Robust Optimization for Intensity Modulated Proton Therapy to Redistribute High Linear Energy Transfer from Nearby Critical Organs to Tumors in Head and Neck Cancer

Robust Optimization for Spot-Scanning Proton Therapy based on Dose-Linear-Energy-Transfer Volume Constraints

PURPOSE

Historically, spot-scanning proton therapy (SSPT) treatment planning uses dose-volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We propose a novel dose-LET-volume constraint (DLVC)-based robust optimization (DLVCRO) method for SSPT in treating prostate cancer to obtain a desirable joint dose and LET distribution to minimize adverse events.

METHODS AND MATERIALS

DLVCRO treats DLVC as soft constraints that control the shapes of the dose-LET volume histogram (DLVH) curves. It minimizes the overlap of high LET and high dose in OARs and redistributes high LET from OARs to targets in a user-defined way. Ten patients with prostate cancer were included in this retrospective study. Rectum and bladder were considered as OARs. DLVCRO was compared with the conventional robust optimization (RO) method. Plan robustness was quantified using the worst-case analysis method. Besides the dose-volume histogram indices, the analogous LET-volume histogram, extrabiological dose (the product of per voxel dose and LET) volume histogram (xBDVH) indices characterizing the joint dose/LET distributions and DLVH indices were also used. The Wilcoxon signed-rank test was performed to measure statistical significance.

RESULTS

In the nominal scenario, DLVCRO significantly improved joint distribution of dose and LET to protect OARs compared with RO. The physical dose distributions in targets and OARs are comparable. In the worst-case scenario, DLVCRO markedly enhanced OAR protection (more robust) while maintaining almost the same plan robustness in target dose coverage and homogeneity.

CONCLUSIONS

DLVCRO upgrades 2D DVH-based to 3D DLVH-based treatment planning to adjust dose/LET distributions simultaneously and robustly. DLVCRO is potentially a powerful tool to improve patient outcomes in SSPT.

Study of linear energy transfer effect on rib fracture in breast cancer patients receiving pencil-beam-scanning proton therapy

BACKGROUND

In breast cancer patients treated with pencil-beam scanning proton therapy (PBS), the increased linear energy transfer (LET) near the end of the proton range can affect nearby ribs. This may associate with a higher risk of rib fractures.

PURPOSE

To study the effect of LET on rib fracture in breast cancer patients treated with PBS using a novel tool of dose-LET volume histogram (DLVH).

METHODS

From a prospective registry of patients treated with post-mastectomy proton therapy to the chest wall and regional lymph nodes for breast cancer between 2015 and 2020, we retrospectively identified rib fracture cases detected after completing treatment. Contemporaneously treated control patients who did not develop rib fracture were matched to patients 2:1 considering prescription dose, boost location, reconstruction status, laterality, chest wall thickness, and treatment year. The DLVH index, V(d, l), defined as volume(V) of the structure with at least dose(d) and dose-averaged LET (l) (LETd), was calculated. DLVH plots between the fracture and control group were compared. Conditional logistic regression (CLR) model was used to establish the relation of V(d, l) and the observed fracture at each combination of d and l. The p-value derived from CLR model shows the statistical difference between fracture patients and the matched control group. Using the 2D p-value map derived from CLR model, the DLVH features associated with the patient outcomes were extracted.

RESULTS

Seven rib fracture patients were identified, and fourteen matched patients were selected for the control group. The median time from the completion of proton therapy to rib fracture diagnosis was 12 months (range 5-14 months). Two patients had grade 2 symptomatic rib fracture while the remaining 5 were grade 1 incidentally detected on imaging. The derived p-value map demonstrated larger V(0-36 Gy[RBE], 4.0-5.0 keV/µm) in patients experiencing fracture (p < 0.1). For example, the p-value for V(30 Gy[RBE], 4.0 keV/um) was 0.069.

CONCLUSION

In breast cancer patients receiving PBS, a larger volume of chest wall receiving moderate dose and high LETd may result in an increased risk of rib fracture.

Linear energy transfer optimized proton therapy for rectal cancer

PURPOSE

To evaluate the feasibility and utility of an LET-optimized proton treatment planning algorithm in locally advanced rectal cancer and to assess whether the degree of LET-optimization achieved in clinical plans improves efficacy and toxicity in preclinical models.

MATERIALS AND METHODS

A series of five rectal cancer patients treated with standard 25 fraction clinical proton plans were re-planned using an LET-optimization treatment planning algorithm and evaluated for dosimetric endpoints. LET-optimized plans were generated using an algorithm which iteratively increases the weights of higher LET spots in GTV and lower LET in OARs. Murine and in vitro preclinical models of tumor efficacy and normal tissue toxicity were evaluated using comparable LETd range to that achieved in clinical LET-optimized plans.

RESULTS

LET-optimized proton plans increased dose-averaged LET (LETd) in the GTV and LET-weighted dose in the GTV, and CTV5625cGy V100% coverage. At the same time, LET-optimization also decreased mean LET-weighted dose to bladder and small bowel, as well as small bowel V30Gy(cc) compared to standard proton plans. Optimizing the LETd to a volume of GTV-3 mm further increased LETd compared to total GTV. LET-optimization in preclinical models increased tumor efficacy in colorectal cancer cell lines in vitro and decreased small bowel radiation enteropathy in murine models of normal tissue toxicity.

CONCLUSIONS

LET-optimized proton plans increased LETd in gross tumor while maintaining or improving target coverage and OAR sparing, with acceptable plan robustness. Preclinical models demonstrated that comparable LET-optimization may increase tumor efficacy and decrease normal tissue toxicity in rectal cancer.

Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-Based Autosegmentation in Pencil Beam Scanning Proton Therapy

PURPOSE

Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy. Artificial intelligence (AI)-based autosegmentation can increase the efficiency and accuracy. Linear energy transfer (LET)-based biological effect evaluation can potentially mitigate possible adverse events caused by high LET. New spot arrangement based on the verification computed tomography (vCT) can further improve the replan quality. We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with patients with prostate cancer.

METHODS AND MATERIALS

AI-based autosegmentation tool AccuContour (Manteia) was seamlessly integrated into oAPT. Initial spot arrangement tool on the vCT for reoptimization was implemented using raytracing. An LET-based biological effect evaluation tool was developed to assess the overlap region of high dose and high LET in selected organs at risk. Eleven patients with prostate cancer were retrospectively selected to verify the efficacy and efficiency of the proposed oAPT workflow. The time cost of each component in the workflow was recorded for analysis.

RESULTS

The verification plan showed significant degradation of the clinical target volume coverage and rectum and bladder sparing due to the interfractional anatomical changes. Reoptimization on the vCT resulted in great improvement of the plan quality. No overlap regions of high dose and high LET distributions were observed in bladder or rectum in replans. Three-dimensional γ analyses in patient-specific quality assurance confirmed the accuracy of the replan doses before delivery (γ passing rate, 99.57% ± 0.46%) and after delivery (98.59% ± 1.29%). The robustness of the replans passed all clinical requirements. The average time for the complete execution of the workflow was 9.12 ± 0.85 minutes, excluding manual intervention time.

CONCLUSIONS

The AI-facilitated oAPT workflow demonstrated to be both efficient and effective by generating a replan that significantly improved the plan quality in prostate cancer treated with pencil beam scanning proton therapy.

Enabling clinical use of linear energy transfer in proton therapy for head and neck cancer - A review of implications for treatment planning and adverse events study

Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously. In conclusion, translating the advances in LET-related research into clinical practice necessitates a better understanding of the LET-related biological mechanisms and the development of clinically relevant LET-related volume constraints directly derived from the clinical outcomes. Future research is needed to refine these models and conduct prospective trials to assess the clinical benefits of LET-guided optimization on patient outcomes.

NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy

This position paper, led by the NRG Oncology Particle Therapy Work Group, focuses on the concept of relative biologic effect (RBE) in clinical proton therapy (PT), with the goal of providing recommendations for the next-generation clinical trials with PT on the best practice of investigating and using RBE, which could deviate from the current standard proton RBE value of 1.1 relative to photons. In part 1, current clinical utilization and practice are reviewed, giving the context and history of RBE. Evidence for variation in RBE is presented along with the concept of linear energy transfer (LET). The intertwined nature of tumor radiobiology, normal tissue constraints, and treatment planning with LET and RBE considerations is then reviewed. Part 2 summarizes current and past clinical data and then suggests the next steps to explore and employ tools for improved dynamic models for RBE. In part 3, approaches and methods for the next generation of prospective clinical trials are explored, with the goal of optimizing RBE to be both more reflective of clinical reality and also deployable in trials to allow clinical validation and interpatient comparisons. These concepts provide the foundation for personalized biologic treatments reviewed in part 4. Finally, we conclude with a summary including short- and long-term scientific focus points for clinical PT. The practicalities and capacity to use RBE in treatment planning are reviewed and considered with more biological data in hand. The intermediate step of LET optimization is summarized and proposed as a potential bridge to the ultimate goal of case-specific RBE planning that can be achieved as a hypothesis-generating tool in near-term proton trials.

Robustness evaluation of pencil beam scanning proton therapy treatment planning: A systematic review

Compared to conventional radiotherapy using X-rays, proton therapy, in principle, allows better conformity of the dose distribution to target volumes, at the cost of greater sensitivity to physical, anatomical, and positioning uncertainties. Robust planning, both in terms of plan optimization and evaluation, has gained high visibility in publications on the subject and is part of clinical practice in many centers. However, there is currently no consensus on the methods and parameters to be used for robust optimization or robustness evaluation. We propose to overcome this deficiency by following the modified Delphi consensus method. This method first requires a systematic review of the literature. We performed this review using the PubMed and Web Of Science databases, via two different experts. Potential conflicts were resolved by a third expert. We then explored the different methods before focusing on clinical studies that evaluate robustness on a significant number of patients. Many robustness assessment methods are proposed in the literature. Some are more successful than others and their implementation varies between centers. Moreover, they are not all statistically or mathematically equivalent. The most sophisticated and rigorous methods have seen more limited application due to the difficulty of their implementation and their lack of widespread availability.

A dosiomics approach to treatment outcome modeling in carbon ion radiotherapy for skull base chordomas

PURPOSE

To investigate the role of dosiomics features extracted from physical dose (DPHYS), RBE-weighted dose (DRBE) and dose-averaged Linear Energy Transfer (LETd), to predict the risk of local recurrence (LR) in skull base chordoma (SBC) treated with Carbon Ion Radiotherapy (CIRT). Thus, define and evaluate dosiomics-driven tumor control probability (TCP) models.

MATERIALS AND METHODS

54 SBC patients were retrospectively selected for this study. A regularized Cox proportional hazard model (r-Cox) and Survival Support Vector Machine (s-SVM) were tuned within a repeated Cross Validation (CV) and patients were stratified in low/high risk of LR. Models' performance was evaluated through Harrell's concordance statistic (C-index), and survival was represented through Kaplan-Meier (KM) curves. A multivariable logistic regression was fit to the selected feature sets to generate a dosiomics-driven TCP model for each map. These were compared to a reference model built with clinical parameters in terms of f-score and accuracy.

RESULTS

The LETd maps reached a test C-index of 0.750 and 0.786 with r-Cox and s-SVM, and significantly separated KM curves. DPHYS maps and clinical parameters showed promising CV outcomes with C-index above 0.8, despite a poorer performance on the test set and patients stratification. The LETd-based TCP showed a significatively higher f-score (0.67[0.52-0.70], median[IQR]) compared to the clinical model (0.4[0.32-0.63], p < 0.025), while DPHYS achieved a significatively higher accuracy (DPHYS: 0.73[0.65-0.79], Clinical: 0.6 [0.52-0.72]).

CONCLUSION

This analysis supports the role of LETd as relevant source of prognostic factors for LR in SBC treated with CIRT. This is reflected in the TCP modeling, where LETd and DPHYS showed an improved performance with respect to clinical models.

NRG Oncology and Particle Therapy Co-Operative Group Patterns of Practice Survey and Consensus Recommendations on Pencil-Beam Scanning Proton Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies

Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally fractionated PBSPT because of concerns of amplified uncertainties at the larger dose per fraction. The NRG Oncology and Particle Therapy Cooperative Group Thoracic Subcommittee surveyed proton centers in the United States to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Among other points, the recommendations highlight the need for volumetric image guidance and multiple computed tomography-based robust optimization and robustness tools to minimize further the effect of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.

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.

Impact of Relative Biologic Effectiveness for Proton Therapy for Head and Neck and Skull-Base Tumors: A Technical and Clinical Review

Proton therapy has emerged as a crucial tool in the treatment of head and neck and skull-base cancers, offering advantages over photon therapy in terms of decreasing integral dose and reducing acute and late toxicities, such as dysgeusia, feeding tube dependence, xerostomia, secondary malignancies, and neurocognitive dysfunction. Despite its benefits in dose distribution and biological effectiveness, the application of proton therapy is challenged by uncertainties in its relative biological effectiveness (RBE). Overcoming the challenges related to RBE is key to fully realizing proton therapy's potential, which extends beyond its physical dosimetric properties when compared with photon-based therapies. In this paper, we discuss the clinical significance of RBE within treatment volumes and adjacent serial organs at risk in the management of head and neck and skull-base tumors. We review proton RBE uncertainties and its modeling and explore clinical outcomes. Additionally, we highlight technological advancements and innovations in plan optimization and treatment delivery, including linear energy transfer/RBE optimizations and the development of spot-scanning proton arc therapy. These advancements show promise in harnessing the full capabilities of proton therapy from an academic standpoint, further technological innovations and clinical outcome studies, however, are needed for their integration into routine clinical practice.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy

BACKGROUND

Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities.

PURPOSE

To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning.

METHODS

PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency.

RESULTS

Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s.

CONCLUSIONS

An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy

Purpose. To enhance an in-house graphic-processing-unit accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS).Methods and materials. A module to simulate VPs passing through patient-specific aperture blocks was developed and integrated in VPMC based on simulation results of realistic particles (primary protons and their secondaries). To validate the aperture block module, VPMC was first validated by an opensource MC code, MCsquare, in eight water phantom simulations with 3 cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4 cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45 mm water equivalent thickness. Then, VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 c.c. with range of 0.4-43.3 c.c.). Finally, 3 typical patients were selected for robust optimization with aperture blocks using VPMC.Results. In the water phantoms, 3D gamma passing rate (2%/2 mm/10%) between VPMC and MCsquare was 99.71 ± 0.23%. In the patient geometries, 3D gamma passing rates (3%/2 mm/10%) between VPMC/MCsquare and RayStation MC were 97.79 ± 2.21%/97.78 ± 1.97%, respectively. Meanwhile, the calculation time was drastically decreased from 112.45 ± 114.08 s (MCsquare) to 8.20 ± 6.42 s (VPMC) with the same statistical uncertainties of ~0.5%. The robustly optimized plans met all the dose-volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 s and the subsequent on-the-fly 'trial-and-error' optimization procedure took only 71.4 s on average for the selected three patients.Conclusion. VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.

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.

The LET trilemma: Conflicts between robust target coverage, uniform dose, and dose-averaged LET in carbon therapy

BACKGROUND

Linear energy transfer (LET) is closely related to the biological effect of ionizing radiation. Increasing the dose-averaged LET (LETd ) within the target volume has been proposed as a means to improve clinical outcome for hypoxic tumors. However, doing so can lead to reduced robustness to range uncertainty.

PURPOSE

To quantify the relationship between robust target coverage, target dose uniformity, and LETd , we employ robust optimization using dose-based and LETd -based functions and allow varying amounts of target non-uniformity.

METHODS AND MATERIALS

Robust carbon therapy optimization is used to create plans for phantom cases with increasing target sizes (radii 1, 3, and 5 cm). First, the influence of respectively range and setup uncertainty on the LETd in the target is studied. Second, we employ strategies allowing overdosage in the clinical target volume (CTV) or gross tumor volume (GTV), which enable increased LETd in the target. The relationship between robust target coverage and LETd in the target is illustrated by tradeoff curves generated by optimization using varying weights for the LETd -based functions.

RESULTS

As the range uncertainty used in the robust optimization increased from 0% to 5%, the near-minimum nominal LETd decreased by 17%-29% (9-21 keV/µm) for the different target sizes. The effect of increasing setup uncertainty was marginal. Allowing 10% overdosage in the CTV enabled 9%-29% (6-12 keV/µm) increased near-minimum worst case LETd for the different target sizes, compared to uniform dose plans. When 10% overdosage was allowed in the GTV only, the increase was 1%-20% (1-8 keV/µm).

CONCLUSIONS

There is an inherent conflict between range uncertainty robustness and high LETd in the target, which is aggravated with increasing target size. For large tumors, it is possible to simultaneously achieve two of the three qualities range robustness, uniform dose, and high LETd in the target.

Proton pencil beam scanning craniospinal irradiation (CSI) with a single posterior brain beam: Dosimetry and efficiency

This study explores the feasibility and potential dosimetric and time efficiency benefit of proton Pencil Beam Scanning (PBS) craniospinal irradiation with a single posterior-anterior (SPA) brain field. The SPA approach was compared to our current clinical protocol using Bilateral Posterior Oblique brain fields (BPO). Ten consecutive patients were simulated in the head-first supine position on a long BOS frame and scanned using 3 mm CT slice thickness. A customized thermoplastic mask immobilized the patient's head, neck, and shoulders. A vac-lock was used to secure the legs. PBS proton plans were robustly optimized with 3mm setup errors and 3.5% range uncertainties in the Eclipse V15.6 treatment planning system (n = 12 scenarios). In order to achieve a smooth gradient dose match at the junction area, at least 5 cm overlap region was maintained between the segments and 5 mm uncertainty along the cranial-cauda direction was applied to each segment independently as additional robust optimization scenarios. The brain doses were planned by SPA or BPO fields. All spine segments were planned with a single PA field. Dosimetric differences between the BPO and SPA approaches were compared, and the treatment efficiency was analyzed according to timestamps of beam delivery. Results: The maximum brain dose increases to 111.1 ± 2.1% for SPA vs. 109.0 ± 1.7% for BPO (p < 0.01). The dose homogeneity index (D5/D95) in brain CTV was comparable between techniques (1.037 ± 0.010 for SPA and 1.033 ± 0.008 for BPO). Lens received lower maximum doses by 2.88 ± 1.58 Gy (RBE) (left) and 2.23 ± 1.37 Gy (RBE) (right) in the SPA plans (p < 0.01). No significant cochlea dose change was observed. SPA reduced the treatment time by more than 4 minutes on average and ranged from 2 to 10 minutes, depending on the beam waiting and allocation time. SPA is dosimetrically comparable to BPO, with reduced lens doses at the cost of slightly higher dose inhomogeneity and hot spots. Implementation of SPA is feasible and can help to improve the treatment efficiency of PBS CSI treatment.

Artificial general intelligence for radiation oncology

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.

Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer

BACKGROUND

Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application.

PURPOSE

A deep-learning-based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. METHODS: A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre-trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per-voxel absolute CT-number-difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively.

RESULTS

The two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per-voxel absolute CT-number-difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5 , within ±0.06% for total lung V5 , ≤1.5cGy[RBE] for heart and esophagus Dmean , and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed.

CONCLUSION

A deep neural network-based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

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.

Validation of robust radiobiological optimization algorithms based on the mixed beam model for intensity-modulated carbon-ion therapy

Currently, treatment planning systems (TPSs) that can compute the intensities of intensity-modulated carbon-ion therapy (IMCT) using scanned carbon-ion beams are limited. In the present study, the computational efficacy of the newly designed IMCT algorithms was analyzed for the first time based on the mixed beam model with respect to the physical and biological doses; moreover, the validity and effectiveness of the robust radiobiological optimization were verified. A dose calculation engine was independently generated to validate a clinical dose determined in the TPS. A biological assay was performed using the HSGc-C5 cell line to validate the calculated surviving fraction (SF). Both spot control (SC) and voxel-wise worst-case scenario (WC) algorithms were employed for robust radiobiological optimization followed by their application in a Radiation Therapy Oncology Group benchmark phantom under homogeneous and heterogeneous conditions and a clinical case for range and position errors. Importantly, for the first time, both SC and WC algorithms were implemented in the integrated TPS platform that can compute the intensities of IMCT using scanned carbon-ion beams for robust radiobiological optimization. For assessing the robustness, the difference between the maximum and minimum values of a dose-volume histogram index in the examined error scenarios was considered as a robustness index. The relative biological effectiveness (RBE) determined by the independent dose calculation engine exhibited a -0.6% difference compared with the RBE defined by the TPS at the isocenter, whereas the measured and the calculated SF were similar. Regardless of the objects, compared with the conventional IMCT, the robust radiobiological optimization enhanced the sensitivity of the examined error scenarios by up to 19% for the robustness index. The computational efficacy of the novel IMCT algorithms was verified according to the mixed beam model with respect to the physical and biological doses. The robust radiobiological optimizations lowered the impact of range and position uncertainties considerably in the examined scenarios. The robustness of the WC algorithm was more enhanced compared with that of the SC algorithm. Nevertheless, the SC algorithm can be used as an alternative to the WC IMCT algorithm with respect to the computational cost.

Dosimetric comparison between proton beam therapy, intensity modulated radiation therapy, and 3D conformal therapy for soft tissue extremity sarcoma

PURPOSE/OBJECTIVES

Proton beam therapy (PBT) may provide a dosimetric advantage in sparing soft tissue and bone for selected patients with extremity soft sarcoma (eSTS). We compared PBT with photons plans generated using intensity-modulated radiotherapy (IMRT) and three-dimensional conformal radiotherapy (3D-CRT).

MATERIALS/METHODS

Seventeen patients previously treated with pencil beam scanning PBT were included in this study. Of these patients, 14 treated with pre-operative 50 Gy in 25 fractions were analyzed. IMRT and 3D-CRT plans were created to compare against the original PBT plans. Dose-volume histogram (DVH) indices were evaluated amongst PBT, IMRT, and 3D plans. Kruskal-Wallis rank sum tests were used to get the statistical significance. A p value smaller than .05 was considered to be statistically significant.

RESULTS

For the clinical target volume (CTV), D2%, D95%, D98%, D_min, D_max, and V50Gy, were assessed. D_min, D1%, D_max, D_mean, V1Gy, V5Gy, and V50Gy were evaluated for the adjacent soft tissue. D1%, D_max, D_mean, and V35-50% were evaluated for bone. All plans met CTV target coverage. The PBT plans delivered less dose to soft tissue and bone. The mean dose to the soft tissue was 2 Gy, 11 Gy, and 13 Gy for PBT, IMRT, and 3D, respectively (p < .001). The mean dose to adjacent bone was 15 Gy, 26 Gy, and 28 Gy for PBT, IMRT, and 3D, respectively (p = .022).

CONCLUSION

PBT plans for selected patients with eSTS demonstrated improved sparing of circumferential soft tissue and adjacent bone compared to IMRT and 3D-CRT. Further evaluation will determine if this improved dosimetry correlates with reduced toxicity and improved quality of life.

Rotating Gantries Provide Individualized Beam Arrangements for Charged Particle Therapy

PURPOSE

This study evaluates beam angles used to generate highly individualized proton therapy treatment plans for patients eligible for carbon ion radiotherapy (CIRT).

METHODS AND MATERIALS

We retrospectively evaluated patients treated with pencil beam scanning intensity modulated proton therapy from 2015 to 2020 who had indications for CIRT. Patients were treated with a 190° rotating gantry with a robotic patient positioning system. Treatment plans were individualized to provide maximal prescription dose delivery to the tumor target volume while sparing organs at risk. The utilized beam angles were grouped, and anatomic sites with at least 10 different beam angles were sorted into histograms.

RESULTS

A total of 467 patients with 484 plans and 1196 unique beam angles were evaluated and characterized by anatomic treatment site and the number of beam angles utilized. The most common beam angles used were 0° and 180°. A wide range of beam angles were used in treating almost all anatomic sites. Only esophageal cancers had a predominantly unimodal grouping of beam angles. Pancreas cancers showed a modest grouping of beam angles.

CONCLUSIONS

The wide distribution of beam angles used to treat CIRT-eligible patients suggests that a rotating gantry is optimal to provide highly individualized beam arrangements.

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 (LETd), and the product between dose and LETd (DLETd) 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 LETd, DLETd, 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 LETd visualization. The relationship between physical dose and LETd and the frequency of LETd hotspots were investigated.

Results

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

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.

Hyperfractionated intensity-modulated proton therapy for pharyngeal cancer with variable relative biological effectiveness: A simulation study

BACKGROUND

The relative biological effectiveness (RBE) of proton is considered to be dependent on biological parameters and fractional dose. While hyperfractionated photon therapy was effective in the treatment of patients with head and neck cancers, its effect in intensity-modulated proton therapy (IMPT) under the variable RBE has not been investigated in detail.

PURPOSE

To study the effect of variable RBE on hyperfractionated IMPT for the treatment of pharyngeal cancer. We investigated the biologically effective dose (BED) to determine the theoretical effective hyperfractionated schedule.

METHODS

The treatment plans of three pharyngeal cancer patients were used to define the ΔBED for the clinical target volume (CTV) and soft tissue (acute and late reaction) as the difference between the BED for the altered schedule with variable RBE and conventional schedule with constant RBE. The ΔBED with several combinations of parameters (treatment days, number of fractions, and prescribed dose) was comprehensively calculated. Of the candidate schedules, the one that commonly gave a higher ΔBED for CTV was selected as the resultant schedule. The BED volume histogram was used to compare the influence of variable RBE and fractionation.

RESULTS

In the conventional schedule, compared with the constant RBE, the variable RBE resulted in a mean 2.6 and 2.7 Gy reduction of BED_mean for the CTV and soft tissue (acute reaction) of the three plans, respectively. Moreover, the BED_mean for soft tissue (late reaction) increased by 7.4 Gy, indicating a potential risk of increased RBE. Comprehensive calculation of the ΔBED resulted in the hyperfractionated schedule of 80.52 Gy (RBE = 1.1)/66 fractions in 6.5 weeks. When variable RBE was used, compared with the conventional schedule, the hyperfractionated schedule increased the BED_mean for CTV by 7.6 Gy; however, this was associated with a 7.8 Gy increase for soft tissue (acute reaction). The BED_mean for soft tissue (late reaction) decreased by 2.4 Gy.

CONCLUSION

The results indicated a potential effect of the variable RBE on IMPT for pharyngeal cancer but with the possibility that hyperfractionation could outweigh this effect. Although biological uncertainties require conservative use of the resultant schedule, hyperfractionation is expected to be an effective strategy in IMPT for pharyngeal cancer.

Technical note: Investigation of dose and LETd effect to rectum and bladder by using non-straight laterals in prostate cancer receiving proton therapy

BACKGROUND

Parallel-opposed lateral beams are the conventional beam arrangements in proton therapy for prostate cancer. However, when considering linear energy transfer (LET) and RBE effects, alternative beam arrangements should be investigated.

PURPOSE

To investigate the dose and dose averaged LET (LET_d ) impact of using new beam arrangements rotating beams 5°-15° posteriorly to the laterals in prostate cancer treated with pencil-beam-scanning (PBS) proton therapy.

METHODS

Twenty patients with localized prostate cancer were included in this study. Four proton treatment plans for each patient were generated utilizing 0°, 5°, 10°, and 15° posterior oblique beam pairs relative to parallel-opposed lateral beams. Dose-volume histograms (DVHs) from posterior oblique beams were analyzed. Dose-LET_d -volume histogram (DLVH) was employed to study the difference in dose and LET_d with each beam arrangement. DLVH indices, V ( d , l ) $V( {d,l} )$ , defined as the cumulative absolute volume that has a dose of at least d (Gy[RBE]) and a LET_d of at least l (keV/µm), were calculated for both the rectum and bladder to the whole group of patients and two-sub groups with and without hydrogel spacer. These metrics were tested using Wilcoxon signed-rank test.

RESULTS

Rotating beam angles from laterals to slightly posterior by 5°-15° reduced high LET_d volumes while it increased the dose volume in the rectum and increased LET_d in bladders. Beam angles rotated five degrees posteriorly from laterals (i.e., gantry in 95° and 265°) are proposed since they achieved the optimal balance of better LET_d sparing and minimal dose increase in the rectum. A reduction of V(50 Gy[RBE], 2.6 keV/µm) from 7.41 to 3.96 cc (p < 0.01), and a slight increase of V(50 Gy[RBE], 0 keV/µm) from 20.1 to 21.6 cc (p < 0.01) were observed for the group without hydrogel spacer. The LET_d sparing was less effective for the group with hydrogel spacer, which achieved the reduction of V(50 Gy[RBE], 2.6 keV/µm) from 4.28 to 2.10 cc (p < 0.01).

CONCLUSIONS

Posterior oblique angle plans improved LET_d sparing of the rectum while sacrificing LET_d sparing in the bladder in the treatment of prostate cancer with PBS. Beam angle modification from laterals to slightly posterior may be a strategy to redistribute LET_d and perhaps reduce rectal toxicity risks in prostate cancer patients treated with PBS. However, the effect is reduced for patients with hydrogel spacer.

Collimating individual beamlets in pencil beam scanning proton therapy, a dosimetric investigation

The purpose of this work is to investigate collimating individual proton beamlets from a dosimetric perspective and to introduce a new device concept, the spot scanning aperture (SSA). The SSA consists of a thin aperture with a small cylindrical opening attached to a robotics system, which allows the aperture to follow and align with individual beamlets during spot delivery. Additionally, a range shifter is incorporated (source-side) for treating shallow depths. Since the SSA trims beamlets spot by spot, the patient-facing portion of the device only needs to be large enough to trim a single proton beamlet. The SSA has been modelled in an open-source Monte-Carlo-based dose engine (MCsquare) to characterize its dosimetric properties in water at depths between 0 and 10 cm while varying the following parameters: the aperture material, thickness, distance to the water phantom, distance between the aperture and attached range shifter, and the aperture opening radius. Overall, the SSA greatly reduced spot sizes for all the aperture opening radii that were tested (1 - 4 mm), especially in comparison with the extended range shifter (ranger shifter placed at 30 cm from patient); greater than 50% when placed less than 10 cm away from the patient at depths in water less than 50 mm. The peak to entrance dose ratio and linear energy transfer was found to depend on the thickness of the aperture and therefore the aperture material. Neutron production rates were also investigated and discussed.

Virtual particle Monte Carlo: A new concept to avoid simulating secondary particles in proton therapy dose calculation

BACKGROUND

In proton therapy dose calculation, Monte Carlo (MC) simulations are superior in accuracy but more time consuming, compared to analytical calculations. Graphic processing units (GPUs) are effective in accelerating MC simulations but may suffer thread divergence and racing condition in GPU threads that degrades the computing performance due to the generation of secondary particles during nuclear reactions.

PURPOSE

A novel concept of virtual particle (VP) MC (VPMC) is proposed to avoid simulating secondary particles in GPU-accelerated proton MC dose calculation and take full advantage of the computing power of GPU.

METHODS

Neutrons and gamma rays were ignored as escaping from the human body; doses of electrons, heavy ions, and nuclear fragments were locally deposited; the tracks of deuterons were converted into tracks of protons. These particles, together with primary and secondary protons, are considered to be the realistic particles. Histories of primary and secondary protons were replaced by histories of multiple VPs. Each VP corresponded to one proton (either primary or secondary). A continuous-slowing-down-approximation model, an ionization model, and a large angle scattering event model corresponding to nuclear interactions were developed for VPs by generating probability distribution functions (PDFs) based on simulation results of realistic particles using MCsquare. For efficient calculations, these PDFs were stored in the Compute Unified Device Architecture textures. VPMC was benchmarked with TOPAS and MCsquare in phantoms and with MCsquare in 13 representative patient geometries. Comparisons between the VPMC calculated dose and dose measured in water during patient-specific quality assurance (PSQA) of the selected 13 patients were also carried out. Gamma analysis was used to compare the doses derived from different methods and calculation efficiencies were also compared.

RESULTS

Integrated depth dose and lateral dose profiles in both homogeneous and inhomogeneous phantoms all matched well among VPMC, TOPAS, and MCsquare calculations. The 3D-3D gamma passing rates with a criterion of 2%/2 mm and a threshold of 10% was 98.49% between MCsquare and TOPAS and 98.31% between VPMC and TOPAS in homogeneous phantoms, and 99.18% between MCsquare and TOPAS and 98.49% between VPMC and TOPAS in inhomogeneous phantoms, respectively. In patient geometries, the 3D-3D gamma passing rates with 2%/2 mm/10% between dose distributions from VPMC and MCsquare were 98.56 ± 1.09% in patient geometries. The 2D-3D gamma analysis with 3%/2 mm/10% between the VPMC calculated dose distributions and the 2D measured planar dose distributions during PSQA was 98.91 ± 0.88%. VPMC calculation was highly efficient and took 2.84 ± 2.44 s to finish for the selected 13 patients running on four NVIDIA Ampere GPUs in patient geometries.

CONCLUSION

VPMC was found to achieve high accuracy and efficiency in proton therapy dose calculation.

Assessment of IMPT versus VMAT plans using different uncertainty scenarios for prostate cancer

Background

To assess the impact of systematic setup and range uncertainties for robustly optimized (RO) intensity modulated proton therapy (IMPT) and volumetric modulated arc therapy (VMAT) plans in patients with localized prostate cancer.

Methods

Twenty-six localized prostate patients previously treated with VMAT (CTV to PTV expansion of 3-5 mm) were re-planned with RO-IMPT with 3 mm and 5 mm geometrical uncertainties coupled with 3% range uncertainties. Robust evaluations (RE) accounting for the geometrical uncertainties of 3 and 5 mm were evaluated for the IMPT and VMAT plans. Clinical target volume (CTV), anorectum, and bladder dose metrics were analyzed between the nominal plans and their uncertainty perturbations.

Results

With geometric uncertainties of 5 mm and accounting for potential inter-fractional perturbations, RO-IMPT provided statistically significant (p < 0.05) sparing at intermediate doses (V_4000cGy) to the anorectum and bladder and high dose sparring (V_8000cGy) to the bladder compared to VMAT. Decreasing the RO and RE parameters to 3 mm improved IMPT sparing over VMAT at all OAR dose levels investigated while maintaining equivalent coverage to the CTV.

Conclusions

For localized prostate treatments, if geometric uncertainties can be maintained at or below 3 mm, RO-IMPT provides clear dosimetric advantages in anorectum and bladder sparing compared to VMAT. This advantage remains even under uncertainty scenarios. As geometric uncertainties increase to 5 mm, RO-IMPT still provides dosimetric advantages, but to a smaller magnitude.

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.

Redefine the Role of Proton Beam Therapy for the Locally-Advanced Non-Small Cell Lung Cancer Assisting the Reduction of Acute Hematologic Toxicity

Purpose

To investigate the potential clinical benefit of utilizing intensity-modulated proton therapy (IMPT) to reduce acute hematologic toxicity for locally advanced non-small cell lung cancer (LA-NSCLC) patients and explore the feasibility of a model-based patient selection approach via the normal tissue complication probability (NTCP).

Methods

Twenty patients with LA-NSCLC were retrospectively selected. Volumetric modulated arc photon therapy (VMAT) and IMPT plans were generated with a prescription dose of 60 Gy in 30 fractions. A wide range of cases with varied tumor size, location, stations of metastatic lymph nodes were selected to represent the general cancer group. Contouring and treatment planning followed RTOG-1308 protocol. Doses to thoracic vertebral bodies (TVB) and other organ at risks were compared. Risk of grade ≥ 3 acute hematologic toxicity (HT3+) were calculated based on the NTCP model, and patients with a reduction on NTCP of HT3+ from VMAT to IMPT (△NTCP_HT3+) ≥ 10% were considered to ‘significantly benefit from proton therapy.’

Results

Compared to VMAT, IMPT significantly reduced the dose to the TVB, the lung, the heart, the esophagus and the spinal cord. Tumor distance to TVB was significantly associated with △NTCP _HT3+ ≥ 10%. For the patients with tumor distance ≤ 0.7 cm to TVB, the absolute reduction of dose (mean, V30 and V40) to TVB was significantly lower than that in patients with tumor distance &gt; 0.7 cm.

Conclusion

IMPT decreased the probability of HT3+ compared to VMAT by reducing the dose to the TVB in LA-NSCLC patients. Patients with tumor distance to TVB less than 0.7 cm are likely to benefit most from proton over photon therapy.

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.

Proton Therapy in the Management of Pancreatic Cancer

Radiation therapy plays a central role in the treatment of pancreatic cancer. While generally shown to be feasible, proton irradiation, particularly when an ablative dose is planned, remains a challenge, especially due to tumor motion and the proximity to organs at risk, like the stomach, duodenum, and bowel. Clinically, standard doses of proton radiation treatment have not been shown to be statistically different from photon radiation treatment in terms of oncologic outcomes and toxicity rates as per non-randomized comparative studies. Fractionation schedules and concurrent chemotherapy combinations are yet to be optimized for proton therapy and are the subject of ongoing trials.

GPU-accelerated Monte Carlo-based online adaptive proton therapy - a feasibility study

PURPOSE

To develop an online Graphic-Processing-Unit (GPU)-accelerated Monte-Carlo-based adaptive radiation therapy (ART) workflow for pencil beam scanning (PBS) proton therapy to address inter-fraction anatomical changes in patients treated with PBS.

METHODS AND MATERIALS

A four-step workflow was developed using our in-house developed GPU-accelerated Monte-Carlo-based treatment planning system to implement online Monte-Carlo-based ART for PBS. The first step conducts diffeomorphic demon-based deformable image registration (DIR) to propagate contours on the initial planning CT (pCT) to the verification CT (vCT) to form a new structure set. The second step performs forward dose calculation of the initial plan on the vCT with the propagated contours after manual approval (possible modifications involved). The third step triggers a re-optimization of the plan depending on whether the verification dose meets the clinical requirements or not. A robust evaluation will be done for both the verification plan in the second step and the re-opotimized plan in the third step. The fourth step involves a two-stage (before and after delivery) patient specific quality assurance (PSQA) of the re-optimized plan. The before-delivery PSQA is to compare the plan dose to the dose calculated using an independent fast open-source Monte Carlo code, MCsquare. The after-delivery PSQA is to compare the plan dose to the dose re-calculated using the log file (spot MU, spot position, and spot energy) collected during the delivery. Jaccard index (JI), Dice similarity coefficients (DSCs), and Hausdorff distance (HD) were used to assess the quality of the propagated contours in the first step. A commercial plan evaluation software, ClearCheck™, was integrated into the workflow to carry out efficient plan evaluation. 3D Gamma analysis was used during the fourth step to ensure the accuracy of the plan dose from re-optimization. Three patients with three different disease sites were chosen to evaluate the feasibility of the online ART workflow for PBS.

RESULTS

For all three patients, the propagated contours were found to have good volume conformance [JI (lowest-highest: 0.833-0.983) and DSC (0.909-0.992)] but sub-optimal boundary coincidence [HD (2.37-20.76 mm)] for organs at risk (OARs). The verification dose evaluated by ClearCheck™ showed significant degradation of the target coverage due to the inter-fractional anatomical changes. Re-optimization on the vCT resulted in great improvement of the plan quality to a clinically acceptable level. 3D Gamma analyses of PSQA confirmed the accuracy of the plan dose before delivery (mean Gamma index = 98.74% with a threshold of 2%/2 mm/10%), and after delivery based on the log files (mean Gamma index = 99.05% with a threshold of 2%/2 mm/10%). The average time cost for the complete execution of the workflow was around 858 seconds, excluding the time for manual intervention.

CONCLUSION

The proposed online ART workflow for PBS was demonstrated to be efficient and effective by generating a re-optimized plan that significantly improved the plan quality. This article is protected by copyright. All rights reserved.

Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis (ORN) in Head and Neck Cancer Patients Treated With Pencil-Beam-Scanning Proton Therapy (PBSPT): A Retrospective, Case-Matched Cohort Study

Purpose

To retrospectively investigate empirical relative biological effectiveness (RBE) for mandible osteoradionecrosis (ORN) in head and neck (H&N) cancer patients treated with pencil-beam-scanning proton therapy (PBSPT).

Methods

We included 1,266 H&N cancer patients, of which, 931 patients were treated with volumetric-modulated arc therapy (VMAT) and 335 were treated with PBSPT. Among them, 26 VMAT and 9 PBSPT patients experienced mandible ORN (ORN group), while all others were included in the control group. To minimize the impact of the possible imbalance in clinical factors between VMAT and PBSPT patients in the dosimetric comparison between these two modalities and the resulting RBE quantification, we formed a 1:1 case-matched patient cohort (335 VMAT patients and 335 PBSPT patients including both the ORN and control groups) using the greedy nearest neighbor matching of propensity scores. Mandible dosimetric metrics were extracted from the case-matched patient cohort and statistically tested to evaluate the association with mandibular ORN to derive dose volume constraints (DVCs) for VMAT and PBSPT, respectively. We sought the equivalent constraint doses for VMAT so that the critical volumes of VMAT were equal to those of PBSPT at different physical doses. Empirical RBEs of PBSPT for ORN were obtained by calculating the ratio between the derived equivalent constraint doses and physical doses of PBSPT. Bootstrapping was further used to get the confidence intervals.

Results

Clinical variables of age, gender, tumor stage, prescription dose, chemotherapy, hypertension or diabetes, dental extraction, smoking history, or current smoker were not statistically related to the incidence of ORN in the overall patient cohort. Smoking history was found to be significantly associated with the ORN incidence in PBSPT patients only. V40Gy[RBE], V50Gy[RBE], and V60Gy[RBE] were statistically different (p<0.05) between the ORN and control group for VMAT and PBSPT. Empirical RBEs of 1.58(95%CI: 1.34-1.64), 1.34(95%CI: 1.23-1.40), and 1.24(95%: 1.15-1.26) were obtained for proton dose at 40 Gy[RBE=1.1], 50 Gy[RBE=1.1] and 60 Gy[RBE=1.1], respectively.

Conclusions

Our study suggested that RBEs were larger than 1.1 at moderate doses (between 40 and 60 Gy[RBE=1.1]) with high LET for mandible ORN. RBEs are underestimated in current clinical practice in PBSPT. The derived DVCs can be used for PBSPT plan evaluation and optimization to minimize the incidence rate of mandible ORN.

Quantifying Systematic RBE-Weighted Dose Uncertainty Arising from Multiple Variable RBE Models in Organ at Risk

PURPOSE

Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models.

METHODS

Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming( α β ) x of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties.

RESULTS

The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences (P <.05) in the RBE-weighted dose uncertainties in the OAR. In both clinical target volume (CTV)-brain stem and CTV-chiasm overlap region, 10 of 15 patients showed a lower median RBE-weighted dose uncertainty in MFO planning compared with SFO planning. In the LET analysis, 8 patients (optic chiasm) and 13 patients (brain stem) showed a lower mean LET in MFO planning compared with SFO planning. It was also observed that lesser RBE-weighted dose uncertainties were present with MFO planning compared with SFO planning technique.

CONCLUSIONS

Calculations of the RBE-weighted dose uncertainties based on 6 RBE models and 2 different( α β ) x revealed that MFO planning is a better option as opposed to SFO planning for cases of overlapping brain tumor with OARs in eliminating RBE-weighted dose uncertainties. Incorporation of RBE models failed to dictate the passing or failing of a treatment plan. To eliminate RBE-weighted dose uncertainties in OARs, the MFO planning technique is recommended for brain tumor when CTV and OARs overlap.

Quantitative analysis of dose-averaged linear energy transfer (LETd ) robustness in pencil beam scanning proton lung plans

PURPOSE

The primary objective of our study was to perform a quantitative robustness analysis of the dose-averaged linear energy transfer (LET_d ) and related RBE-weighted dose in robustly optimized (in terms of the range and set up uncertainties) pencil beam scanning (PBS) proton lung cancer plans.

METHODS

In this study, we utilized the 4DCT data set of six anonymized lung patients. PBS lung plans were generated using a robust optimization technique (range uncertainty: ±3.5% and setup errors: ±5 mm) on the CTV for a total dose of 5000 cGy(RBE) in 5 fractions using RBE of 1.1. For each patient, the LET_d distributions were calculated for the nominal plan and three groups. Group 1: two plan robustness scenarios for range uncertainties of ±3.5%; Group 2: twelve plan robustness scenarios (range uncertainty (±3.5%) in conjunction with setup errors (±5 mm)); and Group 3: ten different breathing phases of the 4DCT data set. RBE-weighted dose to the OARs was evaluated for all robustness scenarios and breathing phases. The variation (∆) in the mean LET_d and mean RBE-weighted dose from each group was recorded.

RESULTS

The mean LET_d in the CTV of nominal PBS lung plans among six patients ranged from 2.2 to 2.6 keV/μm. On average, for the combined range and setup uncertainties, the ∆ in the mean LET_d among 12 scenarios of all six patients was 0.6 keV/μm, which is slightly higher than when only the range uncertainties were considered (0.4 keV/μm). The ∆ in the mean LET_d in a patient was ≤1.7 keV/μm in the heart and ≤1.2 keV/μm in the esophagus and total lung. The ∆ in the mean RBE-weighted dose in a patient was up to 79 cGy for the total lung, 165 cGy for the heart, and 258 cGy for the esophagus. For ten breathing phases, the ∆ in the mean LET_d in a patient was ≤0.3 keV/μm in the CTV, ≤0.5 keV/μm in the heart, ≤0.4 keV/μm in the esophagus, and ≤0.7 keV/μm in the total lung.

CONCLUSION

The addition of setup errors to the range uncertainties resulted in slightly less homogeneous LET_d distributions. The variations in the mean LET_d among ten breathing phases were slightly larger in the total lung than in the heart and esophagus. The combination of setup and range uncertainties had a greater impact than the effect of breathing phases on the variations in the mean RBE-weighted dose to the OARs. Overall, the LET_d distributions in the CTV were less sensitive than those in the OARs to setup errors, range uncertainties, and breathing phases for robustly optimized PBS proton lung cancer plans. This article is protected by copyright. All rights reserved.

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 LETd 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 (DvRBE) and LETd 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 LETd 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 ∆D50,CTV and ∆D2,OAR both ⪅1%. The pediatric case report demonstrated implications of different beam arrangements on DvRBE and LETd distributions. From initial planning in RayStation sharing identical optimization constraints, FRoG analysis led to plan selection of the most conservative approach, i.e., minimized DvRBE,max and LETd,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 DvRBE and LETd 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.

Executive Summary of Clinical and Technical Guidelines for Esophageal Cancer Proton Beam Therapy From the Particle Therapy Co-Operative Group Thoracic and Gastrointestinal Subcommittees

Radiation therapy (RT) is an integral component of potentially curative management of esophageal cancer (EC). However, RT can cause significant acute and late morbidity due to excess radiation exposure to nearby critical organs, especially the heart and lungs. Sparing these organs from both low and high radiation dose has been demonstrated to achieve clinically meaningful reductions in toxicity and may improve long-term survival. Accruing dosimetry and clinical evidence support the consideration of proton beam therapy (PBT) for the management of EC. There are critical treatment planning and delivery uncertainties that should be considered when treating EC with PBT, especially as there may be substantial motion-related interplay effects. The Particle Therapy Co-operative Group Thoracic and Gastrointestinal Subcommittees jointly developed guidelines regarding patient selection, treatment planning, clinical trials, and future directions of PBT for EC.

Can the mean linear energy transfer of organs be directly related to patient toxicities for current head and neck cancer intensity-modulated proton therapy practice?

BACKGROUND AND PURPOSE

The relative biological effectiveness (RBE) of proton therapy is predicted to vary with the dose-weighted average linear energy transfer (LET_d). However, RBE values may substantially vary for different clinical endpoints. Therefore, the aim of this study was to assess the feasibility of relating mean D⋅LET_d parameters to patient toxicity for HNC patients treated with proton therapy.

MATERIALS AND METHODS

The delivered physical dose (D) and the voxel-wise product of D and LET_d (D⋅LET_d) distributions were calculated for 100 head and neck cancer (HNC) proton therapy patients using our TPS (Raystation v6R). The means and covariance matrix of the accumulated D and D⋅LET_d of all relevant organs-at-risk (OARs) were used to simulate 2.500 data sets of different sizes. For each dataset, an attempt was made to add mean D⋅LET_d parameters to a multivariable NTCP model based on mean D parameters of the same OAR for xerostomia, tube feeding and dysphagia. The likelihood of creating an NTCP model with statistically significant parameters (i.e. power) was calculated as a function of the simulated sample size for various RBE models.

RESULTS

The sample size required to have a power of at least 80% to show an independent effect of mean D⋅LET_d parameters on toxicity is over 15,000 patients for all toxicities.

CONCLUSION

For current clinical practice, it is not feasible to directly model NTCP with both mean D and mean D⋅LET_d of OARs. These findings should not be interpreted as a contradiction of previous evidence for the relationship between RBE and LET_d.

Exploratory Investigation of Dose-Linear Energy Transfer (LET) Volume Histogram (DLVH) for Adverse Events Study in Intensity Modulated Proton Therapy (IMPT)

PURPOSE

We proposed a novel tool-a dose linear energy transfer (LET)-volume histogram (DLVH)-and performed an exploratory study to investigate rectal bleeding in prostate cancer treated with intensity modulated proton therapy.

METHODS AND MATERIALS

The DLVH was constructed with dose and LET as 2 axes, and the normalized volume of the structure was contoured in the dose-LET plane as isovolume lines. We defined the DLVH index, DLv%(d,l) (ie, v% of the structure) to have a dose of ≥d Gy and an LET of ≥l keV/μm, similar to the dose-volume histogram index Dv%. Nine patients with prostate cancer with rectal bleeding (Common Terminology Criteria for Adverse Events grade ≥2) were included as the adverse event group, and 48 patients with no complications were considered the control group. A P value map was constructed by comparison of the DLVH indices of all patients between the 2 groups using the Mann-Whitney U test. Dose-LET volume constraints (DLVCs) were derived based on the P value map with a manual selection procedure facilitated by Spearman's correlation tests. The obtained DLVCs were further cross-validated using a multivariate support vector machine (SVM)-based normal tissue complication probability (NTCP) model with an independent testing data set composed of 8 adverse event and 13 control patients.

RESULTS

We extracted 2 DLVC constraints. One DLVC was obtained, Vdose/LETboundary:2.5keVμmat 75 Gy to 3.2keVμmat8.65Gy <1.27% (DLVC1), revealing a high LET volume effect. The second DLVC, V(72.2Gy,0keVμm) < 2.23% (DVLC2), revealed a high dose volume effect. The SVM-based NTCP model with 2 DLVCs provided slightly superior performance than using dose only, with an area under the curve of 0.798 versus 0.779 for the testing data set.

CONCLUSIONS

Our results demonstrated the importance of rectal "hot spots" in both high LET (DLVC1) and high dose (DLVC2) in inducing rectal bleeding. The SVM-based NTCP model confirmed the derived DLVCs as good predictors for rectal bleeding when intensity modulated proton therapy is used to treat prostate cancer.

Initial Experience with Proton Beam Therapy for Differentiated Thyroid Cancer

Purpose

External beam radiotherapy is used in a subset of high-risk patients with differentiated thyroid cancer (DTC). Recurrent, radioactive iodine (RAI)-refractory DTC carries a poor prognosis. We report our initial experience of intensity-modulated proton therapy (IMPT) for recurrent, RAI-refractory DTC.

Patients and Methods

Fourteen patients with recurrent, RAI-refractory DTC were consecutively treated with IMPT from November 2016 to March 2020 at our multisite institution. Patient, tumor, and treatment characteristics were recorded. Overall survival and local-regional recurrence-free survival were recorded and estimated using the Kaplan-Meier method. Acute and late treatment-related toxicities were recorded based on the Common Terminology Criteria for Adverse Events version 5.0. Patients completed the European Organization for Research and Treatment of Cancer Quality of Life Head and Neck Module at baseline and after IMPT. Eleven patients were included in the final analysis.

Results

Median follow-up was 8 months (range, 3-40) for all patients. Median age at treatment with IMPT was 64 years (range, 40-77), and the majority were men (64%). Recurrent histologies included papillary (55%), Hurthle cell (36%), and poorly differentiated (9%) carcinoma; 1 patient had tall cell variant. Concurrent chemotherapy was not administered for any patient in this cohort. At 8 months, all patients were alive without local-regional failure. Acute grade 3 toxicities were limited to 1 patient with dysphagia, requiring feeding tube placement. Two patients experienced late grade 3 esophageal stenosis requiring dilation. There were no grade 4 or 5 toxicities. There were no differences in pretreatment versus posttreatment patient-reported outcomes in terms of dysphagia or hoarseness.

Conclusion

In our early experience, IMPT provided promising local-regional control for recurrent, RAI-refractory DTC. Further study is warranted to evaluate the long-term efficacy and safety of IMPT in this patient population.

A Critical Review of LET-Based Intensity-Modulated Proton Therapy Plan Evaluation and Optimization for Head and Neck Cancer Management

In this review article, we review the 3 important aspects of linear-energy-transfer (LET) in intensity-modulated proton therapy (IMPT) for head and neck (H&N) cancer management. Accurate LET calculation methods are essential for LET-guided plan evaluation and optimization, which can be calculated either by analytical methods or by Monte Carlo (MC) simulations. Recently, some new 3D analytical approaches to calculate LET accurately and efficiently have been proposed. On the other hand, several fast MC codes have also been developed to speed up the MC simulation by simplifying nonessential physics models and/or using the graphics processor unit (GPU)–acceleration approach. Some concepts related to LET are also briefly summarized including (1) dose-weighted versus fluence-weighted LET; (2) restricted versus unrestricted LET; and (3) microdosimetry versus macrodosimetry. LET-guided plan evaluation has been clinically done in some proton centers. Recently, more and more studies using patient outcomes as the biological endpoint have shown a positive correlation between high LET and adverse events sites, indicating the importance of LET-guided plan evaluation in proton clinics. Various LET-guided plan optimization methods have been proposed to generate proton plans to achieve biologically optimized IMPT plans. Different optimization frameworks were used, including 2-step optimization, 1-step optimization, and worst-case robust optimization. They either indirectly or directly optimize the LET distribution in patients while trying to maintain the same dose distribution and plan robustness. It is important to consider the impact of uncertainties in LET-guided optimization (ie, LET-guided robust optimization) in IMPT, since IMPT is sensitive to uncertainties including both the dose and LET distributions. We believe that the advancement of the LET-guided plan evaluation and optimization will help us exploit the unique biological characteristics of proton beams to improve the therapeutic ratio of IMPT to treat H&N and other cancers.

Technical Note: 4D robust optimization in small spot intensity-modulated proton therapy (IMPT) for distal esophageal carcinoma

PURPOSE

To compare the dosimetric performances of small-spot three-dimensional (3D) and four-dimensional (4D) robustly optimized intensity-modulated proton (IMPT) plans in the presence of uncertainties and interplay effect simultaneously for distal esophageal carcinoma.

METHOD AND MATERIALS

Thirteen (13) patients were selected and re-planned with small-spot ( σ  ~ 2-6 mm) 3D and 4D robust optimization in IMPT, respectively. The internal clinical target volumes (CTV_high3d , CTV_low3d ) were used in 3D robust optimization. Different CTVs (CTV_high4d , CTV_low4d ) were generated by subtracting an inner margin of the motion amplitudes in three cardinal directions from the internal CTVs and used in 4D robust optimization. All patients were prescribed the same dose to CTVs (50 Gy[RBE] for CTV_high3d /CTV_high4d and 45 Gy[RBE] for CTV_low3d /CTV_low4d ). Dose-volume histogram (DVH) indices were calculated to assess plan quality. Comprehensive plan robustness evaluations that consisted of 300 perturbed scenarios (10 different motion patterns to consider irregular motion (sampled from a Gaussian distribution) and 30 different uncertainties scenarios (sampled from a 4D uniform distribution) combined), were performed to quantify robustness to uncertainties and interplay effect simultaneously. Wilcoxon signed-rank test was used for statistical analysis.

RESULTS

Compared to 3D robustly optimized plans, 4D robustly optimized plans had statistically improved target coverage and better sparing of lungs and heart (heart D_mean , P = 0.001; heart V_30Gy[RBE] , P = 0.001) in the nominal scenario. 4D robustly optimized plans had better robustness in target dose coverage (CTV_high4d V_100% , P = 0.002) and the protection of lungs and heart (heart D_mean , P = 0.001; heart V_30Gy[RBE] , P = 0.001) when uncertainties and interplay effect were considered simultaneously.

CONCLUSIONS

Even with small spots in IMPT, 4D robust optimization outperformed 3D robust optimization in terms of normal tissue protection and robustness to uncertainties and interplay effect simultaneously. Our findings support the use of 4D robust optimization to treat distal esophageal carcinoma with small spots in IMPT.

Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer

Purpose:

Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT.

Methods and Materials:

For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LET_d) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve.

Results:

An overall statistically significant RAIC association with dose and LET_d was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30–2.72) for dose and 2.68 (1.93–4.93) for LET_d, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LET_d effect demonstrated increased risk of RAIC with increasing LET_d for the majority of patients. Estimated probability of RAIC with LET_d = 1 keV/μm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD₁₅ were estimated to be 63.6 and 50.1 Gy with LET_d equal to 2 and 5 keV/μm, respectively.

Conclusions:

Our results suggest that the LET_d effect could be of clinical significance for some patients; LET_d assessment in clinical treatment plans should therefore be taken into consideration.

Impact of Multiple Beams on Plan Quality, Linear Energy Transfer Distribution, and Plan Robustness of Intensity Modulated Proton Therapy for Lung Cancer

The increase of proton beam number might provide higher degrees of freedom in the optimization of intensity-modulated proton therapy planning. In this study, we aimed to quantitatively explore the potential benefits of the increased beam number, including dose volume histogram (DVH), linear energy transfer volume histogram, and DVH bandwidth metrics. Twelve patients with lung cancer are retrospectively selected. Four plans were created based on internal target volume (ITV) robust optimization for each patient using the RayStation treatment planning system. Four plans were generated using different numbers (three, five, seven, and nine) of evenly separated coplanar beams. The three-beam plan was considered as the reference plan. Biologically equivalent doses were calculated using both constant relative biological effectiveness (RBE) and variable RBE models, respectively. To evaluate plan quality, DVH metrics in the target [ITV: D_2%, CI, HI] and organs-at-risk [Lung: V_5Gy[RBE], V_20Gy[RBE], V_30Gy[RBE]; Heart D_2%; Spinal cord D_2%] were calculated using both RBE models. To evaluate LET distributions, LET volume histogram metrics [ITV LET_mean and LET_2%; Lung LET_mean and LET_2%; Heart LET_2%; Spinal cord LET_2%] were quantified. To evaluate plan robustness, the metrics using DVH bandwidth [ITV: D_2%, D_99%; Lung: V_5Gy[RBE], V_20Gy[RBE], V_30Gy[RBE]; Heart D_2%; Spinal cord D_2%] were also reported. For plan quality, the increase of proton beam number resulted in fewer target hot spots, improved target dose conformity, improved target dose homogeneity, lower median-dose lung volume, and fewer hot spots in spinal cord. As to LET distributions, target mean LET increased significantly as the beam number increased to seven or more. Lung LET hot spots were significantly reduced with the increase of proton beams. With respect to plan robustness, the robustness of target dose coverage, target hot spots, and low-dose lung volume were improved, while the robustness of heart hot spots became worse as the beam number increased to nine. The robustness of cord hot spots became worse using five and seven beams compared to that using three beams. As the proton beam number increased, plan quality and LET distributions were comparable or significantly improved. The robustness of target dose coverage, target dose hot spots, and low-dose lung volume were significantly improved.

Intensity-modulated proton therapy (IMPT) interplay effect evaluation of asymmetric breathing with simultaneous uncertainty considerations in patients with non-small cell lung cancer

PURPOSE

Intensity-modulated proton therapy (IMPT) is sensitive to uncertainties from patient setup and proton beam range, as well as interplay effect. In addition, respiratory motion may vary from cycle to cycle, and also from day to day. These uncertainties can severely degrade the original plan quality and potentially affect patient's outcome. In this work, we developed a new tool to comprehensively consider the impact of all these uncertainties and provide plan robustness evaluation under them.

METHODS

We developed a comprehensive plan robustness evaluation tool that considered both uncertainties from patient setup and proton beam range, as well as respiratory motion simultaneously. To mimic patients' respiratory motion, the time spent in each phase was randomly sampled based on patient-specific breathing pattern parameters as acquired during the four-dimensional (4D)-computed tomography (CT) simulation. Spots were then assigned to one specific phase according to the temporal relationship between spot delivery sequence and patients' respiratory motion. Dose in each phase was calculated by summing contributions from all the spots delivered in that phase. The final 4D dynamic dose was obtained by deforming all doses in each phase to the maximum exhalation phase. Three hundred (300) scenarios (10 different breathing patterns with 30 different setup and range uncertainty scenario combinations) were calculated for each plan. The dose-volume histograms (DVHs) band method was used to assess plan robustness. Benchmarking the tool as an application's example, we compared plan robustness under both three-dimensional (3D) and 4D robustly optimized IMPT plans for 10 nonrandomly selected patients with non-small cell lung cancer.

RESULTS

The developed comprehensive plan robustness tool had been successfully applied to compare the plan robustness between 3D and 4D robustly optimized IMPT plans for 10 lung cancer patients. In the presence of interplay effect with uncertainties considered simultaneously, 4D robustly optimized plans provided significantly better CTV coverage (D_95% , P = 0.002), CTV homogeneity (D_5% -D_95% , P = 0.002) with less target hot spots (D_5% , P = 0.002), and target coverage robustness (CTV D_95% bandwidth, P = 0.004) compared to 3D robustly optimized plans. Superior dose sparing of normal lung (lung D_mean , P = 0.020) favoring 4D plans and comparable normal tissue sparing including esophagus, heart, and spinal cord for both 3D and 4D plans were observed. The calculation time for all patients included in this study was 11.4 ± 2.6 min.

CONCLUSION

A comprehensive plan robustness evaluation tool was successfully developed and benchmarked for plan robustness evaluation in the presence of interplay effect, setup and range uncertainties. The very high efficiency of this tool marks its clinical adaptation, highly practical and versatile nature, including possible real-time intra-fractional interplay effect evaluation as a potential application for future use.