A Preliminary Investigation of Radiation-Sensitive Ultrasound Contrast Agents for Photon Dosimetry

Radiotherapy treatment plans have become highly conformal, posing additional constraints on the accuracy of treatment delivery. Here, we explore the use of radiation-sensitive ultrasound contrast agents (superheated phase-change nanodroplets) as dosimetric radiation sensors. In a series of experiments, we irradiated perfluorobutane nanodroplets dispersed in gel phantoms at various temperatures and assessed the radiation-induced nanodroplet vaporization events using offline or online ultrasound imaging. At 25 °C and 37 °C, the nanodroplet response was only present at higher photon energies (≥10 MV) and limited to <2 vaporization events per cm2 per Gy. A strong response (~2000 vaporizations per cm2 per Gy) was observed at 65 °C, suggesting radiation-induced nucleation of the droplet core at a sufficiently high degree of superheat. These results emphasize the need for alternative nanodroplet formulations, with a more volatile perfluorocarbon core, to enable in vivo photon dosimetry. The current nanodroplet formulation carries potential as an innovative gel dosimeter if an appropriate gel matrix can be found to ensure reproducibility. Eventually, the proposed technology might unlock unprecedented temporal and spatial resolution in image-based dosimetry, thanks to the combination of high-frame-rate ultrasound imaging and the detection of individual vaporization events, thereby addressing some of the burning challenges of new radiotherapy innovations.

Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering

BACKGROUND

Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time.

PURPOSE

This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach without filtering.

METHODS

Three methods of EL filtering were developed: unrestricted, switch-up (SU), and switch-up gap (SU gap) filtering. The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework energy layer selection and spot assignment (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and normal tissue complication probability evaluations, as well as comparisons to intensity-modulated proton therapy (IMPT) plans, were used to assess plan quality.

RESULTS

The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients.

CONCLUSIONS

This study provides insights to accelerate PAT delivery without compromising plan quality. These advancements could enhance treatment efficiency and patient throughput.

[Protontherapy : principles, advantages, limitations, indications, perspectives...and some Belgian peculiarities]

Most radiotherapy treatments are nowadays delivered with linear accelerators producing photons. This robust radiation technique improved outstandingly during the last three decades, allowing treatments for most tumoural indications with an exquisite accuracy, a formidable effectiveness, a low toxicity, and a very low cost for the society. Therefore, the reasons for using and developing the more expensive hadron therapy and more particularly proton therapy may seem futile. In the current article targeting the general practitioners readership, we look at the principles of this innovative technique, its inherent advantages and limitations, the current and future indications, the challenges and perspectives for the future. We conclude with an overview of the Belgian landscape in terms of installation, operation, access and reimbursement procedures.

Evaluation of safety margins for cone beam CT-based adaptive prostate radiotherapy

Adaptive radiotherapy is characterized by the use of a daily imaging system, such as CBCT (Cone-Beam Computed Tomography) images to re-optimize the treatment based on the daily anatomy and position of the patient. By systematically re-delineating the Clinical Target Volume (CTV) at each fraction, target delineation uncertainty features a random component instead of a pure systematic. The goal of this work is to identify the random and systematic contributions of the delineation error and compute a new relevant Planning Target Volume (PTV) safety margin. 169 radiotherapy sessions from 10 prostate cancer patients treated on the Varian ETHOS treatment system have been analyzed. Intra-patient and inter-patient delineation variabilities were computed in six directions, by considering the prostate as a rigid, non-rotating volume. By doing so, we were able to directly compare the delineations done by the physicians on daily CBCT images with the initial delineation done on the CT-sim and MRI, and sort them by direction using the polar coordinates of the points. The computed variabilities were then used to compute a PTV margin based on Van Herk margin recipe. The total margin computed with random and systematic delineation uncertainties was of 2.7, 2.4, 5.6, 4.8, 4.9 and 3.6 mm in the left, right, anterior, posterior, cranial and caudal directions, respectively. According to our results, the gain offered by the separation of the delineation uncertainty into systematic and random contributions due to the adaptive delineation process justifies a reduction of the PTV margin down to 3 to 5 mm in every direction.

Markerless liver online adaptive stereotactic radiotherapy: feasibility analysisCervantes

Objective. Radio-opaque markers are recommended for image-guided radiotherapy in liver stereotactic ablative radiotherapy (SABR), but their implantation is invasive. We evaluate in thisin-silicostudy the feasibility of cone-beam computed tomography-guided stereotactic online-adaptive radiotherapy (CBCT-STAR) to propagate the target volumes without implanting radio-opaque markers and assess its consequence on the margin that should be used in that context.Approach. An emulator of a CBCT-STAR-dedicated treatment planning system was used to generate plans for 32 liver SABR patients. Three target volume propagation strategies were compared, analysing the volume difference between the GTVPropagatedand the GTVConventional, the vector lengths between their centres of mass (lCoM), and the 95th percentile of the Hausdorff distance between these two volumes (HD95). These propagation strategies were: (1) structure-guided deformable registration with deformable GTV propagation; (2) rigid registration with rigid GTV propagation; and (3) image-guided deformable registration with rigid GTV propagation. Adaptive margin calculation integrated propagation errors, while interfraction position errors were removed. Scheduled plans (PlanNon-adaptive) and daily-adapted plans (PlanAdaptive) were compared for each treatment fraction.Main results.The image-guided deformable registration with rigid GTV propagation was the best propagation strategy regarding tolCoM(mean: 4.3 +/- 2.1 mm), HD95 (mean 4.8 +/- 3.2 mm) and volume preservation between GTVPropagatedand GTVConventional. This resulted in a planning target volume (PTV) margin increase (+69.1% in volume on average). Online adaptation (PlanAdaptive) reduced the violation rate of the most important dose constraints ('priority 1 constraints', 4.2 versus 0.9%, respectively;p< 0.001) and even improved target volume coverage compared to non-adaptive plans (PlanNon-adaptive).Significance. Markerless CBCT-STAR for liver tumours is feasible using Image-guided deformable registration with rigid GTV propagation. Despite the cost in terms of PTV volumes, daily adaptation reduces constraints violation and restores target volumes coverage.

Balancing robustness and adaptation rate for proton therapy of lung cancer patients

INTRODUCTION

An increase in plan robustness leads to a higher dose to organs-at-risk (OARs), and an increased chance of post-treatment toxicities. In contrast, more conformal plans lead to sparing of healthy surrounding tissue at the expense of a higher sensitivity to anatomical changes, requiring costly adaptations. In this study, we assess the trade-off and impact of treatment plan robustness on the adaptation rate.

METHOD

Treatment planning was performed for 40 lung cancer patients, each having a planning 4DCT and up to eight weekly repeated 4DCTs (reCTs). For each patient, plans were made with three different levels of robustness based on setup uncertainty of 3, 6 and 9 mm. These plans were robustly re-evaluated on all reCTs to assess whether the clinical constraints were met.

RESULTS

For the 3, 6 and 9 mm robustness levels, adaptation rates of 87.5 %, 70.0 % and 57.5 %, respectively, were observed. A mean absolute normal tissue complication probability (NTCP) gain of 2.9 percentage points (pp) was calculated for pneumonitis grade ≥ 2 when transitioning from 9 mm plans to 3 mm plans, 7.6 pp for esophagitis grade ≥ 2, and 2.5 pp for mortality risk 2 years post-treatment.

CONCLUSION

The lowered risk of post treatment toxicities at lower robustness levels is clinically relevant but comes at the expense of more treatment adaptations, particularly in cases where meeting our clinical goals is not compromised by having a dose that is more conformal to the target. The trade-off between workload and reduced NTCP needs to be individually assessed.

Technical note: Beamlet-free optimization for Monte-Carlo-based treatment planning in proton therapy

BACKGROUND

Dose calculation and optimization algorithms in proton therapy treatment planning often have high computational requirements regarding time and memory. This can hinder the implementation of efficient workflows in clinics and prevent the use of new, elaborate treatment techniques aiming to improve clinical outcomes like robust optimization, arc, and adaptive proton therapy.

PURPOSE

A new method, namely, the beamlet-free algorithm, is presented to address the aforementioned issue by combining Monte Carlo dose calculation and optimization into a single algorithm and omitting the calculation of the time-consuming and costly dose influence matrix.

METHODS

The beamlet-free algorithm simulates the dose in proton batches of randomly chosen spots and evaluates their relative impact on the objective function at each iteration. Based on the approximated gradient, the spot weights are then updated and used to generate a new spot probability distribution. The beamlet-free method is compared against a conventional, beamlet-based treatment planning algorithm on a brain case and a prostate case.

RESULTS

The beamlet-free algorithm maintained a comparable plan quality while largely reducing the dependence of computation time and memory usage on the number of spots.

CONCLUSION

The implementation of a beamlet-free treatment planning algorithm for proton therapy is feasible and capable of achieving treatment plans of comparable quality to conventional methods. Its efficient usage of computational resources and low spot dependence makes it a promising method for large plans, robust optimization, and arc proton therapy.

Comparison of Ethos template-based planning and AI-based dose prediction: General performance, patient optimality, and limitations

PURPOSE

Ethos proposes a template-based automatic dose planning (Etb) for online adaptive radiotherapy. This study evaluates the general performance of Etb for prostate cancer, as well as the ability to generate patient-optimal plans, by comparing it with another state-of-the-art automatic planning method, i.e., deep learning dose prediction followed by dose mimicking (DP + DM).

MATERIALS

General performances and capability to produce patient-optimal plan were investigated through two studies: Study-S1 generated plans for 45 patients using our initial Ethos clinical goals template (EG_init), and compared them to manually generated plans (MG). For study-S2, 10 patients which showed poor performances at study-S1 were selected. S2 compared the quality of plans generated with four different methods: 1) Ethos initial template (EG_init_selected), 2) Ethos updated template-based on S1 results (EG_upd_selected), 3) DP + DM, and 4) MG plans.

RESULTS

EG_init plans showed satisfactory performance for dose level above 50 Gy: reported mean metrics differences (EG_init minus MG) never exceeded 0.6 %. However, lower dose levels showed loosely optimized metrics, mean differences for V30Gy to rectum and V20Gy to anal canal were of 6.6 % and 13.0 %. EG_init_selected showed amplified differences in V30Gy to rectum and V20Gy to anal canal: 8.5 % and 16.9 %, respectively. These dropped to 5.7 % and 11.5 % for EG_upd_selected plans but strongly increased V60Gy to rectum for 2 patients. DP + DM plans achieved differences of 3.4 % and 4.6 % without compromising any V60Gy.

CONCLUSION

General performances of Etb were satisfactory. However, optimizing with template of goals might be limiting for some complex cases. Over our test patients, DP + DM outperformed the Etb approach.

Dosimetrically triggered adaptive radiotherapy for head and neck cancer: Considerations for the implementation of clinical protocols

PURPOSE

Defining dosimetric rules to automatically detect patients requiring adaptive radiotherapy (ART) is not straightforward, and most centres perform ad-hoc ART with no specific protocol. This study aims to propose and analyse different steps to design a protocol for dosimetrically triggered ART of head and neck (H&N) cancer. As a proof-of-concept, the designed protocol was applied to patients treated in TomoTherapy units, using their available software for daily MVCT image and dose accumulation.

METHODS

An initial protocol was designed by a multidisciplinary team, with a set of flagging criteria based only on dose-volume metrics, including two action levels: (1) surveillance (orange flag), and (2) immediate verification (red flag). This protocol was adapted to the clinical needs following an iterative process. First, the protocol was applied to 38 H&N patients with daily imaging. Automatic software generated the daily contours, recomputed the daily dose and flagged the dosimetric differences with respect to the planning dose. Second, these results were compared, by a sensitivity/specificity test, to the answers of a physician. Third, the physician, supported by the multidisciplinary team, performed a self-analysis of the provided answers and translated them into mathematical rules in order to upgrade the protocol. The upgraded protocol was applied to different definitions of the target volume (i.e. deformed CTV + 0, 2 and 4 mm), in order to quantify how the number of flags decreases when reducing the CTV-to-PTV margin.

RESULTS

The sensitivity of the initial protocol was very low, specifically for the orange flags. The best values were 0.84 for red and 0.15 for orange flags. After the review and upgrade process, the sensitivity of the upgraded protocol increased to 0.96 for red and 0.84 for orange flags. The number of patients flagged per week with the final (upgraded) protocol decreased in median by 26% and 18% for red and orange flags, respectively, when reducing the CTV-to-PTV margin from 4 to 2 mm. This resulted in only one patient flagged at the last fraction for both red and orange flags.

CONCLUSION

Our results demonstrate the value of iterative protocol design with retrospective data, and shows the feasibility of automatically-triggered ART using simple dosimetric rules to mimic the physician's decisions. Using a proper target volume definition is important and influences the flagging rate, particularly when decreasing the CTV-to-PTV margin.

A spot-specific range uncertainty framework for robust optimization of proton therapy treatments

PURPOSE

An accurate estimation of range uncertainties is essential to exploit the potential of proton therapy. According to Paganetti's study, a value of 2.4% (1.5 standard deviation) is currently recommended for planning robust treatments with Monte Carlo dose engines. This number is based on a dominant contribution from the mean excitation energy of tissues. However, it was recently shown that expressing tissues as a mixture of water and "dry" material in the CT calibration process allowed for a significant reduction of this uncertainty. We thus propose an adapted framework for pencil beam scanning robust optimization. First, we move towards a spot-specific range uncertainty (SSRU) determination. Second, we use the water-based formalism to reduce range uncertainties and, potentially, to spare better the organs at risk.

METHODS

The stoichiometric calibration was adapted to provide a molecular decomposition (including water) of each voxel of the CT. The SSRU calculation was implemented in MCsquare, a fast Monte Carlo dose engine dedicated to proton therapy. For each spot, a ray-tracing method was used to propagate molecular I-values uncertainties and obtain the corresponding effective range uncertainty. These were then combined with other sources of range uncertainties, according to Paganetti's study of 2012. The method was then assessed on three head-and-neck patients. Two plans were optimized for each patient: the first one with the classical 2.4% flat range uncertainty (FRU), the second one with the variable range uncertainty. Both plans were then compared in terms of target coverage and OAR mean dose reduction. Robustness evaluations were also performed, using the SSRU for both plans in order to simulate errors as realistically as possible.

RESULTS

For patient 1, it was found that the median SSRU was 1.04% (1.5 standard deviation), yielding, therefore, a very large reduction from the 2.4% FRU. All three SSRU plans were found to have a very good robustness level at a 90% confidence interval while sparing OAR better than the classical plan. For instance, in nominal cases, average reductions in the mean dose of 15.7, 8.4, and 13.2% were observed in the left parotid, right parotid, and pharyngeal constrictor muscle, respectively. As expected, the classical plans showed a higher but unnecessary level of robustness.

CONCLUSIONS

Promising results of the SSRU framework were observed on three head-and-neck cases, and more patients should now be considered. The method could also benefit to other tumor sites and, in the long run, the variable part of the range uncertainty could be generalized to other sources of uncertainty in order to move towards more and more patient-specific treatments.

Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer

BACKGROUND

In cancer care, determining the most beneficial treatment technique is a key decision affecting the patient's survival and quality of life. Patient selection for proton therapy (PT) over conventional radiotherapy (XT) currently entails comparing manually generated treatment plans, which requires time and expertise.

PURPOSE

We developed an automatic and fast tool, AI-PROTIPP (Artificial Intelligence Predictive Radiation Oncology Treatment Indication to Photons/Protons), that assesses quantitatively the benefits of each therapeutic option. Our method uses deep learning (DL) models to directly predict the dose distributions for a given patient for both XT and PT. By using models that estimate the Normal Tissue Complication Probability (NTCP), namely the likelihood of side effects to occur for a specific patient, AI-PROTIPP can propose a treatment selection quickly and automatically.

METHODS

A database of 60 patients presenting oropharyngeal cancer, obtained from the Cliniques Universitaires Saint Luc in Belgium, was used in this study. For every patient, a PT plan and an XT plan were generated. The dose distributions were used to train the two dose DL prediction models (one for each modality). The model is based on U-Net architecture, a type of convolutional neural network currently considered as the state of the art for dose prediction models. A NTCP protocol used in the Dutch model-based approach, including grades II and III xerostomia and grades II and III dysphagia, was later applied in order to perform automatic treatment selection for each patient. The networks were trained using a nested cross-validation approach with 11-folds. We set aside three patients in an outer set and each fold consists of 47 patients in training, five in validation and five for testing. This method allowed us to assess our method on 55 patients (five patients per test times the number of folds).

RESULTS

The treatment selection based on the DL-predicted doses reached an accuracy of 87.4% for the threshold parameters set by the Health Council of the Netherlands. The selected treatment is directly linked with these threshold parameters as they express the minimal gain brought by the PT treatment for a patient to be indicated to PT. To validate the performance of AI-PROTIPP in other conditions, we modulated these thresholds, and the accuracy was above 81% for all the considered cases. The difference in average cumulative NTCP per patient of predicted and clinical dose distributions is very similar (less than 1% difference).

CONCLUSIONS

AI-PROTIPP shows that using DL dose prediction in combination with NTCP models to select PT for patients is feasible and can help to save time by avoiding the generation of treatment plans only used for the comparison. Moreover, DL models are transferable, allowing, in the future, experience to be shared with centers that would not have PT planning expertise.

Definition of dose rate for FLASH pencil-beam scanning proton therapy: A comparative study

BACKGROUND

FLASH proton therapy has the potential to reduce side effects of conventional proton therapy by delivering a high dose of radiation in a very short period of time. However, significant progress is needed in the development of FLASH proton therapy. Increasing the dose rate while maintaining dose conformality may involve the use of advanced beam-shaping technologies and specialized equipment such as 3D patient-specific range modulators, to take advantage of the higher transmission efficiency at the highest energy available. The dose rate is an important factor in FLASH proton therapy, but its definition can vary because of the uneven distribution of the dose over time in pencil-beam scanning (PBS).

PURPOSE

Highlight the distinctions, both in terms of concept and numerical values, of the various definitions that can be established for the dose rate in PBS proton therapy.

METHODS

In an in silico study, five definitions of the dose rate, namely the PBS dose rate, the percentile dose rate, the maximum percentile dose rate, the average dose rate, and the dose averaged dose rate (DADR) were analyzed first through theoretical comparison, and then applied to a head and neck case. To carry out this study, a treatment plan utilizing a single energy level and requiring the use of a patient-specific range modulator was employed. The dose rate values were compared both locally and by means of dose rate volume histograms (DRVHs).

RESULTS

The PBS dose rate, the percentile dose rate, and the maximum percentile dose are definitions that are specifically designed to take into account the time structure of the delivery of a PBS treatment plan. Although they may appear similar, our study shows that they can vary locally by up to 10%. On the other hand, the DADR values were approximately twice as high as those of the PBS, percentile, and maximum percentile dose rates, since the DADR disregards the periods when a voxel does not receive any dose. Finally, the average dose rate can be defined in various ways, as discussed in this paper. The average dose rate is found to be lower by a factor of approximately 1/2 than the PBS, percentile, and maximum percentile dose rates.

CONCLUSIONS

We have shown that using different definitions for the dose rate in FLASH proton therapy can lead to variations in calculated values ranging from a few percent to a factor of two. Since the dose rate is a critical parameter in FLASH radiation therapy, it is essential to carefully consider the choice of definition. However, to make an informed decision, additional biological data and models are needed.

A hybrid multi-particle approach to range assessment-based treatment verification in particle therapy

Particle therapy (PT) used for cancer treatment can spare healthy tissue and reduce treatment toxicity. However, full exploitation of the dosimetric advantages of PT is not yet possible due to range uncertainties, warranting development of range-monitoring techniques. This study proposes a novel range-monitoring technique introducing the yet unexplored concept of simultaneous detection and imaging of fast neutrons and prompt-gamma rays produced in beam-tissue interactions. A quasi-monolithic organic detector array is proposed, and its feasibility for detecting range shifts in the context of proton therapy is explored through Monte Carlo simulations of realistic patient models and detector resolution effects. The results indicate that range shifts of [Formula: see text] can be detected at relatively low proton intensities ([Formula: see text] protons/spot) when spatial information obtained through imaging of both particle species are used simultaneously. This study lays the foundation for multi-particle detection and imaging systems in the context of range verification in PT.

Machine learning-based automatic proton therapy planning: impact of post-processing and dose-mimicking in plan robustness

PURPOSE

Automated treatment planning strategies are being widely implemented in clinical routine to reduce inter-planner variability, speed up the optimization process, and improve plan quality. This study aims to evaluate the feasibility and quality of intensity-modulated proton therapy (IMPT) plans generated with four different knowledge-based planning (KBP) pipelines fully integrated into a commercial treatment planning system (TPS).

MATERIALS/METHODS

A data set containing 60 oropharyngeal cancer patients was split into 11 folds, each containing 47 patients for training, 5 patients for validation and 5 patients for testing. A dose prediction model was trained on each of the folds, resulting in a total of 11 models. Three patients were left out in order to assess if the differences introduced between models were significant. From voxel-based dose predictions, we analyze the two steps that follow the dose prediction: post-processing of the predicted dose and dose mimicking (DM). We focused on the effect of post-processing (PP) or no post-processing (NPP) combined with two different DM algorithms for optimization: the one available in the commercial TPS RayStation (RSM) and a simpler isodose-based mimicking (IBM). Using 55 test patients (5 test patients for each model), we evaluated the quality and robustness of the plans generated by the four proposed KBP pipelines (PP-RSM, PP-IBM, NPP-RSM, NPP-IBM). After robust evaluation, dose-volume histogram (DVH) metrics in nominal and worst-case scenarios were compared to those of the manually generated plans.

RESULTS

Nominal doses from the four KBP pipelines showed promising results achieving comparable target coverage and improved dose to organs at risk (OARs) compared to the manual plans. However, too optimistic post-processing applied to the dose prediction (i.e. important decrease of the dose to the organs) compromised the robustness of the plans. Even though RSM seemed to partially compensate for the lack of robustness in the PP plans, still 65% of the patients did not achieve the expected robustness levels. NPP-RSM plans seemed to achieve the best trade-off between robustness and OAR sparing.

DISCUSSION/CONCLUSIONS

PP and DM strategies are crucial steps to generate acceptable robust and deliverable IMPT plans from ML-predicted doses. Before the clinical implementation of any KBP pipeline, the PP and DM parameters predefined by the commercial TPS need to be modified accordingly with a comprehensive feedback loop in which the robustness of the final dose calculations is evaluated. With the right choice of PP and DM parameters, KBP strategies have the potential to generate IMPT plans within clinically acceptable levels comparable to plans manually generated by dosimetrists. This article is protected by copyright. All rights reserved.

Dose mimicking based strategies for online adaptive proton therapy of head and neck cancer

OBJECTIVE

To compare a not adapted (NA) robust planning strategy with three fully automated online adaptive proton therapy (OAPT) workflows based on the same optimization method: dose mimicking (DM). The added clinical value and limitations of the OAPT methods are investigated for head and neck cancer (HNC) patients.&#xD;&#xD;&#xD;Approach: The three OAPT strategies aimed at compensating for inter-fractional anatomical changes by mimiking different dose distributions on corrected cone beam CT images (corrCBCTs). Order by complexity, the OAPTs were: (1) online adaptive dose restoration (OADR) where the approved clinical dose on the planning-CT (pCT) was mimicked, (2) online adaptation using DM of the deformed clinical dose from the pCT to corrCBCTs (OADEF), and (3) online adaptation applying DM to a predicted dose on corrCBCTs (OAML). Adaptation was only applied in fractions where the target coverage criteria were not met (D98% < 95% of the prescribed dose). For 10 HNC patients, the accumulated dose distributions over the 35 fractions were calculated for NA, OADR, OADEF, and OAML.&#xD;&#xD;Main results: Higher target coverage was observed for all OAPT strategies compared to no adaptation. OADEF and OAML outperformed both NA and OADR and were comparable in terms of target coverage to initial clinical plans. However, only OAML provided comparable NTCP values to those from the clinical dose without statistically significant differences. When the NA initial plan was evaluated on corrCBCTs, 51% of fractions needed adaptation. The adaptation rate decreased significantly to 25% when the last adapted plan with OADR was selected for delivery, to 16% with OADEF, and to 21% with OAML. The reduction was even greater when the best plan among previously generated adapted plans (instead of the last one) was selected.&#xD;&#xD;Significance: The implemented OAPT strategies provided superior target coverage compared to no adaptation, higher OAR sparing, and fewer required adaptations.

Biophysical characterization of collimated and uncollimated fields in pencil beam scanning proton therapy

Objective. The lateral dose fall-off in proton pencil beam scanning (PBS) technique remains the preferred choice for sparing adjacent organs at risk as opposed to the distal edge due to the proton range uncertainties and potentially high relative biological effectiveness. However, because of the substantial spot size along with the scattering in the air and in the patient, the lateral penumbra in PBS can be degraded. Combining PBS with an aperture can result in a sharper dose fall-off, particularly for shallow targets.Approach. The aim of this work was to characterize the radiation fields produced by collimated and uncollimated 100 and 140 MeV proton beams, using Monte Carlo simulations and measurements with a MiniPIX-Timepix detector. The dose and the linear energy transfer (LET) were then coupled with publishedin silicobiophysical models to elucidate the potential biological effects of collimated and uncollimated fields.Main results. Combining an aperture with PBS reduced the absorbed dose in the lateral fall-off and out-of-field by 60%. However, the results also showed that the absolute frequency-averaged LET (LET_F) values increased by a maximum of 3.5 keVμm^-1in collimated relative to uncollimated fields, while the dose-averaged LET (LET_D) increased by a maximum of 7 keVμm^-1. Despite the higher LET values produced by collimated fields, the predicted DNA damage yields remained lower, owing to the large dose reduction.Significance. This work demonstrated the dosimetric advantages of combining an aperture with PBS coupled with lower DNA damage induction. A methodology for calculating dose in water derived from measurements with a silicon-based detector was also presented. This work is the first to demonstrate experimentally the increase in LET caused by combining PBS with aperture, and to assess the potential DNA damage which is the initial step in the cascade of events leading to the majority of radiation-induced biological effects.

Analytic prediction of droplet vaporization events to estimate the precision of ultrasound-based proton range verification

BACKGROUND

The safety and efficacy of proton therapy is currently hampered by range uncertainties. The combination of ultrasound imaging with injectable radiation-sensitive superheated nanodroplets was recently proposed for in vivo range verification. The proton range can be estimated from the distribution of nanodroplet vaporization events, which is stochastically related to the stopping distribution of protons, as nanodroplets are vaporized by protons reaching their maximal LET at the end of their range.

PURPOSE

Here, we aim to estimate the range estimation precision of this technique. As for any stochastic measurement, the precision will increase with the sample size, that is, the number of detected vaporizations. Thus, we first develop and validate a model to predict the number of vaporizations, which is then applied to estimate the range verification precision for a set of conditions (droplet size, droplet concentration, and proton beam parameters).

METHODS

Starting from the thermal spike theory, we derived a model that predicts the expected number of droplet vaporizations in an irradiated sample as a function of the droplet size, concentration, and number of protons. The model was validated by irradiating phantoms consisting of size-sorted perfluorobutane droplets dispersed in an aqueous matrix. The number of protons was counted with an ionization chamber, and the droplet vaporizations were recorded and counted individually using high frame rate ultrasound imaging. After validation, the range estimate precision was determined for different conditions using a Monte Carlo algorithm.

RESULTS

A good agreement between theory and experiments was observed for the number of vaporizations, especially for large (5.8 ± 2.2 µm) and medium (3.5 ± 1.1 µm) sized droplets. The number of events was lower than expected in phantoms with small droplets (2.0 ± 0.7 µm), but still within the same order of magnitude. The inter-phantom variability was considerably larger (up to 30x) than predicted by the model. The validated model was then combined with Monte Carlo simulations, which predicted a theoretical range retrieval precision improving with the square-root of the number of vaporizations, and degrading at high beam energies due to range straggling. For single pencil beams with energies between 70 and 240 MeV, a range verification precision below 1% of the range required perfluorocarbon concentrations in the order of 0.3-2.4 µM.

CONCLUSION

We proposed and experimentally validated a model to provide a quick estimate of the number of vaporizations for a given set of conditions (droplet size, droplet concentration, and proton beam parameters). From this model, promising range verification performances were predicted for realistic perfluorocarbon concentrations. These findings are an incentive to move towards preclinical studies, which are critical to assess the achievable droplet distribution in and around the tumor, and hence the in vivo range verification precision.

Radiation dose-escalation and dose-fractionation modulate the immune microenvironment, cancer stem cells and vasculature in experimental high-grade gliomas

BACKGROUND

In the context of high-grade gliomas (HGGs), very little evidence is available concerning the optimal radiotherapy (RT) schedule to be used in radioimmunotherapy combinations. This studied was aimed at shedding new light in this field by analyzing the effects of RT dose escalation and dose fractionation on the tumor microenvironment of experimental HGGs.

METHODS

Neurospheres (NS) CT-2A HGG-bearing C57BL/6 mice were treated with stereotactic RT. For dose-escalation experiments, mice received 2, 4 or 8 Gy as single administrations. For dose-fractionation experiments, mice received 4 Gy as a single fraction or multiple (1.33x3 Gy) fractions. The impact of the RT schedule on murine survival and tumor immunity was evaluated. Modifications of glioma stem cells (GSCs), tumor vasculature and tumor cell replication were also assessed.

RESULTS

RT dose-escalation was associated with an improved immune profile, with higher CD8⁺ T cells and CD8⁺ T cells/regulatory T cells (Tregs) ratio (P=0.0003 and P=0.0022, respectively) and lower total tumor associated microglia/macrophages (TAMs), M2 TAMs and monocytic myeloid derived suppressor cells (mMDSCs) (P=0.0011, P=0.0024 and P<0.0001, respectively). The progressive increase of RT dosages prolonged survival (P<0.0001) and reduced tumor vasculature (P=0.069), tumor cell proliferation (P<0.0001) and the amount of GSCs (P=0.0132 or lower). Compared to the unfractionated regimen, RT dose-fractionation negatively affected tumor immunity by inducing higher total TAMs, M2 TAMs and mMDSCs (P=0.0051, P=0.0036 and P=0.0436, respectively). Fractionation also induced a shorter survival (P=0.0078), a higher amount of GSCs (P=0.0015 or lower) and a higher degree of tumor cell proliferation (P=0.0003).

CONCLUSIONS

This study demonstrates that RT dosage and fractionation significantly influence survival, tumor immunity and GSCs in experimental HGGs. These findings should be taken into account when aiming at designing more synergistic and effective radio-immunotherapy combinations.

Exploring trade-offs in treatment planning for brain tumor cases with a probabilistic definition of the clinical target volume

PURPOSE

This study demonstrates how a novel probabilistic clinical target volume (CTV) concept-the clinical target distribution (CTD)-can be used to navigate the trade-off between target coverage and organ sparing with a semi-interactive treatment planning approach.

METHODS

Two probabilistic treatment planning methods are presented that use tumor probabilities to balance tumor control with organ-at-risk (OAR) sparing. The first method explores OAR dose reduction by systematically discarding x % $x\%$ of CTD voxels with an unfavorable dose-to-probability ratio from the minimum dose coverage objective. The second method sequentially expands the target volume from the GTV edge, calculating the CTD coverage versus OAR sparing trade-off after dosing each expansion. Each planning method leads to estimated levels of tumor control under specific statistical models of tumor infiltration: an independent tumor islets model and contiguous circumferential tumor growth model. The methods are illustrated by creating proton therapy treatment plans for two glioblastoma patients with the clinical goal of sparing the hippocampus and brainstem. For probabilistic plan evaluation, the concept of a dose-expected-volume histogram is introduced, which plots the dose to the expected tumor volume ⟨ v ⟩ $\langle v \rangle$ considering tumor probabilities.

RESULTS

Both probabilistic planning approaches generate a library of treatment plans to interactively navigate the planning trade-offs. In the first probabilistic approach, a significant reduction of hippocampus dose could be achieved by excluding merely 1% of CTD voxels without compromising expected tumor control probability (TCP) or CTD coverage: the hippocampus D 2 % $D_{2\%}$ dose reduces with 9.5 and 5.3 Gy for Patient 1 and 2, while the TCP loss remains below 1%. Moreover, discarding up to 10% of the CTD voxels does not significantly diminish the expected CTD dose, even though evaluation with a binary volume suggests poor CTD coverage. In the second probabilistic approach, the expected CTD D ⟨ 98 % ⟩ $D_{\langle 98\%\rangle }$ and TCP depend more strongly on the extent of the high-dose region: the target volume margin cannot be reduced by more than 2 mm if one aims at keeping the expected CTD D ⟨ 98 % ⟩ $D_{\langle 98\%\rangle }$ loss and TCP loss under 1 Gy and 2%, respectively. Therefore, there is less potential for improved OAR sparing without compromising TCP or expected CTD coverage.

CONCLUSIONS

This study proposes and implements treatment planning strategies to explore trade-offs using tumor probabilities.

Bi-criteria Pareto optimization to balance irradiation time and dosimetric objectives in proton arc therapy

Objective. Proton arc therapy (PAT) is a new delivery technique that exploits the continuous rotation of the gantry to distribute the therapeutic dose over many angular windows instead of using a few static fields, as in conventional (intensity-modulated) proton therapy. Although coming along with many potential clinical and dosimetric benefits, PAT has also raised a new optimization challenge. In addition to the dosimetric goals, the beam delivery time (BDT) needs to be considered in the objective function. Considering this bi-objective formulation, the task of finding a good compromise with appropriate weighting factors can turn out to be cumbersome.Approach. We have computed Pareto-optimal plans for three disease sites: a brain, a lung, and a liver, following a method of iteratively choosing weight vectors to approximate the Pareto front with few points. Mixed-integer programming (MIP) was selected to state the bi-criteria PAT problem and to find Pareto optimal points with a suited solver.Main results. The trade-offs between plan quality and beam irradiation time (staticBDT) are investigated by inspecting three plans from the Pareto front. The latter are carefully picked to demonstrate significant differences in dose distribution and delivery time depending on their location on the frontier. The results were benchmarked against IMPT and SPArc plans showing the strength of degrees of freedom coming along with MIP optimization.Significance. This paper presents for the first time the application of bi-criteria optimization to the PAT problem, which eventually permits the planners to select the best treatment strategy according to the patient conditions and clinical resources available.

Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer

BACKGROUND AND PURPOSE

This study aims to investigate how accurate our deep learning (DL) dose prediction models for intensity modulated radiotherapy (IMRT) and pencil beam scanning (PBS) treatments, when chained with normal tissue complication probability (NTCP) models, are at identifying esophageal cancer patients who are at high risk of toxicity and should be switched to proton therapy (PT).

MATERIALS AND METHODS

Two U-Net were created, for photon (XT) and proton (PT) plans, respectively. To estimate the dose distribution for each patient, they were trained on a database of 40 uniformly planned patients using cross validation and a circulating test set. These models were combined with a NTCP model for postoperative pulmonary complications. The NTCP model used the mean lung dose, age, histology type, and body mass index as predicting variables. The treatment choice is then done by using a ΔNTCP threshold between XT and PT plans. Patients with ΔNTCP ≥ 10% were referred to PT.

RESULTS

Our DL models succeed in predicting dose distributions with a mean error on the mean dose to the lungs (MLD) of 1.14 ± 0.93% for XT and 0.66 ± 0.48% for PT. The complete automated workflow (DL chained with NTCP) achieved 100% accuracy in patient referral. The average residual (ΔNTCP ground truth - ΔNTCP predicted) is 1.43 ± 1.49%.

CONCLUSION

This study evaluates our DL dose prediction models in a broader patient referral context and demonstrates their ability to support clinical decisions.

Low-dose CT allows for accurate proton therapy dose calculation and plan optimization

Objective. Protons offer a more conformal dose delivery compared to photons, yet they are sensitive to anatomical changes over the course of treatment. To minimize range uncertainties due to anatomical variations, a new CT acquisition at every treatment session would be paramount to enable daily dose calculation and subsequent plan adaptation. However, the series of CT scans results in an additional accumulated patient dose. Reducing CT radiation dose and thereby decreasing the potential risk of radiation exposure to patients is desirable, however, lowering the CT dose results in a lower signal-to-noise ratio and therefore in a reduced quality image. We hypothesized that the signal-to-noise ratio provided by conventional CT protocols is higher than needed for proton dose distribution estimation. In this study, we aim to investigate the effect of CT imaging dose reduction on proton therapy dose calculations and plan optimization. Approach. To verify our hypothesis, a CT dose reduction simulation tool has been developed and validated to simulate lower-dose CT scans from an existing standard-dose scan. The simulated lower-dose CTs were then used for proton dose calculation and plan optimization and the results were compared with those of the standard-dose scan. The same strategy was adopted to investigate the effect of CT dose reduction on water equivalent thickness (WET) calculation to quantify CT noise accumulation during integration along the beam. Main results. The similarity between the dose distributions acquired from the low-dose and standard-dose CTs was evaluated by the dose-volume histogram and the 3D Gamma analysis. The results on an anthropomorphic head phantom and three patient cases indicate that CT imaging dose reduction up to 90% does not have a significant effect on proton dose calculation and plan optimization. The relative error was employed to evaluate the similarity between WET maps and was found to be less than 1% after reducing the CT imaging dose by 90%. Significance. The results suggest the possibility of using low-dose CT for proton therapy dose estimation, since the dose distributions acquired from the standard-dose and low-dose CTs are clinically equivalent.

Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging

Objective. External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified by quality assurance methods. Recently, online ultrasound imaging of vaporized radiosensitive nanodroplets was proposed as a promising tool for in vivo dosimetry in radiotherapy. Previously, the detection of sparse vaporization events was achieved by applying differential ultrasound (US) imaging followed by intensity thresholding using subjective parameter tuning, which is sensitive to image artifacts. Approach. A generalized deep learning solution (i.e. BubbleNet) is proposed to localize vaporized nanodroplets on differential US frames, while overcoming the aforementioned limitation. A 5-fold cross-validation was performed on a diversely composed 5747-frame training/validation dataset by manual segmentation. BubbleNet was then applied on a test dataset of 1536 differential US frames to evaluate dosimetric features. The intra-observer variability was determined by scoring the Dice similarity coefficient (DSC) on 150 frames segmented twice. Additionally, the BubbleNet generalization capability was tested on an external test dataset of 432 frames acquired by a phased array transducer at a much lower ultrasound frequency and reconstructed with unconventional pixel dimensions with respect to the training dataset. Main results. The median DSC in the 5-fold cross validation was equal to ∼0.88, which was in line with the intra-observer variability (=0.86). Next, BubbleNet was employed to detect vaporizations in differential US frames obtained during the irradiation of phantoms with a 154 MeV proton beam or a 6 MV photon beam. BubbleNet improved the bubble-count statistics by ∼30% compared to the earlier established intensity-weighted thresholding. The proton range was verified with a −0.8 mm accuracy. Significance. BubbleNet is a flexible tool to localize individual vaporized nanodroplets on experimentally acquired US images, which improves the sensitivity compared to former thresholding-weighted methods.

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.

Ultrasound-assisted carbon ion dosimetry and range measurement using injectable polymer-shelled phase-change nanodroplets: in vitro study

Methods allowing for in situ dosimetry and range verification are essential in radiotherapy to reduce the safety margins required to account for uncertainties introduced in the entire treatment workflow. This study suggests a non-invasive dosimetry concept for carbon ion radiotherapy based on phase-change ultrasound contrast agents. Injectable nanodroplets made of a metastable perfluorobutane (PFB) liquid core, stabilized with a crosslinked poly(vinylalcohol) shell, are vaporized at physiological temperature when exposed to carbon ion radiation (C-ions), converting them into echogenic microbubbles. Nanodroplets, embedded in tissue-mimicking phantoms, are exposed at 37 °C to a 312 MeV/u clinical C-ions beam at different doses between 0.1 and 4 Gy. The evaluation of the contrast enhancement from ultrasound imaging of the phantoms, pre- and post-irradiation, reveals a significant radiation-triggered nanodroplets vaporization occurring at the C-ions Bragg peak with sub-millimeter shift reproducibility and dose dependency. The specific response of the nanodroplets to C-ions is further confirmed by varying the phantom position, the beam range, and by performing spread-out Bragg peak irradiation. The nanodroplets' response to C-ions is influenced by their concentration and is dose rate independent. These early findings show the ground-breaking potential of polymer-shelled PFB nanodroplets to enable in vivo carbon ion dosimetry and range verification.

Evaluation of the clinical value of automatic online dose restoration for adaptive proton therapy of head and neck cancer

INTRODUCTION

Intensity modulated proton therapy (IMPT) is highly sensitive to anatomical variations which can cause inadequate target coverage during treatment. This study compares not-adapted (NA) robust plans to two adaptive IMPT methods - a fully-offline adaptive (FOA) and a simplified automatic online adaptive strategy (dose restoration (DR)) to determine the benefit of DR, in head and neck cancer (HNC).

MATERIAL/METHODS

Robustly optimized clinical IMPT doses in planning-CTs (pCTs) were available for a cohort of 10 HNC patients. During robust re-optimization, DR used isodose contours, generated from the clinical dose on pCTs, and patient specific objectives to reproduce the clinical dose in every repeated-CT(rCT). For each rCT(n=50), NA, DR and FOA plans were robustly evaluated.

RESULTS

An improvement in DVH-metrics and robustness was seen for DR and FOA plans compared to NA plans. For NA plans, 74%(37/50) of rCTs did not fulfill the CTV coverage criteria (D98%>95%Dprescription). DR improved target coverage, target homogeneity and variability on critical risk organs such as the spinal cord. After DR, 52%(26/50) of rCTs met all clinical goals. Because of large anatomical changes and/or inaccurate patient repositioning, 48%(24/50) of rCTs still needed full offline adaptation to ensure an optimal treatment since dose restoration was not able to re-establish the initial plan quality.

CONCLUSION

Robust optimization together with fully-automatized DR avoided offline adaptation in 52% of the cases. Implementation of dose restoration in clinical routine could ensure treatment plan optimality while saving valuable human and material resources to radiotherapy departments.

Development and validation of an automatic commissioning tool for the Monte Carlo dose engine in myQA iON

Independent dose verification with Monte Carlo (MC) simulations is an important feature of proton therapy quality assurance (QA). However, clinical integration of such tools often generates an additional and complex workload for medical physicists. The preparation of the necessary clinical inputs, such as the machine beam model, should therefore be automated. In this work, a methodology for automatic MC commissioning has been devised, validated, and developed into a MATLAB tool for the users of myQA iON, the recent QA platform of IBA Dosimetry. With this workflow, all necessary parameters can easily be tuned using dedicated optimization methods. For the geometrical beam parameters (phase space), the assumption of a single or double Gaussian is made. To model the energy spectrum, a Gaussian function is assumed and parameters are optimized using either MC simulations or a library of pre-computed Bragg peaks. For the absolute dose calibration, commissioning fields can be reproduced with the dose engine to retrieve the necessary parameters. We discuss in a first time the tool efficiency and show that one can optimize all parameters in less than 4 min per energy with excellent accuracy. We then validate a beam model obtained with the tool by simulating homogeneous spread-out Bragg peaks (SOBPs) and patient QA plans previously measured in water. An average range agreement of 0.29 ± 0.34 mm is achieved for the SOBPs while 3%/3 mm local gamma passing rates reach 99.3% on average over all 62 measured patient QA planes, which is well within clinical tolerances.

Spatiotemporal Distribution of Nanodroplet Vaporization in a Proton Beam Using Real-Time Ultrasound Imaging for Range Verification

The potential of proton therapy to improve the conformity of the delivered dose to the tumor volume is currently limited by range uncertainties. Injectable superheated nanodroplets have recently been proposed for ultrasound-based in vivo range verification, as these vaporize into echogenic microbubbles on proton irradiation. In previous studies, offline ultrasound images of phantoms with dispersed nanodroplets were acquired after irradiation, relating the induced vaporization profiles to the proton range. However, the aforementioned method did not enable the counting of individual vaporization events, and offline imaging cannot provide real-time feedback. In this study, we overcame these limitations using high-frame-rate ultrasound imaging with a linear array during proton irradiation of phantoms with dispersed perfluorobutane nanodroplets at 37°C and 50°C. Differential image analysis of subsequent frames allowed us to count individual vaporization events and to localize them with a resolution beyond the ultrasound diffraction limit, enabling spatial and temporal quantification of the interaction between ionizing radiation and nanodroplets. Vaporization maps were found to accurately correlate with the stopping distribution of protons (at 50°C) or secondary particles (at both temperatures). Furthermore, a linear relationship between the vaporization count and the number of incoming protons was observed. These results indicate the potential of real-time high-frame-rate contrast-enhanced ultrasound imaging for proton range verification and dosimetry.

Incorporation of tumor motion directionality in margin recipe: The directional MidP strategy

PURPOSE

Planning target volume (PTV) definition based on Mid-Position (Mid-P) strategy typically integrates breathing motion from tumor positions variances along the conventional axes of the DICOM coordinate system. Tumor motion directionality is thus neglected even though it is one of its stable characteristics in time. We therefore propose the directional MidP approach (MidP dir), which allows motion directionality to be incorporated into PTV margins. A second objective consists in assessing the ability of the proposed method to better take care of respiratory motion uncertainty.

METHODS

11 lung tumors from 10 patients with supra-centimetric motion were included. PTV were generated according to the MidP and MidP dir strategies starting from planning 4D CT.

RESULTS

PTV_{MidP dir} volume didn't differ from the PTV_MidP volume: 31351 mm³ IC95% [17242-45459] vs. 31003 mm³ IC95% [ 17347-44659], p = 0.477 respectively. PTV_{MidP dir} morphology was different and appeared more oblong along the main motion axis. The relative difference between 3D and 4D doses was on average 1.09%, p = 0.011 and 0.74%, p = 0.032 improved with directional MidP for D_99% and D_95%. D_2% was not significantly different between both approaches. The improvement in dosimetric coverage fluctuated substantially from one lesion to another and was all the more important as motion showed a large amplitude, some obliquity with respect to conventional axes and small hysteresis.

CONCLUSIONS

Directional MidP method allows tumor motion to be taken into account more tightly as a geometrical uncertainty without increasing the irradiation volume.

Ultrasound-assisted investigation of photon triggered vaporization of poly(vinylalcohol) phase-change nanodroplets: A preliminary concept study with dosimetry perspective

PURPOSE

We investigate the vaporization of phase-change ultrasound contrast agents using photon radiation for dosimetry perspectives in radiotherapy.

METHODS

We studied superheated perfluorobutane nanodroplets with a crosslinked poly(vinylalcohol) shell. The nanodroplets' physico-chemical properties, and their acoustic transition have been assessed firstly. Then, poly(vinylalcohol)-perfluorobutane nanodroplets were dispersed in poly(acrylamide) hydrogel phantoms and exposed to a photon beam. We addressed the effect of several parameters influencing the nanodroplets radiation sensitivity (energy/delivered dose/dose rate/temperature). The nanodroplets-vaporization post-photon exposure was evaluated using ultrasound imaging at a low mechanical index.

RESULTS

Poly(vinylalcohol)-perfluorobutane nanodroplets show a good colloidal stability over four weeks and remain highly stable at temperatures up to 78 °C. Nanodroplets acoustically-triggered phase transition leads to microbubbles with diameters <10 μm and an activation threshold of mechanical index = 0.4, at 7.5 MHz. A small number of vaporization events occur post-photon exposure (6MV/15MV), at doses between 2 and 10 Gy, leading to ultrasound contrast increase up to 60% at RT. The nanodroplets become efficiently sensitive to photons when heated to a temperature of 65 °C (while remaining below the superheat limit temperature) during irradiation.

CONCLUSIONS

Nanodroplets' core is linked to the degree of superheat in the metastable state and plays a critical role in determining nanodroplet' stability and sensitivity to ionizing radiation, requiring higher or lower linear energy transfer vaporization thresholds. While poly(vinylalcohol)-perfluorobutane nanodroplets could be slightly activated by photons at ambient conditions, a good balance between the degree of superheat and stability will aim at optimizing the design of nanodroplets to reach high sensitivity to photons at physiological conditions.

Introducing a probabilistic definition of the target in a robust treatment planning framework

The 'clinical target distribution' (CTD) has recently been introduced as a promising alternative to the binary clinical target volume (CTV). However, a comprehensive study that considers the CTD, together with geometric treatment uncertainties, was lacking. Because the CTD is inherently a probabilistic concept, this study proposes a fully probabilistic approach that integrates the CTD directly in a robust treatment planning framework. First, the CTD is derived from a reported microscopic tumor infiltration model such that it explicitly features the probability of tumor cell presence in its target definition. Second, two probabilistic robust optimization methods are proposed that evaluate CTD coverage under uncertainty. The first method minimizes the expected-value (EV) over the uncertainty scenarios and the second method minimizes the sum of the expected value and standard deviation (EV-SD), thereby penalizing the spread of the objectives from the mean. Both EV and EV-SD methods introduce the CTD in the objective function by using weighting factors that represent the probability of tumor presence. The probabilistic methods are compared to a conventional worst-case approach that uses the CTV in a worst-case optimization algorithm. To evaluate the treatment plans, a scenario-based evaluation strategy is implemented that combines the effects of microscopic tumor infiltrations with the other geometric uncertainties. The methods are tested for five lung tumor patients, treated with intensity-modulated proton therapy. The results indicate that for the studied patient cases, the probabilistic methods favor the reduction of the esophagus dose but compensate by increasing the high-dose region in a low conflicting organ such as the lung. These results show that a fully probabilistic approach has the potential to obtain clinical benefits when tumor infiltration uncertainties are taken into account directly in the treatment planning process.

Artificial intelligence supported single detector multi-energy proton radiography system

Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus^®One accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4% ± 0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3% ± 4.1% forWET(ITotal),2.2% ± 1.4% forWET(IPrimary),and 2.5% ± 2.0% forWET(ICorrected).

Radiotherapy, Temozolomide, and Antiprogrammed Cell Death Protein 1 Treatments Modulate the Immune Microenvironment in Experimental High-Grade Glioma

BACKGROUND

The lack of immune synergy with conventional chemoradiation could explain the failure of checkpoint inhibitors in current clinical trials for high-grade gliomas (HGGs).

OBJECTIVE

To analyze the impact of radiotherapy (RT), Temozolomide (TMZ) and antiprogrammed cell death protein 1 (αPD1) (as single or combined treatments) on the immune microenvironment of experimental HGGs.

METHODS

Mice harboring neurosphere /CT-2A HGGs received RT (4 Gy, single dose), TMZ (50 mg/kg, 4 doses) and αPD1 (100 μg, 3 doses) as monotherapies or combinations. The influence on survival, tumor volume, and tumor-infiltrating immune cells was analyzed.

RESULTS

RT increased total T cells (P = .0159) and cluster of differentiation (CD)8+ T cells (P = .0078) compared to TMZ. Lymphocyte subpopulations resulting from TMZ or αPD1 treatment were comparable with those of controls. RT reduced M2 tumor-associated macrophages/microglia (P = .0019) and monocytic myeloid derived suppressor cells (mMDSCs, P = .0003) compared to controls. The effect on mMDSC was also seen following TMZ and αPD1 treatment, although less pronounced (P = .0439 and P = .0538, respectively). Combining RT with TMZ reduced CD8+ T cells (P = .0145) compared to RT alone. Adding αPD1 partially mitigated this effect as shown by the increased CD8+ T cells/Tregs ratio, even if this result failed to reach statistical significance (P = .0973). Changing the combination sequence of RT, TMZ, and αPD1 did not alter survival nor the immune effects.

CONCLUSION

RT, TMZ, and αPD1 modify the immune microenvironment of HGG. The combination of RT with TMZ induces a strong immune suppression which cannot be effectively counteracted by αPD1.

Modulating ultrasound contrast generation from injectable nanodroplets for proton range verification by varying the degree of superheat

PURPOSE

Despite the physical benefits of protons over conventional photon radiation in cancer treatment, range uncertainties impede the ability to harness the full potential of proton therapy. While monitoring the proton range in vivo could reduce the currently adopted safety margins, a routinely applicable range verification technique is still lacking. Recently, phase-change nanodroplets were proposed for proton range verification, demonstrating a reproducible relationship between the proton range and generated ultrasound contrast after radiation-induced vaporization at 25°C. In this study, previous findings are extended with proton irradiations at different temperatures, including the physiological temperature of 37°C, for a novel nanodroplet formulation. Moreover, the potential to modulate the linear energy transfer (LET) threshold for vaporization by varying the degree of superheat is investigated, where the aim is to demonstrate vaporization of nanodroplets directly by primary protons.

METHODS

Perfluorobutane nanodroplets with a shell made of polyvinyl alcohol (PVA-PFB) or 10,12-pentacosadyinoic acid (PCDA-PFB) were dispersed in polyacrylamide hydrogels and irradiated with 62 MeV passively scattered protons at temperatures of 37°C and 50°C. Nanodroplet transition into echogenic microbubbles was assessed using ultrasound imaging (gray value and attenuation analysis) and optical images. The proton range was measured independently and compared to the generated contrast.

RESULTS

Nanodroplet design proved crucial to ensure thermal stability, as PVA-shelled nanodroplets dramatically outperformed their PCDA-shelled counterpart. At body temperature, a uniform radiation response proximal to the Bragg peak is attributed to nuclear reaction products interacting with PVA-PFB nanodroplets, with the 50% drop in ultrasound contrast being 0.17 mm ± 0.20 mm (mean ± standard deviation) in front of the proton range. Also at 50°C, highly reproducible ultrasound contrast profiles were obtained with shifts of -0.74 mm ± 0.09 mm (gray value analysis), -0.86 mm ± 0.04 mm (attenuation analysis) and -0.64 mm ± 0.29 mm (optical analysis). Moreover, a strong contrast enhancement was observed near the Bragg peak, suggesting that nanodroplets were sensitive to primary protons.

CONCLUSIONS

By varying the degree of superheat of the nanodroplets' core, one can modulate the intensity of the generated ultrasound contrast. Moreover, a submillimeter reproducible relationship between the ultrasound contrast and the proton range was obtained, either indirectly via the visualization of secondary reaction products or directly through the detection of primary protons, depending on the degree of superheat. The potential of PVA-PFB nanodroplets for in vivo proton range verification was confirmed by observing a reproducible radiation response at physiological temperature, and further studies aim to assess the nanodroplets' performance in a physiological environment. Ultimately, cost-effective online or offline ultrasound imaging of radiation-induced nanodroplet vaporization could facilitate the reduction of safety margins in treatment planning and enable adaptive proton therapy.