Scattering kernels for fast neutron therapy treatment planning.

The University of Washington (UW) Clinical Neutron Therapy System (CNTS) has been used to treat over 3300 patients. Treatment planning for these patients is currently performed using an MV x-ray model in Pinnacle® adapted to fit measurements of fast neutron output factors, wedge factors, depth-dose and lateral profiles. While this model has provided an adequate representation of the CNTS for 3D conformal treatment planning, later versions of Pinnacle did not allow for isocentric gantry rotation machines with a source-to-axis distance of 150 cm. This restriction limited the neutron model to version 9.0 while the photon and electron treatment planning at the UW had moved on to newer versions. Also, intensity modulated neutron therapy (IMNT) is underdevelopment at the UW, and the Pinnacle neutron model developed cannot be used for inverse treatment planning. We have used the MCNP6 Monte Carlo code system to develop Collapsed Cone (CC) and Singular Value Decomposition (SVD) neutron scattering kernels suitable for integration into the RayStation treatment planning system. Kernels were generated for monoenergetic neutrons with energies ranging from 1 keV to 51 MeV, i.e. the energy range most relevant to the CNTS. Percent depth dose (PDD) profiles computed in RayStation for the CC and SVD kernels are in excellent agreement with each other for depths beyond the beam's dose build-up region (depths greater than about 1.7 cm) for open 2.8 × 2.8 cm2, 10.3 × 10.3 cm2, and 28.8 × 32.8 cm2 fields. Lateral profiles at several depths, as well as the PDD, calculated using the CC kernels in RayStation for the full CNTS energy spectrum pass a 3%/3 mm gamma test for field sizes of 2.8 × 2.8 cm2, 10.0 × 10.3 cm2, and 28.8 × 32.8 cm2.

Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system.

PURPOSE: The accuracy of deformable image registration tools can vary widely between imaging modalities and specific implementations of the same algorithms. A biomechanical model-based algorithm initially developed in-house at an academic institution was translated into a commercial radiotherapy treatment planning system and validated for multiple imaging modalities and anatomic sites. METHODS: Biomechanical deformable registration (Morfeus) is a geometry-driven algorithm based on the finite element method. Boundary conditions are derived from the model-based segmentation of controlling structures in each image which establishes a point-to-point surface correspondence. For each controlling structure, material properties and fixed or sliding interfaces are assigned. The displacements of internal volumes for controlling structures and other structures implicitly deformed are solved with finite element analysis. Registration was performed for 74 patients with images (mean vector resolution) of thoracic and abdominal 4DCT (2.8 mm) and MR (5.3 mm), liver CT-MR (4.5 mm), and prostate MR (2.6 mm). Accuracy was quantified between deformed and actual target images using distance-to-agreement (DTA) for structure surfaces and the target registration error (TRE) for internal point landmarks. RESULTS: The results of the commercial implementation were as follows. The mean DTA was ≤ 1.0 mm for controlling structures and 1.0-3.5 mm for implicitly deformed structures on average. TRE ranged from 2.0 mm on prostate MR to 5.1 mm on lung MR on average, within 0.1 mm or lower than the image voxel sizes. Accuracy was not overly sensitive to changes in the material properties or variability in structure segmentations, as changing these inputs affected DTA and TRE by ≤ 0.8 mm. Maximum DTA > 5 mm occurred for 88% of the structures evaluated although these were within the inherent segmentation uncertainty for 82% of structures. Differences in accuracy between the commercial and in-house research implementations were ≤ 0.5 mm for mean DTA and ≤ 0.7 mm for mean TRE. CONCLUSIONS: Accuracy of biomechanical deformable registration evaluated on a large cohort of images in the thorax, abdomen and prostate was similar to the image voxel resolution on average across multiple modalities. Validation of this treatment planning system implementation supports biomechanical deformable registration as a versatile clinical tool to enable accurate target delineation at planning and treatment adaptation.

Toward robust adaptive radiation therapy strategies.

PURPOSE: To set up a framework combining robust treatment planning with adaptive re-optimization in order to maintain high treatment quality, to respond to interfractional geometric variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. METHODS: The authors propose robust adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan applied during the first fractions should be able to handle anticipated systematic and random errors. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient having received a dose that does not satisfy specified plan quality criteria, the plan is re-optimized based on these individually measured errors. The re-optimized plan is then applied during subsequent fractions until a new scheduled adaptation becomes necessary. In this study, three different adaptive strategies are introduced and investigated. (a) In the first adaptive strategy, the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust re-optimization. (b) In the second strategy, the degree of conservativeness is adapted in response to the measured dose delivery errors. (c) In the third strategy, the uncertainty margins around the target are recalculated based on the measured errors. The simulated treatments are subjected to systematic and random errors that are either similar to the anticipated errors or unpredictably larger in order to critically evaluate the performance of these three adaptive strategies. RESULTS: According to the simulations, robustly optimized treatment plans provide sufficient treatment quality for those treatment error scenarios similar to the anticipated error scenarios. Moreover, combining robust planning with adaptation leads to improved organ-at-risk protection. In case of unpredictably larger treatment errors, the first strategy in combination with at most weekly adaptation performs best at notably improving treatment quality in terms of target coverage and organ-at-risk protection in comparison with a non-adaptive approach and the other adaptive strategies. CONCLUSION: The authors present a framework that provides robust plan re-optimization or margin adaptation of a treatment plan in response to interfractional geometric errors throughout the fractionated treatment. According to the simulations, these robust adaptive treatment strategies are able to identify candidates for an adaptive treatment, thus giving the opportunity to provide individualized plans, and improve their treatment quality through adaptation. The simulated robust adaptive framework is a guide for further development of optimally controlled robust adaptive therapy models.

Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning.

PURPOSE: To formulate convex planning objectives of treatment plan multicriteria optimization with explicit relationships to the dose-volume histogram (DVH) statistics used in plan quality evaluation. METHODS: Conventional planning objectives are designed to minimize the violation of DVH statistics thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses-at-volume) used to evaluate plan quality. In this study, we abandon the usual penalty-function framework and propose planning objectives that more closely relate to DVH statistics. The proposed planning objectives are based on mean-tail-dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. RESULTS: We investigated the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives were generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses-at-volume was observed in plans optimized within the proposed framework. CONCLUSION: The initial computational study indicates that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives than using the conventional approach.

Assessment of the quality of very high-energy electron radiotherapy planning.

BACKGROUND AND PURPOSE: To assess the quality of very-high energy electron (VHEE) scanning pencil beam radiation therapy in relation to state-of-the-art volumetric modulated arc therapy (VMAT) and to determine the extent of its application. MATERIAL AND METHODS: We planned five clinical cases with VHEE scanning pencil beams of 100 and 120MeV, equally distributed in a coplanar arrangement around the patient. The clinical cases included acoustic neuroma, and liver, lung, esophagus, and anal cancer cases. We performed Monte Carlo (MC) dose calculations and we optimized the dose in a research version of RayStation. VHEE plan performance was compared against clinically delivered VMAT. RESULTS: With equal target coverage, mean doses to organs at risk (OARs) were on average 22% lower for the VHEE plans compared to the VMAT plans. Dose conformity was equal or superior compared to the VMAT plans and integral dose to the body was in average 14% (9-22%) lower for the VHEE plans. CONCLUSIONS: The dosimetric advantages of VHEE as demonstrated for a variety of clinical cases, combined with the theoretical ultra fast treatment delivery, afford VHEE scanning pencil beam radiotherapy a suitable and potentially superior alternative for cancer radiotherapy.

Maximizing the probability of satisfying the clinical goals in radiation therapy treatment planning under setup uncertainty.

PURPOSE: This paper introduces a method that maximizes the probability of satisfying the clinical goals in intensity-modulated radiation therapy treatments subject to setup uncertainty. METHODS: The authors perform robust optimization in which the clinical goals are constrained to be satisfied whenever the setup error falls within an uncertainty set. The shape of the uncertainty set is included as a variable in the optimization. The goal of the optimization is to modify the shape of the uncertainty set in order to maximize the probability that the setup error will fall within the modified set. Because the constraints enforce the clinical goals to be satisfied under all setup errors within the uncertainty set, this is equivalent to maximizing the probability of satisfying the clinical goals. This type of robust optimization is studied with respect to photon and proton therapy applied to a prostate case and compared to robust optimization using an a priori defined uncertainty set. RESULTS: Slight reductions of the uncertainty sets resulted in plans that satisfied a larger number of clinical goals than optimization with respect to a priori defined uncertainty sets, both within the reduced uncertainty sets and within the a priori, nonreduced, uncertainty sets. For the prostate case, the plans taking reduced uncertainty sets into account satisfied 1.4 (photons) and 1.5 (protons) times as many clinical goals over the scenarios as the method taking a priori uncertainty sets into account. CONCLUSIONS: Reducing the uncertainty sets enabled the optimization to find better solutions with respect to the errors within the reduced as well as the nonreduced uncertainty sets and thereby achieve higher probability of satisfying the clinical goals. This shows that asking for a little less in the optimization sometimes leads to better overall plan quality.

Treatment planning for radiotherapy with very high-energy electron beams and comparison of VHEE and VMAT plans.

PURPOSE: The aim of this work was to develop a treatment planning workflow for rapid radiotherapy delivered with very high-energy electron (VHEE) scanning pencil beams of 60-120 MeV and to study VHEE plans as a function of VHEE treatment parameters. Additionally, VHEE plans were compared to clinical state-of-the-art volumetric modulated arc therapy (VMAT) photon plans for three cases. METHODS: VHEE radiotherapy treatment planning was performed by linking EGSnrc Monte Carlo (MC) dose calculations with inverse treatment planning in a research version of RayStation. In order to study the effect of VHEE treatment parameters on VHEE dose distributions, a matlab graphical user interface (GUI) for calculation of VHEE MC pencil beam doses was developed. Through the GUI, pediatric case MC simulations were run for a number of beam energies (60, 80, 100, and 120 MeV), number of beams (13, 17, and 36), pencil beam spot (0.1, 1.0, and 3.0 mm) and grid (2.0, 2.5, and 3.5 mm) sizes, and source-to-axis distance, SAD (40 and 50 cm). VHEE plans for the pediatric case calculated with the different treatment parameters were optimized and compared. Furthermore, 100 MeV VHEE plans for the pediatric case, a lung, and a prostate case were calculated and compared to the clinically delivered VMAT plans. All plans were normalized such that the 100% isodose line covered 95% of the target volume. RESULTS: VHEE beam energy had the largest effect on the quality of dose distributions of the pediatric case. For the same target dose, the mean doses to organs at risk (OARs) decreased by 5%-16% when planned with 100 MeV compared to 60 MeV, but there was no further improvement in the 120 MeV plan. VHEE plans calculated with 36 beams outperformed plans calculated with 13 and 17 beams, but to a more modest degree (<8%). While pencil beam spacing and SAD had a small effect on VHEE dose distributions, 0.1-3 mm pencil beam sizes resulted in identical dose distributions. For the 100 MeV VHEE pediatric plan, OAR doses were up to 70% lower and the integral dose was 33% lower for VHEE compared to 6 MV VMAT. Additionally, VHEE conformity indices (CI100 = 1.09 and CI50 = 4.07) were better than VMAT conformity indices (CI100 = 1.30 and CI50 = 6.81). The 100 MeV VHEE lung plan resulted in mean dose decrease to all OARs by up to 27% for the same target coverage compared to the clinical 6 MV flattening filter-free (FFF) VMAT plan. The 100 MeV prostate plan resulted in 3% mean dose increase to the penile bulb and the urethra, but all other OAR mean doses were lower compared to the 15 MV VMAT plan. The lung case CI100 and CI50 conformity indices were 3% and 8% lower, respectively, in the VHEE plan compared to the VMAT plan. The prostate case CI100 and CI50 conformity indices were 1% higher and 8% lower, respectively, in the VHEE plan compared to the VMAT plan. CONCLUSIONS: The authors have developed a treatment planning workflow for MC dose calculation of pencil beams and optimization for treatment planning of VHEE radiotherapy. The authors have demonstrated that VHEE plans resulted in similar or superior dose distributions for pediatric, lung, and prostate cases compared to clinical VMAT plans.

A model for the relative biological effectiveness of protons: the tissue specific parameter α/ß of photons is a predictor for the sensitivity to LET changes.

BACKGROUND: The biological effects of particles are often expressed in relation to that of photons through the concept of relative biological effectiveness, RBE. In proton radiotherapy, a constant RBE of 1.1 is usually assumed. However, there is experimental evidence that RBE depends on various factors. The aim of this study is to develop a model to predict the RBE based on linear energy transfer (LET), dose, and the tissue specific parameter α/ß of the linear-quadratic model for the reference radiation. Moreover, the model should capture the basic features of the RBE using a minimum of assumptions, each supported by experimental data. MATERIAL AND METHODS: The α and ß parameters for protons were studied with respect to their dependence on LET. An RBE model was proposed where the dependence of LET is affected by the (α/ß)phot ratio of photons. Published cell survival data with a range of well-defined LETs and cell types were selected for model evaluation rendering a total of 10 cell lines and 24 RBE values. RESULTS AND CONCLUSION: A statistically significant relation was found between α for protons and LET. Moreover, the strength of that relation varied significantly with (α/ß)phot. In contrast, no significant relation between ß and LET was found. On the whole, the resulting RBE model provided a significantly improved fit (p-value < 0.01) to the experimental data compared to the standard constant RBE. By accounting for the α/ß ratio of photons, clearer trends between RBE and LET of protons were found, and our results suggest that late responding tissues are more sensitive to LET changes than early responding tissues and most tumors. An advantage with the proposed RBE model in optimization and evaluation of treatment plans is that it only requires dose, LET, and (α/ß)phot as input parameters. Hence, no proton specific biological parameters are needed.

Minimax optimization for handling range and setup uncertainties in proton therapy.

PURPOSE: Intensity modulated proton therapy (IMPT) is sensitive to errors, mainly due to high stopping power dependency and steep beam dose gradients. Conventional margins are often insufficient to ensure robustness of treatment plans. In this article, a method is developed that takes the uncertainties into account during the plan optimization. METHODS: Dose contributions for a number of range and setup errors are calculated and a minimax optimization is performed. The minimax optimization aims at minimizing the penalty of the worst case scenario. Any optimization function from conventional treatment planning can be utilized by the method. By considering only scenarios that are physically realizable, the unnecessary conservativeness of other robust optimization methods is avoided. Minimax optimization is related to stochastic programming by the more general minimax stochastic programming formulation, which enables accounting for uncertainties in the probability distributions of the errors. RESULTS: The minimax optimization method is applied to a lung case, a paraspinal case with titanium implants, and a prostate case. It is compared to conventional methods that use margins, single field uniform dose (SFUD), and material override (MO) to handle the uncertainties. For the lung case, the minimax method and the SFUD with MO method yield robust target coverage. The minimax method yields better sparing of the lung than the other methods. For the paraspinal case, the minimax method yields more robust target coverage and better sparing of the spinal cord than the other methods. For the prostate case, the minimax method and the SFUD method yield robust target coverage and the minimax method yields better sparing of the rectum than the other methods. CONCLUSIONS: Minimax optimization provides robust target coverage without sacrificing the sparing of healthy tissues, even in the presence of low density lung tissue and high density titanium implants. Conventional methods using margins, SFUD, and MO do not utilize the full potential of IMPT and deliver unnecessarily high doses to healthy tissues.

Development and evaluation of an efficient approach to volumetric arc therapy planning.

An efficient method for volumetric intensity modulated arc therapy (VMAT) planning was developed, where a single arc (360 degrees or less) is delivered under continuous variation of multileaf collimator (MLC) segments, dose rate, and gantry speed. Plans can be generated for any current linear accelerator that supports these degrees of freedom. MLC segments are derived from fluence maps at relatively coarsely sampled angular positions. The beam segments, dose rate, and gantry speed are then optimized using direct machine parameter optimization based on dose volume objectives and leaf motion constraints to minimize arc delivery time. The method can vary both dose rate and gantry speed or alternatively determine the optimal plan at constant dose rate and gantry speed. The method was used to retrospectively generate variable dose rate VMAT plans to ten patients (head and neck, prostate, brain, lung, and tonsil). In comparison to step-and-shoot intensity modulated radiation therapy, dosimetric plan quality was comparable or improved, estimated delivery times ranged from 70 to 160 s, and monitor units were consistently reduced in nine out of the ten cases by an average of approximately 6%. Optimization and final dose calculation took between 5 and 35 min depending on plan complexity.

MRI/linac integration.

PURPOSE/OBJECTIVES: In radiotherapy the healthy tissue involvement still poses serious dose limitations. This results in sub-optimal tumour dose and complications. Daily image guided radiotherapy (IGRT) is the key development in radiation oncology to solve this problem. MRI yields superb soft-tissue visualization and provides several imaging modalities for identification of movements, function and physiology. Integrating MRI functionality with an accelerator can make these capacities available for high precision, real time IGRT. DESIGN AND RESULTS: The system being built at the University Medical Center Utrecht is a 1.5T MRI scanner, with diagnostic imaging functionality and quality, integrated with a 6MV radiotherapy accelerator. The realization of a prototype of this hybrid system is a joint effort between the Radiotherapy Department of the University of Utrecht, the Netherlands, Elekta, Crawley, U.K., and Philips Research, Hamburg, Germany. Basically, the design is a 1.5 T Philips Achieva MRI scanner with a Magnex closed bore magnet surrounded by a single energy (6 MV) Elekta accelerator. Monte Carlo simulations are used to investigate the radiation beam properties of the hybrid system, dosimetry equipment and for the construction of patient specific dose deposition kernels in the presence of a magnetic field. The latter are used to evaluate the IMRT capability of the integrated MRI linac. CONCLUSIONS: A prototype hybrid MRI/linac for on-line MRI guidance of radiotherapy (MRIgRT) is under construction. The aim of the system is to deliver the radiation dose with mm precision based on diagnostic quality MR images.

The IMRT information process-mastering the degrees of freedom in external beam therapy.

The techniques and procedures for intensity-modulated radiation therapy (IMRT) are reviewed in the context of the information process central to treatment planning and delivery of IMRT. A presentation is given of the evolution of the information based radiotherapy workflow and dose delivery techniques, as well as the volume and planning concepts for relating the dose information to image based patient representations. The formulation of the dose shaping process as an optimization problem is described. The different steps in the calculation flow for determination of machine parameters for dose delivery are described starting from the formulation of optimization objectives over dose calculation to optimization procedures. Finally, the main elements of the quality assurance procedure necessary for implementing IMRT clinically are reviewed.