Towards a generalised development of synthetic CT images and assessment of their dosimetric accuracy.

Background: The interest in generating "synthetic computed tomography (CT) images" from magnetic resonance (MR) images has been increasing over the past years due to advances in MR guidance for radiotherapy. A variety of methods for synthetic CT creation have been developed, from simple bulk density assignment to complex machine learning algorithms.Material and methods: In this study, we present a general method to determine simplistic synthetic CTs and evaluate them according to their dosimetric accuracy. It separates the requirements on the MR image and the associated calculation effort to generate a synthetic CT. To evaluate the significance of the dosimetric accuracy under realistic conditions, clinically common uncertainties including position shifts and Hounsfield lookup table (HLUT) errors were simulated. To illustrate our approach, we first translated CT images from a test set of six pelvic cancer patients to relative electron density (ED) via a clinical HLUT. For each patient, seven simplified ED images (simED) were generated at different levels of complexity, ranging from one to four tissue classes. Then, dose distributions optimised on the reference ED image and the simEDs were compared to each other in terms of gamma pass rates (2 mm/2% criteria) and dose volume metrics.Results: For our test set, best results were obtained for simEDs with four tissue classes representing fat, soft tissue, air, and bone. For this simED, gamma pass rates of 99.95% (range: 99.72-100%) were achieved. The decrease in accuracy from ED simplification was smaller in this case than the influence of the uncertainty scenarios on the reference image, both for gamma pass rates and dose volume metrics.Conclusions: The presented workflow helps to determine the required complexity of synthetic CTs with respect to their dosimetric accuracy. The investigated cases showed potential simplifications, based on which the synthetic CT generation could be faster and more reproducible.

Correlation between intrafractional motion and dosimetric changes for prostate IMRT: Comparison of different adaptive strategies.

PURPOSE: To retrospectively analyze and estimate the dosimetric benefit of online and offline motion mitigation strategies for prostate IMRT. METHODS: Intrafractional motion data of 21 prostate patients receiving intensity-modulated radiotherapy was acquired with an electromagnetic tracking system. Target trajectories of 734 fractions were analyzed per delivered multileaf-collimator segment in five motion metrics: three-dimensional displacement, distance from beam axis (DistToBeam), and three orthogonal components. Time-resolved dose calculations have been performed by shifting the target according to the sampled motion for the following scenarios: without adaptation, online-repositioning with a minimum threshold of 3 mm, and an offline approach using a modified field order applying horizontal before vertical beams. Change of D95 (targets) or V65 (organs at risk) relative to the static case, that is, ΔD95 or ΔV65, was extracted per fraction in percent. Correlation coefficients (CC) between the motion metrics and the dose metrics were extracted. Mean of patient-wise CC was used to evaluate the correlation of motion metric and dosimetric changes. Mean and standard deviation of the patient-wise correlation slopes (in %/mm) were extracted. RESULTS: For ΔD95 of the prostate, mean DistToBeam per fraction showed the highest correlation for all scenarios with a relative change of -0.6 ± 0.7%/mm without adaptation and -0.4 ± 0.5%/mm for the repositioning and field order strategies. For ΔV65 of the bladder and the rectum, superior-inferior and posterior-anterior motion components per fraction showed the highest correlation, respectively. The slope of bladder (rectum) was 14.6 ± 5.8 (15.1 ± 6.9) %/mm without adaptation, 14.0 ± 4.9 (14.5 ± 7.4) %/mm for repositioning with 3 mm, and 10.6 ± 2.5 (8.1 ± 4.6) %/mm for the field order approach. CONCLUSIONS: The correlation slope is a valuable concept to estimate dosimetric deviations from static plan quality directly based on the observed motion. For the prostate, both mitigation strategies showed comparable benefit. For organs at risk, the field order approach showed less sensitive response regarding motion and reduced interpatient variation.

Impact of respiratory motion on variable relative biological effectiveness in 4D-dose distributions of proton therapy.

BACKGROUND: Organ motion during radiation therapy with scanned protons leads to deviations between the planned and the delivered physical dose. Using a constant relative biological effectiveness (RBE) of 1.1 linearly maps these deviations into RBE-weighted dose. However, a constant value cannot account for potential nonlinear variations in RBE suggested by variable RBE models. Here, we study the impact of motion on recalculations of RBE-weighted dose distributions using a phenomenological variable RBE model. MATERIAL AND METHODS: 4D-dose calculation including variable RBE was implemented in the open source treatment planning toolkit matRad. Four scenarios were compared for one field and two field proton treatments for a liver cancer patient assuming (α∕ß)x = 2 Gy and (α∕ß)x = 10 Gy: (A) the optimized static dose distribution with constant RBE, (B) a static recalculation with variable RBE, (C) a 4D-dose recalculation with constant RBE and (D) a 4D-dose recalculation with variable RBE. For (B) and (D), the variable RBE was calculated by the model proposed by McNamara. For (C), the physical dose was accumulated with direct dose mapping; for (D), dose-weighted radio-sensitivity parameters of the linear quadratic model were accumulated to model synergistic irradiation effects on RBE. RESULTS: Dose recalculation with variable RBE led to an elevated biological dose at the end of the proton field, while 4D-dose recalculation exhibited random deviations everywhere in the radiation field depending on the interplay of beam delivery and organ motion. For a single beam treatment assuming (α∕ß)x = 2 Gy, D95% was 1.98 Gy (RBE) (A), 2.15 Gy (RBE) (B), 1.81 Gy (RBE) (C) and 1.98 Gy (RBE) (D). The homogeneity index was 1.04 (A), 1.08 (B), 1.23 (C) and 1.25 (D). CONCLUSION: For the studied liver case, intrafractional motion did not reduce the modulation of the RBE-weighted dose postulated by variable RBE models for proton treatments.

Parotid gland mean dose as a xerostomia predictor in low-dose domains.

PURPOSE: Xerostomia is a common side effect of radiotherapy resulting from excessive irradiation of salivary glands. Typically, xerostomia is modeled by the mean dose-response characteristic of parotid glands and prevented by mean dose constraints to either contralateral or both parotid glands. The aim of this study was to investigate whether normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands are suitable for the prediction of xerostomia in a highly conformal low-dose regime of modern intensity-modulated radiotherapy (IMRT) techniques. MATERIAL AND METHODS: We present a retrospective analysis of 153 head and neck cancer patients treated with radiotherapy. The Lyman Kutcher Burman (LKB) model was used to evaluate predictive power of the parotid gland mean dose with respect to xerostomia at 6 and 12 months after the treatment. The predictive performance of the model was evaluated by receiver operating characteristic (ROC) curves and precision-recall (PR) curves. RESULTS: Average mean doses to ipsilateral and contralateral parotid glands were 25.4 Gy and 18.7 Gy, respectively. QUANTEC constraints were met in 74% of patients. Mild to severe (G1+) xerostomia prevalence at both 6 and 12 months was 67%. Moderate to severe (G2+) xerostomia prevalence at 6 and 12 months was 20% and 15%, respectively. G1 + xerostomia was predicted reasonably well with area under the ROC curve ranging from 0.69 to 0.76. The LKB model failed to provide reliable G2 + xerostomia predictions at both time points. CONCLUSIONS: Reduction of the mean dose to parotid glands below QUANTEC guidelines resulted in low G2 + xerostomia rates. In this dose domain, the mean dose models predicted G1 + xerostomia fairly well, however, failed to recognize patients at risk of G2 + xerostomia. There is a need for the development of more flexible models able to capture complexity of dose response in this dose regime.

Physically constrained voxel-based penalty adaptation for ultra-fast IMRT planning.

Conventional treatment planning in intensity-modulated radiation therapy (IMRT) is a trial-and-error process that usually involves tedious tweaking of optimization parameters. Here, we present an algorithm that automates part of this process, in particular the adaptation of voxel-based penalties within normal tissue. Thereby, the proposed algorithm explicitly considers a priori known physical limitations of photon irradiation. The efficacy of the developed algorithm is assessed during treatment planning studies comprising 16 prostate and 5 head and neck cases. We study the eradication of hot spots in the normal tissue, effects on target coverage and target conformity, as well as selected dose volume points for organs at risk. The potential of the proposed method to generate class solutions for the two indications is investigated. Run-times of the algorithms are reported. Physically constrained voxel-based penalty adaptation is an adequate means to automatically detect and eradicate hot-spots during IMRT planning while maintaining target coverage and conformity. Negative effects on organs at risk are comparably small and restricted to lower doses. Using physically constrained voxel-based penalty adaptation, it was possible to improve the generation of class solutions for both indications. Considering the reported run-times of less than 20 s, physically constrained voxel-based penalty adaptation has the potential to reduce the clinical workload during planning and automated treatment plan generation in the long run, facilitating adaptive radiation treatments.

Long short-term memory networks for proton dose calculation in highly heterogeneous tissues.

PURPOSE: To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANNs) in challenging three-dimensional (3D) anatomies. METHODS: A novel proton dose calculation approach was designed based on the application of a long short-term memory (LSTM) network. It processes the 3D geometry as a sequence of two-dimensional (2D) computed tomography slices and outputs a corresponding sequence of 2D slices that forms the 3D dose distribution. The general accuracy of the approach is investigated in comparison to Monte Carlo reference simulations and pencil beam dose calculations. We consider both artificial phantom geometries and clinically realistic lung cases for three different pencil beam energies. RESULTS: For artificial phantom cases, the trained LSTM model achieved a 98.57% γ-index pass rate ([1%, 3 mm]) in comparison to MC simulations for a pencil beam with initial energy 104.25 MeV. For a lung patient case, we observe pass rates of 98.56%, 97.74%, and 94.51% for an initial energy of 67.85, 104.25, and 134.68 MeV, respectively. Applying the LSTM dose calculation on patient cases that were fully excluded from the training process yields an average γ-index pass rate of 97.85%. CONCLUSIONS: LSTM networks are well suited for proton dose calculation tasks. Further research, especially regarding model generalization and computational performance in comparison to established dose calculation methods, is warranted.

Joint Optimization of Photon-Carbon Ion Treatments for Glioblastoma.

PURPOSE: Carbon ions are radiobiologically more effective than photons and are beneficial for treating radioresistant gross tumor volumes (GTV). However, owing to a reduced fractionation effect, they may be disadvantageous for treating infiltrative tumors, in which healthy tissue inside the clinical target volume (CTV) must be protected through fractionation. This work addresses the question: What is the ideal combined photon-carbon ion fluence distribution for treating infiltrative tumors given a specific fraction allocation between photons and carbon ions? METHODS AND MATERIALS: We present a method to simultaneously optimize sequentially delivered intensity modulated photon (IMRT) and carbon ion (CIRT) treatments based on cumulative biological effect, incorporating both the variable relative biological effect of carbon ions and the fractionation effect within the linear quadratic model. The method is demonstrated for 6 glioblastoma patients in comparison with the current clinical standard of independently optimized CIRT-IMRT plans. RESULTS: Compared with the reference plan, joint optimization strategies yield inhomogeneous photon and carbon ion dose distributions that cumulatively deliver a homogeneous biological effect distribution. In the optimal distributions, the dose to CTV is mostly delivered by photons and carbon ions are restricted to the GTV with variations depending on tumor size and location. Improvements in conformity of high-dose regions are reflected by a mean EQD2 reduction of 3.29 ± 1.22 Gy in a dose fall-off margin around the CTV. Carbon ions may deliver higher doses to the center of the GTV, and photon contributions are increased at interfaces with CTV and critical structures. This results in a mean EQD2 reduction of 8.3 ± 2.28 Gy, in which the brain stem abuts the target volumes. CONCLUSIONS: We have developed a biophysical model to optimize combined photon-carbon ion treatments. For 6 glioblastoma patient cases, we show that our approach results in a more targeted application of carbon ions that (1) reduces dose in normal tissues within the target volume, which can only be protected through fractionation; and (2) boosts central target volume regions to reduce integral dose. Joint optimization of IMRT-CIRT treatments enable the exploration of a new spectrum of plans that can better address physical and radiobiological treatment planning challenges.

Accounting for Range Uncertainties in the Optimization of Combined Proton-Photon Treatments Via Stochastic Optimization.

PURPOSE: Proton treatment slots are a limited resource. Combined proton-photon treatments, in which most fractions are delivered with photons and only a few with protons, may represent a practical solution to optimize the allocation of proton resources over the patient population. We demonstrate how a limited number of proton fractions can be optimally used in multimodality treatments and address the issue of the robustness of combined treatments against proton range uncertainties. METHODS AND MATERIALS: Combined proton-photon treatments are planned by simultaneously optimizing intensity modulated radiation therapy and proton therapy plans while accounting for the fractionation effect through the biologically effective dose model. The method was investigated for different tumor sites (a spinal metastasis, a sacral chordoma, and an atypical meningioma) in which organs at risk (OARs) were located within or near the tumor. Stochastic optimization was applied to mitigate range uncertainties. RESULTS: In optimal combinations, proton and photon fractions deliver similar doses to OARs overlaying the target volume to protect these dose-limiting normal tissues through fractionation. Meanwhile, parts of the tumor are hypofractionated with protons. Thus, the total dose delivered with photons is reduced compared with simple combinations in which each modality delivers the prescribed dose per fraction to the target volume. The benefit of optimal combinations persists when range errors are accounted for via stochastic optimization. CONCLUSIONS: Limited proton resources are optimally used in combined treatments if parts of the tumor are hypofractionated with protons and near-uniform fractionation is maintained in serial OARs. Proton range uncertainties can be efficiently accounted for through stochastic optimization and are not an obstacle for clinical application.

Analytical probabilistic modeling of dose-volume histograms.

PURPOSE: Radiotherapy, especially with charged particles, is sensitive to executional and preparational uncertainties that propagate to uncertainty in dose and plan quality indicators, for example, dose-volume histograms (DVHs). Current approaches to quantify and mitigate such uncertainties rely on explicitly computed error scenarios and are thus subject to statistical uncertainty and limitations regarding the underlying uncertainty model. Here we present an alternative, analytical method to approximate moments, in particular expectation value and (co)variance, of the probability distribution of DVH-points, and evaluate its accuracy on patient data. METHODS: We use Analytical Probabilistic Modeling (APM) to derive moments of the probability distribution over individual DVH-points based on the probability distribution over dose. By using the computed moments to parameterize distinct probability distributions over DVH-points (here normal or beta distributions), not only the moments but also percentiles, that is, α - DVHs, are computed. The model is subsequently evaluated on three patient cases (intracranial, paraspinal, prostate) in 30- and single-fraction scenarios by assuming the dose to follow a multivariate normal distribution, whose moments are computed in closed-form with APM. The results are compared to a benchmark based on discrete random sampling. RESULTS: The evaluation of the new probabilistic model on the three patient cases against a sampling benchmark proves its correctness under perfect assumptions as well as good agreement in realistic conditions. More precisely, ca. 90% of all computed expected DVH-points and their standard deviations agree within 1% volume with their empirical counterpart from sampling computations, for both fractionated and single fraction treatments. When computing α - DVH, the assumption of a beta distribution achieved better agreement with empirical percentiles than the assumption of a normal distribution: While in both cases probabilities locally showed large deviations (up to ±0.2), the respective - DVHs for α={0.05,0.5,0.95} only showed small deviations in respective volume (up to ±5% volume for a normal distribution, and up to 2% for a beta distribution). A previously published model from literature, which was included for comparison, exhibited substantially larger deviations. CONCLUSIONS: With APM we could derive a mathematically exact description of moments of probability distributions over DVH-points given a probability distribution over dose. The model generalizes previous attempts and performs well for both choices of probability distributions, that is, normal or beta distributions, over DVH-points.

Analytical modeling of depth-dose degradation in heterogeneous lung tissue for intensity-modulated proton therapy planning.

BACKGROUND AND PURPOSE: Proton therapy may be promising for treating non-small-cell lung cancer due to lower doses to the lung and heart, as compared to photon therapy. A reported challenge is degradation, i.e., a smoothing of the depth-dose distribution due to heterogeneous lung tissue. For pencil beams, this causes a distal falloff widening and a peak-to-plateau ratio decrease, not considered in clinical treatment planning systems. MATERIALS AND METHODS: We present a degradation model implemented into an analytical dose calculation, fully integrated into a treatment planning workflow. Degradation effects were investigated on target dose, distal dose falloffs, and mean lung dose for ten patient cases with varying anatomical characteristics. RESULTS: For patients with pronounced range straggling (in our study large tumors, or lesions close to the mediastinum), degradation effects were restricted to a maximum decrease in target coverage (D 95 of the planning target volume) of 1.4%. The median broadening of the distal 80-20% dose falloffs was 0.5 mm at the maximum. For small target volumes deep inside lung tissue, however, the target underdose increased considerably by up to 26%. The mean lung dose was not negatively affected by degradation in any of the investigated cases. CONCLUSION: For most cases, dose degradation due to heterogeneous lung tissue did not yield critical organ at risk overdosing or overall target underdosing. However, for small and deep-seated tumors which can only be reached by penetrating lung tissue, we have seen substantial local underdose, which deserves further investigation, also considering other prevalent sources of uncertainty.

Simultaneous optimization of RBE-weighted dose and nanometric ionization distributions in treatment planning with carbon ions.

Inverse treatment planning in intensity modulated particle therapy (IMPT) with scanned carbon-ion beams is currently based on the optimization of RBE-weighted dose to satisfy requirements of target coverage and limited toxicity to organs-at-risk (OARs) and healthy tissues. There are many feasible IMPT plans that meet these requirements, which allows the introduction of further criteria to narrow the selection of a biologically optimal treatment plan. We propose a novel treatment planning strategy based on the simultaneous optimization of RBE-weighted dose and nanometric ionization details (ID) as a new physical characteristic of the delivered plan beyond LET. In particular, we focus on the distribution of large ionization clusters (more than 3 ionizations) to enhance the biological effect across the target volume while minimizing biological effect in normal tissues. Carbon-ion treatment plans for different patient geometries and beam configurations generated with the simultaneous optimization strategy were compared against reference plans obtained with RBE-weighted dose optimization alone. Quality indicators, inhomogeneity index and planning volume histograms of RBE-weighted dose and large ionization clusters were used to evaluate the treatment plans. We show that with simultaneous optimization, ID distributions can be optimized in carbon-ion radiotherapy without compromising the RBE-weighted dose distributions. This strategy can potentially be used to account for optimization of endpoints closely related to radiation quality to achieve better tumor control and reduce risks of complications.

Incorporation of Dosimetric Gradients and Parotid Gland Migration Into Xerostomia Prediction.

Purpose: Due to the sharp gradients of intensity-modulated radiotherapy (IMRT) dose distributions, treatment uncertainties may induce substantial deviations from the planned dose during irradiation. Here, we investigate if the planned mean dose to parotid glands in combination with the dose gradient and information about anatomical changes during the treatment improves xerostomia prediction in head and neck cancer patients. Materials and methods: Eighty eight patients were retrospectively analyzed. Three features of the contralateral parotid gland were studied in terms of their association with the outcome, i.e., grade ≥ 2 (G2) xerostomia between 6 months and 2 years after radiotherapy (RT): planned mean dose (MD), average lateral dose gradient (GRADX), and parotid gland migration toward medial (PGM). PGM was estimated using daily megavoltage computed tomography (MVCT) images. Three logistic regression models where analyzed: based on (1) MD only, (2) MD and GRADX, and (3) MD, GRADX, and PGM. Additionally, the cohort was stratified based on the median value of GRADX, and a univariate analysis was performed to study the association of the MD with the outcome for patients in low- and high-GRADX domains. Results: The planned MD failed to recognize G2 xerostomia patients (AUC = 0.57). By adding the information of GRADX (second model), the model performance increased to AUC = 0.72. The addition of PGM (third model) led to further improvement in the recognition of the outcome (AUC = 0.79). Remarkably, xerostomia patients in the low-GRADX domain were successfully identified (AUC = 0.88) by the MD alone. Conclusions: Our results indicate that GRADX and PGM, which together serve as a proxy of dosimetric changes, provide valuable information for xerostomia prediction.

Analytical incorporation of fractionation effects in probabilistic treatment planning for intensity-modulated proton therapy.

PURPOSE: We show that it is possible to explicitly incorporate fractionation effects into closed-form probabilistic treatment plan analysis and optimization for intensity-modulated proton therapy with analytical probabilistic modeling (APM). We study the impact of different fractionation schemes on the dosimetric uncertainty induced by random and systematic sources of range and setup uncertainty for treatment plans that were optimized with and without consideration of the number of treatment fractions. METHODS: The APM framework is capable of handling arbitrarily correlated uncertainty models including systematic and random errors in the context of fractionation. On this basis, we construct an analytical dose variance computation pipeline that explicitly considers the number of treatment fractions for uncertainty quantitation and minimization during treatment planning. We evaluate the variance computation model in comparison to random sampling of 100 treatments for conventional and probabilistic treatment plans under different fractionation schemes (1, 5, 30 fractions) for an intracranial, a paraspinal and a prostate case. The impact of neglecting the fractionation scheme during treatment planning is investigated by applying treatment plans that were generated with probabilistic optimization for 1 fraction in a higher number of fractions and comparing them to the probabilistic plans optimized under explicit consideration of the number of fractions. RESULTS: APM enables the construction of an analytical variance computation model for dose uncertainty considering fractionation at negligible computational overhead. It is computationally feasible (a) to simultaneously perform a robustness analysis for all possible fraction numbers and (b) to perform a probabilistic treatment plan optimization for a specific fraction number. The incorporation of fractionation assumptions for robustness analysis exposes a dose to uncertainty trade-off, i.e., the dose in the organs at risk is increased for a reduced fraction number and/or for more robust treatment plans. By explicit consideration of fractionation effects during planning, we demonstrate that it is possible to exploit this trade-off during optimization. APM optimization considering the fraction number reduced the dose in organs at risk compared to conventional probabilistic optimization neglecting the fraction number. CONCLUSION: APM enables computationally efficient incorporation of fractionation effects in probabilistic uncertainty analysis and probabilistic treatment plan optimization. The consideration of the fractionation scheme in probabilistic treatment planning reveals the trade-off between number of fractions, nominal dose, and treatment plan robustness.

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia.

PURPOSE: The purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands. MATERIAL AND METHODS: A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0-6 months (early), 6-15 months (late), 15-24 months (long-term), and at any time (a longitudinal model) after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis. RESULTS: NTCP models based on the parotid mean dose failed to predict xerostomia (AUCs < 0.60). The most informative predictors were found for late and long-term xerostomia. Late xerostomia correlated with the contralateral dose gradient in the anterior-posterior (AUC = 0.72) and the right-left (AUC = 0.68) direction, whereas long-term xerostomia was associated with parotid volumes (AUCs > 0.85), dose gradients in the right-left (AUCs > 0.78), and the anterior-posterior (AUCs > 0.72) direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose-volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing methods. CONCLUSION: We demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments. Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia. The facilitated machine learning pipeline is described in detail and can serve as a valuable reference for future work in radiomic and dosiomic NTCP modeling.

Optimization of combined proton-photon treatments.

PURPOSE: Proton treatment slots are a limited resource. Therefore, we consider combined proton-photon treatments in which most fractions are delivered with photons and only a few with protons. We demonstrate how both modalities can be combined to optimally capitalize on the proton's ability to reduce normal tissue dose. METHODS: An optimal combined treatment must account for fractionation effects. We therefore perform simultaneous optimization of intensity-modulated proton (IMPT) and photon (IMRT) plans based on their cumulative biologically effective dose (BED). We demonstrate the method for a sacral chordoma patient, in whom the gross tumor volume (GTV) abuts bowel and rectum. RESULTS: In an optimal combination, proton and photon fractions deliver similar doses to bowel and rectum to protect these dose-limiting normal tissues through fractionation. However, proton fractions deliver, on average, higher doses to the GTV. Thereby, the photon dose bath is reduced. An optimized 30-fraction treatment with 10 IMPT fractions achieved more than 50% of the integral dose reduction in the gastrointestinal tract that is possible with 30 IMPT fractions (compared to 33% for a simple proton-photon combination in which both modalities deliver the same target dose). CONCLUSIONS: A limited number of proton fractions can best be used if protons hypofractionate parts of the GTV while maintaining near-uniform fractionation in dose-limiting normal tissues.

The effect of density overrides on magnetic resonance-guided radiation therapy planning for lung cancer.

BACKGROUND AND PURPOSE: Inverse treatment planning for lung cancer can be challenging since density heterogeneities may appear inside the planning target volume (PTV). One method to improve the quality of intensity modulation is the override of low density tissues inside the PTV during plan optimization. For magnetic resonance-guided radiation therapy (MRgRT), where the influence of the magnetic field on secondary electrons is sensitive to the tissue density, the reliability of density overrides has not yet been proven. This work, therefore, gains a first insight into density override strategies for MRgRT. MATERIAL AND METHODS: Monte Carlo-based treatment plans for five lung cancer patients were generated based on free-breathing CTs and two density override strategies. Different magnetic field configurations were considered with their effect being accounted for during optimization. Optimized plans were forward calculated to 4D-CTs and accumulated for the comparison of planned and expected delivered dose. RESULTS: For MRgRT, density overrides led to a discrepancy between the delivered and planned dose. The tumor volume coverage deteriorated for perpendicular magnetic fields of 1.5 T to 93.6% (D98%). For inline fields a maximal increase of 2.2% was found for the mean dose. In terms of organs at risk, a maximal sparing of 0.6 Gy and 0.9 Gy was observed for lung and heart, respectively. CONCLUSIONS: In this work, first results on the effect of density overrides on treatment planning for MRgRT are presented. It was observed that the underestimation of magnetic field effects in overridden densities during treatment planning resulted in an altered delivered dose, depending on the field strength and orientation.

Robust radiotherapy planning.

Motion and uncertainty in radiotherapy is traditionally handled via margins. The clinical target volume (CTV) is expanded to a larger planning target volume (PTV), which is irradiated to the prescribed dose. However, the PTV concept has several limitations, especially in proton therapy. Therefore, robust and probabilistic optimization methods have been developed that directly incorporate motion and uncertainty into treatment plan optimization for intensity modulated radiotherapy (IMRT) and intensity modulated proton therapy (IMPT). Thereby, the explicit definition of a PTV becomes obsolete and treatment plan optimization is directly based on the CTV. Initial work focused on random and systematic setup errors in IMRT. Later, inter-fraction prostate motion and intra-fraction lung motion became a research focus. Over the past ten years, IMPT has emerged as a new application for robust planning methods. In proton therapy, range or setup errors may lead to dose degradation and misalignment of dose contributions from different beams - a problem that cannot generally be addressed by margins. Therefore, IMPT has led to the first implementations of robust planning methods in commercial planning systems, making these methods available for clinical use. This paper first summarizes the limitations of the PTV concept. Subsequently, robust optimization methods are introduced and their applications in IMRT and IMPT planning are reviewed.

Development of the open-source dose calculation and optimization toolkit matRad.

PURPOSE: We report on the development of the open-source cross-platform radiation treatment planning toolkit matRad and its comparison against validated treatment planning systems. The toolkit enables three-dimensional intensity-modulated radiation therapy treatment planning for photons, scanned protons and scanned carbon ions. METHODS: matRad is entirely written in Matlab and is freely available online. It re-implements well-established algorithms employing a modular and sequential software design to model the entire treatment planning workflow. It comprises core functionalities to import DICOM data, to calculate and optimize dose as well as a graphical user interface for visualization. matRad dose calculation algorithms (for carbon ions this also includes the computation of the relative biological effect) are compared against dose calculation results originating from clinically approved treatment planning systems. RESULTS: We observe three-dimensional γ-analysis pass rates ≥ 99.67% for all three radiation modalities utilizing a distance to agreement of 2 mm and a dose difference criterion of 2%. The computational efficiency of matRad is evaluated in a treatment planning study considering three different treatment scenarios for every radiation modality. For photons, we measure total run times of 145 s-1260 s for dose calculation and fluence optimization combined considering 4-72 beam orientations and 2608-13597 beamlets. For charged particles, we measure total run times of 63 s-993 s for dose calculation and fluence optimization combined considering 9963-45574 pencil beams. Using a CT and dose grid resolution of 0.3 cm3 requires a memory consumption of 1.59 GB-9.07 GB and 0.29 GB-17.94 GB for photons and charged particles, respectively. CONCLUSION: The dosimetric accuracy, computational performance and open-source character of matRad encourages a future application of matRad for both educational and research purposes.

Accelerated iterative beam angle selection in IMRT.

PURPOSE: Iterative methods for beam angle selection (BAS) for intensity-modulated radiation therapy (IMRT) planning sequentially construct a beneficial ensemble of beam directions. In a naïve implementation, the nth beam is selected by adding beam orientations one-by-one from a discrete set of candidates to an existing ensemble of (n - 1) beams. The best beam orientation is identified in a time consuming process by solving the fluence map optimization (FMO) problem for every candidate beam and selecting the beam that yields the largest improvement to the objective function value. This paper evaluates two alternative methods to accelerate iterative BAS based on surrogates for the FMO objective function value. METHODS: We suggest to select candidate beams not based on the FMO objective function value after convergence but (1) based on the objective function value after five FMO iterations of a gradient based algorithm and (2) based on a projected gradient of the FMO problem in the first iteration. The performance of the objective function surrogates is evaluated based on the resulting objective function values and dose statistics in a treatment planning study comprising three intracranial, three pancreas, and three prostate cases. Furthermore, iterative BAS is evaluated for an application in which a small number of noncoplanar beams complement a set of coplanar beam orientations. This scenario is of practical interest as noncoplanar setups may require additional attention of the treatment personnel for every couch rotation. RESULTS: Iterative BAS relying on objective function surrogates yields similar results compared to naïve BAS with regard to the objective function values and dose statistics. At the same time, early stopping of the FMO and using the projected gradient during the first iteration enable reductions in computation time by approximately one to two orders of magnitude. With regard to the clinical delivery of noncoplanar IMRT treatments, we could show that optimized beam ensembles using only a few noncoplanar beam orientations often approach the plan quality of fully noncoplanar ensembles. CONCLUSIONS: We conclude that iterative BAS in combination with objective function surrogates can be a viable option to implement automated BAS at clinically acceptable computation times.

Worst case optimization for interfractional motion mitigation in carbon ion therapy of pancreatic cancer.

INTRODUCTION: The efficacy of radiation therapy treatments for pancreatic cancer is compromised by abdominal motion which limits the spatial accuracy for dose delivery - especially for particles. In this work we investigate the potential of worst case optimization for interfractional offline motion mitigation in carbon ion treatments of pancreatic cancer. METHODS: We implement a worst case optimization algorithm that explicitly models the relative biological effectiveness of carbon ions during inverse planning. We perform a comparative treatment planning study for seven pancreatic cancer patients. Treatment plans that have been generated using worst case optimization are compared against (1) conventional intensity-modulated carbon ion therapy, (2) single field uniform dose carbon ion therapy, and (3) an ideal yet impractical scenario relying on daily re-planning. The dosimetric quality and robustness of the resulting treatment plans is evaluated using reconstructions of the daily delivered dose distributions on fractional control CTs. RESULTS: Idealized daily re-planning consistently gives the best dosimetric results with regard to both target coverage and organ at risk sparing. The absolute reduction of D 95 within the gross tumor volume during fractional dose reconstruction is most pronounced for conventional intensity-modulated carbon ion therapy. Single field uniform dose optimization exhibits no substantial reduction for six of seven patients and values for D 95 for worst case optimization fall in between. The treated volume (D>95 % prescription dose) outside of the gross tumor volume is reduced by a factor of two by worst case optimization compared to conventional optimization and single field uniform dose optimization. Single field uniform dose optimization comes at an increased radiation exposure of normal tissues, e.g. ≈2 Gy (RBE) in the mean dose in the kidneys compared to conventional and worst case optimization and ≈4 Gy (RBE) in D 1 in the spinal cord compared to worst case optimization. CONCLUSION: Interfractional motion substantially deteriorates dose distributions for carbon ion treatments of pancreatic cancer patients. Single field uniform dose optimization mitigates the negative influence of motion on target coverage at an increased radiation exposure of normal tissue. Worst case optimization enables an exploration of the trade-off between robust target coverage and organ at risk sparing during inverse treatment planning beyond margin concepts.