Proton Treatment Planning Research Articles

Preliminary results of the non-invasive cardiac radioablation for ventricular tachycardia study; an in silico dosimetric comparison of photon vs proton radiotherapy for ventricular arrhythmias

Abstract Background Stereotactic arrhythmia radio-ablation (STAR) has been proposed as an alternative treatment for refractory ventricular arrhythmias (VA) in patients who are unsuitable or refractory to standard catheter ablation (CA). It consists in the application of external beam radiotherapy in a single dose of 25 Gy to the target areas. Most of the STAR treatments performed so far used photon radiotherapy. As compared to photons, particle therapy with protons, based on its finite penetration depth in tissue, might have the potential to reduce the dose to surrounding healthy tissues thus lowering the risk of side effects. Purpose The purpose of this in silico simulation study was to evaluate the feasibility of STAR with protons in patients with VA and to compare the doses of the organs at risk (OARs) in photon and proton treatment plans. Methods 14 patients (11 males) candidates to standard CA for VA were enrolled. ECG gated CT scans in expiratory breath-hold, and standard electroanatomical mapping were performed in each patients to reconstruct a 3D bipolar voltage map and identify the ablation target. Based on the information obtained by the contouring of the ablation target three treatment plans were simulated: one with photons, and two with protons - with and without cardiac gating- to assess the doses at the OARs. Results The median age was 68.5 (63.25-71.5), all patients had a history of recurrent VA and the indication for treatment with CA. 7 patients had an ischemic cardiomyopathy, 4 an idiopathic cardiomyopathy and 3 ventricular ectopic beats without structural heart disease. The mean left ventricular ejection fraction was 40±11% and 8 out of 14 patients were ICD carriers. Both photons and protons treatment plans reached the goals for target coverage (25 Gy on 98% of the target volume). A statistically significant reduction in the mean maximum dose (Dmax) to some OARs was observed in proton compared to photon plans (see table 1). Specifically, the Dmax delivered to the mitral valve, vena cava, descending aorta, esophagus and to the lungs have been largely reduced with the proton plans. However, in some specific cases, a higher maximum dose was observed for protons in structures close or overlapping with the target, mainly due to the need of an additional margin in the treatment plan to account for proton range uncertainty. The use of cardiac gating did not show a significant dose reduction to OARs compared to the non-gated proton plan. Conclusion The preliminary results of our in silico simulation study suggest that STAR therapy with the use of protons might be associated with a significant dose reduction to the surrounding organs. Nevertheless, the choice of the best treatment should be evaluated on a case by-case strategy since some specific cases may be better suitable for photon treatment.Table 1

Cardiac motion in patients with ventricular tachycardia: potential implications for the treatment of patients candidates to stereotactic arrhythmia radio-ablation (STAR)

Abstract Background Stereotactic arrhythmia radio-ablation (STAR) has proven to be a valid alternative treatment for refractory ventricular tachycardia (VT) in patients who are unsuitable to undergo standard catheter ablation (CA). It consists in the application of external beam radiotherapy in a single dose of 25 Gy to the target areas. There is increased research activity on better understanding the complex and fast motion of the heart and on reducing radiation toxicity. Purpose In the context of an in silico study to evaluate the feasibility of STAR with protons in patients with VT, the initial findings related to cardiac motion on the first 10 patients are here presented. Methods Prior to CA, each patient underwent ECG gated CT scans in expiratory breath-hold, with and without contrast and reconstructed at 30% (systole) and 80% (diastole) of the cardiac R-R cycle. Contouring of the ablation target as a clinical target volume (CTV) for proton treatment planning was performed based on a standard electrophysiological study with 3D eletroanatomical mapping. Two different treatment plans were created: the first with only the diastolic CTV as target (gated treatment), the second including both diastolic and systolic CTVs to create an Internal Target Volume (ITV) in the hypothesis of non-gated treatment. Robust optimization was used to create the Planning Target Volume (PTV). Results The target volumes used for treatment planning are given in Table 1. The median CTV was 10.90 (2.93-36.94) cm3 for diastole and 14.26 (1.73-36.31) cm3 for systole. These volumes are somewhat smaller than those reported in patients treated previously with STAR: this difference may be due to the fact that no respiratory motion was included in the target contour and that the study population includes many patients referred for ventricular ectopic beats without structural heart disease, where the ablation target is expected to be smaller as compared to patients with VT in structural heart disease. Despite a small median difference between the diastolic and systolic CTV of 0.53 (0.03-9.61) cm3, the ITV approach resulted in a median increase in volume compared to the largest of the two CTVs of 29 % (1%-44%). When considering the proton range uncertainty and potential errors in patient positioning (PTV), the target is this time enlarged by a factor 2.9 (1.9-3.9). This data indicates that both cardiac motion and uncertainties in patient positioning before treatment will have a large impact on the treatment volume thus affecting the amount of surrounding tissue exposed to radiation. Conclusion The ablation target contour at systole and diastole are of similar magnitude but their non-overlap results in a large increase in treated volume when radiation delivery is not gated for cardiac motion. Further analysis shall investigate on a case-by-case basis the potential reduction in risk of healthy tissue toxicity with cardiac-gated proton delivery.Table 1

An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system

Objective. Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for PG range verification compares the detected PG profile with a predicted one. Recently, a novel analytical PG prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. This work validates the performance of the filtering PG prediction approach. Approach. The said algorithm is validated against experimental data and benchmarked with another well-established PG prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation. Main results. The mean shifts between the experimental data and the predicted PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be 1.5±2.1 mm and −0.6±2.2 mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach. Significance. The novel filtering approach is successfully validated against experimental data and another widely used PG prediction algorithm. The workflow designed in this work selects spots with high-quality PGD shift calculation results, and performs sensitivity and specificity analyses to assist clinical decisions.

Dosimetric evaluation of intensity modulated photon versus proton reirradiation of head and neck cancer

BackgroundReirradiation of head and neck cancer (HNC) became more accessible in the last decade, owing to modern irradiation techniques which offer a reduction in treatment related toxicities. The aim of this paper was to comparatively evaluate the dosimetric aspects derived from intensity modulated photon vs. proton treatment planning in reirradiated HNC patients. MethodsSix recurrent HNC patients were enrolled in this retrospective study. For each patient two treatment plans were created: one IMRT/VMAT and one IMPT plan. The prescribed dose for the second irradiation was between 50 and 70 Gy RBE. The study comparatively analyzed the CTV coverage, doses to organs at risk (OARs) and low doses received by the healthy tissue (other than OAR). ResultsSimilar CTV coverage was achieved for photon vs proton plans, with the latter presenting better homogeneity in four cases. Maximum dose to CTV was generally higher for photon plans, with differences ranging from 0.3 to 1.9%. For parotid glands and body, the mean dose was lower for proton plans. A notable reduction of low dose to healthy tissue (other than OARs) could be achieved with protons, with an average of 60% and 64% for D10% and Dmean, respectively. ConclusionThe dosimetric comparison between photon and proton reirradiation of HNC showed a great need for treatment individualization, concluding that protons should be considered for reirradiation on an individual basis.

Proton CT on biological phantoms for x-ray CT calibration in proton treatment planning

Objective. To present and characterize a novel method for x-ray computed tomography (xCT) calibration in proton treatment planning, based on proton CT (pCT) measurements on biological phantoms. Approach. A pCT apparatus was used to perform direct measurements of 3D stopping power relative to water (SPR) maps on stabilized, biological phantoms. Two single-energy xCT calibration curves—i.e. tissue substitutes and stoichiometric—were compared to pCT data. Moreover, a new calibration method based on these data was proposed, and verified against intra- and inter-species variability, dependence on stabilization, beam-hardening conditions, and analysis procedures. Main results. Biological phantoms were verified to be stable in time, with a dependence on temperature conditions, especially in the fat region: (−2.5 土 0.5) HU °C−1. The pCT measurements were compared with standard xCT calibrations, revealing an average SPR discrepancy within ±1.60% for both fat and muscle regions. In the bone region the xCT calibrations overestimated the pCT-measured SPR of the phantom, with a maximum discrepancy of about +3%. As a result, a new cross-calibration curve was directly extracted from the pCT data. Overall, the SPR uncertainty margin associated with this curve was below 3%; fluctuations in the uncertainty values were observed across the HU range. Cross-calibration curves obtained with phantoms made of different animal species and anatomical parts were reproducible with SPR discrepancies within 3%. Moreover, the stabilization procedure did not affect the resulting curve within a 2.2% SPR deviation. Finally, the cross-calibration curve was affected by the beam-hardening conditions on xCTs, especially in the bone region, while dependencies below 2% resulted from the image registration procedure. Significance. Our results showed that pCT measurements on biological phantoms may provide an accurate method for the verification of current xCT calibrations and may represent a tool for the implementation of a new calibration method for proton treatment planning.

A reference-point-method-based online proton treatment plan re-optimization strategy and a novel solution to planning constraint infeasibility problem

Objective. Propose a highly automated treatment plan re-optimization strategy suitable for online adaptive proton therapy. The strategy includes a rapid re-optimization method that generates quality replans and a novel solution that efficiently addresses the planning constraint infeasibility issue that can significantly prolong the re-optimization process. Approach. We propose a systematic reference point method (RPM) model that minimizes the l-infinity norm from the initial treatment plan in the daily objective space for online re-optimization. This model minimizes the largest objective value deviation among the objectives of the daily replan from their reference values, leading to a daily replan similar to the initial plan. Whether a set of planning constraints is feasible with respect to the daily anatomy cannot be known before solving the corresponding optimization problem. The conventional trial-and-error-based relaxation process can cost a significant amount of time. To that end, we propose an optimization problem that first estimates the magnitude of daily violation of each planning constraint. Guided by the violation magnitude and clinical importance of the constraints, the constraints are then iteratively converted into objectives based on their priority until the infeasibility issue is solved. Main results. The proposed RPM-based strategy generated replans similar to the offline manual replans within the online time requirement for six head and neck and four breast patients. The average target D 95 and relevant organ at risk sparing parameter differences between the RPM replans and clinical offline replans were −0.23, −1.62 Gy for head and neck cases and 0.29, −0.39 Gy for breast cases. The proposed constraint relaxation solution made the RPM problem feasible after one round of relaxation for all four patients who encountered the infeasibility issue. Significance. We proposed a novel RPM-based re-optimization strategy and demonstrated its effectiveness on complex cases, regardless of whether constraint infeasibility is encountered.

Deep learning based linear energy transfer calculation for proton therapy

Objective. This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LETd) using patient anatomy and dose-to-water (DW) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems. Approach. 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LETd distributions from CT images and DW. The accuracy of the LETd of the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis. Main results. The proposed model accurately inferred LETd distributions for each proton field in the test dataset. A single-field LETd calculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 ± 0.14 MeV cm−1 and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest. Significance. This study demonstrates that deep-learning-based models can efficiently calculate LETd with high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model’s performance and evaluating its adaptability to different clinical scenarios.

1855: Automated Beam Angle Selection for Proton Treatment Planning in Breast Cancer

1855: Automated Beam Angle Selection for Proton Treatment Planning in Breast Cancer

Creating uniform cluster dose spread-out Bragg peaks for proton and carbon beams.

Preliminary data have shown a close association of the generalized ionization cluster size dose (in short, cluster dose) with cell survival, independent of particle type, and energy, when cluster dose is derived from an ionization detail parameter preferred for its association with cell survival. Such results suggest cluster dose has the potential to replace RBE-weighted dose in proton and ion beam radiotherapy treatment plan optimization, should a uniform cluster dose lead to comparable biological effects. However, further preclinical investigations are warranted to confirm this premise. To present an analytical approach to create uniform cluster dose spread-out Bragg peaks (SOBP) for evaluation of the potential of cluster dose to result in uniform biological effect. We modified the coefficients of the Bortfeld and Schlegel weight formula, an analytical method typically used for the creation of radiation dose SOBP in particle therapy, to produce uniform cluster dose SOBP of different widths (1-5cm) at relevant clinical proton and carbon beam energies. Optimum parameters were found by minimization of the ratio between the maximum and minimum cluster dose in the SOBP region using the Nelder-Mead method. The coefficients of the Bortfeld and Schlegel weight formula leading to uniform cluster dose SOBPs were determined for each combination of beam energy and SOBP width studied. The uniformity of the resulting cluster dose SOBPs, calculated as the relative difference between the maximum and minimum cluster dose within the SOBP, was within 0.3%-3.5% for the evaluated proton beams and 1.3%-3.4% for the evaluated carbon beams. The modifications to the analytical approach to create radiation dose SOBPs resulted in uniform cluster dose proton and carbon SOBPs over a wide range of beam energies and SOBP widths. Such SOBPs should prove valuable in preclinical investigations for the selection of nanodosimetric quantities to be used in proton and ion therapy treatment planning.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETd distributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETd distribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETd map generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETd maps generated by the proposed method are 0.096 ± 0.019 keV μm−1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keV μm−1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETd maps from dose maps and has the potential to improve proton therapy planning by providing accurate LETd information.

Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy.

Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep-learning-based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. Two deep-learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT-MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU-, range-, WET-, and dose-error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90-degree angle. To evaluate the dose distribution, a mask based on the 5%-isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs RESULTS: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty-HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty-range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty-WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD-based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning-based sCTs.

A systematic approach for calibrating a Monte Carlo code to a treatment planning system for obtaining dose, LET, variable proton RBE and out-of-field dose

Objective. While integration of variable relative biological effectiveness (RBE) has not reached full clinical implementation, the importance of having the ability to recalculate proton treatment plans in a flexible, dedicated Monte Carlo (MC) code cannot be understated . Here we provide a step-wise method for calibrating dose from a MC code to a treatment planning system (TPS), to obtain required parameters for calculating linear energy transfer (LET), variable RBE and in general enabling clinical realistic research studies beyond the capabilities of a TPS. Approach. Initially, Pristine Bragg peaks (PBP) were calculated in both the Eclipse TPS and the FLUKA MC code. A rearranged Bortfeld energy-range relation was applied to the initial energy of the beam to fine-tune the range of the MC code at 80% dose level distal to the PBP. The energy spread was adapted by dividing the TPS range by the MC range for dose level 80%–20% distal to the PBP. Density and relative proton stopping power were adjusted by comparing the TPS and MC for different Hounsfield units. To find the relationship of dose per primary particle from the MC to dose per monitor unit in the TPS, integration was applied to the area of the Bragg curve. The calibration was validated for spread-out Bragg peaks (SOBP) in water and patient treatment plans. Following the validation, variable RBE were calculated using established models. Main results. The PBPs ranges were within ± mm threshold, and a maximum of 5.5% difference for the SOBPs was observed. The patient validation showed excellent dose agreement between the TPS and MC, with the greatest differences for the lung tumor patient. Significance. A procedure for calibrating a MC code to a TPS was developed and validated. The procedure enables MC-based calculation of dose, LET, variable RBE, advanced (secondary) particle tracking and more from treatment plans.

High-resolution three-dimensional dosimetry in clinically relevant volumes utilizing optically stimulated luminescence.

The continued development of new radiotherapy techniques requires dosimetry systems that satisfy increasingly rigorous requirements, such as high sensitivity, wide dose range, and high spatial resolution. An emerging requirement is the ability to read out doses in three dimensions (3D) with high precision and spatial resolution. A few dosimetry systems with 3D capabilities are available, but their application in a clinical workflow is limited for various reasons, primarily originating from their chemical nature. The search for a 3D dosimetry system with potential for clinical implementation is thusongoing. To demonstrate the capabilities of a novel optically-stimulated-luminescence (OSL)-based 3D dosimetry system capable of measuring radiation doses in clinically relevantvolumes. A laser-based readout system was used to measure dose distributions delivered by both photons and protons, utilizing the OSL from a mm YSO:Ce crystal. A homogeneous treatment plan consisting of two opposing photon fields was used to establish an inhomogeneity correction map of the crystal response and demonstrated the accuracy and precision of the system. The crystal was additionally irradiated with a photon treatment plan consisting of three overlapping mm fields delivered from different angles, and a proton treatment plan consisting of four pencil beams with energies 90 MeV ( ), 115 MeV, and 140 MeV. The system abilities were quantified by comparing the 3D-resolved measurements to Monte Carlosimulations. The dose map reproducibility of the system was found to be within 2% including both statistical and systematic errors. The measurements yielded integrated doses from a volume of mm with voxel volumes of just mm . An excellent agreement between the 3D-resolved measurements and the simulations was found for both photon- andproton-irradiation. The capabilities of the devised system for measuring clinically relevant fields of photons and proton pencil beams within a clinically relevant volume were demonstrated. The system poses as a promising candidate for clinical applications, and enables future research in the field of OSL-based tissue-equivalent 3Ddosimetry.

The dirty and clean dose concept: Towards creating proton therapy treatment plans with a photon-like dose response.

Applying tolerance doses for organs at risk (OAR) from photon therapy introduces uncertainties in proton therapy when assuming a constant relative biological effectiveness (RBE) of 1.1. This work introduces the novel dirty and clean dose concept, which allows for creating treatment plans with a more photon-like dose response for OAR and, thus, less uncertainties when applying photon-based tolerance doses. The concept divides the 1.1-weighted dose distribution into two parts: the clean and the dirty dose. The clean and dirty dose are deposited by protons with a linear energy transfer (LET) below and above a set LET threshold, respectively. For the former, a photon-like dose response is assumed, while for the latter, the RBE might exceed 1.1. To reduce the dirty dose in OAR, a MaxDirtyDose objective was added in treatment plan optimization. It requires setting two parameters: LET threshold and max dirty dose level. A simple geometry consisting of one target volume and one OAR in water was used to study the reduction in dirty dose in the OAR depending on the choice of the two MaxDirtyDose objective parameters during plan optimization. The best performing parameter combinations were used to create multiple dirty dose optimized (DDopt) treatment plans for two cranial patient cases. For each DDopt plan, 1.1-weighted dose, variable RBE-weighted dose using the Wedenberg RBE model and dose-average LETd distributions as well as resulting normal tissue complication probability (NTCP) values were calculated and compared to the reference plan (RefPlan) without MaxDirtyDose objectives. In the water phantom studies, LET thresholds between 1.5 and 2.5keV/µm yielded the best plans and were subsequently used. For the patient cases, nearly all DDopt plans led to a reduced Wedenberg dose in critical OAR. This reduction resulted from an LET reduction and translated into an NTCP reduction of up to 19 percentage points compared to the RefPlan. The 1.1-weighted dose in the OARs was slightly increased (patient 1: 0.45Gy(RBE), patient 2: 0.08Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1-weighted dose in healthy brain tissue was observed (patient 1: 0.81Gy(RBE), patient 2: 0.53Gy(RBE)). The variation of NTCP values due to variation of α/β from 2 to 3Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp). The novel dirty and clean dose concept allows for creating biologically more robust proton treatment plans with a more photon-like dose response. The reduced uncertainties in RBE can, therefore, mitigate uncertainties introduced by using photon-based tolerance doses for OAR.

AI Beam Angle Prediction in Proton Therapy for Brain Patients.

Patients receiving brain Intensity-modulated Proton Therapy (IMPT) are treated with 1-3 fields. Typically, beam angles must be selected manually, requiring substantial planner time and experience. The choice of beam angles has a major impact on the treatment plan quality. An AI model was developed to automate this process aiming to improve efficiency and potentially plan quality. AI performance was tested by comparing predicted beam angles to the human ground truth angles and evaluating plans resulting from each set of beam angles. Previously treated IMPT brain patients were divided into a training (n = 40) and validation set (n = 10) for the AI. Beam-angle selection in the AI was cast as a multi-label classification problem, training the convolution neural network with a Circular Earth Mover's Distance based regularization and multi-label circular-smooth label technique. An analytical post-processing algorithm is employed to minimize distance to the target and avoid organs-at-risk. Finalized gantry angle predictions are discretized in steps of 5°. For an independent test set of n = 10 patients beam angles were predicted by the AI and compared to those chosen by human planners. Both sets of angles were used to create treatment plans with an automated knowledge-based planning (KBP) tool for brain IMPT utilizing single-field optimization and robust optimization. Plan differences are therefore solely due to the choice of beam angles as automated KBP removes human optimization variability. Resulting plan quality was compared by standard clinical dosimetric parameters to the CTV, brain, brainstem, cochlea, cord, eye, lens, optic chiasm, optic nerves, pituitary, and temporal lobes. All Human and AI selected beam angles are shown in the table. For cases 1-6, AI and Human selected beam angles were within ±15° and resulting plans showed minimal dosimetric differences. In cases 7-9 AI beam angles reduced max dose while keeping organ-at-risk dose within ±2 Gy. In case 10 the AI chosen beam angles reduced cord Dmax by 9.1 Gy but increased left eye Dmax by 4.9 Gy, other organs-at-risk showed minimal differences. Gantry beam angle selection was automated by a newly developed AI model and tested on 10 brain IMPT patients. The comparison showed that the AI often chooses similar beam angles to the human planners. When there are differences, dosimetric analysis demonstrated that plans created from AI beam angles have at least the same quality as the human ones. Results motivate further research into this approach showing the AI being a promising tool to fill a current gap in the strive for automating proton treatment planning, increasing planning efficiency and potentially quality.

Inaugural Experience with Real-time Gated Liver Proton SBRT and Treatment Plan Quality Improvement

DIBH SBRT is routinely used for liver proton therapy. While intra-fraction target motion is limited with DIBH, acquisition of DIBH CT simulations in triplicate, as is done at our institution, reveals that variation does exist between each DIBH scan. The related target position can also vary correspondingly. The most common setup uncertainty for robust proton SBRT liver plan used at our institution is 5 mm sup-inf (SI) and 3 mm radially. Real-time gated proton therapy (RGPT) has the potential to provide instantaneous feedback for intra-fraction target motion to maximize patient safety and inform optimal treatment planning. Our first RGPT liver SBRT with intra-fraction motion under deep inspiration breath hold (DIBH). The potential treatment plan quality improvement brought by RGPT is investigated. The following metrics were used in establishing our RGPT proton DIBH SBRT liver program: the iso center is always set at the fiducial mark; the beam orientation is selected to achieve both good plan quality and tracking performance; daily CBCTs are acquired and verified using fiducial maker position with kV images; robust uncertainty is determined by the gating tolerance; SBRT plan has three beams with uniform dose. Target motion was monitored throughout treatment. To evaluate dose sparing for surrounding OARs, a plan with tighter gating tolerance (3 mm SI and 2 mm radially) is optimized for dosimetric comparison. Statistical analyses were conducted using a programming environment. Each of the three proton beams were delivered using DIBH over a total of 120-140 seconds. The average beam on time were 61.4, 66.9 and 62.8 seconds. The intra-fraction motion showed that targets could move up to 3 mm within the same DIBH. The motion increased with time. The table details the mean, maximum, standard deviation, and estimated upper 95% of directional shifts for three beams. Based on these results, plan delivery efficiency was maintained even with tighter gating tolerance. The comparison plan with tight gating tolerance showed significantly less dose (-25%) to the stomach in coronal view. RGPT successfully tracked fiducial marker motion for DIBH SBRT liver treatment. Despite target drift during DIBH, the uncertainty of our DIBH SBRT procedure was sufficient to cover target motion throughout treatment. Based on the target drift value, a maximum of 25 seconds for breath hold time should be employed. Utilizing a tighter gating tolerance of 3 mm SI and 2 mm radially has the potential to maintain target coverage while significantly reducing OAR dose. Aggregated RGPT-derived data may provide optimal treatment planning parameters such as variable uncertainty based on target location.

Ionization detail parameters and cluster dose: a mathematical model for selection of nanodosimetric quantities for use in treatment planning in charged particle radiotherapy

Objective. To propose a mathematical model for applying ionization detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP). Approach. Our model provides for selection of preferred ID parameters (I p ) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopic I p and macroscopic RTP. Selection of I p is demonstrated using published cell survival measurements for protons through argon, comparing results for nineteen I p : N k , k = 2, 3, …, 10, the number of ionizations in clusters of k or more per particle, and F k , k = 1, 2, …, 10, the number of clusters of k or more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation. Main results. The preferred I p were N 4 and F 5 for aerobic cells, N 5 and F 7 for hypoxic cells. Significant differences were found in cell survival for beams having the same LET or the preferred N k . Conversely, there was no significant difference for F 5 for aerobic cells and F 7 for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for these I p had the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert that I p exist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation. Significance. Our model provides a practical means to select preferred I p from radiobiological data, and to convert I p to the macroscopic cluster dose for particle RTP.

Evaluation of decentralised model-based selection of head and neck cancer patients for a proton treatment study. DAHANCA 35

IntroductionProton treatment can potentially spare patients with H&N cancer for substantial treatment-related toxicities. The current study investigated the reproducibility of a decentralised model-based selection of patients for a proton treatment study when the selection plans were compared to the clinical treatment plans performed at the proton centre. MethodsSixty-three patients were selected for proton treatment in the six Danish Head and Neck Cancer (DAHANCA) centres. The patients were selected based on normal tissue complication probability (NTCP) estimated from local photon and proton treatment plans, which showed a ΔNTCP greater than 5%-point for either grade 2 + dysphagia or grade 2 + xerostomia at six months. The selection plans were compared to the clinical treatment plans performed at the proton centre. ResultsOf the 63 patients, 49 and 25 were selected based on an estimated benefit in risk of dysphagia and xerostomia, respectively. Eleven patients had a potential gain in both toxicities. The mean ΔNTCP changed from the local selection plan comparison to the clinical comparison from 6.9 to 5.3 %-points (p = 0.01) and 7.3 to 4.9 %-points (p = 0.03) for dysphagia and xerostomia, respectively. Volume differences in both CTV and OAR could add to the loss in ΔNTCP. 61 of the 63 clinical plans had a positive ΔNTCP, and 38 had a ΔNTCP of 5%-points for at least one of the two endpoints. ConclusionA local treatment plan comparison can be used to select candidates for proton treatment. The local comparative proton plan overestimates the potential benefit of the clinical proton plan. Continuous quality assurance of the delineation procedures and planning is crucial in the subsequent randomised clinical trial setting.

Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients

Objective. Respiration negatively affects the outcome of a radiation therapy treatment, with potentially severe effects especially in particle therapy (PT). If compensation strategies are not applied, accuracy cannot be achieved. To support the clinical practice based on 4D computed tomography (CT), 4D magnetic resonance imaging (MRI) acquisitions can be exploited. The purpose of this study was to validate a method for virtual 4DCT generation from 4DMRI data for lung cancers on a porcine lung phantom, and to apply it to lung cancer patients in PT. Approach. Deformable image registration was used to register each respiratory phase of the 4DMRI to a reference phase. Then, a static 3DCT was registered to this reference MR image set, and the virtual 4DCT was generated by warping the registered CT according to previously obtained deformation fields. The method was validated on a physical phantom for which a ground truth 4DCT was available and tested on lung tumor patients, treated with gated PT at end-exhale, by comparing the virtual 4DCT with a re-evaluation 4DCT. The geometric and dosimetric evaluation was performed for both proton and carbon ion treatment plans. Main results. The phantom validation exhibited a geometrical accuracy within the maximum resolution of the MRI and mean dose deviations, with respect to the prescription dose, up to 3.2% for target D 95%, with a mean gamma pass rate of 98%. For patients, the virtual and re-evaluation 4DCTs showed good correspondence, with errors on target D 95% up to 2% within the gating window. For one patient, dose variations up to 10% at end-exhale were observed due to relevant inter-fraction anatomo-pathological changes that occurred between the planning and re-evaluation CTs. Significance. Results obtained on phantom data showed that the virtual 4DCT method was accurate, allowing its application on patient data for testing within a clinical scenario.

Comparison of 3D and 4D robustly optimized proton treatment plans for non-small cell lung cancer patients with tumour motion amplitudes larger than 5mm.

There is no consensus about an ideal robust optimization (RO) strategy for proton therapy of targets with large intrafractional motion. We investigated the plan robustness of 3D and different 4D RO strategies. For eight non-small cell lung cancer patients with clinical target volume (CTV) motion >5mm, different RO approaches were investigated: 3DRO considering the average CT (AvgCT) with a target density override, 4DRO considering three/all 4DCT phases, and 4DRO considering the AvgCT and three/all 4DCT phases. Robustness against setup/range errors, interplay effects based on breathing and machine log file data for deliveries with/without rescanning, and interfractional anatomical changes were analyzed for target coverage and OAR sparing. All nominal plans fulfilled the clinical requirements with individual CTV coverage differences <2pp; 4DRO without AvgCT generated the most conformal dose distributions. Robustness against setup/range errors was best for 4DRO with AvgCT (18% more passed error scenarios than 3DRO). Interplay effects caused fraction-wise median CTV coverage loss of 3pp and missed maximum dose constraints for heart and esophagus in 18% of scenarios. CTV coverage and OAR sparing fulfilled requirements in all cases when accumulating four interplay scenarios. Interfractional changes caused less target misses for RO with AvgCT compared to 4DRO without AvgCT (≤42%/33% vs. ≥56%/44% failed single/accumulated scenarios). All RO strategies provided acceptable plans with equally low robustness against interplay effects demanding other mitigation than rescanning to ensure fraction-wise target coverage. 4DRO considering three phases and the AvgCT provided best compromise on planning effort and robustness.