. In IMRT, in which dose is being escalated to unprecedented levels, where dose distributions are highly nonuniform, and where plans are generated and evaluated by the computer during the iterative optimization process, all structures to which the dose must be constrained need to be delineated.
Beam Configurations
Systems Using Fixed Intensity-Modulated Fields
The beam configuration can have a significant impact on the quality of an optimized IMRT plan. It may be argued that, because of the greater control over dose distributions afforded by optimized intensity modulation, the fine-tuning of beam angles may not be as important for IMRT as it is for standard radiotherapy. However, optimization of beam angles may find paths least obstructed by critical normal tissues, thus facilitating the achievement of desired distribution with a minimum of compromise.
Beam-angle optimization, however, is not a trivial problem. There have been some attempts to solve this problem (108,121), but a satisfactory general solution has not yet been found. To appreciate the magnitude of the problem, consider the following example. If the angle range is divided into 5-degree steps, nearly 60,000 combinations would need to be tested for three beams, nearly 14 million combinations for five beams, nearly 1.5 billion combinations for seven beams, and so on. Considering the magnitude of the search space, none of the optimization methods is likely to be able to demonstrate a significant improvement in treatment plans, let alone find a truly optimum combination when the number of beams is five or more. Furthermore, the beam-angle optimization problem is known to have multiple minima, which means that fast gradient-based optimization techniques may fail.
Another question that may be asked is how many beams are optimal. In principle, a larger number of beams would provide a larger number of parameters to adjust and therefore a greater opportunity to achieve desired dose distributions. (Thus, in theory, a rotational beam would be the ultimate.) However, for fixed-beam IMRT, it may be desirable to minimize the number of beams to reduce the time and effort required for planning, QA, dosimetric verification, and delivery of treatments. Fewer intensity-modulated beams would be needed if beam angles were optimized than if the beams were placed at equiangular steps.
Considering the difficulties of optimizing beam angles, beam directions are selected intuitively, based on conventional experience, or placed at equiangular steps. For equiangular beams,
P.243
in general, the quality of a plan improves as the number of beams increases; but as the number of beams increases, the additional gain achieved diminishes. It is not evident a priori how many intensity-modulated beams would be adequate. The ideal minimum number would depend in a complex manner on a combination of geometric and biologic factors including the size, shape, and location of the target volume; the sizes, shapes, tolerances, tissue architectures, and relative locations of the surrounding normal tissues; and the prescription dose. This number may have to be determined by trial and error for each class of radiotherapy problems.
Figure 9.7 (a) through (d) compare head and neck IMRT plans designed with 5, 7, 9, and 15 equally spaced beams. Consistent with published experience, the plan quality improves but the incremental improvement diminishes with increasing number of beams. Optimum nonuniform placement of beams can further improve dose distribution. Figure 9.8A and B shows another head and neck IMRT case for two different beam angles. The patient, treated with beam configuration shown in Figure 9.8A, developed significant mucositis at the early phase of treatment. This was consistent with the “horn” in dose distribution pointed to by the arrow. Revising beam angle arrangement as shown in Figure 9.8C led to improved dose distribution shown in Figure 9.8D.
In general, it is most advantageous to place beams so that they are maximally avoiding each other and the opposing beams with the stipulation that directions that overlap significant obstructions, such as heavily attenuating bars in the treatment couch, be avoided. For simplicity, beams often are constrained to lie in the same transverse plane. However, noncoplanar beams will provide an additional degree of freedom and potentially an additional gain in the quality of treatments.
Although reducing the number of beams is a desirable goal for IMRT delivered with several fixed-gantry angles and dynamic MLC, it should not be the overriding consideration. IMRT can be planned and delivered automatically in times not significantly different from the times for much simpler conventional treatments. Therefore, the delivery times for 6 to 20 beams may be quite acceptable. Keep in mind, however, that some of the current linear accelerators are limited in their ability to accurately deliver a large number of intensity-modulated beams each with a very small number of monitor units (MUs).
Systems Using Rotating Slit (Tomotherapy) Approach
Tomotherapy delivery has substantial differences from fixed-portal IMRT. The linear accelerator rotates during delivery, and the beam is modulated during rotation. Typically, the modulation is subdivided into small gantry angle ranges (e.g., 5 degrees) and the beam is independently modulated at each gantry angle. Each leaf is used to deliver a single rotating pencil. The pencil-beam modulation is conducted for each leaf by opening that leaf for a fraction of the gantry range consistent with the fractional fluence to be delivered from that gantry angle. For example, for a 5-degree angle range bin, if a leaf is to deliver 50% fluence, the leaf will be open for 2.5 degrees over the 5-degree range. Because of geometric constraints of modulating the radiation fan beams, only one or two thin planes can be treated with each rotation. The Peacock system, for instance, uses two banks of opposing leaves projecting to 1.7 or 3.4 cm, depending on user-selected mechanical stops. This delivers modulated beams to two abutting, independently modulated planes. The helical tomotherapy unit uses a single leaf bank with a backup collimator that allows the radiation field width to be continuously adjusted. Narrower leaf widths provide higher spatial resolution for modulation but require more treatment arcs and consequently more delivery time.
Aperture Margins
IMRT has the inherent capacity to reduce margins attributable to the beam penumbra. When a photon beam traverses the body, it is scattered, depositing dose not only along the path of each ray of the beam but also at points away from it. The electrons knocked out by the incident photons travel laterally to points in the neighborhood of each ray, depositing dose along the way. Near the middle of a uniform beam, outgoing electrons are offset by incoming electrons and equilibrium exists. However, at and just inside the boundaries of the beam, there are no incoming electrons to balance electrons flowing out of the beam. Therefore, a “lateral disequilibrium” exists that leads to a dose deficit inside the boundaries of beams. For lower energy beams and at large depths, scattered photons significantly contribute to this effect also. The conventional approach to overcome this deficiency is to add a margin for the “beam penumbra” to the PTV so that the tumor dose is maintained at the required level.
For IMRT plans, there is another method to counterbalance the dose deficit. The intensity of rays just inside the beam boundary may be increased. Because some of the increased energy must also flow out, a very large increase would be required if the margin for the penumbra were set to zero or to a very small value. Therefore, an increase in boundary fluence alone is not enough. A combination of an increased fluence and the addition of a margin, albeit a much smaller one, is a better solution. This reduction in margin can be exploited quite usefully to reduce the volume of normal tissues exposed to high doses
P.244
of radiation with a corresponding reduction in toxicity and a further potential for dose escalation.
The beam–boundary-sharpening and margin-reduction feature of IMRT can be taken advantage of only if the dose-computation method is able to adequately take into account the lateral transport of radiation and if the intensity matrix grid size is sufficiently small. Initially, dose distribution for a given configuration of beams is computed by taking lateral transport into consideration. In each optimization iteration, the intensity distribution first is designed ignoring lateral transport. At the end of the iteration, the dose distribution is recalculated, thereby incorporating the effects of field-shaping devices on lateral transport and revealing the resulting deviations from the anticipated dose distribution. In the next iteration, ray intensities are adjusted further to rectify the deviations, and so on (25,98).
A schematic example shown in Figure 9.9A through C illustrates the issues involved. Figure 9.9A shows a normal organ overlapping the target volume. The target volume is being irradiated by two parallel-opposed beams. It is desired that the dose to the region of overlap be 60% of the target dose. If more dose is delivered, damage to the normal organ may result; but lower than the desired dose may cause local failure. If the role of lateral transport in optimization is ignored, the intensity resulting from the optimization process is essentially a step function, as shown in Fig. 9.9B (solid curve). The corresponding dose distribution (the dotted curve) shows a dose deficit inside the high-dose target volume as well as the outside edge of the region of overlap and an excess of dose in the region of overlap adjacent to the high-dose volume. If lateral transport is incorporated by adjusting fluence, the fluence and dose patterns shown
P.245
in Figure 9.9C result. Fluence is increased at both boundaries. It also is increased in the high-dose side of the interface with the overlap region and decreased on the lower-dose side. The dose is now much closer to the desired dose. Comparing Figures 9.9B and C, it also appears that a modest increase in fluence just inside the boundary does not lead to a perceptible increase in dose outside the beam boundary. This is presumably the result of the fact that the excess dose flowing out of the target periphery is deposited in a much larger volume of tissue. A reduction of margins attributable to penumbra by as much as 8 mm has been found to be feasible for prostate treatments (25,98).
IMRT Fractionation
In principle, conventional fractionation strategies can be used to design IMRT plans as well. For example, in a strategy similar to the conventional 1.8-Gy to 2-Gy/fx schedule, a major portion of the dose could be delivered in the initial phase using uniform fields designed with standard 3D conformal methods followed by an IMRT boost. Alternatively, separate IMRT plans could be designed for both the initial large-field treatment and the boost treatment. It may be intuitively obvious that, if a large portion of the dose already has been delivered using large fields, it may be very difficult, if not impossible, to achieve a high level of dose conformation with the remaining fractions in the IMRT-boost phase (97). As indicated earlier in this chapter, IMRT may be most conformal if all targets volumes (gross disease, subclinical extensions, and electively treated nodes) are treated simultaneously using different fraction sizes (97). Such a treatment strategy has been called the simultaneous integrated boost (SIB) (97). The SIB IMRT strategy not only produces superior dose distributions; it is also an easier, more efficient, and perhaps less error-prone way of planning and delivering IMRT because it involves the use of the same plan for the entire course of treatment. Furthermore, in many cases, there is no need for electron fields and the nodal volumes can be included in the IMRT fields, thus avoiding the perennial problem of field matching encountered in the treatment of many sites.
Because each of the target regions receives different doses per fraction in the SIB IMRT strategy, prescribed nominal (physical) dose and dose per fraction must be adjusted appropriately. The adjusted nominal dose and fraction size for each target region depends on the number of IMRT fractions. The fraction sizes may be estimated using an isoeffect relationship based on the linear-quadratic model and the values of its parameters (such as α/β ratios, tumor doubling time).
The effect of the modified fractionation on acute and late toxicity of normal tissues both outside and within the volumes to be treated also should be considered. Because of the improved conformality of IMRT plans, dose to normal tissues outside the target volume is typically lower than for conventional treatment plans. In addition, if the number of fractions is greater than the number of fractions used to deliver large fields in conventional therapy, the dose per fraction to normal tissues is lower. Therefore, the biologically effective dose would be lower still. However, normal tissues embedded within or adjacent to the target volumes would receive high doses per fraction and may be at higher risk. Isoeffect formulae for normal tissues also may be derived to estimate the effect of a particular fractionation strategy. These formalisms would need to incorporate regeneration and change in sensitivity over the treatment course.
The values of parameters for the computation of altered fractionation may, in theory, be obtained from published studies. Studies by Maciejewski et al. (84) and Withers et al. (138,139,140), for example, have yielded important information for estimating tumor parameters for head and neck carcinoma. In general, the data available are limited. Furthermore, there is considerable uncertainty in the data, and there are concerns
P.246
about the validity of numerous assumptions in the linear-quadratic model and the isoeffect formalism, especially with regard to normal tissues. Nevertheless, various investigators have carried out the necessary calculations and adopted SIB IMRT fractionation strategies. Continued investigations and clinical trials are needed to develop more reliable time-dose fractionation models, to produce better estimates of their parameters, and to evaluate alternate SIB IMRT fractionation strategies for all sites. The following are some examples of IMRT fractionation strategies currently being used for IMRT of head and neck cancers.
In Radiation Therapy Oncology Group H-0022 protocol, 30 daily fractions are used to simultaneously deliver 66 Gy (2.2. Gy per fraction) to the PTV, 60 Gy (2 Gy per fraction) to the high-risk subclinical disease (“first echelon nodes or dissected neck area containing lymph node metastases”), and 54 Gy (1.8 Gy per fraction) to subclinical disease. These are biologically equivalent to 70, 60, and 50 Gy, respectively, if given in 2 Gy per fraction. For normal structures, brainstem, spinal cord, and mandible are maintained below 54, 45, and 70 Gy, respectively. The mean dose to the parotid glands is maintained below 26 Gy and/or 50% of one of the parotids is maintained below 30 Gy and/or at least 20 mL of the combined volume of both parotids is constrained to receive no more than 20 Gy (43).
The SIB strategy at Virginia Commonwealth University involves a dose-escalation protocol in which primary nominal dose levels of 68.1, 70.8, and 73.8 Gy, given in 30 fractions (biologically equivalent to 74, 79, and 85 Gy, respectively, if given in 2 Gy per fraction) are used (143). Simultaneously, the subclinical disease and electively treated nodes were prescribed 60 and 54 Gy, respectively (biologically equivalent to 60 and 50 Gy, respectively, if given in 2 Gy fractions). Spinal cord and brainstem are maintained below 45 and 55 Gy, respectively, and an attempt is made to allow no more than 50% of at least one parotid to receive higher than 26 Gy.
At the Mallinckrodt Institute of Radiology, the SIB strategy for definitive IMRT prescribes 70 Gy in 35 fractions in 2 Gy per fraction to the volume of gross disease with margins. The adjacent soft tissue and nodal volumes at high risk were treated to 63 Gy in 1.8 Gy per fraction and simultaneously 56 Gy in 1.6 Gy per fraction to the elective nodal regions. This regimen has been shown to be well tolerated when combined with concurrent chemotherapy (25).
Optic nerve and optic chiasm are maintained below 55 Gy, retina below 45 Gy, brainstem below 50 to 55 Gy, spinal cord to below 45 to 48 Gy, parotid glands to below 20 to 30 Gy, and mandible to below 70 Gy.
Optimization of Intensity Maps
The optimization of ray intensities may be carried out using one of several mathematical formalisms and algorithms (also termed optimization engines). Each method has its strengths and weaknesses. The choice depends in part on the nature of the objective function and in part on individual preference. Although the details are complex, the basic principles are not difficult to comprehend. As depicted schematically in Figure 9.10, each ray of each beam is traced from the source of radiation through the patient. Only the rays that pass through the target volume need to be traced (plus through a small margin assigned to ensure that the lateral loss of scattered radiation does not compromise the treatment). Others are set to a weight of zero.
P.247
The patient's 3D image is divided into voxels. The dose at every voxel in the patient is calculated for an initial set of ray weights. The resulting dose distribution is used to compute the “score” of the treatment plan (i.e., the value of the objective function that mathematically states the clinical objectives of the intended treatment).
The ray-tracing process identifies the tumor and normal tissue voxels that lie along the path of the ray. The effect of a small change in a ray weight on the score then is calculated. If the increase in ray weight would result in favorable consequences for the patient, the weight is increased, and vice versa. Mathematically speaking, the ray weight is changed by an amount proportional to the gradient of the score with respect to the ray weight. Realizing that the improvement in the plan at each point comes from rays from many beams and that each ray affects many points, only a small change in ray weight may be permitted at a time. This process is repeated for each ray. At the end of each complete cycle (an iteration), a small improvement in the treatment plan results. The new pattern of ray intensities then is used to calculate a new dose distribution and the new score of the plan, which then is used as the basis of further improvement in the next iteration. The iterative process continues until no further improvement takes place, the optimization process is assumed to have converged, and the optimum plan is assumed to have been achieved.
Many current optimization systems use variations of gradient techniques to optimize IMRT plans. Methods of this type are by far the fastest computationally. However, the use of gradient techniques assumes that there is a single extremum (a minimum or a maximum, depending on the form of the objective function). This is indeed the case for objective functions based on variance of dose and when only ray weights are optimized. For other cases, it would be necessary to determine whether multiple extrema exist and whether such multiple extrema have an impact on the quality of the solution found. Multiple extrema have been found to exist when beam directions are optimized or when dose–response-based objective functions are used to optimize weights of uniform beams (88,94,120). One can expect that multiple minima also exist when dose–response-based objective functions are used to optimize IMRT plans. Using simple schematic examples, it also has been shown that multiple minima exist when dose–volume-based objectives are used (40). Although this may be the case in theory, the existence of multiple minima has not been found to be a serious impediment in dose–volume-based or dose–response-based optimization using gradient techniques. In fact, in a recent study of dose–volume-based IMRT optimization, Wu and Mohan (142) found that, starting from vastly different initial intensities, the solutions converged to nearly the same plans. The reasons for this have been speculated but not conclusively proven and need to be investigated further.
It is possible that multiple minima do become a factor under a specific set of circumstances. These circumstances need to be determined. If multiple minima are discovered to be a factor, then some form of stochastic optimization technique may need to be considered. At the simplest, one may use a random search technique in conjunction with one of the gradient techniques. A more sophisticated stochastic technique is “simulated annealing” or its variation, the “fast simulated annealing” (88,94). These techniques allow the optimization process to escape from the local minima traps. Other forms of stochastic approaches, such as “genetic algorithms,” also have been proposed (45). In principle, the simulated annealing technique and other stochastic approaches can find the global minimum, but, practically, there is no guarantee that the absolute optimum has been found, only that the best among the solutions examined has been found. Furthermore, stochastic techniques tend to be extremely slow and should be used in routine work only if it is established that they are necessary. Nevertheless, some commercial systems have implemented the simulated annealing approach for IMRT optimization (21).
K. S. Clifford Chao
Radhe Mohan
Nancy A. Lee
Gregory Chronowski
Daniel Low
Qiuwen Wu
Lei Dong
Since its introduction into clinical use (21,76,77), intensity-modulated radiation therapy (IMRT) has generated widespread interest. IMRT optimally assigns nonuniform intensities (i.e., weights) to tiny subdivisions of beams, which have been called rays or “beamlets.” The ability to optimally manipulate the intensities of individual rays within each beam permits greatly increased control over the radiation fluence, enabling custom design of optimum dose distributions. These improved dose distributions potentially may lead to improved tumor control and reduced normal tissue toxicity. IMRT requires the settings of the relative intensities of tens of thousands of rays comprising an intensity-modulated treatment plan. This task cannot be accomplished manually and requires the use of specialized computer-aided optimization methods.
The optimum beamlet intensities are determined using a systematic iterative process during which the computer sequentially generates intensity-modulated plans one by one, evaluates each of them according to user-selected criteria (“desired objectives”), and makes incremental changes in the ray intensities based on the deviation from the desired objectives. The quality of an intensity-modulated treatment plan produced in this manner depends on a number of factors. These include the mathematical function and its parameters used by the optimization process to evaluate and compare competing treatment plans; the mathematics and algorithms of optimization; the number, orientation, and energy of radiation beams; margins assigned to the planning target volume (PTV) and to normal structures; dose-calculation algorithms; and so on. We will discuss many of these in detail in this chapter.
The term IMRT is used to mean much more than its literal meaning might suggest. Strictly speaking, the use of wedges and conventional compensators to compensate for surface curvature is also intensity modulation. In this chapter, IMRT is a form of three-dimensional (3D) conformal radiation therapy (CRT) in which a computer-aided optimization process is used to determine customized nonuniform fluence distributions to attain certain specified dosimetric and clinical objectives.
IMRT Rationale
IMRT has many potential advantages. It can be used to produce dose distributions that are far more conformal than those possible with standard 3D conformal radiation therapy (3DCRT). Dose distributions within the PTV, in theory, can be made more homogeneous and, if so desired, a sharper fall-off of dose at the PTV boundary can be achieved. Experience with current IMRT systems has led to an impression among many that IMRT inherently produces inhomogeneous dose distribution within the target volume. Inhomogeneity commonly observed is the result of the overriding need to partially or wholly protect one or more critical organs. In other words, the dose distributions tend to be more heterogeneous because the homogeneity criterion is made less important than the normal structure avoidance criterion. If all things were equal, the IMRT plan always should produce more homogeneous dose distribution than a plan made with uniform beams. A sharper fall-off of dose at the PTV boundary, In turn, means that the volume of normal tissues exposed to high doses may be reduced significantly. These factors may allow escalation of tumor dose, reduction of normal tissue dose, or both, hopefully leading to an improved outcome. A lower rate of complications also may mean lower cost of patient care following the treatment. In addition, IMRT has the potential to be more efficient with regard to treatment planning and delivery than standard 3DCRT, although gains in this direction are being realized rather slowly. The treatment design process is relatively insensitive to the choice of planning parameters, such as beam directions. There are no secondary field-shaping devices other than the computer-controlled multileaf collimator (MLC). Furthermore, large fields and boosts can be integrated into a single treatment plan, and, in many cases, electrons can be dispensed with, permitting the use of the same integrated boost plan for the entire course of treatment. An integrated boost treatment may offer an additional radiobiologic advantage in terms of lower dose per fraction to normal tissues while delivering higher dose per fraction to the target volume. Higher dose per fraction also reduces the number of fractions and hence lowers the cost of a treatment course.
IMRT Limitations and Risks
We should recognize, however, that IMRT has limitations. There are many dose distributions (or dose-volume combinations) that are simply not physically achievable. Furthermore, our knowledge about what is clinically optimal and achievable and how best to define clinical and dosimetric objectives of IMRT is limited. Moreover, the best solution may elude us because of the limitations of the mathematical formalism used or because of the practical limits of computer speed and the time required for finding it.
Uncertainties of various types (e.g., those related to daily, or interfraction, positioning; displacement and distortions of internal anatomy; intrafraction motion; and changes in physical and radiobiologic characteristics of tumors and normal tissues during the course of treatment) may limit the applicability and efficacy of IMRT. Dosimetry characteristics of a delivery device, such as radiation scattering and transmission through the MLC leaves, introduce some limitations in the accuracy and deliverability of IMRT fluence distributions. In addition, the limited spatial and temporal coverage and overall accuracy of current IMRT dosimetric verification systems (based principally on radiographic film) diminish the confidence in the delivered dose. Furthermore, most current dose-calculation models are limited in their accuracy, especially for the small, complex shapes required for IMRT. It is quite conceivable that inaccuracies in dose calculations may yield a solution different from the one derived if dose calculations were accurate. Perhaps the most important factor that may limit the immediate success of IMRT is the inadequacy of imaging technology to define the true extent of the tumor, its extensions, and radiobiologic characteristics as well as geometric, dose-response, and functional characteristics of normal tissues.
P.240
We also should be aware of the risks of IMRT. The effect of large fraction sizes used in integral boost IMRT on tissues embedded within the gross tumor volume (GTV) is uncertain and may present an increased risk of injury (97). There also may be an increased risk that improper use of spatial margins, coupled with the high degree of conformation with IMRT, may lead to geographic misses of the disease and recurrences, especially for disease sites where positioning and motion uncertainties play a large role or where there are significant changes in anatomy and radiobiology during the course of radiotherapy. Similarly, high doses in close proximity to normal critical structures may pose a greater risk of normal tissue injury. In addition, IMRT dose distributions are unusual and highly complex and existing experience is too limited to interpret them properly and evaluate their efficacy. This may lead to unforeseen sequelae.
These limitations and risks point to the need for continued investigations to improve the method and to minimize the uncertainties. Such investigations are essential to exploit the full power of IMRT.
IMRT—An Unconventional Paradigm
The application, process, and dose distributions of IMRT are significantly different from those of conventional two-dimensional (2D) CRT or 3DCRT. This means the traditional methods of specification and fractionation of treatments, evaluation of treatment plans, and reporting of results are limited and new methods need to be introduced.
The traditional 3DCRT process involves “forward planning,” in which beam parameters (directions, apertures and their margins, beam weights, beam modifiers) are specified and dose distributions are computed. The treatment plan is evaluated by a human being, and, if necessary, beam parameters are modified to achieve a satisfactory dose distribution. In IMRT, an inverse process (“inverse planning”) is used in which the desired dosimetric and clinical objectives are stated mathematically (in the form of an “objective function”). The term inverse planning sometimes is confused with matrix inversion of a given dose distribution. In the present context it is used to distinguish it from forward planning for conventional 3DCRT. The IMRT optimization software iteratively adjusts beam parameters with the aim of obtaining the best possible approximation of the desired dose distribution. In each optimization iteration, the optimization software computes the value of the objective function (i.e., the IMRT plan score) to judge the overall quality of each of a large number of plans to choose the optimum one.
IMRT is most conformal and most efficient if all target volumes (gross disease, subclinical extensions, and electively treated nodes) are treated simultaneously using different fraction sizes. Such a treatment strategy has been called the simultaneous integrated boost (SIB) (97). This is in contrast to conventional radiotherapy in which the same fraction size (typically 1.8 or 2 Gy) is used for all target volumes with successive reductions in field sizes to protect critical normal structures and to limit the dose to electively treated and subclinical disease regions.
Alternative IMRT Approaches
During the past 15 years, a variety of techniques have been explored for designing and delivering optimized IMRT (5,8,9,10,11,13,14,15,16,20,21,27,30,34,35,53,60,76,77,86,91,92,93,96,97,115,116,117,118,119,120,128,131,132,133,134,135,136,147,148). Many of these are implemented in commercial IMRT systems. The most significant differences among the various approaches are in terms of the mechanisms they use for the delivery of nonuniform fluences. Although the merits of each often are speculated, the superiority of any of the approaches is difficult to assess because there have been no systematic comparisons of clinical treatment plans.
Of the various approaches proposed, two dominant but significantly different methods have emerged. Mackie et al. (85,86) proposed an approach called tomotherapy in which intensity-modulated photon therapy is delivered using a rotating slit beam. A temporally modulated slit MLC is used to rapidly move leaves in or out of the slit. Like a computed tomography (CT) unit, the radiation source and the collimator continuously revolve around the patient. Either the patient is translated between successive rotations (serial tomotherapy) or during rotation (helical tomotherapy). For helical tomotherapy, the system looks like a conventional CT scanner and includes a megavoltage portal detector to provide for the tomographic reconstruction of the delivered dose distribution. The first clinical tomotherapy machine is in the process of being implemented.
A commercial slit collimator (called MIMiC) of the type proposed by Mackie et al. (85,86) has been designed and built by the NOMOS Corporation (North American Scientific Chatsworth, CA). It has been incorporated into their serial tomotherapy system, known as Peacock (Fig. 9.1), for planning and rotational delivery of intensity-modulated treatments (21).
In the second approach, implemented first into clinical use at Memorial Sloan-Kettering Cancer Center (76,77,91,92,96,98,131,132), a standard MLC is used to deliver the optimized fluence distribution in either dynamic mode (defined as the leaves moving while the radiation is on) or static mode, i.e., “step-and-shoot” mode (defined as sequential delivery of radiation subportals that combine to deliver the desired fluence distribution), to deliver a set of intensity-modulated fields incident from fixed-gantry angles. These techniques are gaining wide acceptance rapidly. Every major commercial treatment-planning system manufacturer has implemented one or both of these approaches (Fig. 9.2).
A third approach, called intensity-modulated arc therapy or IMAT, developed by Yu (147), uses a combination of dynamic multileaf collimation and arc therapy. The shape of the field formed by the MLC changes continuously during gantry rotation. Multiple superimposing arcs are used, and the field shape for a specific gantry angle changes from one arc to the next appropriately so that the cumulative fluence distribution of all arcs is equal to the desired distribution.
In addition to these approaches, the University of Michigan has used the so-called multisegment approach in which each of a number of beams is divided into multiple segments. One segment for each beam frames the entire target while the others spare one or more normal structures. Each segment is uniform in intensity. The weights of segments of all beams are optimized to produce the desired treatment plan. The treatments are delivered as a sequence of multiple uniform field segments. A similar approach previously was proposed by Mohan et al. (94). In almost all of these significantly different treatment-delivery approaches, the underlying principles of optimization are similar, although the specifics may be quite different.
The IMRT Process Overview
As mentioned previously, there are significant differences in 3DCRT and IMRT concepts and processes. However, there are also many similarities. In particular, IMRT relies on many of the same imaging, dose calculations, plan evaluation, quality assurance (QA), and delivery tools as 3DCRT.
The IMRT planning, QA, and delivery phases of the dynamic or static MLC process are shown in Figure 9.3. Figures 9.4, 9.5 and 9.6 show the steps in each phase of the IMRT process. The tomotherapy process is similar, except that the fixed-beam angle selection is replaced by selection of the slice thickness and, for serial tomotherapy, the gantry rotation angles.
P.241
In the preparatory phase of the IMRT process, volumes of interest (such as tumors and normal organs) are delineated on 3D CT images, often with assistance from other coregistered imaging modalities. Also, the desired objectives in the form of an objective function, its parameter values, and the IMRT fractionation strategy are specified, and beam configuration is defined.
In the treatment-plan optimization phase, an iterative process is used to adjust and set the intensities of rays of each beam (or portion of the arc) so that the resulting intensity distributions yield the best approximation of the desired objectives. The IMRT plan then is evaluated to ensure that the trade-offs made by the optimization system are acceptable. If further improvement is deemed necessary and possible, the objective function parameters are modified and the optimization process is repeated until a satisfactory treatment plan is achieved.
In the leaf sequence-generation phase, the intensity distributions are converted into sequences of leaf positions. It is conceivable that certain dose distributions cannot be delivered as a result of the leakage characteristics of the delivery devices. Therefore, in most treatment-planning systems, the leaf sequences are used in a reverse process to calculate the dose distributions they are expected to deliver. These dose distributions, called the deliverable dose distributions, are evaluated for clinical adequacy. If necessary, objective function parameters are further adjusted to produce an intensity distribution that leads to a deliverable dose distribution that meets the desired objectives. This is the practice in most systems. However, in some systems, the leaf sequence-generation process is incorporated into the IMRT plan optimization loop so that the optimized and deliverable dose distributions are identical. More details on this are given later in this chapter.
The leaf sequences then are transmitted to the treatment machine and used to verify that the dose distribution that will be delivered to the patient is correct and accurate. The patient then is set up in the usual fashion and treated. In general, the
P.242
entire treatment is delivered remotely without the need to re-enter the treatment room in between fields.
Preparatory and IMRT Planning Phases
This section discusses each of the steps of the preparatory and IMRT plan design phases. For reasons of clarity, the order in which these steps are discussed is not the same as the order in which they occur as shown in Figs. 9.4, 9.5 and 9.6.
Imaging and Volumes of Interest
As with 3DCRT, the treatment-planning process begins with the delineation of the outlines of the GTV, clinical target volume (CTV), and the critical normal structures considered to be at risk on a sequence of CT image sections. In an important contrast with standard 3DCRT, regions of subclinical extension (volume typically enclosed by a 1- to 2-cm margin around the gross tumor) and potential subclinical disease (e.g., electively treated lymph–node-bearing regions) also need to be outlined. The optimization process considers explicitly and simultaneously both the gross disease and the larger volumes of occult or microscopic disease to design an IMRT plan. As will be explained later, this strategy has some distinct advantages. A supplementary margin is added to allow for uncertainties related to the movement of the tumor volume from one day to the next and for intrafraction motion to obtain the PTVs. The number of normal structures that need to be drawn also increases. In conventional radiotherapy, in which the use of large uniform fields is typical and the treatment plans are evaluated manually, a clinician can make a reasonable estimate of dose received by a volume of interest even if it is not explicitly drawn. In IMRT, in which dose is being escalated to unprecedented levels, where dose distributions are highly nonuniform, and where plans are generated and evaluated by the computer during the iterative optimization process, all structures to which the dose must be constrained need to be delineated.
Beam Configurations
Systems Using Fixed Intensity-Modulated Fields
The beam configuration can have a significant impact on the quality of an optimized IMRT plan. It may be argued that, because of the greater control over dose distributions afforded by optimized intensity modulation, the fine-tuning of beam angles may not be as important for IMRT as it is for standard radiotherapy. However, optimization of beam angles may find paths least obstructed by critical normal tissues, thus facilitating the achievement of desired distribution with a minimum of compromise.
Beam-angle optimization, however, is not a trivial problem. There have been some attempts to solve this problem (108,121), but a satisfactory general solution has not yet been found. To appreciate the magnitude of the problem, consider the following example. If the angle range is divided into 5-degree steps, nearly 60,000 combinations would need to be tested for three beams, nearly 14 million combinations for five beams, nearly 1.5 billion combinations for seven beams, and so on. Considering the magnitude of the search space, none of the optimization methods is likely to be able to demonstrate a significant improvement in treatment plans, let alone find a truly optimum combination when the number of beams is five or more. Furthermore, the beam-angle optimization problem is known to have multiple minima, which means that fast gradient-based optimization techniques may fail.
Another question that may be asked is how many beams are optimal. In principle, a larger number of beams would provide a larger number of parameters to adjust and therefore a greater opportunity to achieve desired dose distributions. (Thus, in theory, a rotational beam would be the ultimate.) However, for fixed-beam IMRT, it may be desirable to minimize the number of beams to reduce the time and effort required for planning, QA, dosimetric verification, and delivery of treatments. Fewer intensity-modulated beams would be needed if beam angles were optimized than if the beams were placed at equiangular steps.
Considering the difficulties of optimizing beam angles, beam directions are selected intuitively, based on conventional experience, or placed at equiangular steps. For equiangular beams,
P.243
in general, the quality of a plan improves as the number of beams increases; but as the number of beams increases, the additional gain achieved diminishes. It is not evident a priori how many intensity-modulated beams would be adequate. The ideal minimum number would depend in a complex manner on a combination of geometric and biologic factors including the size, shape, and location of the target volume; the sizes, shapes, tolerances, tissue architectures, and relative locations of the surrounding normal tissues; and the prescription dose. This number may have to be determined by trial and error for each class of radiotherapy problems.
Figure 9.7 (a) through (d) compare head and neck IMRT plans designed with 5, 7, 9, and 15 equally spaced beams. Consistent with published experience, the plan quality improves but the incremental improvement diminishes with increasing number of beams. Optimum nonuniform placement of beams can further improve dose distribution. Figure 9.8A and B shows another head and neck IMRT case for two different beam angles. The patient, treated with beam configuration shown in Figure 9.8A, developed significant mucositis at the early phase of treatment. This was consistent with the “horn” in dose distribution pointed to by the arrow. Revising beam angle arrangement as shown in Figure 9.8C led to improved dose distribution shown in Figure 9.8D.
In general, it is most advantageous to place beams so that they are maximally avoiding each other and the opposing beams with the stipulation that directions that overlap significant obstructions, such as heavily attenuating bars in the treatment couch, be avoided. For simplicity, beams often are constrained to lie in the same transverse plane. However, noncoplanar beams will provide an additional degree of freedom and potentially an additional gain in the quality of treatments.
Although reducing the number of beams is a desirable goal for IMRT delivered with several fixed-gantry angles and dynamic MLC, it should not be the overriding consideration. IMRT can be planned and delivered automatically in times not significantly different from the times for much simpler conventional treatments. Therefore, the delivery times for 6 to 20 beams may be quite acceptable. Keep in mind, however, that some of the current linear accelerators are limited in their ability to accurately deliver a large number of intensity-modulated beams each with a very small number of monitor units (MUs).
Systems Using Rotating Slit (Tomotherapy) Approach
Tomotherapy delivery has substantial differences from fixed-portal IMRT. The linear accelerator rotates during delivery, and the beam is modulated during rotation. Typically, the modulation is subdivided into small gantry angle ranges (e.g., 5 degrees) and the beam is independently modulated at each gantry angle. Each leaf is used to deliver a single rotating pencil. The pencil-beam modulation is conducted for each leaf by opening that leaf for a fraction of the gantry range consistent with the fractional fluence to be delivered from that gantry angle. For example, for a 5-degree angle range bin, if a leaf is to deliver 50% fluence, the leaf will be open for 2.5 degrees over the 5-degree range. Because of geometric constraints of modulating the radiation fan beams, only one or two thin planes can be treated with each rotation. The Peacock system, for instance, uses two banks of opposing leaves projecting to 1.7 or 3.4 cm, depending on user-selected mechanical stops. This delivers modulated beams to two abutting, independently modulated planes. The helical tomotherapy unit uses a single leaf bank with a backup collimator that allows the radiation field width to be continuously adjusted. Narrower leaf widths provide higher spatial resolution for modulation but require more treatment arcs and consequently more delivery time.
Aperture Margins
IMRT has the inherent capacity to reduce margins attributable to the beam penumbra. When a photon beam traverses the body, it is scattered, depositing dose not only along the path of each ray of the beam but also at points away from it. The electrons knocked out by the incident photons travel laterally to points in the neighborhood of each ray, depositing dose along the way. Near the middle of a uniform beam, outgoing electrons are offset by incoming electrons and equilibrium exists. However, at and just inside the boundaries of the beam, there are no incoming electrons to balance electrons flowing out of the beam. Therefore, a “lateral disequilibrium” exists that leads to a dose deficit inside the boundaries of beams. For lower energy beams and at large depths, scattered photons significantly contribute to this effect also. The conventional approach to overcome this deficiency is to add a margin for the “beam penumbra” to the PTV so that the tumor dose is maintained at the required level.
For IMRT plans, there is another method to counterbalance the dose deficit. The intensity of rays just inside the beam boundary may be increased. Because some of the increased energy must also flow out, a very large increase would be required if the margin for the penumbra were set to zero or to a very small value. Therefore, an increase in boundary fluence alone is not enough. A combination of an increased fluence and the addition of a margin, albeit a much smaller one, is a better solution. This reduction in margin can be exploited quite usefully to reduce the volume of normal tissues exposed to high doses
P.244
of radiation with a corresponding reduction in toxicity and a further potential for dose escalation.
The beam–boundary-sharpening and margin-reduction feature of IMRT can be taken advantage of only if the dose-computation method is able to adequately take into account the lateral transport of radiation and if the intensity matrix grid size is sufficiently small. Initially, dose distribution for a given configuration of beams is computed by taking lateral transport into consideration. In each optimization iteration, the intensity distribution first is designed ignoring lateral transport. At the end of the iteration, the dose distribution is recalculated, thereby incorporating the effects of field-shaping devices on lateral transport and revealing the resulting deviations from the anticipated dose distribution. In the next iteration, ray intensities are adjusted further to rectify the deviations, and so on (25,98).
A schematic example shown in Figure 9.9A through C illustrates the issues involved. Figure 9.9A shows a normal organ overlapping the target volume. The target volume is being irradiated by two parallel-opposed beams. It is desired that the dose to the region of overlap be 60% of the target dose. If more dose is delivered, damage to the normal organ may result; but lower than the desired dose may cause local failure. If the role of lateral transport in optimization is ignored, the intensity resulting from the optimization process is essentially a step function, as shown in Fig. 9.9B (solid curve). The corresponding dose distribution (the dotted curve) shows a dose deficit inside the high-dose target volume as well as the outside edge of the region of overlap and an excess of dose in the region of overlap adjacent to the high-dose volume. If lateral transport is incorporated by adjusting fluence, the fluence and dose patterns shown
P.245
in Figure 9.9C result. Fluence is increased at both boundaries. It also is increased in the high-dose side of the interface with the overlap region and decreased on the lower-dose side. The dose is now much closer to the desired dose. Comparing Figures 9.9B and C, it also appears that a modest increase in fluence just inside the boundary does not lead to a perceptible increase in dose outside the beam boundary. This is presumably the result of the fact that the excess dose flowing out of the target periphery is deposited in a much larger volume of tissue. A reduction of margins attributable to penumbra by as much as 8 mm has been found to be feasible for prostate treatments (25,98).
IMRT Fractionation
In principle, conventional fractionation strategies can be used to design IMRT plans as well. For example, in a strategy similar to the conventional 1.8-Gy to 2-Gy/fx schedule, a major portion of the dose could be delivered in the initial phase using uniform fields designed with standard 3D conformal methods followed by an IMRT boost. Alternatively, separate IMRT plans could be designed for both the initial large-field treatment and the boost treatment. It may be intuitively obvious that, if a large portion of the dose already has been delivered using large fields, it may be very difficult, if not impossible, to achieve a high level of dose conformation with the remaining fractions in the IMRT-boost phase (97). As indicated earlier in this chapter, IMRT may be most conformal if all targets volumes (gross disease, subclinical extensions, and electively treated nodes) are treated simultaneously using different fraction sizes (97). Such a treatment strategy has been called the simultaneous integrated boost (SIB) (97). The SIB IMRT strategy not only produces superior dose distributions; it is also an easier, more efficient, and perhaps less error-prone way of planning and delivering IMRT because it involves the use of the same plan for the entire course of treatment. Furthermore, in many cases, there is no need for electron fields and the nodal volumes can be included in the IMRT fields, thus avoiding the perennial problem of field matching encountered in the treatment of many sites.
Because each of the target regions receives different doses per fraction in the SIB IMRT strategy, prescribed nominal (physical) dose and dose per fraction must be adjusted appropriately. The adjusted nominal dose and fraction size for each target region depends on the number of IMRT fractions. The fraction sizes may be estimated using an isoeffect relationship based on the linear-quadratic model and the values of its parameters (such as α/β ratios, tumor doubling time).
The effect of the modified fractionation on acute and late toxicity of normal tissues both outside and within the volumes to be treated also should be considered. Because of the improved conformality of IMRT plans, dose to normal tissues outside the target volume is typically lower than for conventional treatment plans. In addition, if the number of fractions is greater than the number of fractions used to deliver large fields in conventional therapy, the dose per fraction to normal tissues is lower. Therefore, the biologically effective dose would be lower still. However, normal tissues embedded within or adjacent to the target volumes would receive high doses per fraction and may be at higher risk. Isoeffect formulae for normal tissues also may be derived to estimate the effect of a particular fractionation strategy. These formalisms would need to incorporate regeneration and change in sensitivity over the treatment course.
The values of parameters for the computation of altered fractionation may, in theory, be obtained from published studies. Studies by Maciejewski et al. (84) and Withers et al. (138,139,140), for example, have yielded important information for estimating tumor parameters for head and neck carcinoma. In general, the data available are limited. Furthermore, there is considerable uncertainty in the data, and there are concerns
P.246
about the validity of numerous assumptions in the linear-quadratic model and the isoeffect formalism, especially with regard to normal tissues. Nevertheless, various investigators have carried out the necessary calculations and adopted SIB IMRT fractionation strategies. Continued investigations and clinical trials are needed to develop more reliable time-dose fractionation models, to produce better estimates of their parameters, and to evaluate alternate SIB IMRT fractionation strategies for all sites. The following are some examples of IMRT fractionation strategies currently being used for IMRT of head and neck cancers.
In Radiation Therapy Oncology Group H-0022 protocol, 30 daily fractions are used to simultaneously deliver 66 Gy (2.2. Gy per fraction) to the PTV, 60 Gy (2 Gy per fraction) to the high-risk subclinical disease (“first echelon nodes or dissected neck area containing lymph node metastases”), and 54 Gy (1.8 Gy per fraction) to subclinical disease. These are biologically equivalent to 70, 60, and 50 Gy, respectively, if given in 2 Gy per fraction. For normal structures, brainstem, spinal cord, and mandible are maintained below 54, 45, and 70 Gy, respectively. The mean dose to the parotid glands is maintained below 26 Gy and/or 50% of one of the parotids is maintained below 30 Gy and/or at least 20 mL of the combined volume of both parotids is constrained to receive no more than 20 Gy (43).
The SIB strategy at Virginia Commonwealth University involves a dose-escalation protocol in which primary nominal dose levels of 68.1, 70.8, and 73.8 Gy, given in 30 fractions (biologically equivalent to 74, 79, and 85 Gy, respectively, if given in 2 Gy per fraction) are used (143). Simultaneously, the subclinical disease and electively treated nodes were prescribed 60 and 54 Gy, respectively (biologically equivalent to 60 and 50 Gy, respectively, if given in 2 Gy fractions). Spinal cord and brainstem are maintained below 45 and 55 Gy, respectively, and an attempt is made to allow no more than 50% of at least one parotid to receive higher than 26 Gy.
At the Mallinckrodt Institute of Radiology, the SIB strategy for definitive IMRT prescribes 70 Gy in 35 fractions in 2 Gy per fraction to the volume of gross disease with margins. The adjacent soft tissue and nodal volumes at high risk were treated to 63 Gy in 1.8 Gy per fraction and simultaneously 56 Gy in 1.6 Gy per fraction to the elective nodal regions. This regimen has been shown to be well tolerated when combined with concurrent chemotherapy (25).
Optic nerve and optic chiasm are maintained below 55 Gy, retina below 45 Gy, brainstem below 50 to 55 Gy, spinal cord to below 45 to 48 Gy, parotid glands to below 20 to 30 Gy, and mandible to below 70 Gy.
Optimization of Intensity Maps
The optimization of ray intensities may be carried out using one of several mathematical formalisms and algorithms (also termed optimization engines). Each method has its strengths and weaknesses. The choice depends in part on the nature of the objective function and in part on individual preference. Although the details are complex, the basic principles are not difficult to comprehend. As depicted schematically in Figure 9.10, each ray of each beam is traced from the source of radiation through the patient. Only the rays that pass through the target volume need to be traced (plus through a small margin assigned to ensure that the lateral loss of scattered radiation does not compromise the treatment). Others are set to a weight of zero.
P.247
The patient's 3D image is divided into voxels. The dose at every voxel in the patient is calculated for an initial set of ray weights. The resulting dose distribution is used to compute the “score” of the treatment plan (i.e., the value of the objective function that mathematically states the clinical objectives of the intended treatment).
The ray-tracing process identifies the tumor and normal tissue voxels that lie along the path of the ray. The effect of a small change in a ray weight on the score then is calculated. If the increase in ray weight would result in favorable consequences for the patient, the weight is increased, and vice versa. Mathematically speaking, the ray weight is changed by an amount proportional to the gradient of the score with respect to the ray weight. Realizing that the improvement in the plan at each point comes from rays from many beams and that each ray affects many points, only a small change in ray weight may be permitted at a time. This process is repeated for each ray. At the end of each complete cycle (an iteration), a small improvement in the treatment plan results. The new pattern of ray intensities then is used to calculate a new dose distribution and the new score of the plan, which then is used as the basis of further improvement in the next iteration. The iterative process continues until no further improvement takes place, the optimization process is assumed to have converged, and the optimum plan is assumed to have been achieved.
Many current optimization systems use variations of gradient techniques to optimize IMRT plans. Methods of this type are by far the fastest computationally. However, the use of gradient techniques assumes that there is a single extremum (a minimum or a maximum, depending on the form of the objective function). This is indeed the case for objective functions based on variance of dose and when only ray weights are optimized. For other cases, it would be necessary to determine whether multiple extrema exist and whether such multiple extrema have an impact on the quality of the solution found. Multiple extrema have been found to exist when beam directions are optimized or when dose–response-based objective functions are used to optimize weights of uniform beams (88,94,120). One can expect that multiple minima also exist when dose–response-based objective functions are used to optimize IMRT plans. Using simple schematic examples, it also has been shown that multiple minima exist when dose–volume-based objectives are used (40). Although this may be the case in theory, the existence of multiple minima has not been found to be a serious impediment in dose–volume-based or dose–response-based optimization using gradient techniques. In fact, in a recent study of dose–volume-based IMRT optimization, Wu and Mohan (142) found that, starting from vastly different initial intensities, the solutions converged to nearly the same plans. The reasons for this have been speculated but not conclusively proven and need to be investigated further.
It is possible that multiple minima do become a factor under a specific set of circumstances. These circumstances need to be determined. If multiple minima are discovered to be a factor, then some form of stochastic optimization technique may need to be considered. At the simplest, one may use a random search technique in conjunction with one of the gradient techniques. A more sophisticated stochastic technique is “simulated annealing” or its variation, the “fast simulated annealing” (88,94). These techniques allow the optimization process to escape from the local minima traps. Other forms of stochastic approaches, such as “genetic algorithms,” also have been proposed (45). In principle, the simulated annealing technique and other stochastic approaches can find the global minimum, but, practically, there is no guarantee that the absolute optimum has been found, only that the best among the solutions examined has been found. Furthermore, stochastic techniques tend to be extremely slow and should be used in routine work only if it is established that they are necessary. Nevertheless, some commercial systems have implemented the simulated annealing approach for IMRT optimization (21).
posted by stefanoxx at 12:55 AM
0 Comments:
Post a Comment
<< Home