Fraction optimization for hybrid proton-photon treatment planning

TEAM: Triangular-mEsh Adaptive and Multiscale proton spot generation method

BACKGROUND

Proton therapy is preferred for its dose conformality to spare normal tissues and organs-at-risk (OAR) via Bragg peaks with negligible exit dose. However, proton dose conformality can be further optimized: (1) the spot placement is based on the structured (e.g., Cartesian) grid, which may not offer conformal shaping to complex tumor targets; (2) the spot sampling pattern is uniform, which may be insufficient at the tumor boundary to provide the sharp dose falloff, and at the same time may be redundant at the tumor interior to provide the uniform dose coverage, for example, due to multiple Coulomb scattering (MCS); and (3) the lateral spot penumbra increases with respect to the depth due to MCS, which blurs the lateral dose falloff. On the other hand, while (1) the deliverable spots are subject to the minimum-monitor-unit (MMU) constraint, and (2) the dose rate is proportional to the MMU threshold, the current spot sampling method is sensitive to the MMU threshold and can fail to provide satisfactory plan quality for a large MMU threshold (i.e., high-dose-rate delivery).

PURPOSE

This work will develop a novel Triangular-mEsh-based Adaptive and Multiscale (TEAM) proton spot generation method to address these issues for optimizing proton dose conformality and plan delivery efficiency.

METHODS

Compared to the standard clinically-used spot placement method, three key elements of TEAM are as follows: (1) a triangular mesh instead of a structured grid: the triangular mesh is geometrically more conformal to complex target shapes and therefore more efficient and accurate for dose shaping inside and around the target; (2) adaptive sampling instead of uniform sampling: the adaptive sampling consists of relatively dense sampling at the tumor boundary to create the sharp dose falloff, which is more accurate, and coarse sampling at the tumor interior to uniformly cover the target, which is more efficient; and (3) depth-dependent sampling instead of depth-independent sampling: the depth-dependent sampling is used to compensate for MCS, that is, with increasingly dense sampling at the tumor boundary to improve dose shaping accuracy, and increasingly coarse sampling at the tumor interior to improve dose shaping efficiency, as the depth increases. In the TEAM method the spot locations are generated for each energy layer and layer-by-layer in the multiscale fashion; and then the spot weights are derived by solving the IMPT problem of dose-volume planning objectives, MMU constraints, and robustness optimization with respect to range and setup uncertainties.

RESULTS

Compared to the standard clinically-used spot placement method UNIFORM, TEAM achieved (1) better plan quality using <60% number of spots of UNIFORM; (2) better robustness to the number of spots; (3) better robustness to a large MMU threshold. Furthermore, TEAM provided better plan quality with fewer spots than other adaptive methods (Cartesian-grid or triangular-mesh).

CONCLUSIONS

A novel triangular-mesh-based proton spot placement method called TEAM is proposed, and it is demonstrated to improve plan quality, robustness to the number of spots, and robustness to the MMU threshold, compared to the clinically-used spot placement method and other adaptive methods.

Simultaneous dose and dose rate optimization via dose modifying factor modeling for FLASH effective dose

BACKGROUND

Although the FLASH radiotherapy (FLASH) can improve the sparing of organs-at-risk (OAR) via the FLASH effect, it is generally a tradeoff between the physical dose coverage and the biological FLASH coverage, for which the concept of FLASH effective dose (FED) is needed to quantify the net improvement of FLASH, compared to the conventional radiotherapy (CONV).

PURPOSE

This work will develop the first-of-its-kind treatment planning method called simultaneous dose and dose rate optimization via dose modifying factor modeling (SDDRO-DMF) for proton FLASH that directly optimizes FED.

METHODS

SDDRO-DMF models and optimizes FED using FLASH dose modifying factor (DMF) models, which can be classified into two categories: (1) the phenomenological model of the FLASH effect, such as the FLASH effectiveness model (FEM); (2) the mechanistic model of the FLASH radiobiology, such as the radiolytic oxygen depletion (ROD) model. The general framework of SDDRO-DMF will be developed, with specific DMF models using FEM and ROD, as a demonstration of general applicability of SDDRO-DMF for proton FLASH via transmission beams (TB) or Bragg peaks (BP) with single-field or multi-field irradiation. The FLASH dose rate is modeled as pencil beam scanning dose rate. The solution algorithm for solving the inverse optimization problem of SDDRO-DMF is based on iterative convex relaxation method.

RESULTS

SDDRO-DMF is validated in comparison with IMPT and a state-of-the-art method called SDDRO, with demonstrated efficacy and improvement for reducing the high dose and the high-dose volume for OAR in terms of FED. For example, in a SBRT lung case of the dose-limiting factor that the max dose of brachial plexus should be no more than 26 Gy, only SDDRO-DMF met this max dose constraint; moreover, SDDRO-DMF completely eliminated the high-dose (V70%) volume to zero for CTV10mm (a high-dose region as a 10 mm ring expansion of CTV).

CONCLUSION

We have proposed a new proton FLASH optimization method called SDDRO-DMF that directly optimizes FED using phenomenological or mechanistic models of DMF, and have demonstrated the efficacy of SDDO-DMF in reducing the high-dose volume or/and the high-dose value for OAR, compared to IMPT and a state-of-the-art method SDDRO.

A novel treatment planning method via scissor beams for uniform-target-dose proton GRID with peak-valley-dose-ratio optimization

BACKGROUND

Proton spatially fractionated RT (SFRT) can potentially synergize the unique advantages of using proton Bragg peak and SFRT peak-valley dose ratio (PVDR) to reduce the radiation-induced damage for normal tissues. Uniform-target-dose (UTD) proton GRID is a proton SFRT modality that can be clinically desirable and conveniently adopted since its UTD resembles target dose distribution in conventional proton RT (CONV). However, UTD proton GRID is not used clinically, which is likely due to the lack of an effective treatment planning method.

PURPOSE

This work will develop a novel treatment planning method using scissor beams (SB) for UTD proton GRID, with the joint optimization of PVDR and dose objectives.

METHODS

The SB method for spatial dose modulation in normal tissues with UTD has two steps: (1) a primary beam (PB) is halved with interleaved beamlets, to generate spatial dose modulation in normal tissues; (2) a complementary beam (CB) is added to fill in previously valley-dose positions in the target to generate UTD, while the CB is angled slightly from the PB, to maintain spatial dose modulation in normal tissues. A treatment planning method with PVDR optimization via the joint total variation and L1 (TVL1) regularization is developed to jointly optimize PVDR and dose objectives. The plan optimization solution is obtained using an iterative convex relaxation algorithm.

RESULTS

The new methods SB and SB-TVL1 were validated in comparison with CONV. Compared to CONV of relatively homogeneous dose distribution, SB had modulated spatial dose pattern in normal tissues with UTD and comparable plan quality. Compared to SB, SB-TVL1 further maximized PVDR, with comparable dose-volume parameters.

CONCLUSIONS

A novel SB method is proposed that can generate modulated spatial dose pattern in normal tissues to achieve UTD proton GRID. A treatment planning method with PVDR optimization capability via TVL1 regularization is developed that can jointly optimize PVDR and dose objectives for proton GRID.

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.

Single high-energy arc proton therapy with Bragg peak boost (SHARP)

INTRODUCTION

Because of the co-location of critical organs at risk, base of skull tumours require steep dose gradients to achieve the prescribed dosimetric criteria. When available, proton beam therapy (PBT) is often considered a desirable modality for these cases, but in many instances, compromises in target coverage are still required to achieve critical organ at risk (OAR) tolerance doses. A number of techniques have been proposed to further improve the penumbra of PBT. In the current study, we propose a novel, collimator-free treatment planning technique that combines high-energy shoot-through proton beams with conventional Bragg peak spot placement. The small spot size of the high-energy pencil beams provides a sharp penumbra at the target boundary, and the Bragg peak spots provide a higher linear energy transfer (LET) boost to the target centre.

METHODS

Three base of skull chordoma patients were retrospectively planned with three different PBT treatment planning techniques: (1) conventional intensity-modulated proton therapy (IMPT); (2) high-energy proton arc therapy (HE-PAT); and (3) the novel technique combining HE-PAT and IMPT, referred to as single high-energy arc with Bragg peak boost (SHARP). The Monaco 6 treatment planning system was used.

RESULTS

SHARP was found to improve the PBT penumbra in the plane perpendicular to the HE-PAT beams. Minimal penumbra differences were observed in the plane of the HE-PAT beams. SHARP reduced dose-averaged LET to surrounding organs at risk.

CONCLUSION

A novel PBT treatment planning technique was successfully implemented. Initial results indicate the potential for SHARP to improve the penumbra of PBT treatments for base of skull tumours.

Combined BNCT-CIRT treatment planning for glioblastoma using the effect-based optimization

Objective. Boron neutron capture therapy (BNCT) and carbon ion radiotherapy (CIRT) are emerging treatment modalities for glioblastoma. In this study, we investigated the methodology and feasibility to combine BNCT and CIRT treatments. The combined treatment plan illustrated how the synergistic utilization of BNCT's biological targeting and CIRT's intensity modulation capabilities could lead to optimized treatment outcomes.Approach. The Monte Carlo toolkit, TOPAS, was employed to calculate the dose distribution for BNCT, while matRad was utilized for the optimization of CIRT. The biological effect-based approach, instead of the dose-based approach, was adopted to develop the combined BNCT-CIRT treatment plans for six patients diagnosed with glioblastoma, considering the different radiosensitivity and fraction. Five optional combined treatment plans with specific BNCT effect proportions for each patient were evaluated to identify the optimal treatment that minimizes damage on normal tissue.Main results. Individual BNCT exhibits a significant effect gradient along with the beam direction in the large tumor, while combined BNCT-CIRT treatments can achieve uniform effect delivery within the clinical target volume (CTV) through the effect filling with reversed gradient by the CIRT part. In addition, the increasing BNCT effect proportion in combined treatments can reduce damage in the normal brain tissue near the CTV. Besides, the combined treatments effectively minimize damage to the skin compared to individual BNCT treatments.Significance. The initial endeavor to combine BNCT and CIRT treatment plans is achieved by the effect-based optimization. The observed advantages of the combined treatment suggest its potential applicability for tumors characterized by pleomorphic, infiltrative, radioresistant and voluminous features.

Lattice position optimization for LATTICE therapy

BACKGROUND

LATTICE radiation therapy delivers 3D heterogenous dose of high peak-to-valley dose ratio (PVDR) to the tumor target, with peak dose at lattice vertices inside the target and valley dose for the rest of the target. Although the lattice vertex positions can impact PVDR inside the target and sparing of organs-at-risk (OAR), they are fixed as constants and not optimized during treatment planning in current clinical practice.

PURPOSE

This work proposes a new LATTICE plan optimization method that can optimize lattice vertex positions during LATTICE treatment planning, which is the first lattice position optimization study to the best of our knowledge.

METHODS

The new LATTICE treatment planning method optimizes lattice vertex positions as well as other plan variables (e.g., photon fluences or proton spot weights), with optimization objectives for target PVDR and OAR sparing. To satisfy mathematical differentiability, the lattice vertices are approximated in sigmoid functions. For geometric feasibility, proper geometry constraints are enforced onto lattice vertex positions. The lattice position optimization problem is solved by iterative convex relaxation (ICR) method and alternating direction method of multipliers (ADMM), and lattice vertex positions and photon/proton plan variables are jointly updated via the Quasi-Newton method.

RESULTS

Both photon and proton LATTICE RT were considered, and the optimal lattice vertex positions in terms of plan objectives were found by solving all possible combinations on given discrete positions via exhaustive searching based on standard IMRT/IMPT, which served as the ground truth for validating the new LATTICE method. The results show that the new method indeed provided the optimal lattice vertex positions with the smallest optimization objective, the largest target PVDR, and the best OAR sparing.

CONCLUSIONS

A new LATTICE treatment planning method is proposed and validated that can optimize lattice vertex positions as well as other photon or proton plan variables for improving target PVDR and OAR sparing.