Feasibility of extreme dose escalation for glioblastoma multiforme using 4π radiotherapy

Advances in Automated Treatment Planning

Treatment planning in radiation therapy has progressed enormously over the past several decades. Such advancements came in the form of innovative hardware and algorithms, giving rise to modalities such as intensity-modulated radiation therapy and volume modulated arc therapy, greatly improving patient outcome and quality of life. While these developments have improved the overall plan quality, they have also given rise to higher treatment planning complexity. This has resulted in increased treatment planning time and higher variability in the final approved plan quality. Radiation oncology, as an already technologically advanced field, has much research and implementation involving the use of AI. The field has begun to show the efficacy of using such technologies in many of its sub-areas, such as in diagnosis, imaging, segmentation, treatment planning, quality assurance, treatment delivery, and follow-up. Some AI technologies have already been clinically implemented by commercial systems. In this article, we will provide an overview to methods involved with treatment planning in radiation therapy. In particular, we will review the recent research and literature related to automation of the treatment planning process, leading to potentially higher efficiency and higher quality plans. We will then present the current and future challenges, as well as some future perspectives.

Mean Arc Distance (MAD): a quantity to compare trajectory 4πsampling in single target cranial stereotactic radiotherapy

Purpose:C-arm linac-based radiotherapy has seen a recent interest in 4 methods of delivery using simultaneous rotations of couch and gantry to reduce doses to organs-at-risk (OARs) and increase dose compactness. While many methods use heuristics to generate trajectories that avoid OARs, combined with arbitrary trajectory restrictions to prevent oversampling, a quantity has not yet been developed to succinctly compare sampling of the 4 space for candidate trajectories as a surrogate for dosimetric compactness.Methods:Evenly spaced sampling points were distributed across a 4 sphere centred on isocentre. A metric, mean arc distance (MAD), was defined that quantifies the average arc distance between all fields in a radiotherapy trajectory and their nearest sampling point. The relationship between isodose volume and MAD was examined in 2,047 plans: 900 unique trajectories of fixed port DCA plans, 900 unique trajectories of contiguous field DCA plans, 192 VMAT plans (eight volumes in four locations, each with six trajectories) in matRad with 5 VMAT plans repeated for validation in a clinical planning system, and 10 clinical VMAT cases replanned with five trajectories in a clinical treatment planning system.Results:All isodose volumes greater than 10 % of the prescription dose decreased with decreasing MAD in all comparisons. In the range of 10 % to 100 % of the prescription dose, the rate of isodose volume decrease was exponential as a function of MAD in all comparisons. Reduction of absolute isodose volume is seen with increased 4 sampling, with larger target volumes exhibiting larger absolute reductions. Very low isodoses (0 % to 10 % of prescription) increased with decreasing MAD.Conclusions:MAD is a 4 sampling quantity useful in quantifying the decrease of isodose volume, relevant for sparing normal tissues. By quantifying this feature, candidate dynamic trajectories can be efficiently compared for 4 sampling. This quantity is explored here for single target cranial radiotherapy but may have applications to other radiotherapy treatment site.

Patient-Specific Collision Zones for 4π Trajectory Optimized Radiation Therapy

PURPOSE

The 4π methodology determines optimized non-coplanar sub arcs for stereotactic radiation therapy which minimize dose to organs-at-risk. Every combination of treatment angle is examined, but some angles are not appropriate as a collision would occur between the gantry and the couch or the gantry and the patient. Those combinations of couch and gantry angles are referred to as collision zones. A major barrier to applying 4π to stereotactic body radiation therapy (SBRT) is the unknown shape of the collision zones, which are significant as patients take up a large volume within the 4π sphere. This study presents a system which determines patient-specific collision zones, without additional clinical steps, to enable safe and deliverable non-coplanar treatment trajectories for SBRT patients.

METHODS

To augment patient's computed tomography (CT) scan, full body scans of patients in treatment position were acquired using an optical scanner. A library of a priori scans (N = 25) was created. Based on the patients treatment position and their body dimensions, a library scan is selected and registered to the CT scan of the patient. Next, a model of the couch and immobilization equipment is added to the patient model. This results in a patient model that is then aligned with a model of the treatment linac in a "virtual treatment room", where both components can be rotated to test for collisions. To test the collision detection algorithm, an end-to-end test was performed using a cranial phantom. The registration algorithm was tested by comparing the registered patient collision zones to those generated by using the patient's matching scan.

RESULTS

The collision detection algorithm was found to have a 97.80% accuracy, a 99.99% sensitivity and a 99.99% negative predictive value (NPV). Analysis of the registration algorithm determined that a 6 cm buffer was required to achieve a 99.65% mean sensitivity, where a sensitivity of unity is considered to be a requirement for safe treatment delivery. With a 6 cm buffer the mean accuracy was 86.70% and the mean NPV was 99.33%.

CONCLUSIONS

Our method of determining patient-specific collision zones can be accomplished with minimal user intervention based on an a priori library of body surface scans, thus enabling the safe application of 4π SBRT.

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T1-weighted, and T2-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: - 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.

Automated Non-Coplanar VMAT for Dose Escalation in Recurrent Head and Neck Cancer Patients

Simple Summary

The ability to escalate the radiation dose to head and neck tumors has been shown to offer improved local control, and consequently, survival for recurrent head and neck cancer (rHNC) patients. This study evaluates the HyperArc automated non-coplanar planning technique (originally developed for intracranial treatment) for 20 rHNC patients, and compares this technique to conventional planning methods. HyperArc enables significant tumor dose escalation, with average increases in mean target dose of over 11.5 Gy (26%), while maintaining clinically-equivalent doses to nearby organs. Our results show that the average probability of tumor control is 23% higher for HyperArc than conventional techniques.

Abstract

This study evaluates the potential for tumor dose escalation in recurrent head and neck cancer (rHNC) patients with automated non-coplanar volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) planning (HyperArc). Twenty rHNC patients are planned with conventional VMAT SBRT to 40 Gy while minimizing organ-at-risk (OAR) doses. They are then re-planned with the HyperArc technique to match these minimal OAR doses while escalating the target dose as high as possible. Then, we compare the dosimetry, tumor control probability (TCP), and normal tissue complication probability (NTCP) for the two plan types. Our results show that the HyperArc technique significantly increases the mean planning target volume (PTV) and gross tumor volume (GTV) doses by 10.8 ± 4.4 Gy (25%) and 11.5 ± 5.1 Gy (26%) on average, respectively. There are no clinically significant differences in OAR doses, with maximum dose differences of <2 Gy on average. The average TCP is 23% (± 21%) higher for HyperArc than conventional plans, with no significant differences in NTCP for the brainstem, cord, mandible, or larynx. HyperArc can achieve significant tumor dose escalation while maintaining minimal OAR doses in the head and neck—potentially enabling improved local control for rHNC SBRT patients without increased risk of treatment-related toxicities.

Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy

PURPOSE

Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity-modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep learning model that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities.

METHODS

We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35 000 plans. We studied and compared two different models, Models I and II. Although they both used the same anatomical structures - including the planning target volume (PTV), organs at risk (OARs), and body - these models were designed with two different methods for representing beam angles. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. We divided the 70 patients into 54 training, 6 validation, and 10 testing patients, thus yielding 27 000 training, 3000 validation, and 5000 testing plans. Mean square loss (MSE) was taken as the loss function. We used the Adam optimizer with a default learning rate of 0.01 to optimize the network's performance. We evaluated the models' performance by comparing their predicted dose distributions with the ground truth (Pareto optimal) dose distribution, in terms of dose volume histogram (DVH) plots and evaluation metrics such as PTV D98 , D95 , D50 , D2 , Dmax , Dmean , Paddick Conformation Number, R50, and Homogeneity index.

RESULTS

Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. The DVHs generated also precisely matched the ground truth. Evaluation metrics such as PTV statistics, dose conformity, dose spillage (R50), and homogeneity index also confirmed the accuracy of PTV curves on the DVH. Quantitatively, Model I's prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), and 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), and 6.30% (D2). Model I also outperformed Model II in terms of the mean dose error and the max dose error on the PTV, bladder, rectum, left femoral head, and right femoral head.

CONCLUSIONS

Treatment planners who use our models will be able to use deep learning to control the trade-offs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning.

Artificial intelligence in radiotherapy: a technological review

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.

A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy

PURPOSE

Beam orientation selection, whether manual or protocol-based, is the current clinical standard in radiation therapy treatment planning, but it is tedious and can yield suboptimal results. Many algorithms have been designed to optimize beam orientation selection because of its impact on treatment plan quality, but these algorithms suffer from slow calculation of the dose influence matrices of all candidate beams. We propose a fast beam orientation selection method, based on deep learning neural networks (DNN), capable of developing a plan comparable to those developed by the state-of-the-art column generation (CG) method. Our model's novelty lies in its supervised learning structure (using CG to teach the network), DNN architecture, and ability to learn from anatomical features to predict dosimetrically suitable beam orientations without using dosimetric information from the candidate beams. This may save hours of computation.

METHODS

A supervised DNN is trained to mimic the CG algorithm, which iteratively chooses beam orientations one-by-one by calculating beam fitness values based on Karush-Kush-Tucker optimality conditions at each iteration. The DNN learns to predict these values. The dataset contains 70 prostate cancer patients - 50 training, 7 validation, and 13 test patients - to develop and test the model. Each patient's data contains 6 contours: PTV, body, bladder, rectum, and left and right femoral heads. Column generation was implemented with a GPU-based Chambolle-Pock algorithm, a first-order primal-dual proximal-class algorithm, to create 6270 plans. The DNN trained over 400 epochs, each with 2500 steps and a batch size of 1, using the Adam optimizer at a learning rate of 1 × 10-5 and a sixfold cross-validation technique.

RESULTS

The average and standard deviation of training, validation, and testing loss functions among the six folds were 0.62 ± 0.09%, 1.04 ± 0.06%, and 1.44 ± 0.11%, respectively. Using CG and supervised DNN, we generated two sets of plans for each scenario in the test set. The proposed method took at most 1.5 s to select a set of five beam orientations and 300 s to calculate the dose influence matrices for 5 beams and finally 20 s to solve the fluence map optimization (FMO). However, CG needed around 15 h to calculate the dose influence matrices of all beams and at least 400 s to solve both the beam orientation selection and FMO problems. The differences in the dose coverage of PTV between plans generated by CG and by DNN were 0.2%. The average dose differences received by organs at risk were between 1 and 6 percent: Bladder had the smallest average difference in dose received (0.956 ± 1.184%), then Rectum (2.44 ± 2.11%), Left Femoral Head (6.03 ± 5.86%), and Right Femoral Head (5.885 ± 5.515%). The dose received by Body had an average difference of 0.10 ± 0.1% between the generated treatment plans.

CONCLUSIONS

We developed a fast beam orientation selection method based on a DNN that selects beam orientations in seconds and is therefore suitable for clinical routines. In the training phase of the proposed method, the model learns the suitable beam orientations based on patients' anatomical features and omits time intensive calculations of dose influence matrices for all possible candidate beams. Solving the FMO to get the final treatment plan requires calculating dose influence matrices only for the selected beams.

Many-isocenter optimization for robotic radiotherapy

Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hindered by limited clearance, and the need to perform complex coordinated gantry and couch motion. A robotic radiotherapy platform would be conducive to such treatment but a new conflict between field size and MLC modulation resolution needs to be managed for versatile applications. This study investigates the dosimetry and delivery efficiency of purposefully creating many isocenters to achieve simultaneously high MLC modulation resolution and large tumor coverage. An integrated optimization framework was proposed for simultaneous beam orientation optimization (BOO), isocenter selection, and fluence map optimization (FMO). The framework includes a least-square dose fidelity objective, a total variation term for regularizing the fluence smoothness, and a group sparsity term for beam selection. A minimal number of isocenters were identified for efficient target coverage. Colliding beams excluded, high-resolution small-field 4π intensity-modulated radiotherapy (IMRT) treatment plans with 50 cm source-to-isocenter distance (SID-50) on 10 Head and Neck (H&N) cancer patients were compared with low-resolution large-field plans with 100 cm SID (SID-100). With the same or better target coverage, the average reduction of [Dmean, Dmax] of 20-beam SID-50 plans from 20-beam SID-100 plans were [2.09 Gy, 1.19 Gy] for organs at risk (OARs) overall, [3.05 Gy, 0.04 Gy] for parotid gland, [3.62 Gy, 5.19 Gy] for larynx, and [3.27 Gy, 1.10 Gy] for mandible. R50 and integral dose were reduced by 5.3% and 9.6%, respectively. Wilcoxon signed-rank test showed significant difference (p   <  0.05) in planning target volume (PTV) homogeneity, PTV Dmax, R50, Integral dose, and OAR Dmean and Dmax. The estimated delivery time of 20-beam [SID-50, SID-100] plans were [19, 18] min and [14, 9] min, assuming 5 fractions and 30 fractions, respectively. With clinically acceptable delivery efficiency, many-isocenter optimization is dosimetrically desirable for treating large targets with high modulation resolution on the robotic platform.

Lateral head flexion as a noncoplanar solution for ring gantry stereotactic radiosurgery

PURPOSE

Ring gantry radiotherapy devices are often limited to deliver beams in the axial plane, severely limiting beam entrance angles and rendering noncoplanar beam delivery impossible. However, a ring gantry geometry greatly simplifies delivery machines and increases the efficiency of treatment with the potential to decrease the overall costs of radiotherapy. This study explores the use of lateral head flexion in order to increase beam entrance angles and extend the available solid angle space for a ring gantry stereotactic radiosurgery (SRS) application.

MATERIALS AND METHODS

A 1.5 T magnetic resonance imaging scanner was used to scan seven healthy volunteers at three different head positions: a neutral position, a left lateral flexion position and a right lateral flexion position. The lateral flexion scans were co-registered to the neutral head position scan using rigid registration and extracting the rotational transformation. The head pitch, roll, and yaw were computed for each registration to evaluate the natural range of motion for all volunteers. A ring gantry plan geometry was used to generate two sets of single fraction SRS plans (21 Gy): one coplanar set for head neutral scans, and a three-arc plan set using the head neutral and lateral head flexion scans. The conformity index (CI), intermediate dose fall-off (R50), low dose spillage (R10), and gradient measure (GM) were used to evaluate both sets of plans. The treatment plans were generated for a ring-gantry linear accelerator (linac) (Varian Halcyon 2.0) as well as radiosurgery linac (Varian Edge) for comparison.

RESULTS

The average pitch, yaw, and roll for the lateral head flexion scans were 4.1° ± 4.7°, 16.9° ± 3.7°, and 2.5° ± 4.9° for the right flexion and 4.9° ± 4.3°, 14.0° ± 3.7° and 2.8° ± 5.4° for left flexion. When comparing the head flexion technique with a fully coplanar geometry, the ring gantry plans showed an average improvement in CI of 7.3% (1.46 ± 0.25 vs 1.36 ± 0.28), a decrease of 13% in R50 (5.46 ± 1.14 vs 4.78 ± 1.12), a decrease of 32% in R10 (85.7 ± 20.3 vs 58.2 ± 15.1), and a decrease of 7.8% in GM (0.53 ± 0.05 vs 0.49 ± 0.04). The Edge plans showed an average improvement in CI of 3.0% (1.49 ± 0.26 vs 1.45 ± 0.25), a decrease of 6.8% in R50 (5.19 ± 1.03 vs 4.82 ± 0.83), a decrease of 29% in R10 (84.1 ± 16.3 vs 59.9 ± 12.5), and a decrease of 5.0% in GM (0.50 ± 0.04 vs 0.47 ± 0.03).

CONCLUSION

Lateral head flexion was shown to increase beam entrance angles considerably improving plan conformity and normal tissue sparing in this pilot study of seven sets of plans. Rigid registrations demonstrated each lateral flexion to be analogous to a 15° couch kick. The head flexion technique outlined here was shown to be a feasible solution for SRS treatments being delivered on ring gantry devices.

4π Radiotherapy Using a Linear Accelerator: A Misnomer in Violation of the Solid Geometric Boundary Conditions in Three-Dimensional Euclidean Space

Purpose:

The concept of 4π^c radiotherapy is a radiotherapy planning technique receiving much attention in recent times. The aim of this article is to disprove the feasibility of the 4π radiotherapy using a cantilever-type linear accelerator or any other external-beam delivery machines.

Materials and Methods:

A surface integral-based mathematical derivation for the maximum achievable solid angle for a linear accelerator was carried out respecting the rotational boundary conditions for gantry and couch in three-dimensional Euclidean space. The allowed movements include a gantry rotation of 0–2π^c and a table rotation of .

Results:

Total achievable solid angle by cantilever-type linear accelerator (or any teletherapy machine employing a cantilever design) is , which is applicable only for the foot and brain radiotherapy where the allowed table rotation is 90°–0°–270°. For other sites such as pelvis, thorax, or abdomen, achievable solid angle as the couch rotation comes down significantly. Practically, only suitable couch angle is 0° by avoiding gantry–couch–patient collision.

Conclusions:

Present cantilever design of linear accelerator prevents achieving a 4π radian solid angle at any point in the patient. Even the most modern therapy machines like CyberKnife which has a robotic arm also cannot achieve 4π geometry. Maximum achievable solid angle under the highest allowable boundary condition(s) cannot exceed 2π^c, which is restricted for only extremities such as foot and brain radiotherapy. For other parts of the body such as pelvis, thorax, and abdomen, the solid angle is reduced to 1/5^th (maximum value) of the 4π^c. To obtain a 4π^c solid angle in a three-dimensional Euclidean space, the patient has to be a zero-dimensional point and X-ray head of the linear accelerator has a freedom to rotate in every point of a hypothetical sphere of radius 1 m. This article establishes geometrically why it is not possible to achieve a 4π^c solid angle.

A sparse orthogonal collimator for small animal intensity-modulated radiation therapy. Part II: hardware development and commissioning

PURPOSE

A dose-modulation device for small animal radiotherapy is required to use clinically analogous treatment techniques, which will likely increase the translatability of preclinical research results. Because the clinically used multileaf collimator (MLC) is impractical for miniaturization, we have developed a simpler, better-suited sparse orthogonal collimator (SOC) for delivering small animal intensity-modulated radiation therapy (IMRT) using a rectangular aperture optimization (RAO) treatment planning system.

METHODS

The SOC system was modeled in computer-aided design software and fabricated with machined tungsten leaves and three-dimensional (3D) printed leaf housing. A graphical user interface was developed for controlling and calibrating the SOC leaves, which are driven by Arduino-controlled stepper motors. A Winston-Lutz test was performed to assess mechanical alignment, and abutting field and grid dose patterns were created to analyze intra- and intercalibration leaf positioning error. Leaf transmission and penumbra were measured over the full range of gantry angles and leaf positions, respectively. Three SOC test plans were delivered, and film measurements were compared to the intended dose distributions. The differences in maximum, mean, and minimum, as well as pixelwise absolute dose differences, were compared for each structure, and a gamma analysis was performed for the target structures using criteria of 4% dose difference and 0.3 mm distance to agreement.

RESULTS

The Winston-Lutz test revealed maximum directional offsets between the SOC and primary collimator axes of 0.53 mm at 0° and 0.68 mm over the full 360°. Upper and lower abutting field patterns had maximum dose deviations of 18.8 ± 3.1% and 15.5 ± 2.9%, respectively, and grid patterns showed intra- and intercalibration repeatability of 93% and 91%, respectively. Extremely low midleaf (0.15 ± 0.05%) and interleaf (0.27 ± 0.22%) transmission was measured, with no significant rotational variation. The average penumbra was ~0.8 mm for all leaves at field center, with a range of 0.17 mm for all leaf positions. A highly concave test plan was delivered with a ~ 95% gamma analysis pass rate, and a realistic mouse phantom liver irradiation plan achieved a pass rate of ~98%. A highly complex dose distribution was also created with 551 SOC apertures averaging 2.4 mm in size.

CONCLUSIONS

A sparse orthogonal collimator was developed and commissioned, with promising preliminary dosimetry results. The SOC design, with its limited moving components and high dose-modulation resolution, is ideal for delivering high-quality small animal IMRT with our RAO-based treatment planning system.

A sparse orthogonal collimator for small animal intensity-modulated radiation therapy part I: Planning system development and commissioning

PURPOSE

To achieve more translatable preclinical research results, small animal irradiation needs to more closely simulate human radiotherapy. Although the clinical gold standard is intensity-modulated radiation therapy (IMRT), the direct translation of this method for small animals is impractical. In this study we describe the treatment planning system for a novel dose modulation device to address this challenge.

METHODS

Using delineated target and avoidance structures, a rectangular aperture optimization (RAO) problem was formulated to penalize deviations from a desired dose distribution and limit the number of selected rectangular apertures. RAO was used to create IMRT plans with highly concave targets in the mouse brain, and the plan quality was compared to that using a hypothetical miniaturized multileaf collimator (MLC). RAO plans were also created for a realistic application of mouse whole liver irradiation and for a highly complex two-dimensional (2D) dose distribution as a proof-of-principle. Beam commissioning data, including output and off-axis factors and percent depth dose (PDD) curves, were acquired for our small animal irradiation system and incorporated into the treatment planning system. A plan post-processing step was implemented for aperture size-specific dose recalculation and aperture weighting reoptimization.

RESULTS

The first RAO test case achieved highly conformal doses to concave targets in the brain, with substantially better dose gradient, conformity, and target dose homogeneity than the hypothetical miniaturized MLC plans. In the second test case, a highly conformal dose to the liver was achieved with significant sparing of the kidneys. RAO also successfully replicated a complex 2D dose distribution with three prescription dose levels. Energy spectra for field sizes 1 to 20 mm were calculated to match the measured PDD curves, with maximum and mean dose deviations of 4.47 ± 0.30% and 1.71 ± 0.18%. The final reoptimization of aperture weightings for the complex RAO test plan was able to reduce the maximum and mean dose deviations between the optimized and recalculated dose distributions from 10.3% to 6.6% and 4.0% to 2.8%, respectively.

CONCLUSIONS

Using the advanced optimization techniques, complex IMRT plans were achieved using a simple dose modulation device. Beam commissioning data were incorporated into the treatment planning process to more accurately predict the resulting dose distribution. This platform substantially reduces the gap in treatment plan quality between clinical and preclinical radiotherapy, potentially increasing the value and flexibility of small animal studies.

Repeat Resection and Intraoperative Radiotherapy for Malignant Gliomas of the Brain: A History and Review of Current Techniques

The degree of primary resection of malignant brain gliomas (MBGs) has correlated positively with progression-free and overall survival. The indications for surgery and reoperation in MBG relapse remain controversial. Surgery will not be curative and should be followed by adjuvant treatment. We reviewed the reported studies with respect to repeat resection and the various methods of intraoperative radiotherapy for MBGs from the initial experience with high-energy linear accelerators in Japan to modern, integrated brachytherapy solutions using solid and balloon applicators. Because of the findings from our review, we have begun to research into the use of intraoperative balloon brachytherapy for recurrent MBGs.

Automated 4π radiotherapy treatment planning with evolving knowledge-base

PURPOSE

Non-coplanar 4π radiotherapy generalizes intensity modulated radiation therapy (IMRT) to automate beam geometry selection but requires complicated hyperparameter tuning to attain superior plan quality, which can be tedious and inconsistent. In this study, a fully automated 4π treatment planning was developed using evolving knowledge-base (EKB) planning guided by dose prediction.

METHODS

Twenty 4π lung and twenty 4π head and neck (HN) cases were included. A statistical voxel dose learning model was initially trained on low-quality plans created using generic hyperparameter templates without manual tuning. To improve the automated plan quality without being limited by the training data quality, a new 4π optimization problem was formulated to include a one-sided penalty on the organ-at-risk (OAR) dose deviation from the predicted dose. This directional OAR penalty encourages superior OAR sparing. The fast iterative shrinkage-thresholding algorithm (FISTA) was used to solve the large-scale beam orientation optimization problem. With the improved plans, new predictions were created to guide the next loop of EKB planning for a total of 10 loops. Plan quality was evaluated using a plan quality metric (PQM) points system based on clinical dose constraints and compared with automated planning approaches guided by manual high-quality plans using all non-coplanar beams, automated plans using individually evolved targeted dose, and manually created 4π plans.

RESULTS

For the lung cases, the final EKB plans had significantly higher PQM than manually created 4π (+2.60%). The improvements plateaued after the third loop. The final HN EKB plans and manually created 4π plans had comparable PQMs, but had lower PQM compared to automated plans using a high-quality training set (-3.00% and -4.44%, respectively). The PQM consistently increased up to the sixth loop. Individually evolved plans were able to improve the plan quality from initial condition due to the one-sided cost function but the 60% of them were trapped in undesired local minima that were substantially worse than their corresponding EKB plans.

CONCLUSION

Evolving knowledge-base planning is a novel automated planning technique guided by the predicted three-dimensional dose distribution, which can evolve from low-quality plans. EKB allows new beams to be used in the automated planning workflow for superior plan quality.

Single-isocenter volumetric-modulated Dynamic WaveArc therapy for two brain metastases

PURPOSE

A new irradiation technique, volumetric-modulated Dynamic WaveArc therapy (VMDWAT), based on sequential non-coplanar trajectories, can be performed using the Vero4DRT. This planning study compared the dose distribution and treatment time between single-isocenter volumetric-modulated arc therapy (VMAT) with multiple straight non-coplanar arcs and single-isocenter VMDWAT in patients with two brain metastases.

MATERIALS AND METHODS

Twenty patients with two planning target volumes exceeding 2.0 cm³ were included. Both VMAT and VMDWAT plans were created with single isocenter and a prescribed dose of 28 Gy delivered in five fractions. Target conformity was evaluated using indices modified from the RTOG-CI (mRTOG-CI) and IP-CI (mIP-CI).

RESULTS

VMDWAT significantly improved both mRTOG-CI and mIP-CI and reduced the volume of normal brain tissue receiving 25 and 28 Gy compared to VMAT. The two modalities did not significantly differ in terms of the volume of normal brain tissue receiving 5, 10, 12, 15, and 20 Gy. The mean treatment time was significantly shorter in the VMDWAT group.

CONCLUSION

VMDWAT significantly improved dose distribution in a shorter treatment time compared to VMAT in patients treated for two brain metastases. Single-isocenter VMDWAT may thus be a promising treatment for two brain metastases.

Recent developments in non-coplanar radiotherapy

This paper gives an overview of recent developments in non-coplanar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Modern linear accelerators are capable of automating motion around multiple axes, allowing efficient delivery of highly non-coplanar radiotherapy techniques. Novel techniques developed for C-arm and non-standard linac geometries, methods of optimization, and clinical applications are reviewed. The additional degrees of freedom are shown to increase the therapeutic ratio, either through dose escalation to the target or dose reduction to functionally important organs at risk, by multiple research groups. Although significant work is still needed to translate these new non-coplanar radiotherapy techniques into the clinic, clinical implementation should be prioritized. Recent developments in non-coplanar radiotherapy demonstrate that it continues to have a place in modern cancer treatment.

Quality assurance of non-coplanar, volumetric-modulated arc therapy employing a C-arm linear accelerator, featuring continuous patient couch rotation

PURPOSE

To perform quality assurance of non-coplanar, volumetric-modulated arc therapy featuring continuous couch rotation (CCR-VMAT) using a C-arm linear accelerator.

METHODS

We planned and delivered CCR-VMAT using the TrueBeam Developer Mode. Treatment plans were created for both a C-shaped phantom and five prostate cancer patients using seven CCR trajectories that lacked collisions; we used RayStation software (ver. 4.7) to this end. Subsequently, verification plans were generated. The mean absolute error (MAE) between the center of an MV-imaged steel ball and the radiation field was calculated using the Winston-Lutz test. The MAEs between planned and actual irradiation values were also calculated from trajectory logs. In addition, correlation coefficients (r values) among the MAEs of gantry angle, couch angle, and multi-leaf collimator (MLC) position, and mechanical parameters including gantry speed, couch speed, MLC speed, and beam output, were estimated. The dosimetric accuracies of planned and measured values were also assessed using ArcCHECK.

RESULTS

The MAEs ±2 standard deviations as revealed by the Winston-Lutz test for all trajectories were 0.3 ± 0.3 mm in two dimensions. The MAEs of the gantry, couch, and MLC positions calculated from all trajectory logs were within 0.04°, 0.08°, and 0.02 mm, respectively. Deviations in the couch angle (r = 0.98, p < 0.05) and MLC position (r = 0.86, p < 0.05) increased significantly with speed. The MAE of the beam output error was less than 0.01 MU. The mean gamma passing rate ± 2 SD (range) of the 3%/3 mm, 3%/1 mm, and 5%/1 mm was 98.1 ± 1.9% (95.7-99.6%), 87.2 ± 2.8% (80.2-96.7%), and 96.3 ± 2.8% (93.9-99.6%), respectively.

CONCLUSIONS

CCR-VMAT delivered via the TrueBeam Developer Mode was associated with high-level geometric and mechanical accuracy, thus affording to high dosimetric accuracy. The CCR-VMAT performance was stable regardless of the trajectory chosen.

Performance Comparison of Knowledge-Based Dose Prediction Techniques Based on Limited Patient Data

Purpose:

The accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data.

Methods:

Statistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy.

Results:

Statistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (−43.9%) and support vector regression (−42.8%) and lung support vector regression (−24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases.

Conclusion:

Compared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method.

Part 1: Optimization and evaluation of dynamic trajectory radiotherapy

PURPOSE

Although volumetric modulated arc therapy (VMAT) is a well-accepted treatment technique in radiotherapy using a coplanar delivery approach, VMAT might be further improved by including dynamic table and collimator rotations leading to dynamic trajectory radiotherapy (DTRT). In this work, an optimization procedure for DTRT was developed and the potential benefit of DTRT was investigated for different treatment sites.

METHODS

For this purpose, a dedicated optimization framework for DTRT was developed using the Eclipse Scripting Research Application Programming Interface (ESRAPI). The contours of the target and organs at risk (OARs) structures were exported by applying the ESRAPI and were used to determine the fractional volume-overlap of the OARs with the target from several potential beam directions. Thereby, an additional weighting was applied taking into account the relative position of the OAR with respect to the target and radiation beam, that is, penalizing directions where the OAR is proximal to the target. The resulting two-dimensional gantry-table map was used as input for an A* path finding algorithm returning an optimized gantry-table path. Thereby, the process is also taking into account CT scan length and collision restrictions. The A* algorithm was used again to determine the dynamic collimator angle path by optimizing the area between the MLC leaves and the target contour for each gantry-table path leading to gantry-collimator paths. The resulting gantry-table and gantry-collimator paths are combined and serve as input for the intensity modulation optimization using a research VMAT optimizer and the ESRAPI resulting in dynamic trajectories. This procedure was evaluated for five clinically motivated cases: two head and neck, one lung, one esophagus, and one prostate. Final dose calculations were performed using the Swiss Monte Carlo Plan (SMCP). Resulting dose distributions for the DTRT treatment plans and for the standard VMAT plans were compared based on dose distributions and dose volume histogram (DVH) parameters. For this comparison, the dose distribution for the VMAT plans were recalculated using the SMCP. In addition, the suitability of the delivery of a DTRT treatment plan was demonstrated by means of gafchromic film measurements on a TrueBeam linear accelerator.

RESULTS

DVHs for the target volumes showed similar or improved coverage and dose homogeneity for DTRT compared with VMAT using equal or less number of dynamic trajectories for DTRT than arcs for VMAT for all cases studied. Depending on the case, improvements in mean and maximum dose for the DTRT plans were achieved for almost all OARs compared with the VMAT plans. Improvements in DTRT treatment plans for mean and maximum dose compared to VMAT plans were up to 16% and 38% relative to the prescribed dose, respectively. The measured and calculated dose values resulted in a passing rate of more than 99.5% for the two-dimensional gamma analysis using 2% and 2 mm criteria and a threshold of 10%.

CONCLUSIONS

DTRT plans for different treatment sites were generated and compared with VMAT plans. The delivery is suitable and dose comparisons demonstrate a high potential of DTRT to reduce dose to OARs using less dynamic trajectories than arcs, while target coverage is preserved.

In regard to "Tran A, Zhang J, Woods K, Yu V, Nguyen D, Gustafson G, Rosen L, Sheng K. Treatment planning comparison of IMPT, VMAT and 4π radiotherapy for prostate cases. Radiation oncology. 2017 Jan 11; 12(1):10"

This article describe the three dimensional geometrical incompetency of the term "4π radiotherapy"; frequently used in radiation oncology to establish the superiority (or rather complexity) of particular kind of external beam delivery technique. It was claimed by several researchers, to obtain 4πc solid angle at target centre created by the tele-therapy delivery machine in three dimensional Euclidian space. However with the present design of linear accelerator (or any other tele-therapy machine) it is not possible to achieve more than 2πc with the allowed boundary condition of 0 ≤ Gnatry position≤πc and [Formula: see text]≤Couch Position≤[Formula: see text] .This article describes why it is not possible to achieve a 4πc solid angle at any point in three dimensional Euclidian spaces. This article also recommends not to use the terminology "4π radiotherapy" for describing any external beam technique or its complexity as this term is geometrically wrong.

4π plan optimization for cortical-sparing brain radiotherapy

BACKGROUND AND PURPOSE

Incidental irradiation of normal brain tissue during radiotherapy is linked to cognitive decline, and may be mediated by damage to healthy cortex. Non-coplanar techniques may be used for cortical sparing. We compared normal brain sparing and probability of cortical atrophy using 4π radiation therapy planning vs. standard fixed gantry intensity-modulated radiotherapy (IMRT).

MATERIAL AND METHODS

Plans from previously irradiated brain tumor patients ("original IMRT", n = 13) were re-planned to spare cortex using both 4π optimization ("4π") and IMRT optimization ("optimized IMRT"). Homogeneity index (HI), gradient measure, doses to cortex and white matter (excluding tumor), brainstem, optics, and hippocampus were compared with matching PTV coverage. Probability of three grades of post-treatment cortical atrophy was modeled based on previously established dose response curves.

RESULTS

With matching PTV coverage, 4π significantly improved HI by 27% (p = 0.005) and gradient measure by 8% (p = 0.001) compared with optimized IMRT. 4π optimization reduced mean and equivalent uniform doses (EUD) to all standard OARs, with 14-15% reduction in hippocampal EUD (p ≤ 0.003) compared with the other two plans. 4π significantly reduced dose to fractional cortical volumes (V50, V40 and V30) compared with the original IMRT plans, and reduced cortical V30 by 7% (p = 0.008) compared with optimized IMRT. White matter EUD, mean dose, and fractional volumes V50, V40 and V30 were also significantly lower with 4π (p ≤ 0.001). With 4π, probability of grade 1, 2 and 3 cortical atrophy decreased by 12%, 21% and 26% compared with original IMRT and by 8%, 14% and 3% compared with optimized IMRT, respectively (p ≤ 0.001).

CONCLUSIONS

4π radiotherapy significantly improved cortical sparing and reduced doses to standard brain OARs, white matter, and the hippocampus. This was achieved with superior PTV dose homogeneity. Such sparing could reduce the probability of cortical atrophy that may lead to cognitive decline.

In silico assessment of the dosimetric quality of a novel, automated radiation treatment planning strategy for linac-based radiosurgery of multiple brain metastases and a comparison with robotic methods

Background

To appraise the dosimetric features and the quality of the treatment plan for radiosurgery of multiple brain metastases optimized with a novel automated engine and to compare with plans optimized for robotic-based delivery.

Methods

A set of 15 patients with multiple brain metastases was selected for this in silico study. The technique under investigation is the recently introduced HyperArc. For all patients, three treatment plans were computed and compared: i: a HyperArc; ii: a standard VMAT; iii) a CyberKnife. Dosimetric features were computed for the clinical target volumes as well as for the healthy brain tissue and the organs at risk.

Results

The data showed that the best dose homogeneity was achieved with the VMAT technique. HyperArc allowed to minimize the volume of brain receiving 4Gy (as well as for the mean dose and the volume receiving 12Gy, although not statistically significant). The smallest dose on 1 cm³ volume for all organs at risk is for CK techniques, and the biggest for VMAT (p < 0.05). The Radiation Planning Index coefficient indicates that, there are no significant differences among the techniques investigated, suggesting an equivalence among these.

Conclusion

At treatment planning level, the study demonstrates that the use of HyperArc technique can significantly improve the sparing of the healthy brain while maintaining a full coverage of the target volumes.

Fraction-variant beam orientation optimization for non-coplanar IMRT

Conventional beam orientation optimization (BOO) algorithms for IMRT assume that the same set of beam angles is used for all treatment fractions. In this paper we present a BOO formulation based on group sparsity that simultaneously optimizes non-coplanar beam angles for all fractions, yielding a fraction-variant (FV) treatment plan. Beam angles are selected by solving a multi-fraction fluence map optimization problem involving 500-700 candidate beams per fraction, with an additional group sparsity term that encourages most candidate beams to be inactive. The optimization problem is solved using the fast iterative shrinkage-thresholding algorithm. Our FV BOO algorithm is used to create five-fraction treatment plans for digital phantom, prostate, and lung cases as well as a 30-fraction plan for a head and neck case. A homogeneous PTV dose coverage is maintained in all fractions. The treatment plans are compared with fraction-invariant plans that use a fixed set of beam angles for all fractions. The FV plans reduced OAR mean dose and D 2 values on average by 3.3% and 3.8% of the prescription dose, respectively. Notably, mean OAR dose was reduced by 14.3% of prescription dose (rectum), 11.6% (penile bulb), 10.7% (seminal vesicle), 5.5% (right femur), 3.5% (bladder), 4.0% (normal left lung), 15.5% (cochleas), and 5.2% (chiasm). D 2 was reduced by 14.9% of prescription dose (right femur), 8.2% (penile bulb), 12.7% (proximal bronchus), 4.1% (normal left lung), 15.2% (cochleas), 10.1% (orbits), 9.1% (chiasm), 8.7% (brainstem), and 7.1% (parotids). Meanwhile, PTV homogeneity defined as D 95/D 5 improved from .92 to .95 (digital phantom), from .95 to .98 (prostate case), and from .94 to .97 (lung case), and remained constant for the head and neck case. Moreover, the FV plans are dosimetrically similar to conventional plans that use twice as many beams per fraction. Thus, FV BOO offers the potential to reduce delivery time for non-coplanar IMRT.

HyperArc VMAT planning for single and multiple brain metastases stereotactic radiosurgery: a new treatment planning approach

Purpose

The HyperArc VMAT (HA-VMAT) planning approach was newly developed to fulfill the demands of dose delivery for brain metastases stereotactic radiosurgery. We compared the dosimetric parameters of the HA-VMAT plan with those of the conventional VMAT (C-VMAT).

Material and methods

For 23 patients (1–4 brain metastases), C-VMAT and HA-VMAT plans with a prescription dose of 20–24 Gy were retrospectively generated, and dosimetric parameters for PTV (homogeneity index, HI; conformity index, CI; gradient index, GI) and brain tissue (V_2Gy-V_16Gy) were evaluated. Subsequently, the physical characteristics (modulation complexity score for VMAT, MCSV; Monitor unit, MU) of both treatment approaches were compared.

Results

HA-VMAT provided higher HI (1.41 ± 0.07 vs. 1.24 ± 0.07, p < 0.01), CI (0.93 ± 0.02 vs. 0.90 ± 0.05, p = 0.01) and lower GI (3.06 ± 0.42 vs. 3.91 ± 0.55, p < 0.01) values. Moderate-to-low dose spreads (V_4Gy-V_16Gy) were significantly reduced (p < 0.01) in the HA-VMAT plan over that of C-VMAT. HA-VMAT plans resulted in more complex MLC patterns (lower MCSV, p < 0.01) and higher MU (p < 0.01).

Conclusions

HA-VMAT plans provided significantly higher conformity and rapid dose falloff with respect to the C-VMAT plans.

An evaluation of the consistency of shifts reported by three different systems for non-coplanar treatments

Treatment of intra-cranial lesions sometimes requires a non-coplanar beam configuration. One of the most commonly used IGRT modalities, kV conebeam CT, cannot typically be used when large couch rotations are introduced. However, multiple other systems allow for imaging/tracking the patient for such situations. This work compares shift consistency from three independent systems, namely Varian's Advanced Imaging, Brainlab's Exactrac and Varian's OSMS, all installed on the same linear accelerator. After a phantom was first positioned using conebeam CT, the three systems were used to determine shifts at different couch positions. This was done with and without intentional shifts inserted in the original phantom position. Results show that the difference in shifts between the three systems was never more than 0.7 mm (average of 0.2 mm, standard deviation of 0.2 mm). These results confirm that all three systems are equivalent to within 1 mm and may potentially be uses interchangeably, especially in cases where the PTV margin is on the order of 1 mm.

Deterministic direct aperture optimization using multiphase piecewise constant segmentation

PURPOSE

Direct aperture optimization (DAO) attempts to incorporate machine constraints in the inverse optimization to eliminate the post-processing steps in fluence map optimization (FMO) that degrade plan quality. Current commercial DAO methods utilize a stochastic or greedy approach to search a small aperture solution space. In this study, we propose a novel deterministic direct aperture optimization that integrates the segmentation of fluence map in the optimization problem using the multiphase piecewise constant Mumford-Shah formulation.

METHODS

The Mumford-Shah based direct aperture optimization problem was formulated to include an L2-norm dose fidelity term to penalize differences between the projected dose and the prescribed dose, an anisotropic total variation term to promote piecewise continuity in the fluence maps, and the multiphase piecewise constant Mumford-Shah function to partition the fluence into pairwise discrete segments. A proximal-class, first-order primal-dual solver was implemented to solve the large scale optimization problem, and an alternating module strategy was implemented to update fluence and delivery segments. Three patients of varying complexity-one glioblastoma multiforme (GBM) patient, one lung (LNG) patient, and one bilateral head and neck (H&N) patient with 3 PTVs-were selected to test the new DAO method. For each patient, 20 non-coplanar beams were first selected using column generation, followed by the Mumford-Shah based DAO (DAO_MS ). For comparison, a popular and successful approach to DAO known as simulated annealing-a stochastic approach-was replicated. The simulated annealing DAO (DAO_SA ) plans were then created using the same beam angles and maximum number of segments per beam. PTV coverage, PTV homogeneity D95D5, and OAR sparing were assessed for each plan. In addition, high dose spillage, defined as the 50% isodose volume divided by the tumor volume, as well as conformity, defined as the van't Riet conformation number, were evaluated.

RESULTS

DAO_MS achieved essentially the same OAR doses compared with the DAO_SA plans for the GBM case. The average difference of OAR D_max and D_mean between the two plans were within 0.05% of the plan prescription dose. The lung case showed slightly improved critical structure sparing using the DAO_MS approach, where the average OAR D_max and D_mean were reduced by 3.67% and 1.08%, respectively, of the prescription dose. The DAO_MS plan substantially improved OAR dose sparing for the H&N patient, where the average OAR D_max and D_mean were reduced by over 10% of the prescription dose. The DAO_MS and DAO_SA plans were comparable for the GBM and LNG PTV coverage, while the DAO_MS plan substantially improved the H&N PTV coverage, increasing D99 by 6.98% of the prescription dose. For the GBM and LNG patients, the DAO_MS and DAO_SA plans had comparable high dose spillage but slightly worse conformity with the DAO_MS approach. For the H&N plan, DAO_MS was considerably superior in high dose spillage and conformity to the DAO_SA . The deterministic approach is able to solve the DAO problem substantially faster than the simulated annealing approach, with a 9.5- to 40-fold decrease in total solve time, depending on the patient case.

CONCLUSIONS

A novel deterministic direct aperture optimization formulation was developed and evaluated. It combines fluence map optimization and the multiphase piecewise constant Mumford-Shah segmentation into a unified framework, and the resulting optimization problem can be solved efficiently. Compared to the widely and commercially used simulated annealing DAO approach, it showed comparable dosimetry behavior for simple plans, and substantially improved OAR sparing, PTV coverage, PTV homogeneity, high dose spillage, and conformity for the more complex head and neck plan.

Multi-isocentric 4π volumetric-modulated arc therapy approach for head and neck cancer

Objectives

To explore the feasibility of multi‐isocentric 4π volumetric‐modulated arc therapy (MI4π‐VMAT) for the complex targets of head and neck cancers.

Methods

Twenty‐five previously treated patients of HNC underwent re‐planning to improve the dose distributions with either coplanar VMAT technique (CP‐VMAT) or noncoplanar MI4π‐VMAT plans. The latter, involving 3–6 noncoplanar arcs and 2–3 isocenters were re‐optimized using the same priorities and objectives. Dosimetric comparison on standard metrics from dose‐volume histograms was performed to appraise relative merits of the two techniques. Pretreatment quality assurance was performed with IMRT phantoms to assess deliverability and accuracy of the MI4π‐VMAT plans. The gamma agreement index (GAI) analysis with criteria of 3 mm distance to agreement (DTA) and 3% dose difference (DD) was applied.

Results

CP‐VMAT and MI4π‐VMAT plans achieved the same degree of coverage for all target volumes related to near‐to‐minimum and near‐to‐maximum doses. MI4π‐VΜΑΤ plans resulted in an improved sparing of organs at risk. The average mean dose reduction to the parotids, larynx, oral cavity, and pharyngeal muscles were 3 Gy, 4 Gy, 5 Gy, and 4.3 Gy, respectively. The average maximum dose reduction to the brain stem, spinal cord, and oral cavity was 6.0 Gy, 3.8 Gy, and 2.4 Gy. Pretreatment QA results showed that plans can be reliably delivered with mean gamma agreement index of 97.0 ± 1.1%.

Conclusions

MI4π‐VMAT plans allowed to decrease the dose‐volume‐metrics for relevant OAR and results are reliable from a dosimetric standpoint. Early clinical experience has begun and future studies will report treatment outcome.

A priori patient-specific collision avoidance in radiotherapy using consumer grade depth cameras

PURPOSE

In this study, we demonstrate and evaluate a low cost, fast, and accurate avoidance framework for radiotherapy treatments. Furthermore, we provide an implementation which is patient specific and can be implemented during the normal simulation process.

METHODS

Four patients and a treatment unit were scanned with a set of consumer depth cameras to create a polygon mesh of each object. Using a fast polygon interference algorithm, the models were virtually collided to map out feasible treatment positions of the couch and gantry. The actual physical collision space was then mapped in the treatment room by moving the gantry and couch until a collision occurred with either the patient or hardware. The physical and virtual collision spaces were then compared to determine the accuracy of the system. To improve the collision predictions, a buffer geometry was added to the scanned gantry mesh and performance was assessed as a function of buffer thickness.

RESULTS

Each patient was optically scanned during simulation in less than 1 min. The average time to virtually map the collision space for 64, 800 gantry/couch states was 5.40 ± 2.88 s. The system had an average raw accuracy and negative prediction rate (NPR) across all patients of 97.3% ± 2.4% and 96.9% ± 2.2% respectively. Using a polygon buffer of 6 cm over the gantry geometry, the NPR was raised to unity for all patients, signifying the detection of all collision events. However, the average accuracy fell from 95.3% ± 3.1% to 91.5% ± 3.6% between the 3 and 6 cm buffer as more false positives were detected.

CONCLUSIONS

We successfully demonstrated a fast and low cost framework which can map an entire collision space a priori for a given patient during the time of simulation. All collisions can be avoided using polygon interference, but a polygon buffer may be required to account for geometric uncertainties of scanned objects.

Functional image-guided dose escalation in gliomas using of state-of-the-art photon vs. proton therapy

BACKGROUND

Recurrences of glioma are usually local, suggesting the need for higher tumor dose. We investigated the boundaries for dose escalation of an ¹⁸F-fluoro-ethyl-tyrosine positron emission tomography defined target by intensity-modulated photon therapy (IMRT), volumetric modulated arc therapy (VMAT) and intensity-modulated proton therapy (IMPT).

MATERIALS AND METHODS

Standard dose (60 Gy) and dose-escalated plans were calculated for seven patients using IMRT, VMAT and IMPT. The achieved boost dose, the dose to the organs at risk (OAR), the dose homogeneity (defined as overdose volume, ODV) and the ratio of the 30 Gy isodose curve and the boost volume (R30) were compared. The risk of radionecrosis was estimated using the ratio of the dose volume histograms of the brain (range 30-60 Gy).

RESULTS

The mean boost dose was 77.1 Gy for IMRT, 79.2 Gy for VMAT and 85.1 GyE for IMPT. Compared with the standard plan, the ODV was unchanged and the R30 increased (17%) for IMRT. For VMAT, the ODV decreased (7%) and the R30 was unchanged whereas IMPT substantially decreased ODV (61%), R30 (22%), OAR doses as well as the risk of radionecrosis.

CONCLUSIONS

Dose escalation can be achieved with IMRT, VMAT and IMPT while respecting normal tissue constraints, yet with IMPT being most favorable.

Computerized triplet beam orientation optimization for MRI-guided Co-60 radiotherapy

PURPOSE

Magnetic resonance imaging (MRI)-guided Co-60 provides daily and intrafractional MRI soft tissue imaging for improved target and critical organ tracking. To increase delivery efficiency, the system uses three Co-60 sources at 120° apart, allowing up to 600 cGy combined dose rate at isocenter. Despite the potential tripling in output, creating a delivery plan that uses all three sources is considerably unintuitive. Here, the authors computerize the triplet orientation optimization using column generation, an approach that was demonstrated effective in integrated beam orientation and fluence optimization for noncoplanar therapies. To achieve a better plan quality without increasing the treatment time, the authors then solve a fluence map optimization (FMO) problem while regularizing the fluence maps to reduce the number of deliverable MLC segments.

METHODS

Three patients-one prostate, one lung, and one head and neck boost plan (H&N_Boost)-were evaluated in this study. For each patient, the beamlet doses were calculated using Monte Carlo, under a 0.35 T magnetic field, for 180 equally spaced coplanar beams grouped into 60 triplets. The beamlet size is 1.05 × 0.5 cm determined by the MLC leaf thickness and step size. The triplets were selected using the column generation algorithm. The FMO problem was formulated using an L2-norm dose fidelity term and an L1-norm anisotropic total variation regularization term, which allows controlling the number of MLC segments, and hence the treatment time, with minimal degradation to the dose. The authors' Fluence Regularization and Optimized Selection of Triplets (FROST) plans were compared against the clinical treatment plans (CLNs) produced by an experienced dosimetrist. PTV homogeneity, max dose, mean dose, D95, D98, and D99 were evaluated. OAR max and mean doses, as well as R50, defined as the ratio of the 50% isodose volume over the planning target volume were investigated.

RESULTS

The mean PTV D95, D98, and D99 differ by +0.04%, +0.07%, and +0.25% of the prescription dose between planning methods. The mean PTV homogeneity was virtually same with values at 0.8788 (FROST) and 0.8812 (CLN). R50 decreased by 0.67 comparing FROST to CLN. On average, FROST reduced D_max and D_mean of OARs by 7.30% and 6.08% of the prescription dose, respectively. The manual CLN planning processes required numerous trial and error runs. The FROST plans on the other hand required minimal human intervention.

CONCLUSIONS

Efficient delivery of MRI-guided Co-60 therapy needs the output of multiple sources yet suffers from unintuitive and laborious manual beam selection processes. Computerized triplet orientation optimization improves both planning efficiency and plan dosimetry. The novel fluence map regularization provides additional controls over the number of MLC segments and treatment time.

A simple and robust trajectory-based stereotactic radiosurgery treatment

INTRODUCTION

We present a Trajectory-based Volumetric Modulated Arc Therapy (TVMAT) technique for Stereotactic Radiosurgery (SRS) that takes advantage of a modern linacs ability to modulate dose rate and move the couch dynamically. In addition, we investigate the quality of the developed TVMAT method and the dosimetric accuracy of the technique.

METHODS

The main feature of the TVMAT technique is a standard beam trajectory formed by dynamic motion of the treatment couch and the linac gantry. The couch rotates slowly through 180 degrees while the gantry delivers radiation through continuous sweeps of the gantry. The number of partial arcs that constitute the trajectory can be varied between two and eight and as the number of partial arcs increases, the trajectory more finely samples 4π geometry. Along these trajectories, the multi-leaf collimator (MLC) and dose rate are optimized through an inverse planning framework. The TVMAT method was tested on ten cranial SRS patients who were previously treated with the Dynamic Conformal Arc (DCA) technique. The plans were compared with the DCA and a four- arc VMAT technique with regards to dose to the OAR, dose falloff, V12Gy, and V4Gy. Validation measurements were performed using ion-chamber and Gafchromic film. In addition, the trajectory-log files were analyzed and compared with the treatment plan beam data.

RESULTS

The TVMAT treatment plans were successfully delivered with a treatment time between 3-8 min which mostly depended on total cumulated dose. Ion chamber measurements had an average measured error of 1.1 ± 0.6% and a maximum value of 2.2% of the delivered dose. The 2%, 2 mm gamma pass rates for the film measurements were 96% or greater. In a preliminary comparison of ten patients who underwent SRS treatments with the DCA technique, the TVMAT and VMAT techniques were able to produce plans with comparable dose falloff and OAR doses, while achieving better dose conformality, V4Gy and V12Gy when compared to the original DCA plans. The improvement of the TVMAT plans were as follows (mean % improvement ± standard err): Conformity (10 ± 2%), V4 (20 ± 20%), V12 (27 ± 10%), volume weighted mean dose to organs at risk (13 ± 13%), homogeneity index (2 ± 2%) and falloff (4 ± 2%).

CONCLUSION

We have developed and validated a trajectory-based dose delivery method which has dose distribution improvements while having a treatment time of 3-8 min. In addition, it has the potential for a simpler planning experience while maintaining an accurate delivery on the Varian Truebeam Linac.

Treatment planning comparison of IMPT, VMAT and 4π radiotherapy for prostate cases

Background

Intensity-modulated proton therapy (IMPT), non-coplanar 4π intensity-modulated radiation therapy (IMRT), and volumetric-modulated arc therapy (VMAT) represent the most advanced treatment methods based on heavy ion and X-rays, respectively. Here we compare their performance for prostate cancer treatment.

Methods

Ten prostate patients were planned using IMPT with robustness optimization, VMAT, and 4π to an initial dose of 54 Gy to a clinical target volume (CTV) that encompassed the prostate and seminal vesicles, then a boost prescription dose of 25.2 Gy to the prostate for a total dose of 79.2 Gy. The IMPT plans utilized two coplanar, oblique scanning beams 10° posterior of the lateral beam positions. Range uncertainties were taken into consideration in the IMPT plans. VMAT plans used two full, coplanar arcs to ensure sufficient PTV coverage. 4π plans were created by inversely selecting and optimizing 30 beams from 1162 candidate non-coplanar beams using a greedy column generation algorithm. CTV doses, bladder and rectum dose volumes (V40, V45, V60, V65, V70, V75, and V80), R100, R50, R10, and CTV homogeneity index (D95/D5) were evaluated.

Results

Compared to IMPT, 4π resulted in lower anterior rectal wall mean dose as well as lower rectum V40, V45, V60, V65, V70, and V75. Due to the opposing beam arrangement, IMPT resulted in significantly (p < 0.05) greater femoral head doses. However, IMPT plans had significantly lower bladder, rectum, and anterior rectal wall max dose. IMPT doses were also significantly more homogeneous than 4π and VMAT doses.

Conclusion

Compared to the VMAT and 4π plans, IMPT treatment plans are superior in CTV homogeneity and maximum point organ-at-risk (OAR) doses with the exception of femur heads. IMPT is inferior in rectum and bladder volumes receiving intermediate to high doses, particularly to the 4π plans, but significantly reduced low dose spillage and integral dose, which are correlated to secondary cancer for patients with expected long survival. The dosimetric benefits of 4π plans over VMAT are consistent with the previous publication.

The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

PURPOSE

Significant dosimetric benefits had been previously demonstrated in highly noncoplanar treatment plans. In this study, the authors developed and verified an individualized collision model for the purpose of delivering highly noncoplanar radiotherapy and tested the feasibility of total delivery automation with Varian TrueBeam developer mode.

METHODS

A hand-held 3D scanner was used to capture the surfaces of an anthropomorphic phantom and a human subject, which were positioned with a computer-aided design model of a TrueBeam machine to create a detailed virtual geometrical collision model. The collision model included gantry, collimator, and couch motion degrees of freedom. The accuracy of the 3D scanner was validated by scanning a rigid cubical phantom with known dimensions. The collision model was then validated by generating 300 linear accelerator orientations corresponding to 300 gantry-to-couch and gantry-to-phantom distances, and comparing the corresponding distance measurements to their corresponding models. The linear accelerator orientations reflected uniformly sampled noncoplanar beam angles to the head, lung, and prostate. The distance discrepancies between measurements on the physical and virtual systems were used to estimate treatment-site-specific safety buffer distances with 0.1%, 0.01%, and 0.001% probability of collision between the gantry and couch or phantom. Plans containing 20 noncoplanar beams to the brain, lung, and prostate optimized via an in-house noncoplanar radiotherapy platform were converted into XML script for automated delivery and the entire delivery was recorded and timed to demonstrate the feasibility of automated delivery.

RESULTS

The 3D scanner measured the dimension of the 14 cm cubic phantom within 0.5 mm. The maximal absolute discrepancy between machine and model measurements for gantry-to-couch and gantry-to-phantom was 0.95 and 2.97 cm, respectively. The reduced accuracy of gantry-to-phantom measurements was attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac.

CONCLUSIONS

An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

Dose domain regularization of MLC leaf patterns for highly complex IMRT plans

PURPOSE

The advent of automated beam orientation and fluence optimization enables more complex intensity modulated radiation therapy (IMRT) planning using an increasing number of fields to exploit the expanded solution space. This has created a challenge in converting complex fluences to robust multileaf collimator (MLC) segments for delivery. A novel method to regularize the fluence map and simplify MLC segments is introduced to maximize delivery efficiency, accuracy, and plan quality.

METHODS

In this work, we implemented a novel approach to regularize optimized fluences in the dose domain. The treatment planning problem was formulated in an optimization framework to minimize the segmentation-induced dose distribution degradation subject to a total variation regularization to encourage piecewise smoothness in fluence maps. The optimization problem was solved using a first-order primal-dual algorithm known as the Chambolle-Pock algorithm. Plans for 2 GBM, 2 head and neck, and 2 lung patients were created using 20 automatically selected and optimized noncoplanar beams. The fluence was first regularized using Chambolle-Pock and then stratified into equal steps, and the MLC segments were calculated using a previously described level reducing method. Isolated apertures with sizes smaller than preset thresholds of 1-3 bixels, which are square units of an IMRT fluence map from MLC discretization, were removed from the MLC segments. Performance of the dose domain regularized (DDR) fluences was compared to direct stratification and direct MLC segmentation (DMS) of the fluences using level reduction without dose domain fluence regularization.

RESULTS

For all six cases, the DDR method increased the average planning target volume dose homogeneity (D95/D5) from 0.814 to 0.878 while maintaining equivalent dose to organs at risk (OARs). Regularized fluences were more robust to MLC sequencing, particularly to the stratification and small aperture removal. The maximum and mean aperture sizes using the DDR were consistently larger than those from DMS for all tested number of segments.

CONCLUSIONS

The fluence map to MLC segmentation conversion problem was formulated as a secondary optimization problem in the dose domain to minimize the smoothness-regularized dose discrepancy. The large scale optimization problem was solved using a primal-dual algorithm that transformed complicated fluences into maps that were more robust to the MLC segmentation and sequencing, affording fewer and larger segments with minimal degradation to dose distribution.