Browsing by Subject "Volumetric Modulated Arc Therapy"

In external beam radiation therapy (EBRT) for cancer patients, it is highly desirable to completely eradicate the cancerous cells for the purpose of improving the patient’s quality of life and increasing the patient’s likelihood of survival. However, there can be significant side effects when large regions of healthy cells are irradiated during EBRT, particularly for organs-at-risk (OARs). Due to the juxtaposition of the cancerous and non-cancerous tissue, trade-offs need to be made between target coverage and OAR sparing during treatment planning. For this reason, the treatment planning process can be posed as a multi-criterial optimization (MCO) problem, which has previously been studied extensively with several exact solutions existing specifically for radiation therapy. Typical MCO implementations for EBRT involve creating, optimizing, and calculating many treatment plans to infer the set of feasible best radiation doses, or the Pareto surface. However, each optimization and calculation can take 10-30 minutes per plan. As a result, generating enough plans to attain an accurate representation of the Pareto surface can be very time-consuming, particularly in higher-dimensions with many possible trade-offs.

The purpose of this study is to streamline the MCO workflow by using a machine-learning model to quickly predict the Pareto surface plan doses, rather than exactly computing them. The primary focus of this study focuses on the development and analysis of the dose prediction model. The secondary focus of this study is to develop new metrics for analyzing the similarity between different Pareto surface interpolations. The tertiary focus of this study is to investigate the feasibility of deliberately irradiating the epidural space in spine stereotactic radiosurgery (SRS), as well as estimate its potential effect on preventing tumor recurrence.

For the primary focus of this study, the model’s architecture proceeds as follows. The model begins by creating an initial dose distribution via an inverse fit of inter-slice and intra-slice PTV distance maps on a voxel-wise basis. The model proceeds by extracting three sets of transverse patches from all structure maps and the initialized dose map at each voxel. The model then uses the patch vectors as inputs for a neural network which updates and refines the dose initialization to achieve a final dose prediction. The primary motivation behind our model is to use our understanding of the general shape of dose distributions to remove much of the nonlinearity of the dose prediction problem, decreasing the difficulty of subsequent network predictions. Our model is able to take the optimization priorities into account during dose prediction and infer feasible dose distributions across a range of optimization priority combinations, allowing for indirect Pareto surface inference.

The model’s performance was analyzed on conventional prostate volumetric modulated arc therapy (VMAT), pancreas stereotactic body radiation therapy (SBRT), and spine stereotactic radiosurgery (SRS) with epidural space irradiation. For each of these treatment paradigms, the Pareto surfaces of many patients were thoroughly sampled to train and test the model. On all of these cases, our model achieved good performance in terms of speed and accuracy. Overfitting was shown to be minimal in all cases, and dose distribution slices and dose-volume histograms (DVHs) were shown for comparison, confirming the proficiency of our model. This model is relatively fast (0.05-0.20 seconds per plan), and it is capable of sampling the entire Pareto surface much faster than commercial dose optimization and calculation engines.

While these results were generally promising, the model achieved lower error on the prostate VMAT treatment plans compared to the pancreas SBRT and spine SRS treatment plans. This is likely due to the existence of heavier beam streaks in the stereotactic treatment plans which are generated by a sharper control of the delivered dose distribution. However, the Pareto surface errors were similar across all three cases, so these dose distribution errors did not propagate to the Pareto objective space.

The secondary focus of this study is the development and analysis of Pareto surface similarity metrics. The dose prediction model can be used to rapidly estimate many Pareto-optimal plans for quick Pareto surface inference. This could allow for a potentially significant increase in the speed at which Pareto surfaces are inferenced to provide treatment planning assistance and acceleration. However, previous investigations into Pareto surface analysis typically do not compare a ground truth Pareto surface with a Pareto surface prediction. Therefore, there is a need to develop a Pareto surface metric in order to evaluate the ability of the model to generate accurate Pareto surfaces in addition to accurate dose distributions.

To address these needs, we developed four Pareto surface similarity metrics, emphasizing the ability to represent distances between the interpolations rather than the sampled points. The most straightforward metric is the root-mean-square error (RMSE) evaluated between matched, sampled points on the Pareto surfaces, augmented by intra-simplex upsampling of the barycentric dimensions of each simplex. The second metric is the Hausdorff distance, which evaluates the maximum closest distance between the sets of sampled points. The third metric is the average projected distance (APD), which evaluates the displacements between the sampled points and evaluates their projections along the mean displacement. The fourth metric is the average nearest-point distance (ANPD), which numerically integrates point-to-simplex distances over the upsampled simplices of the Pareto surfaces. These metrics are compared by their convergence rates as a function of intra-simplex upsampling, the calculation times required to achieve convergence, and their qualitative meaningfulness in representing the underlying interpolated surfaces. For testing, several simplex pairs were constructed abstractly, and Pareto surfaces were constructed using inverse optimization and our dose prediction model applied to conventional prostate VMAT, pancreas SBRT, and spine SRS with epidural irradiation.

For the abstract simplex pairs, convergence within 1% was typically achieved at approximately 50 and 100 samples per barycentric dimension for the ANPD and the RMSE, respectively. The RMSE and the ANPD required approximately 50 milliseconds and 3 seconds to calculate to these sampling rates, respectively, while the APD and HD required much less than 1 millisecond. Additionally, the APD values closely resembled the ANPD limits, while the RMSE limits and HD tended to be more different. The ANPD is likely more meaningful than the RMSE and APD, as the ANPD’s point-to-simplex distance functions more closely represent the dissimilarity between the underlying interpolated surfaces rather than the sampling points on the surfaces. However, in situations requiring high-speed evaluations, the APD may be more desirable due to its speed, lack of subjective specification of intra-simplex upsampling rates, and similarity to the ANPD limits.

The tertiary focus of this study is the analysis of the feasibility of epidural space irradiation in spine SRS. The epidural space is a frequent site of cancer recurrence after spine SRS. This may be due to microscopic disease in the epidural space which is under-dosed to obey strict spinal cord dose constraints. We hypothesized that the epidural space could be purposefully irradiated to prescription dose levels, potentially reducing the risk of recurrence in the epidural space without increasing toxicity. To address this, we sought to analyze the feasibility of irradiating the epidural space in spine SRS. Analyzing the data associated with this study is synergistic to our MCO acceleration study, since the range of trade-offs between epidural space irradiation and spinal cord sparing represents an MCO problem which our dose prediction model may quickly solve.

Spine SRS clinical treatment plans with associated spinal PTV (PTVspine) and spinal cord contours, and prior delivered dose distributions were identified retrospectively. An epidural space PTV (PTVepidural) was contoured to avoid the spinal cord and focus on regions near the PTVspine. Clinical plan constraints included PTVspine constraints (D95% = 1800 cGy, D5% < 1950 cGy) and spinal cord constraints (Dmax < 1300 cGy, D10% < 1000 cGy). Prior clinical plan doses were mapped onto the new PTVepidural contour for analysis. Plans were copied and revised to additionally target the PTVepidural, optimizing PTVepidural D95% after meeting clinical plan constraints. Tumor control probabilities (TCPs) were estimated for the PTVepidural using a radiobiological linear-quadratic model of cell survival for both clinical and revised plans. Clinical and revised plans were compared according to their PTVepidural DVH distributions, D95% distributions, and TCPs.

Seventeen SSRS plans were identified and included in this study. Revised plan DVHs demonstrated higher doses to the epidural low-dose regions, with D95% improving from 10.96 Gy ± 1.76 Gy to 16.84 Gy ± 0.87 Gy (p < 10-5). Our TCP modeling set the clinical plan TCP average to 85%, while revised plan TCPs were all greater than 99.99%. Therefore, irradiating the epidural space in spine SRS is likely feasible, and purposefully targeting the epidural space in SSRS should increase control in the epidural space without significantly increasing the risk of spinal cord toxicity.

With the advent of new, more efficient, rotational therapy techniques such as volumetric modulated arc therapy (VMAT), radiation therapy treatment precision requires evolving quality assurance. Two dimensional (2D) detector arrays have shown angular dependence that must be compensated for by the creation of angular correction factor tables. Currently available correction factor tables have several underlying assumptions that leave room for improvement: first, these correction factors assume that the response of all ion chambers is identical for each angle; second, that the ion chamber array response from gantry angles 0°-180° are equivalent to the response from 180°-360° and, third, that the response is independent of the direction of rotation.

Measurements were acquired using a 2D ion chamber array (MatriXX®, IBA Dosimetry) for static open fields delivered every 5° around the MatriXX while dose was calculated using Eclipse v8.6 (analytic anisotropic algorithm, Varian Medical Systems). Customized correction factors were created by dividing the calculated dose by the measured dose for each ion chamber. Two measurement positions were used in the creation of the custom correction factors: a coronal position in which the couch was included, and two sagittal orientations in which the couch was not included.

The correction factors were verified using open field arcs and VMAT patient plans, where measured doses were compared to calculated doses using gamma analysis (3%, 3 mm). Narrow fields were also delivered clockwise and counterclockwise in order to investigate the effect of the internal structure of the ion chamber array.

The angular response of the individual ion chambers appears to vary significantly (1 &sigma &le 4.6%). The response from 0°-180° vs. 180°-360° is significantly different (paired t-test yields p<0.0001). Custom correction factors do enhance the agreement between measured and calculated doses for open field arcs and VMAT patient plans compared to the default correction factors. The direction of rotation appears to affect the dose to the penumbra region of narrow fields, which could affect VMAT patient specific quality assurance.

The custom correction factor tables, using measurements for individual ion chambers over a full 0°-360° range, allows for improved accuracy in measurements by the 2D ion chamber array. However, even the corrected measurements still showed discrepancies with the calculated doses for VMAT plans.

Purpose: Linear accelerator-based Stereotactic Radiosurgery (SRS) is often performed using either dynamic conformal arcs or VMAT. For multifocal disease, multifocal conformal arc techniques can struggle to deliver the desired dose with high conformity for all targets simultaneously. While VMAT may improve coverage and conformity, and can offer the planner more flexibility, it can result in highly modulated treatment plans with non-intuitive MLC trajectories. The complex MLC modulation trajectories can often struggle to shield healthy areas between targets, thus leaving open gaps being irradiated between targets. The purpose of this research is to overcome these limitations by developing a technique for SRS of multifocal targets that combines the intuitive MLC trajectories of dynamic conformal arcs with the flexibility of VMAT.

Methods: A Conformal Arc Informed VMAT (CAVMAT) planning technique was developed in which arcs are assigned subgroups of targets, for which the MLCs are able to effectively conform to all targets in the subgroup. Arc weights are optimized to achieve desired dose per target while minimizing the variation in MU per arc. The optimized conformal arc plan then serves as the starting point in a VMAT inverse optimization to fine tune the dose to each target, optimize conformity, and meet any plan specific objectives. To demonstrate feasibility, ten multifocal VMAT cases were re-planned using the CAVMAT technique. The following metrics of plan quality were used to compare VMAT with CAVMAT: volume of healthy brain receiving 6Gy, 12Gy, and 16Gy, conformity index, and total number of monitor units.

Results: The V6Gy of the healthy brain was 10±13% lower in CAVMAT than in VMAT (range 25% lower to 15% higher for CAVMAT plans than VMAT plans). V12Gy of healthy brain tissue showed 5±14% lower in CAVMAT than in VMAT (range 16% lower to 24% higher in CAVMAT plans than VMAT plans). The V16Gy of healthy brain tissue was 3±16% lower in CAVMAT than in VMAT (range 16% to 4% lower and 41% higher in CAVMAT plans than VMAT plans in one case). The MU (Monitor Units) for the CAVMAT plans were 6156.4MU with a standard deviation of 878.41MU compared to 7031.3MU with a deviation of 1788.89MUs for VMAT. The CI (Conformity Index) for CAVMAT are 1.31 with a standard deviation of 0.13, the VMAT plan has a mean conformity of 1.28 with a standard deviation of 0.18. The mean maximum dose of the CAVMAT plan is 2445.37cGy with a standard deviation of 107.22cGy compared to 2309.28cGy with a standard deviation of 114.72cGy for VMAT.

Conclusion: CAVMAT plans succeeded in lessening low dose spill with lower MUs on average compared to VMAT plans. The conformity indexes are comparable to VMAT plans and maximum doses to patients are higher in the CAVMAT plans than in the VMAT plans.