Improving radiotherapy planning, delivery accuracy, and normal tissue sparing using cutting edge technologies

Introduction

In the United States, lung cancer constitutes 56% of all new invasive cancers diagnosed, accounting for ~30% of deaths resulting from all cancers (1). Non-small cell lung cancers (NSCLC) account for 80-85% of all lung cancers (2), with locally advanced, stage III disease representing about 40% of the total cases. The prognosis of these patients, even with aggressive chemoradiation techniques, is quite poor, with 5-year overall survival rates of only 10-15% (3). Given the recent seminal finding that low-dose computed tomography (CT) for lung cancer screening reduces lung cancer mortality ~20% when compared to radiography (4), with widespread acceptance, it may be postulated that lung cancers will be found more frequently, and at earlier stages. For early-stage, medically inoperable NSCLC, stereotactic ablative radiation therapy (SABR, also known as stereotactic body radiotherapy, SBRT) has shown remarkable promise, yielding ~90% local tumor control and, in one study, ~55% overall survival at a time point of three years (5).

Recent retrospective research has shown a dose-effect correlation for lung tumors (6-8), however safe radiation dose escalation is complicated by the close proximity of critical organs, and is further complicated by respiration-induced tumor displacement. However, interim analysis of Radiation Therapy Oncology Group (RTOG) 0617, comparing high dose (74 Gy) versus standard dose (60 Gy) radiation therapy (RT) with and without Cetuximab for Stage III NSCLC patients (9), revealed that the high dose arm did not improve overall survival, with no significant differences in toxicity between treatment arms (10). While mature results are still lacking, the results of this clinical trial prompted a considerable amount of uncertainty in the Radiation Oncology community (11). It has been suggested that requiring the use of technical advances such as image-guided radiation therapy (IGRT), patient-specific dose levels based on nearby organs at risk (i.e., healthy lung tissue and heart), and motion management may be advantageous in future trials (11,12). Motion management is currently recommended on a patient-specific basis for tumor excursions greater than 5 mm in any direction (13). To further facilitate dose escalation and increase local control, considerable effort has been made to characterize patient-specific tumor motion using the tumor (14-16), the organ in which it is embedded (17), implanted fiducial markers (18,19), or another part of the anatomy presumed to be related to tumor motion (i.e., diaphragm or abdomen surface) (20-22).

Advances in imaging, including four-dimensional computed tomography (4DCT) and volumetric cone-beam computed tomography (CBCT) have enabled more accurate target definition and precise tumor localization for both advanced stage lung cancer treatment and SBRT to further support dose escalation efforts while sparing nearby organs at risk. In addition, advances in dose calculation algorithms have allowed for more accurate dosimetry in heterogeneous media, thereby providing a clearer picture of dose distributions. Finally, new delivery approaches, such as tumor tracking or gating, offer additional mechanisms to reduce target margins. This work will provide an overview of the current state of the art for lung cancer volume definition, treatment planning, localization, and treatment plan adaptation.

Internal target volume (ITV)

In 1999, ICRU Report 62 introduced the concept of the “internal margin”, which is meant to incorporate uncertainties arising from physiological variations, such as respiratory motion (23). When the internal margin is combined with the clinical target volume, or CTV, the ITV is formed, which represents the “envelope” encompassing tumor movement determined during the simulation 4D-CT acquisition. The internal margin is expanded to form the planning target volume (PTV), which accounts for geometric variation in the CTV due to day-to-day (interfraction) uncertainties in the patient setup. A margin (planning risk volume, PRV) should also be added to an organ-at-risk to account for interfraction variation in the OAR position (23). Margins for the PTV must be designed with an understanding of the random and systematic errors associated with patient setup (24). For locally advanced stage NSCLC, typical margins for the PTV are on the order of 5-10 mm if an ITV is used for motion compensation and daily IGRT is often employed during treatment. In the absence of motion compensation or IGRT, margins should be much larger (10-20 mm) to minimize the chance of missing the target as a result of motion.

The American Association of Physicists in Medicine (AAPM) Task Group Report No. 76 (13) recommends a variety of approaches to account for respiratory motion. One such example is respiratory-correlated or 4DCT (14,25-27), where organ and tumor motion are both inherently provided during different phases of the respiratory cycle, often sampling data over 10-20 breathing cycles. Figure 1A and 1B illustrate the end-inhale and end-exhale phases of respiratory motion, respectively, for a highly mobile lung tumor. Tumors can be delineated on all 4DCT phases, and a union can be derived to generate the ITV as shown in Figure 1C. By contrast, conventional free-breathing CTs (FBCTs) are acquired at arbitrary states of the breathing cycle, during which tumors, nearby critical structures, and corresponding tissue densities are not static, as shown in Figure 2. Furthermore, due to the fast acquisition time of FBCT, it is possible to acquire imaging data at an extreme phase of the breathing cycle (i.e., end-inhale or end-exhale). Typically, conventional CT-simulator software employs retrospective temporal (i.e., phase-based) 4DCT sorting into 2-10 different phases, although artifact reduction has been realized through the use of amplitude-based 4DCT binning, particularly for irregular breathing patterns (28). Ten-phase 4DCTs often contain >1,000 CT slices, and may result in reconstruction and sorting artifacts introduced by varied respiratory patterns during a single 4DCT acquisition. This is of particular consequence in lung cancer radiotherapy due to patients presenting with compromised pulmonary function. 4DCT artifacts can lead to discrepancies in target and critical structure delineation, as well as impact the accuracy of dose calculation.

Figure 1 4DCT images of an early-stage lung cancer patient at end-inhalation (A); end exhalation (B); and contours from all 10 phases of the 4DCT combined (C). Abbreviation: 4DCT, four-dimensional computed tomography.

Figure 2 (A) Positional differences between the tumor position on the free-breathing CT; (B) maximum intensity projection (MIP); and (C) AVG-CT, indicating that the FBCT was acquired at an extreme phase of the breathing cycle. Contours show the ITV and PTV. Abbreviations: AVG-CT, average computed tomography; ITV, internal target volume; PTV, planning target volume.

Furthermore, the vast amount of data generated via 4DCT may substantially increase the time needed for image review and target/critical structure delineation. Therefore, a problem arises in how to fully exploit 4DCT data for treatment planning with an emphasis on clinical efficiency without compromising accuracy. To reduce the workload of contouring multiple target volumes in 4DCT, post-processing can be conducted to generate derivative datasets such as the average CT (AVG-CT) and maximum intensity projection (MIP). The AVG-CT data set provides a 3DCT scan with voxels equal to the arithmetic mean of the 4DCT, while the MIP image corresponds to the greatest voxel intensity values throughout the 4DCT. Another commonly used dataset is the mid-ventilation CT scan, corresponding to the specific 4DCT phase with the tumor center of mass closely representing the time-averaged position over the respiratory cycle (29). To further address large 4DCT datasets, several groups have worked toward developing automated contour delineation (30,31), deformable image registration (DIR) techniques (32-34), treatment planning on fewer breathing phases (35), the mid-ventilation phase (29,36), or AVG-CT over the entire breathing cycle (37,38). If 4DCT is not available, end-inspiration and end-exhalation images can be acquired to assess tumor excursion, or the tumor can be observed under fluoroscopy, such as with a conventional simulator.

Dose calculation

Dose calculation accuracy is of paramount importance in the clinical treatment process. The AAPM Report No. 85 (39) on Tissue Inhomogeneity Corrections for Megavoltage (MV) Beams notes that a 5% change in dose may result in a 10% to 20% change in tumor control probability (TCP) at 50%, and 20% to 30% impact on normal tissue complication probabilities (NTCP). The report further cites two examples where a 7% difference in dose delivered to different groups of patients was discovered by a radiation oncologist through clinical observations (39).

Dosimetric considerations

The presence of low-density lung tissue surrounding thoracic tumors complicates radiation dose computation in lung cancer treatment planning. Conditions of loss of charged-particle equilibrium (CPE) are produced when the field size is reduced such that the lateral ranges of the secondary electrons become comparable to (or greater than) the field size; such conditions occur for larger field sizes in lung than in water-equivalent tissues due to the increased electron range in lung. Under such circumstances, the dose to the target is determined primarily by secondary electron interactions and dose deposition. Because conventional dose algorithms do not explicitly account for transport of secondary electrons, they can be severely limited in accuracy under non-equilibrium conditions. In low density, lung-equivalent tissues, the reduction of dose due to electron scattering in the lung and the “re-buildup” of dose in the tumor at the lung-tumor interface, as electrons begin to stop in the tumor over a finite range, can produce significant underdosage at the tumor periphery (Figure 3). The reduction of dose at the tumor periphery is also exacerbated at higher beam energies, due to the increased electron range. Based on these dosimetric considerations, the RTOG No. 0236 (40) excluded the use of radiation field sizes less than 3.5 cm and restricted the use of beam energies above 10 MV. The article by Reynaert et al. (41) and the AAPM Task Group No. 105 (42) provide examples of numerous studies reported on the inaccuracies associated with conventional algorithms for dose calculations in the lung. For lung cancer treatment planning, and especially when dealing with smaller tumors with field sizes <5×5 cm², algorithms including three-dimensional (3D) scatter integration such as convolution/superposition, or the Monte Carlo (MC) method are necessary-the latter accounts explicitly for electron transport (43,44).

Figure 3 Geometry of an “island-like” lung tumor where electrons scatter laterally into lower density lung tissue, carrying dose away from the tumor. Electrons “stopping” within the tumor deposit dose over a finite range, resulting in an underdosage at the periphery of the tumor. Dose algorithms incorporating 3D scatter corrections, including the effects of electron scattering, must be used to properly characterize dose deposition within the tumor and surrounding healthy lung tissue. Abbreviation: 3D, three-dimensional.

The AAPM TG Report No. 101 (43) and other articles (45) recommend that pencil-beam algorithms not be utilized for SBRT-based lung dose calculations. The report also states that for the most complex situations, involving small, peripheral lung tumors, surrounded entirely by lung (“island-like” lesions), the MC method is ideal (43). Figure 4 provides a comparison of the 100% isodose line in a treatment plan for a patient with locally advanced stage NSCLC. Dose calculations were performed using a pencil-beam-type algorithm (dashed line) and the MC method (solid line). Whereas the pencil-beam-based calculation shows good dose coverage of the PTV, significant underdosage is noted with the MC algorithm. This example illustrates that PB-based algorithms are relatively insensitive to the presence of low-density lung tissue and do not account for electron scattering within the surrounding lung tissues. Consequently dose to the tumor is overestimated using PB algorithms, and the “actual” dose delivered, as properly predicted with the MC method, is much lower.

Figure 4 Comparison of 100% isodose line in a treatment plan for a patient with locally advanced stage non-small cell lung cancer, shown in the axial (A) and sagittal (B) views. Dose calculations performed using a pencil-beam-type algorithm (dashed line) and the Monte Carlo (MC) method (solid line). Significant underdosage of the PTV (solid line) is noted with the MC algorithm using UMPlan (University of Michigan) treatment planning system.

Figure 5 shows dose volume histograms (DVHs) for the PTV for a peripherally located lung tumor with PTV dimensions of ~4.5 cm planned with six MV photons. The prescription dose was 48 Gy (delivered in four 12 Gy fractions) to the 95% line. The initial 3D conformal (3D-CRT) treatment plan was computed with the 1-D PB algorithm. When re-computed with the convolution/superposition and MC-type algorithms, the “actual” dose to the PTV was much lower than that predicted with the PB algorithm. Both the MC and CCC algorithms show underdosage of the minimum PTV dose of 75% relative to PB (27 vs. 48 Gy). Differences in the minimum PTV dose of 25% were noted between MC or CCC and the AAA algorithm; the former which were lower. The substantial differences observed between pencil beam and convolution/superposition or MC-based algorithms for this particular case can be attributed to several factors, including “island-like” geometry (where the tumor is surrounded entirely by lung), relatively small tumor size, and beam arrangements/trajectories. Such conditions amplify the effects of electron scattering and the importance of electron transport; differences are therefore not unexpected.

Figure 5 Dose volume histograms (DVHs) for the planning target volume (PTV) for a peripherally located lung tumor with PTV dimensions of ~4.5 cm planned with 6 MV photons. Algorithms include pencil beam-type (1D-PB and 3D-PB), convolution/superposition type (AAA and CCC) and Monte Carlo (MC). All calculations were done using treatment planning systems at the Henry Ford Hospital. Figure adapted from reference 46.

Table 1 provides the results of a retrospective dose calculation study consisting of 135 patients with early stage NSCLC treated with SBRT (46). As in the example provided in Figure 5, doses were planned initially using a 1D-PB algorithm to a total dose of 48 Gy (in 12 Gy fractions); treatment plans were recomputed using convolution/superposition type and MC-based algorithms. A recently available algorithm, AcurosXB, uses a discrete-ordinates approach to solve the radiation transport equation. It is similar to the MC method but is deterministic in nature. Results in Table 1 show that the convolution/superposition, MC and discrete ordinates algorithms predict differences of ≈10% and ≈20% in the PTV mean and dose to 95% of the volume (D95) values relative to the 1D-PB algorithm. 1D and 3D PB algorithms are generally within 5% agreement. Differences in mean lung dose (MLD) are not significant, in part because the MLD values are low (~3 Gy). These results confirm that pencil-beam type algorithms should be avoided for thoracic cancer treatment planning, particularly for SBRT.

Table 1. Absolute dose values (in Gy) of the PTV mean (Dmean), D95, and MLD early stage NSCLC treatment plans treated with SBRT.
Full table

Treatment planning considerations

Beam arrangements for treatment planning of lung cancers can range from simple two-field, parallel opposed fields (e.g., anterior-posterior, opposed, AP/PA) for late stage NSCLC to complex multiple gantry angle, intensity modulated beams for local or locally advanced disease. Beams are shaped with a multileaf collimator (MLC) which enables conformation of radiation to the target. Treatment plans should be designed to minimize dose to surrounding normal organs and thereby limit the risk of treatment toxicity, implying sharp gradients in the dose fall-off outside the target (43). AP/PA fields may be considered with more extensive, centrally located disease to help reduce dose to the unaffected lung volume. The goal in such cases is to produce a homogeneous dose distribution across the treated volume to encompass the extent of the disease. However, AP/PA beams can only be used for cumulative PTV doses in the range of 45-50 Gy (in 1.8-2 Gy per fraction) due to spinal cord tolerance. “Off-cord” fields are required beyond 45-50 Gy. When treating large volumes of lung, it is especially important to design treatment plans that adhere to normal lung tolerance doses. Dose indices, such as V20, V5 and MLD must be closely observed to avoid radiation pneumonitis and other catastrophic consequences (47,48). For treatment planning of local or locally advanced NSCLC, more conformal dose distributions employing multiple beam angles are warranted. Treatment plans can be developed using 3D-CRT or intensity modulated radiation therapy (IMRT) techniques and must include beams from multiple gantry angles (five or more beams), particularly in the context of SBRT (43), to limit normal tissue sequelae, such as skin erythema, which has been observed clinically.

For IMRT-based planning, one must bear in mind the interplay effect, which describes the interplay between a given MLC position and instance of radiation delivery with the position of the tumor in the respiratory-induced motion cycle at the same instance (49). For conventional 3D treatment, small dose gradients can be expected and moving anatomy within the treatment field will blur the dose distribution, effectively increasing the beam penumbra (13). Conversely, for IMRT, this effect is more marked due to the interplay between the MLC leaf motions and the target motion perpendicular to the treatment beam. To account for this, the dose deposited for each respiratory phase can be computed by the subset of MLC sequences delivered to that specific phase, rather than by the entire MLC sequence delivered in aggregate. The interplay effect has been evaluated for intra-fraction cumulative dose and while the interplay effect was significant for individual phases, it “washed out” in dose accumulation over ten phases. The interplay effect caused less than 1% discrepancy in the PTV and ITV minimum doses using an energy mapping algorithm (50). Similarly, the interplay effect averages out over 30 or more treatment fractions (49,51). However, in the SBRT setting, where 3-5 dose fractions are delivered, it is not clear how the interplay will impact dose distributions.

Treatment planning for SBRT must be done with an understanding of the dose gradients so as to develop dose distributions with sharp gradients. This is typically achieved using multiple non-overlapping, and non-coplanar beams as necessary, and a MLC with 5 mm or smaller leaf width (43). The dose prescription line can be low (e.g., 80%) with much smaller margins for beam penumbra (“block edge”) than conventional radiotherapy; the motivation is to produce a faster dose falloff and thereby improve sparing of surrounding healthy tissues (43). AAPM Task Group No. 101 discourages the use of calculation grid sizes greater than 3 mm for SBRT planning (43).

Recently, volumetric modulated arc therapies (VMAT) have become available for SBRT-based treatments. The delivery of radiation in significantly less time with VMAT is likely to substantially mitigate patient movement on the treatment table as a result of discomfort during a long treatment procedure, and thereby improve delivery quality (52). Another advantage of VMAT is the ability to deliver multiple beams in different directions and preferentially spare neighboring critical structures. However, one must be cognizant of “low-dose” spread with VMAT, which may be higher than IMRT due to the rotational delivery. As such, parameters such as V5 to the healthy lung tissue must be carefully assessed when using VMAT. Nevertheless, comparisons of VMAT and 3DCRT have revealed no early clinical or radiographic changes in the lung post-treatment (53). Also, as with conventional IMRT, VMAT-based plans are subject to the interplay effect, which must be considered depending on the mobility of the tumor and the degree of modulation of the MLC fields.

4D dose accumulation

With widespread 4DCT implementation, a natural progression has been made to estimating the delivered dose during respiration through the use of 4D treatment planning and dose accumulation (32,54,55). Because the tumor and nearby organs at risk change in density and shape during the different phases of respiration, it is advantageous to calculate dose on each, or a subset, of breathing phases, and accumulate the dose to a reference phase. To accomplish this, DIR is necessary to generate the displacement vector field (DVF) between the source and reference images. DVFs describe the voxel-by-voxel correlation across multiple CT sets, and can be used to map the doses deposited during other phases back to the reference phase. The most straightforward, although not efficient, implementation of 4D dose accumulation is to perform a full 4D dose calculation and calculate the weighted average over the breathing course (35). In an effort to simplify 4D dose calculation and computational expense, reduction in datasets have been proposed such as coupling the DVFs with the AVG-CT to estimate cumulative dose (56), using fewer breathing phases (35), or using the midventilation phase (54,57). All of these approaches have revealed close approximations to a full 4D dose accumulation, thereby supporting integration of cumulative dose into clinical treatment planning. For example, in a patient case that was considered to be the worst-case scenario (tumor abutted the diaphragm with ~2 cm of superior-inferior motion), the largest deviation observed between DIR coupled with full 4D dose accumulation or the AVG-CT was 2% for the maximum dose and dose to 1% of the gross target volume (56) as shown in Figure 6.

Figure 6 Dose volume histogram (A) and coronal 4DCT data set (B) demonstrating the close association between deformable image registration coupled with full 4D dose summation or using the AVG-CT as an approximation for a patient with 2 cm superior-inferior tumor excursion. Isodose washes represent the AVG-CT approximation while the black isodose lines represent the corresponding full 4D dose summation. Figure adapted from Ref (56). Abbreviations: 4DCT, four-dimensional computed tomography; AVG-CT, average computed tomography; 4D, four-dimensional.

Another method that has been proposed is to determine the actual energy and mass transferred to that voxel, and then divide the energy by mass to get the dose (termed energy/mass transfer mapping) (58-61). A comparison of direct dose mapping and energy/mass transfer mapping in ten patients with demonstrable tumor excursion revealed similar cumulative doses to the ITV and PTV, although minimum dose differences of up to 11% in the PTV and 4% in the ITV minimum doses were observed between the two dose mapping algorithms with treatment plans computed with AAA (62).

While DIR facilitates cumulative dose estimation, propagated DIR errors will lead to irregularities in automatic contouring, dose warping, and overall dose accumulation. However, verification of DIR is challenging due to the absence of “ground truth”. Commonly, visual assessment of the DIR results is conducted, sometimes evaluating propagated contours or the deformed image set (63,64). Others have evaluated DIR performance against physician delineations or noted landmarks (65,66). However, large registration errors are often observed in regions of uniform intensity, and errors estimated by feature-guided evaluation methods may not represent voxel registration accuracy away from those landmarks. Approaches such as evaluating the curl vector (67) or warping images with known DVFs and evaluate the recovered deformations have been implemented (64). Stanley et al. benchmarked and evaluated DIR algorithms using patient-specific finite element models (FEM) and a physical deformable phantom (68). Figure 7A shows a programmable deformable phantom that contains a heterogeneous sponge with average density equivalent to lung (Figure 7B) that can be deformed. The modular phantom can be disassembled to insert film or thermoluminescent dosimeters for 4D dose verification.

Figure 7 In-house developed deformable lung phantom (A) and coronal cross section (B) showing implanted tumor embedded in the lung material (Courtesy of Hualiang Zhong, Henry Ford Health System).

On-line IGRT

On-line IGRT verifies the target volume and organ at risk locations before daily treatment (inter-fraction) and can also be used to monitor the target during treatment (intra-fraction). Daily IGRT-based setup has been shown to significantly reduce residual errors, and consequently planning margins (69,70). For SBRT-based treatments, where motion management and IGRT are the recommended standard-of-care (43), PTV margins can range from 3-6 mm (69,71-73). On-board imaging can include a kilovoltage (kV) source and flat-panel detector mounted orthogonal to the MV therapy beam axis on the linear accelerator gantry. Image acquisition includes planar radiographic (i.e., kV images), fluoroscopic (cine loops of triggered planar kV images), and volumetric (series of angular projection images reconstructed to generate CBCT datasets (74-78). A chief advantage of kV imaging, particularly CBCT, is the soft tissue visibility, which has been a key component of implementing lung SBRT (70,79,80). Furthermore, because CBCTs are acquired over ~1 minute, the 3D volume represents a time-averaged scan, often indicating the average position of the tumor. Most linear accelerators are also equipped with MV electronic portal imaging devices (EPIDs) mounted at the exit of the treatment beam, which can be used to verify bony landmarks. MV CBCT is also available using an EPID mounted on the treatment beam axis, allowing for volumetric MV imaging.

At Henry Ford Hospital, volumetric CBCT-based imaging is employed to visualize the tumor with respect to organs at risk, for lung SBRT cases. The localization procedure includes setting the patient to tattoos, acquiring a CBCT image, and using automatic image registration tools to align the CBCT to the reference CT. Bony alignment is first verified by the physicist, and manually adjusted if deemed necessary. The physician and physicist then review the registration using soft-tissue window/level and verify that the ITV contour encompasses the lesion. If the lesion falls outside the ITV contour, the physician will manually adjust the registration until the targets are aligned. The image registration is then approved by the physician, and resulting couch corrections are applied. Verification imaging is performed via an orthogonal pair of MV/kV images that are automatically registered to the digitally reconstructed radiograph (DRR). MV/kV matching ensures the proper couch shift has been applied and the patient has not moved between the original CBCT acquisition and treatment. If the registration result is <2 mm/1 degree (not including shifts made for soft tissue matching in the previous step), treatment commences at the CBCT position. Otherwise, another CBCT is performed and the process is repeated.

Ideally, respiratory-correlated CBCT (or 4D-CBCT) would be implemented to mitigate breathing artifacts while providing the tumor mean position, trajectory, and shape over respiration (81). While the feasibility of 4D-CBCT has been demonstrated on different linear accelerators (82,83), scan times can be on the order of four minutes, yielding ~700 projections of data for sorting, and delivering 2-4 cGy/scan depending on area of interest evaluated (81). Another solution that has been integrated into some clinical workflows include a multiple breath-hold CBCT, often called the “stop and go” CBCT (84,85). Here, CBCT acquisition is paused over multiple breath-holds and the resulting datasets are combined into one final reconstruction.

Tracking

Tumor tracking

Lung tumor motion can be measured and monitored using techniques such as fluoroscopy (15,86), real-time tumor tracking radiotherapy (RT-RT) (18,19), or using implanted fiducials. An example of an in-house analysis program designed to track the tumor and diaphragm in fluoroscopy frames is shown in Figure 8A and B, respectively. Details and validation can be found elsewhere (20,36), but briefly, a region of interest (ROI) is contoured on a single frame, and a template-matching technique using rigid-body registration and nearest-neighbor interpolation propagated the ROI to all other frames. For patients, ROIs can include the tumor or nearby ROI, apex of the diaphragm, or any other anatomy of interest. Centroids of the propagated contours can then be exported to generate the tumor or surrogate trajectories over fluoroscopic frames.

Figure 8 AP fluoroscopy images of an advanced stage lung cancer patient with the tumor (A) and diaphragm (B) tracked using automated in-house software [Courtesy of Jian Liang, William Beaumont Hospital, adapted from Reference (86)].

The fluoroscopic real-time tumor-tracking system (RTRT system) (Mitsubishi Electronics Co. Ltd., Tokyo, Japan) uses four sets of diagnostic X-ray systems oriented with the central axis at isocenter to track gold markers implanted at or near moving tumors (15,87-90). 3D marker positioning is determined via a template-matching algorithm applied to the digital images, and if the measured and expected marker positions do not match inside pre-determined tolerances, a machine interlock is asserted. Clinical outcome data suggests similar local control and overall survival rates for RTRT as compared to SBRT without gating (91). One caveat is that significant skin surface doses (29-1,182 mGy/h) have been reported (92).

Another external-internal tumor tracking modality is the Synchrony™ Respiratory Tracking System (Accuray, Inc., Sunnyvale, CA, USA) integrated with the CyberKnife robotic linear accelerator. Briefly, the Synchrony camera array tracks three external LED markers affixed to the patient’s chest while orthogonal stereoscopic X-ray images are obtained to localize two to four fiducial markers implanted at or near the tumor (93). Real-time feedback from patient monitoring is used to develop a correspondence model, inferring internal tumor positioning from the external surrogates. The correspondence model predicts tumor position, sends feedback to the robotic linear accelerator, and the robot realigns the beam with the tumor. A soft-tissue tracking algorithm has also been reported that can be used for peripheral tumors (diameter >15 mm) in the lung (94). A few disadvantages include the use of ionizing radiation and the additional margin required to account for deformation (94).

The implantation of electromagnetic transponders [e.g., Calypso wireless transponders (Beacons™) currently part of Varian Medical Systems, Palo Alto, CA] at or near the tumor has been widely implemented in prostate cancer RT (95). Briefly, the system uses an array of AC magnetic coils to generate a resonant response in implanted transponders (8 mm length, 2 mm diameter) subsequently detected using a separate array of receiver coils. Beacons’ coordinates are identified on a treatment planning CT, and the offset between the beacons’ centroid and intended isocenter is reported. During treatment, the Calypso system continuously monitors and reports the 3D offset between the actual and desired isocenter locations at a frequency of 10 Hz. Transponders have been implanted into canine lungs, although migration and transponder expulsion were challenges for the original beacon design (96,97). As a result, a new anchored beacon was devised under an Investigational Device Exemption (IDE) granted by the FDA, and clinical trials are currently underway (98). While tracking implanted markers within the tumor is optimal, the invasiveness of implantation, increased risk of pneumothorax (99), and potential “dropping” or migration of markers from the implantation location (87) can also be deterrents.

External surrogate tracking

External surrogates can infer tumor motion, although they can be limited by the need to verify the relationship with the tumor motion, the potential for external marker placement to affect this correlation (100), and time-dependent characteristics (101). External surrogates of the abdomen can be derived from pressure-sensitive belts, infrared blocks, or surface images. One such example is the Real-Time Position Management Respiratory Gating System (RPM) (Varian Medical Systems, Palo Alto, CA, USA). Briefly, the RPM system uses a plastic block containing two to six markers that reflect infrared light (Figure 9A). These markers are subsequently tracked with an infrared-sensitive charge-coupled device camera, and this video signal is transferred back to the RPM computer. RPM can be used for 4DCT sorting, or coupled with respiratory gating with linear accelerators. Another device that derives an external surrogate includes a pneumatic belt (bellows) (Philips Medical Systems, Cleveland, OH, USA) consisting of a rubber belt that expands and contracts as patients’ breathing volumes change (Figure 9A). Changes in the pressure are converted via a transducer to a voltage signal that is then digitized and sent to the CT scanner system for 4DCT sorting. In a simultaneous comparison of bellows and RPM, slight differences in waveform and latency analyses were observed, particularly for low amplitude motions. However, these did not adversely impact image quality or delineations (102). Another example of a pressure sensor is Anzai Medical’s small pneumatic sensor.

Figure 9 Examples of external surrogates used for patient monitoring. (A) Pneumatic belt placed superiorly of the RPM block; (B) surface images obtained from AlignRT [adapted from Reference (86)]. Abbreviation: RPM, Respiratory Gating System.

Video camera-based, 3D imaging systems are available that are used to derive 3D surface images during RT, for example AlignRT (VisionRT Ltd., London, UK) and C-Rad Sentinel^™ (C-RAD AB, Uppsala, Sweden). AlignRT uses two or three cameras combined with a projected speckled-light pattern to derive 3D surface images (shown in Figure 9B), whereas C-Rad uses a line scanning mode with a single camera and laser system. Reference datasets can be derived from RT structure sets (i.e., a CT external structure) or from a previously acquired 3D surface acquisition. Rigid body transformations are used by the systems to perform a least square fit to minimize the difference between the planned 3D model of the patient relative to isocenter and the observed surface model of the patient (103). In a study of simultaneous surface imaging and kV fluoroscopy acquisition of three lung cancer patients in the treatment position, most patient fractions studied showed associations between the abdomen and tumor were equivalent or better than those observed between the diaphragm and tumor. Improved internal-to-external associations have been observed when multiple markers or deformed surface images were used as external surrogates (104-106), although these approaches can be computationally expensive and are not currently incorporated into standard clinical practice. One study explored implementing multiple internal surrogates, such as the air content, lung area, lung density, and body area for 4D CT sorting, and found strong agreement with external surrogates recorded by RPM (107).

Image-guided adaptive radiation therapy (IGART)

While IGRT, such as CBCT, has improved target localization accuracy by providing daily positional information used for online repositioning, daily target and critical structure deformation cannot be fully accounted for using IGRT alone. To combat this, IGART can be implemented. IGART uses patient-specific dynamic/temporal information for potential treatment plan modification during the treatment course (108-110). IGART can address tumor volume and positional changes, as well as other pathologic changes and deformations occurring during the RT treatment course. For lung cancer, inter-fraction baseline variability in lung tumor position, its respiratory trajectory, and normal structures relative to the bony anatomy have been observed (20,36,111-115). Without adjustment, marginal misses can occur. Two cases in point are where a bronchial obstruction is relieved and collapsed lung is re-expanded, resulting in possible tumor shift (116) or in a patient with fluid accumulation in the lungs over the treatment course due to pneumonia (115). Significant reduction in tumor size, particularly for large tumors, has been observed throughout treatment for conventional fractionated radiotherapy of NSCLC (117,118), suggesting that this lung cancer population may benefit most from ART techniques. Conversely, for SBRT, ART has been shown to offer limited value due to the small amount of target volume changes over the shortened time course (119).

To accomplish IGART, a workflow is needed that includes high-quality, temporal volumetric information that is used as a feedback loop in the DIR, dose reconstruction, dose accumulation, and plan adaptation processes (120) as shown in Figure 10. An offline IGART framework has been implemented consisting of a closed-loop system incorporating feedback from updated patient geometry (i.e., CBCTs) and anatomical information to recompute dose and determine the actual dose delivered to the target and surrounding healthy tissues (120). Similar concepts have been proposed previously (108,121), although a unique feature of the presented framework is that it includes a systematic validation of the DIR algorithm and dose accumulation techniques.

Figure 10 Image-guided adaptive radiation therapy framework developed at Henry Ford Health System. Figure adapted from Ref (120).

On-line plan re-optimization using an “anatomy of the day” approach has also been implemented. Li et al. have developed new IMRT plans using daily IGRT images using a two-step process: segment aperture morphing (SAM), to correct for target deformation/translation using the MLC, and segment weight optimization (SWO), to determine the optimal MU for each segment (122). Full plan re-optimization can be accomplished in ~10 minutes. While this would be challenging to implement in the clinic, on-line IGART is becoming more realistic due to recent advances in computing such as implementing the graphics processing unit (GPU) (123-125), which has reduced online optimization time from minutes down to seconds.

A prospective, randomized, multi-institutional clinical trial is currently underway to incorporate a during-RT PET/CT-adapted boost for patients with large lung tumors that may potentially benefit from dose escalation (12). In this manner, individualized ART will be performed for patients with inoperable or unresectable stage III NSCLC, a population in which overall prognosis currently remains quite poor despite advances in RT techniques including IMRT and IGRT. Controlled clinical trials such as this will help streamline IGART approaches into clinical practice.

Conclusions and future directions

Lung cancer RT is complicated by tumor motion, challenges of accurate dose calculation in low density media, and changing anatomy over the treatment course, in addition to radiobiologic and individual patient-response-specific issues. As tumor localization improves, whether via high quality daily IGRT images or tumor tracking, margin reduction and further dose escalation is possible. Furthermore, dose calculation accuracy has substantially improved in recent years, including the ability to incorporate 3D scatter and implement MC for modeling electron transport, and these algorithms are now available in the clinic. 4DCT and DIR have made dose accumulation and IGART possible, and advances in computational speed will continue to make on-line IGART more clinically plausible over the treatment course.

Some promising new techniques currently being evaluated include incorporating biological feedback into treatment planning, such as dynamic contrast-enhanced MRI (DCE-MRI) as an early indicator of treatment response and perfusion changes (126,127), exploring the role of nanoparticles in lung cancer (128), and exploiting radiosensitizers during RT (129). Finding new ways to assess dose response, normal tissue sparing, and identify opportunities for dose escalation, particularly for advanced stage lung cancer patients, is advantageous.

Acknowledgements

Disclosure: HFHS, Department of Radiation Oncology receives grant funding from the NIH/NCI and industrial partners, Varian Medical Systems and Philips Health Care.

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