Fraction optimization for hybrid proton-photon treatment planning

A biological optimization method for carbon therapy via iterative Jacobian-based linearization

Objective.Carbon ion radiotherapy (CIRT) can provide higher biological effectiveness and cause more damage to cancer cells compared to photon or proton radiotherapy, especially for radio-resistant tumors. The optimization of biological dose is essential for CIRT, to achieve the desirable tumoricidal dose while mitigating biological damage to normal tissues and organs at risk (OAR). However, the biological optimization for CIRT is mathematically challenging, due to the nonlinear nature of biological dose model, which can lead to computational inaccuracy and inefficiency. This work will develop an accurate and efficient biological optimization method for CIRT.Approach.The proposed method is called iterative Jacobian-based linearization (IJL). In IJL, the biological dose is modeled as the product of the physical dose and relative biological effect, which is based on the linear-quadratic model via the local effect model in this work, and the optimization objective consists of dose-volume histogram based biological dose objectives within clinical target volume and OAR. The optimization algorithm for IJL is through iterative convex relaxation, in which the nonlinear biological dose is iteratively linearized using Jacobian-based approximations and the linear subproblems are solved using alternating direction method of multipliers. To compare with IJL, the limited-memory quasi-Newton (QN) method (limited-memory version) is developed that directly solves the same nonlinear biological optimization problem.Main results.Compared to the QN, IJL demonstrated superior plan accuracy, e.g. better OAR sparing with the reduction of biological dose in the CTV-surrounding volume (PTV1cm) to 89.7%, 95.0%, 88.3% for brain, lung, and abdomen, respectively; IJL also had higher computational efficiency, with approximately 1/10 the computational time per iteration and continuously decreasing objectives (while being stagnated for QN after certain number of iterations).Significance.A novel optimization algorithm, IJL, incorporating iterative linearization of biological dose, is proposed to accurately and efficiently solve the biological optimization problem for CIRT. It demonstrates superior plan accuracy and computational efficiency compared to the direct nonlinear QN optimization method.

An energy optimization method based on mixed-integer model and variational quantum computing algorithm for faster IMPT

Intensity-modulated proton therapy (IMPT) offers superior dose conformity with reduced exposure to surrounding healthy tissues compared to conventional photon therapy. Improving IMPT delivery efficiency reduces motion-related uncertainties, enhances plan robustness, and benefits breath-hold techniques by shortening treatment time. Among various factors, energy switching time plays a critical role, making energy layer optimization (ELO) essential. This work develops an energy layer optimization method based on mixed integer model and variational quantum computing algorithm to enhance the efficiency of IMPT. The energy layer optimization problem is modeled as a mixed-integer program, where continuous variables optimize the dose distribution and binary variables indicate energy layer selection. To solve it, iterative convex relaxation decouples the dose-volume constraints, followed by the alternating direction method of multipliers (ADMM) to separate mixed-variable optimization and the minimum monitor unit (MMU) constraint. The resulting beam intensity subproblem, subject to MMU, either admits a closed-form solution or is efficiently solvable via conjugate gradient. The binary subproblem is cast as a quadratic unconstrained binary optimization (QUBO) problem, solvable using variational quantum computing algorithms. With nearly the same plan quality, the proposed method noticeable reduces the number of the used energies. For example, compared to conventional IMPT, QC can reduce the number of energy layers from 61 to 35 in HN case, from 56 to 35 in lung case, and from 59 to 32 to abdomen case. The reduced number of energies also results in fewer delivery time, e.g., the delivery time is reduced from 100.6, 232.0, 185.3 seconds to 90.7, 215.4, 154.0 seconds, respectively.

Direct minimization of normal-tissue toxicity via an NTCP-based IMPT planning method

BACKGROUND

Intensity-modulated proton therapy (IMPT) planning often relies on physical dose constraints to balance tumor control and sparing of organs at risk (OARs). However, focusing solely on these dose objectives does not always minimize the normal-tissue toxicity, which is quantified as normal tissue complication probability (NTCP). NTCP is also a quantitative criterion for patient selection between proton and photon treatments.

PURPOSE

This study introduces an NTCP-based IMPT planning (NTCP-IMPT) method designed to directly minimize normal-tissue toxicity while maintaining tumor coverage.

METHODS

NTCP-IMPT simultaneously optimizes NTCP and dose-volume histogram (DVH)-based physical dose objectives while adhering to the minimum-monitor-unit (MMU) constraint for plan deliverability. The optimization problem is solved by the interior-point method. To assess its efficacy in reducing normal-tissue toxicity, NTCP-IMPT is compared with standard IMPT (without NTCP optimization) for four head-and-neck (HN) cancer patients in terms of physical dose quality and NTCP of xerostomia and dysphagia.

RESULTS

Across all four patients, NTCP-IMPT plans met target dose criteria (D95% ≥ 100% and D2% ≤ 110%) while maintaining maximum doses to the spinal cord and brainstem comparable to standard IMPT. NTCP-IMPT also reduced mean doses to parotid glands, submandibular glands, oral cavity, and pharyngeal constrictor muscles (PCMs). Compared to the standard IMPT, NTCP-IMPT achieved average reductions in NTCP for xerostomia (grade ≥ 2: 3.67%; grade ≥3: 1.07%) and dysphagia (grade ≥ 2: 7.54%; grade ≥ 3: 3.72%).

CONCLUSIONS

NTCP-IMPT effectively minimizes normal-tissue toxicity and improves the sparing of OARs associated with side effects while maintaining comparable tumor coverage compared to standard IMPT.

Minibeam-pLATTICE: A novel proton LATTICE modality using minibeams

Purpose

LATTICE, a form of spatially fractionated radiation therapy (SFRT) that delivers high-dose peaks and low-dose valleys within the target volume, has been clinically utilized for treating bulky tumors. However, its application to small-to-medium-sized target volumes remains challenging due to beam size limitations. To address this challenge, this work proposes a novel proton LATTICE (pLATTICE) modality using minibeams, namely minibeam-pLATTICE, that can extend the LATTICE approach for small-to-medium target volumes.

Methods

Three minibeam-pLATTICE methods are introduced. (1) M0: a fixed minibeam aperture orientation (e.g., 0°) for all beam angles; (2) M1: alternated minibeam aperture orientations (e.g., between 0° and 90°), for consecutive beam angles; (3) M2: multiple minibeam aperture orientations (e.g., 0° and 90°) for each beam angle. The purpose of M1 or M2 is to correct anisotropic dose distribution at lattice peaks due to the planar spatial modulation of minibeams. For each minibeam-pLATTICE method, an optimization problem is formulated to optimize dose uniformity in target peaks and valleys, as well as dose-volume-histogram-based objectives. This optimization problem is solved using iterative convex relaxation and alternating direction method of multipliers (ADMM).

Results

Three minibeam-pLATTICE methods are validated to demonstrate the feasibility of minibeam-pLATTICE for the head-and-neck (HN) patients. The advantages of this modality over conventional beam (CONV) pLATTICE are evaluated by comparing peak-to-valley dose ratio (PVDR) and dose delivered to organs at risk (OAR). All three minibeam-pLATTICE modalities achieved improved plan quality compared to CONV, with M2 yielding the best results. For example, in terms of PVDR, M2=5.89, compared to CONV=4.13, M0=4.87 and M1=4.7; in terms of max brainstem dose, M2=5.8 Gy, compared to CONV=16.57 Gy, M0=6.54 Gy and M1=7.04 Gy.

Conclusions

A novel minibeam-pLATTICE modality is proposed that can generate lattice dose patterns for small-to-medium target volumes, which are not achievable with conventional pLATTICE due to beam size limitations. Peak dose anisotropy due to 1D planar minibeam apertures is corrected through inverse treatment planning with alternating or multiple minibeam apertures per beam angle.

Multi-collimator proton minibeam radiotherapy with joint dose and PVDR optimization

BACKGROUND

The clinical translation of proton minibeam radiation therapy (pMBRT) presents significant challenges, particularly in developing an optimal treatment planning technique. A uniform target dose is crucial for maximizing anti-tumor efficacy and facilitating the clinical acceptance of pMBRT. However, achieving a high peak-to-valley dose ratio (PVDR) in organs-at-risk (OAR) is essential for sparing normal tissue. This balance becomes particularly difficult when OARs are located distal to the beam entrance or require patient-specific collimators.

PURPOSE

This work proposes a novel pMBRT treatment planning method that can achieve high PVDR at OAR and uniform dose at target simultaneously, via multi-collimator pMBRT (MC-pMBRT) treatment planning method with joint dose and PVDR optimization (JDPO).

METHODS

MC-pMBRT utilizes a set of generic and premade multi-slit collimators with different center-to-center distances and does not need patient-specific collimators. The collimator selection per field is OAR-specific and tailored to maximize PVDR in OARs while preserving target dose uniformity. Then, the inverse optimization method JDPO is utilized to jointly optimize target dose uniformity, PVDR, and other dose-volume-histogram based dose objectives, which is solved by iterative convex relaxation optimization algorithm and alternating direction method of multipliers.

RESULTS

The need and efficacy of MC-pMBRT is demonstrated by comparing the single-collimator (SC) approach with the multi-collimator (MC) approach. While SC degraded either PVDR for OAR or dose uniformity for the target, MC provided a good balance of PVDR and target dose uniformity. The proposed JDPO method is validated in comparison with the dose-only optimization (DO) method for MC-pMBRT, in reference to the conventional (CONV) proton RT (no pMBRT). Compared to CONV, MC-pMBRT (DO and JDPO) preserved target dose uniformity and plan quality, while providing unique PVDR in OAR. Compared to DO, JDPO further improved PVDR via PVDR optimization during treatment planning.

CONCLUSION

A novel pMBRT treatment planning method called MC-pMBRT is proposed that utilizes a set of generic and premade collimators with joint dose and PVDR optimization algorithm to optimize OAR-specific PVDR and target dose uniformity simultaneously.

Optimal use of limited proton resources for liver cancer patients in combined proton-photon treatments

Objective. Liver cancer patients may benefit from proton therapy through increase of the tumor control probability (TCP). However, proton therapy is a limited resource and may not be available for all patients. We consider combined proton-photon liver SBRT treatments (CPPT) where only some fractions are delivered with protons. It is investigated how limited proton fractions can be used best for individual patients and optimally allocated within a patient group.Approach. Photon and proton treatment plans were created for five liver cancer patients. In CPPT, limited proton fractions may be optimally exploited by increasing the fraction dose compared to the photon fraction dose. To determine a patient's optimal proton and photon fraction doses, we maximize the target biologically effective dose (BED) while constraining the mean normal liver BED, which leads to an up- or downscaling of the proton and photon plan, respectively. The resulting CPPT balances the benefits of fractionation in the normal liver versus exploiting the superior proton dose distributions. After converting the target BED to TCP, the optimal number of proton fractions per patient is determined by maximizing the overall TCP of the patient group.Main results. For the individual patient, a CPPT treatment that delivers a higher fraction dose with protons than photons allows for dose escalation in the target compared to delivering the same proton and photon fraction dose. On the level of a patient group, CPPT may allow to distribute limited proton slots over several patients. Through an optimal use and allocation of proton fractions, CPPT may increase the average patient group TCP compared to a proton patient selection strategy where patients receive single-modality proton or photon treatments.Significance. Limited proton resources can be optimally exploited via CPPT by increasing the target dose in proton fractions and allocating available proton slots to patients with the highest TCP increase.

Dosimetric optimization for dynamic mixed beam arc therapy (DYMBARC)

BACKGROUND

Non-coplanarity and mixed beam modality could be combined to further enhance dosimetric treatment plan quality. We introduce dynamic mixed beam arc therapy (DYMBARC) as an innovative technique that combines non-coplanar photon and electron arcs, dynamic gantry and collimator rotations, and intensity modulation with photon multileaf collimator (MLC). However, finding favorable beam directions for DYMBARC is challenging due to the large solution space, machine component constraints, and optimization parameters, posing a highly non-convex optimization problem.

PURPOSE

To establish DYMBARC and solve the pathfinding challenge by employing direct aperture optimization (DAO) to determine the table angles and gantry angle ranges of photon and electron arcs for different clinically motivated cases.

METHODS

The method starts by generating a grid of beam directions based on user-defined resolutions along the gantry and table angle axes for each beam quality considered. Beam directions causing collisions or entering through the end of CT are excluded. For electrons, a fixed source-to-surface distance of 80 cm is used to reduce in-air scatter. Electron beam energies with insufficient range to reach the target or beam directions impinging on the table before reaching the patient are excluded. The remaining beam directions form the pathfinding solution space. Promising photon and electron MLC-defined apertures, with associated monitor unit (MU) weights, are iteratively added using a hybrid-DAO algorithm. This algorithm combines column generation to add apertures and simulated annealing to further refine aperture shapes and weights. Apertures are added until the requested number of paths are formed and the user-defined maximum total gantry angle range is reached. Paths are resampled to a finer gantry angle resolution and subject to DAO for simultaneous optimization of beam intensities along the photon/electron arcs. Subsequent final dose calculation and MU weight reoptimization result in a deliverable DYMBARC plan. DYMBARC plans are created for three clinically motivated cases (brain, breast, and pelvis) and compared to DYMBARC variants: colli-DTRT (dynamic collimator trajectory radiotherapy) using non-coplanar photon arcs; and Arc-MBRT (mixed beam radiotherapy) using photons and electrons but restricted to coplanar setup. Additionally, a manually defined volumetric modulated arc therapy (VMAT) setup serves as a reference clinical technique. Dose distributions, dose-volume histograms, and dosimetric endpoints are evaluated. Dosimetric validation with radiochromic film measurements (gamma evaluation, 3% / 2 mm (global), 10% dose threshold) is performed on a TrueBeam system in developer mode for one case.

RESULTS

While maintaining similar target coverage and homogeneity, DYMBARC reduced mean doses to organs-at-risk compared to VMAT by an average of 3.2, 0.5, and 2.9 Gy for the brain, breast, and pelvis cases, respectively. Similar or smaller mean dose reductions were observed for Arc-MBRT or colli-DTRT, compared to VMAT. Electron contributions to the mean planning target volume dose ranged from 2% to 34% for DYMBARC and from 11% to 40% for Arc-MBRT. Measurement validation showed >99.7% gamma passing rate.

CONCLUSIONS

DYMBARC was successfully established using a dosimetrically optimized pathfinding approach, combining non-coplanarity with mixed beam modality. DYMBARC facilitated the determination of photon and electron contributions on a case-by-case basis, enhancing more personalized treatment modalities.

Proton ARC based LATTICE radiation therapy: feasibility study, energy layer optimization and LET optimization

Objective.LATTICE, a spatially fractionated radiation therapy (SFRT) modality, is a 3D generalization of GRID and delivers highly modulated peak-valley spatial dose distribution to tumor targets, characterized by peak-to-valley dose ratio (PVDR). Proton LATTICE is highly desirable, because of the potential synergy of the benefit from protons compared to photons, and the benefit from LATTICE compared to GRID. Proton LATTICE using standard proton RT via intensity modulated proton therapy (IMPT) (with a few beam angles) can be problematic with poor target dose coverage and high dose spill to organs-at-risk (OAR). This work will develop novel proton LATTICE method via proton ARC (with many beam angles) to overcome these challenges in target coverage and OAR sparing, with optimized delivery efficiency via energy layer optimization and optimized biological dose distribution via linear energy transfer (LET) optimization, to enable the clinical use of proton LATTICE.Approach.ARC based proton LATTICE is formulated and solved with energy layer optimization, during which plan quality and delivery efficiency are jointly optimized. In particular, the number of energy jumps (NEJ) is explicitly modelled and minimized during plan optimization for improving delivery efficiency, while target dose conformality and OAR dose objectives are optimized. The plan deliverability is ensured by considering the minimum-monitor-unit (MMU) constraint, and the plan robustness is accounted for using robust optimization. The biological dose is optimized via LET optimization. The optimization solution algorithm utilizes iterative convex relaxation method to handle the dose-volume constraint and the MMU constraint, with spot-weight optimization subproblems solved by proximal descent method.Main results.ARC based proton LATTCE substantially improved plan quality from IMPT based proton LATTICE, such as (1) improved conformity index (CI) from 0.47 to 0.81 for the valley target dose and from 0.62 to 0.97 for the peak target dose, (2) reduced esophagus dose from 0.68 Gy to 0.44 Gy (a 12% reduction with respect to 2 Gy valley prescription dose) and (3) improved PVDR from 4.15 to 4.28 in the lung case. Moreover, energy layer optimization improved plan delivery efficiency for ARC based proton LATTICE, such as (1) reduced NEJ from 71 to 56 and (2) reduction of energy layer switching time by 65% and plan delivery time by 52% in the lung case. The biological target and OAR dose distributions were further enhanced via LET optimization. On the other hand, proton ARC LATTCE also substantially improved plan quality from VMAT LATTICE, such as (1) improved CI from 0.45 to 0.81 for the valley target dose and from 0.63 to 0.97 for the peak target dose, (2) reduced esophagus dose from 0.59 Gy to 0.38 Gy (a 10.5% reduction with respect to 2 Gy valley prescription dose) and (3) improved PVDR from 3.88 to 4.28 in the lung case.Significance.The feasibility of high-plan-quality proton LATTICE is demonstrated via proton ARC with substantially improved target dose coverage and OAR sparing compared to IMPT, while the plan delivery efficiency for ARC based proton LATTICE can be optimized using energy layer optimization.

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.

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.

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.