Linear energy transfer weighted beam orientation optimization for intensity-modulated proton therapy

Biological optimization for hybrid proton-photon radiotherapy

Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

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 LETddistributions from CT images and DW. The accuracy of the LETdof 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 LETddistributions for each proton field in the test dataset. A single-field LETdcalculation 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-1and 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 LETdwith 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.

Development of a standalone delivery sequence model for proton arc therapy

BACKGROUND

Spot-scanning proton arc (SPArc) has been drawing significant interests in recent years because of its capability of continuous proton irradiation during the gantry rotation. Previous studies demonstrated SPArc plans were delivered on a prototype of the DynamicARC solution, IBA ProteusONE.

PURPOSE

We built a novel delivery sequence model through an independent experimental approach: the first SPArc delivery sequence model (DSMSPArc). Based on the model, we investigated SPArc treatment efficiency improvement in the routine proton clinical operation.

METHODS

SPArc test plans were generated and delivered on a prototype of the DynamicARC solution, IBA ProteusONE. An independent gantry inclinometer and the machine logfiles were used to derive the DSMSPArc. Seventeen SPArc plans were used to validate the model's accuracy independently. Two random clinical operation dates (6th January and 22nd March, 2021) from a single-room proton therapy center (PTC) were selected to quantitatively assess the improvement of treatment efficiency compared to the IMPT.

RESULTS

The difference between the logfile and DSMSPArc is about 3.2 ± 4.8%. SPArc reduced 58.1% of the average treatment delivery time per patient compared to IMPT (p < 0.01). Daily treatment throughput could be increased by 30% using SPArc using a single-room proton therapy system.

CONCLUSIONS

The first model of dynamic arc therapy is established in this study through an independent experimental approach using logfiles and measurements which allows clinical users and investigators to simulate the dynamic treatment delivery and assess the daily treatment throughput improvement.

Hypoxia-informed RBE-weighted beam orientation optimization for intensity modulated proton therapy

BACKGROUND

Variable relative biological effectiveness (RBE) models in treatment planning have been proposed to optimize the therapeutic ratio of proton therapy. It has been reported that proton RBE decreases with increasing tumor oxygen level, offering an opportunity to address hypoxia-related radioresistance with RBE-weighted optimization.

PURPOSE

Here, we obtain a voxel-level estimation of partial oxygen pressure to weigh RBE values in a single biologically informed beam orientation optimization (BOO) algorithm.

METHODS

Three glioblastoma patients with [18 F]-fluoromisonidazole (FMISO)-PET/CT images were selected from the institutional database. Oxygen values were derived from tracer uptake using a nonlinear least squares curve fitting. McNamara RBE, calculated from proton dose, was then weighed using oxygen enhancement ratios (OER) for each voxel and incorporated into the dose fidelity term of the BOO algorithm. The nonlinear optimization problem was solved using a split-Bregman approach, with FISTA as the solver. The proposed hypoxia informed RBE-weighted method (HypRBE) was compared to dose fidelity terms using the constant RBE of 1.1 (cRBE) and the normoxic McNamara RBE model (RegRBE). Tumor homogeneity index (HI), maximum biological dose (Dmax), and D95%, as well as OAR therapeutic index (TI = gEUDCTV /gEUDOAR ) were evaluated along with worst-case statistics after normalization to normal tissue isotoxicity.

RESULTS

Compared to [cRBE, RegRBE], HypRBE increased tumor HI, Dmax, and D95% across all plans by on average [31.3%, 31.8%], [48.6%, 27.1%], and [50.4%, 23.8%], respectively. In the worst-case scenario, the parameters increase on average by [12.5%, 14.7%], [7.3%,-8.9%], and [22.3%, 2.1%]. Despite increased OAR Dmean and Dmax by [8.0%, 3.0%] and [13.1%, -0.1%], HypRBE increased average TI by [22.0%, 21.1%]. Worst-case OAR Dmean, Dmax, and TI worsened by [17.9%, 4.3%], [24.5%, -1.2%], and [9.6%, 10.5%], but in the best cases, HypRBE escalates tumor coverage significantly without compromising OAR dose, increasing the therapeutic ratio.

CONCLUSIONS

We have developed an optimization algorithm whose dose fidelity term accounts for hypoxia-informed RBE values. We have shown that HypRBE selects bE:\Alok\aaeams better suited to deliver high physical dose to low RBE, hypoxic tumor regions while sparing the radiosensitive normal tissue.

Incorporating variable RBE in IMPT optimization for ependymoma

PURPOSE

To study the dosimetric impact of incorporating variable relative biological effectiveness (RBE) of protons in optimizing intensity-modulated proton therapy (IMPT) treatment plans and to compare it with conventional constant RBE optimization and linear energy transfer (LET)-based optimization.

METHODS

This study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning. Four plans were generated for each patient according to different optimization strategies: (1) constant RBE optimization (ConstRBEopt) considering standard-of-care dose requirements; (2) LET optimization (LETopt) using a composite cost function simultaneously optimizing dose-averaged LET (LETd ) and dose; (3) variable RBE optimization (VarRBEopt) using a recent phenomenological RBE model developed by McNamara et al.; and (4) hybrid RBE optimization (hRBEopt) assuming constant RBE for the target and variable RBE for organs at risk. By normalizing each plan to obtain the same target coverage in either constant or variable RBE, we compared dose, LETd , LET-weighted dose, and equivalent uniform dose between the different optimization approaches.

RESULTS

We found that the LETopt plans consistently achieved increased LET in tumor targets and similar or decreased LET in critical organs compared to other plans. On average, the VarRBEopt plans achieved lower mean and maximum doses with both constant and variable RBE in the brainstem and spinal cord for all 10 patients. To compensate for the underdosing of targets with 1.1 RBE for the VarRBEopt plans, the hRBEopt plans achieved higher physical dose in targets and reduced mean and especially maximum variable RBE doses compared to the ConstRBEopt and LETopt plans.

CONCLUSION

We demonstrated the feasibility of directly incorporating variable RBE models in IMPT optimization. A hybrid RBE optimization strategy showed potential for clinical implementation by maintaining all current dose limits and reducing the incidence of high RBE in critical normal tissues in ependymoma patients.

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

BACKGROUND

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.

PURPOSE

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.

METHODS

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.

RESULTS

In the water phantom studies, LET thresholds between 1.5 and 2.5 keV/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: 0.45 Gy(RBE), patient 2: 0.08 Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1-weighted dose in healthy brain tissue was observed (patient 1: 0.81 Gy(RBE), patient 2: 0.53 Gy(RBE)). The variation of NTCP values due to variation of α/β from 2 to 3 Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp).

CONCLUSIONS

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.

A Systematic Review of LET-Guided Treatment Plan Optimisation in Proton Therapy: Identifying the Current State and Future Needs

The well-known clinical benefits of proton therapy are achieved through higher target-conformality and normal tissue sparing than conventional radiotherapy. However, there is an increased sensitivity to uncertainties in patient motion/setup, proton range and radiobiological effect. Although recent efforts have mitigated some uncertainties, radiobiological effect remains unresolved due to a lack of clinical data for relevant endpoints. Therefore, RBE optimisations may be currently unsuitable for clinical treatment planning. LET optimisation is a novel method that substitutes RBE with LET, shifting LET hotspots outside critical structures. This review outlines the current status of LET optimisation in proton therapy, highlighting knowledge gaps and possible future research. Following the PRISMA 2020 guidelines, a search of the MEDLINE® and Scopus databases was performed in July 2023, identifying 70 relevant articles. Generally, LET optimisation methods achieved their treatment objectives; however, clinical benefit is patient-dependent. Inconsistencies in the reported data suggest further testing is required to identify therapeutically favourable methods. We discuss the methods which are suitable for near-future clinical deployment, with fast computation times and compatibility with existing treatment protocols. Although there is some clinical evidence of a correlation between high LET and adverse effects, further developments are needed to inform future patient selection protocols for widespread application of LET optimisation in proton therapy.

Proposing a Clinical Model for RBE Based on Proton Track-End Counts

PURPOSE

In proton therapy, the clinical application of linear energy transfer (LET) optimization remains contentious, in part because of challenges associated with the definition and calculation of LET and its exact relationship with relative biological effectiveness (RBE) because of large variation in experimental in vitro data. This has raised interest in other metrics with favorable properties for biological optimization, such as the number of proton track ends in a voxel. In this work, we propose a novel model for clinical calculations of RBE, based on proton track end counts.

METHODS AND MATERIALS

We developed an effective dose concept to translate between the total proton track-end count per unit mass in a voxel and a proton RBE value. Dose, track end, and dose-averaged LET (LETd) distributions were simulated using Monte Carlo models for a series of water phantoms, in vitro radiobiological studies, and patient treatment plans. We evaluated the correlation between track ends and regions of elevated biological effectiveness in comparison to LETd-based models of RBE.

RESULTS

Track ends were found to correlate with biological effects in in vitro experiments with an accuracy comparable to LETd. In patient simulations, our track end model identified the same biological hotspots as predicted by LETd-based radiobiological models of proton RBE.

CONCLUSIONS

These results suggest that, for clinical optimization and evaluation, an RBE model based on proton track end counts may match LETd-based models in terms of information provided while also offering superior statistical properties.

An iterative convex relaxation method for proton LET optimization

Objective:A constant relative biological effectiveness of 1.1 in current clinical practice of proton radiotherapy (RT) is a crude approximation and may severely underestimate the biological dose from proton RT to normal tissues, especially near the treatment target at the end of Bragg peaks that exhibits high linear energy transfer (LET). LET optimization can account for biological effectiveness of protons during treatment planning, for minimizing biological proton dose and hot spots to normal tissues. However, the LET optimization is usually nonlinear and nonconvex to solve, for which this work will develop an effective optimization method based on iterative convex relaxation (ICR).Approach: In contrast to the generic nonlinear optimization method, such as Quasi-Newton (QN) method, that does not account for specific characteristics of LET optimization, ICR is tailored to LET modeling and optimization in order to effectively and efficiently solve the LET problem. Specifically, nonlinear dose-averaged LET term is iteratively linearized and becomes convex during ICR, while nonconvex dose-volume constraint and minimum-monitor-unit constraint are also handled by ICR, so that the solution for LET optimization is obtained by solving a sequence of convex and linearized convex subproblems. Since the high LET mostly occurs near the target, a 1 cm normal-tissue expansion of clinical target volume (CTV) (excluding CTV), i.e. CTV1cm, is defined to as an auxiliary structure during treatment planning, where LET is minimized.Main results: ICR was validated in comparison with QN for abdomen, lung, and head-and-neck cases. ICR was effective for LET optimization, as ICR substantially reduced the LET and biological dose in CTV1cm the ring, with preserved dose conformality to CTV. Compared to QN, ICR had smaller LET, physical and biological dose in CTV1cm, and higher conformity index values; ICR was also computationally more efficient, which was about 3 times faster than QN.Significance: A LET-specific optimization method based on ICR has been developed for solving proton LET optimization, which has been shown to be more computationally efficient than generic nonlinear optimizer via QN, with better plan quality in terms of LET, biological and physical dose conformality.

Linear Energy Transfer and Relative Biological Effectiveness Investigation of Various Structures for a Cohort of Proton Patients With Brain Tumors

Purpose

The current knowledge on biological effects associated with proton therapy is limited. Therefore, we investigated the distributions of dose, dose-averaged linear energy transfer (LET_d), and the product between dose and LET_d (DLET_d) for a patient cohort treated with proton therapy. Different treatment planning system features and visualization tools were explored.

Methods and Materials

For a cohort of 24 patients with brain tumors, the LET_d, DLET_d, and dose was calculated for a fixed relative biological effectiveness value and 2 variable models: plan-based and phenomenological. Dose threshold levels of 0, 5, and 20 Gy were imposed for LET_d visualization. The relationship between physical dose and LET_d and the frequency of LET_d hotspots were investigated.

Results

The phenomenological relative biological effectiveness model presented consistently higher dose values. For lower dose thresholds, the LET_d distribution was steered toward higher values related to low treatment doses. Differences up to 26.0% were found according to the threshold. Maximum LET_d values were identified in the brain, periventricular space, and ventricles. An inverse relationship between LET_d and dose was observed. Frequency information to the domain of dose and LET_d allowed for the identification of clusters, which steer the mean LET_d values, and the identification of higher, but sparse, LET_d values.

Conclusions

Identifying, quantifying, and recording LET distributions in a standardized fashion is necessary, because concern exists over a link between toxicity and LET hotspots. Visualizing DLET_d or dose × LET_d during treatment planning could allow for clinicians to make informed decisions.

Exploratory study of seed spots analysis to characterize dose and linear energy transfer effect in adverse event initialization of pencil beam scanning proton therapy

BACKGROUND

Both dose and linear-energy-transfer (LET) could play a substantial role in adverse event (AE) initialization of cancer patients treated with pencil-beam-scanning proton therapy (PBS). However, not all the voxels within the AE regions are directly induced from the dose and LET effect. It is important to study the synergistic effect of dose and LET in AE initialization by only including a subset of voxels that are dosimetrically important.

PURPOSE

To perform exploratory investigation of the dose and LET effects upon AE initialization in PBS using seed spots analysis.

METHODS

113 head and neck (H&N) cancer patients receiving curative PBS were included. Among them, 20 patients experienced unanticipated CTCAEv4.0 grade≥3 AEs (AE group) and 93 patients did not (control group). Within the AE group, 13 AE patients were included in the seed spot analysis to derive the descriptive features of AE initialization and the remaining 7 mandible osteoradionecrosis patients and 93 control patients were used to derive the feature-based volume constraint of mandible osteoradionecrosis. The AE regions were contoured and the corresponding dose-LET volume histograms (DLVHs) of AE regions were generated for all patients in the AE group. We selected high LET voxels (the highest 5% of each dose bin) with a range of moderate to high dose (≥∼40 Gy[RBE]) as critical voxels. Critical voxels which were contiguous with each other were grouped into clusters. Each cluster was considered as a potential independent seed spot for AE initialization. Seed spots were displayed in a 2D dose-LET plane based on their mean dose and LET to derive the descriptive features of AE initialization. A volume constraint of mandible osteoradionecrosis was then established based on the extracted features using a receiver operating characteristic curve.

RESULTS

The product of dose and LET (xBD) was found to be a descriptive feature of seed spots leading to AE initialization in this preliminary study. The derived xBD volume constraint for mandible osteoradionecrosis showed good performance with an area-under-curve of 0.87 (sensitivity of 0.714 and specificity of 0.807 in the leave-one-out cross validation) for the very limited patient data included in this study.

CONCLUSION

Our exploratory study showed that both dose and LET were observed to be important in AE initializations. The derived xBD volume constraint could predict mandible osteoradionecrosis reasonably well in the very limited H&N cancer patient data treated with PBS included in this study. This article is protected by copyright. All rights reserved.

Reflections on beam configuration optimization for intensity-modulated proton therapy

Presumably, intensity-modulated proton radiotherapy (IMPT) is the most powerful form of proton radiotherapy. In the current state of the art, IMPT beam configurations (i.e. the number of beams and their directions) are, in general, chosen subjectively based on prior experience and practicality. Beam configuration optimization (BCO) for IMPT could, in theory, significantly enhance IMPT’s therapeutic potential. However, BCO is complex and highly computer resource-intensive. Some algorithms for BCO have been developed for intensity-modulated photon therapy (IMRT). They are rarely used clinically mainly because the large number of beams typically employed in IMRT renders BCO essentially unnecessary. Moreover, in the newer form of IMRT, volumetric modulated arc therapy, there are no individual static beams. BCO is of greater importance for IMPT because it typically employs a very small number of beams (2-4) and, when the number of beams is small, BCO is critical for improving plan quality. However, the unique properties and requirements of protons, particularly in IMPT, make BCO challenging. Protons are more sensitive than photons to anatomic changes, exhibit variable relative biological effectiveness along their paths, and, as recently discovered, may spare the immune system. Such factors must be considered in IMPT BCO, though doing so would make BCO more resource intensive and make it more challenging to extend BCO algorithms developed for IMRT to IMPT. A limited amount of research in IMPT BCO has been conducted; however, considerable additional work is needed for its further development to make it truly effective and computationally practical. This article aims to provide a review of existing BCO algorithms, most of which were developed for IMRT, and addresses important requirements specific to BCO for IMPT optimization that necessitate the modification of existing approaches or the development of new effective and efficient ones.

Reformulated McNamara RBE-weighted beam orientation optimization for intensity modulated proton therapy

PURPOSE

Empirical relative biological effectiveness (RBE) models have been used to estimate the biological dose in proton therapy but do not adequately capture the factors influencing RBE values for treatment planning. We reformulate the McNamara RBE model such that it can be added as a linear biological dose fidelity term within our previously developed sensitivity-regularized and heterogeneity-weighted beam orientation optimization (SHBOO) framework.

METHODS

Based on our SHBOO framework, we formulated the biological optimization problem to minimize total McNamara RBE dose to OARs. We solve this problem using two optimization algorithms: FISTA (McNam-FISTA) and Chambolle-Pock (McNam-CP). We compare their performances with a physical dose optimizer assuming RBE = 1.1 in all structures (PHYS-FISTA) and an LET-weighted dose model (LET-FISTA). Three head and neck patients were planned with the four techniques and compared on dosimetry and robustness.

RESULTS

Compared to Phys-FISTA, McNam-CP was able to match CTV [HI, Dmax, D95%, D98%] by [0.00, 0.05%, 1.4%, 0.8%]. McNam-FISTA and McNam-CP were able to significantly improve overall OAR [Dmean, Dmax] by an average of [36.1%,26.4%] and [29.6%, 20.3%], respectively. Regarding CTV robustness, worst [Dmax, V95%, D95%, D98%] improvement of [-6.6%, 6.2%, 6.0%, 4.8%] was reported for McNam-FISTA and [2.7%, 2.7%, 5.3%, -4.3%] for McNam-CP under combinations of range and setup uncertainties. For OARs, worst [Dmax, Dmean] were improved by McNam-FISTA and McNam-CP by an average of [25.0%, 19.2%] and [29.5%, 36.5%], respectively. McNam-FISTA considerably improved dosimetry and CTV robustness compared to LET-FISTA, which achieved better worst-case OAR doses.

CONCLUSION

The four optimization techniques deliver comparable biological doses for the head and neck cases. Besides modest CTV coverage and robustness improvement, OAR biological dose and robustness were substantially improved with both McNam-FISTA and McNam-CP, showing potential benefit for directly incorporating McNamara RBE in proton treatment planning.

How can we consider variable RBE and LETd prediction during clinical practice? A pediatric case report at the Normandy Proton Therapy Centre using an independent dose engine

Background

To develop an auxiliary GPU-accelerated proton therapy (PT) dose and LET_d engine for the IBA Proteus®ONE PT system. A pediatric low-grade glioma case study is reported using FRoG during clinical practice, highlighting potential treatment planning insights using variable RBE dose (D_vRBE) and LET_d as indicators for clinical decision making in PT.

Methods

The physics engine for FRoG has been modified for compatibility with Proteus®ONE PT centers. Subsequently, FRoG was installed and commissioned at NPTC. Dosimetric validation was performed against measurements and the clinical TPS, RayStation (RS-MC). A head patient cohort previously treated at NPTC was collected and FRoG forward calculations were compared against RS-MC for evaluation of 3D-Γ analysis and dose volume histogram (DVH) results. Currently, treatment design at NPTC is supported with fast variable RBE and LET_d calculation and is reported in a representative case for pediatric low-grade glioma.

Results

Simple dosimetric tests against measurements of iso-energy layers and spread-out Bragg Peaks in water verified accuracy of FRoG and RS-MC. Among the patient cohort, average 3D-Γ applying 2%/2 mm, 3%/1.5 mm and 5%/1 mm were > 97%. DVH metrics for targets and OARs between FRoG and RayStation were in good agreement, with ∆D_50,CTV and ∆D_2,OAR both ⪅1%. The pediatric case report demonstrated implications of different beam arrangements on D_vRBE and LET_d distributions. From initial planning in RayStation sharing identical optimization constraints, FRoG analysis led to plan selection of the most conservative approach, i.e., minimized D_vRBE,max and LET_d,max in OARs, to avoid optical system toxicity effects (i.e., vision loss).

Conclusion

An auxiliary dose calculation system was successfully integrated into the clinical workflow at a Proteus®ONE IBA facility, in excellent agreement with measurements and RS-MC. FRoG may lead to further insight on D_vRBE and LET_d implications to help clinical decision making, better understand unexpected toxicities and establish novel clinical procedures with metrics currently absent from the standard clinical TPS.

Per-voxel constraints to minimize hot spots in linear energy transfer-guided robust optimization for base of skull head and neck cancer patients in IMPT

PURPOSE

Due to the employment of quadratic programming using soft constraints to implement dose volume constraints and the "trial-and-error" procedure needed to achieve a clinically acceptable plan, conventional dose volume constraints (upper limit) are not adequately effective in controlling small and isolated hot spots in the dose/linear energy transfer (LET) distribution. Such hot spots can lead to adverse events. In order to mitigate the risk of brain necrosis, one of the most clinically significant adverse events in patients receiving intensity-modulated proton therapy (IMPT) for base of skull (BOS) cancer, we propose per-voxel constraints to minimize hot spots in LET-guided robust optimization.

METHODS AND MATERIALS

Ten BOS cancer patients treated with IMPT were carefully selected by meeting one of the following conditions: (1) diagnosis of brain necrosis during follow-up; and (2) considered high risk for brain necrosis by not meeting dose constraints to the brain. An optimizing structure (BrainOPT) and an evaluating structure (BrainROI) that both contained the aforementioned hot dose regions in the brain were generated for optimization and evaluation, respectively. Two plans were generated for every patient: one using conventional dose-only robust optimization, the other using LET-guided robust optimization. The impact of LET was integrated into the optimization via a term of extra biological dose (xBD). A novel optimization tool of per-voxel constraints to control small and isolated hot spots in either the dose, LET, or combined (dose/LET) distribution was developed and used to minimize dose/LET hot spots of the selected structures. Indices from dose-volume histogram (DVH) and xBD dose-volume histogram (xBDVH) were used in the plan evaluation. A newly developed tool of the dose-LET-volume histogram (DLVH) was also adopted to illustrate the underlying mechanism. Wilcoxon signed-rank test was used for statistical comparison of the DVH and xBDVH indices between the conventional dose-only and the LET-guided robustly optimized plans.

RESULTS

Per-voxel constraints effectively and efficiently minimized dose hot spots in both dose-only and LET-guided robust optimization and LET hot spots in LET-guided robust optimization. Compared to the conventional dose-only robust optimization, the LET-guided robust optimization could generate plans with statistically lower xBD hot spots in BrainROI (VxBD,50 Gy[RBE], p = 0.009; VxBD,60 Gy[RBE], p = 0.025; xBD1cc, p = 0.017; xBD2cc, p = 0.022) with comparable dose coverage, dose hot spots in the target, and dose hot spots in BrainROI. DLVH analysis indicated that LET-guided robust optimization could either reduce LET at the same dose level or redistribute high LET from high dose regions to low dose regions.

CONCLUSION

Per-voxel constraint is a powerful tool to minimize dose/LET hot spots in IMPT. The LET-guided robustly optimized plans outperformed the conventional dose-only robustly optimized plans in terms of xBD hot spots control.