Uncertainty incorporated beam angle optimization for IMPT treatment planning

'iCycle-pBAO': Automated patient-specific beam-angle selection in proton therapy applied to oropharyngeal cancer

OBJECTIVE

This study aimed to develop a fully-automated patient tailored beam-angle optimisation approach for intensity-modulated proton therapy (IMPT). For oropharynx cancer patients, the dosimetric impact of increasing the number of fields from 4 to 12 was systematically assessed.

APPROACH

A total-beam-space heuristic was developed to simultaneously select optimal patient specific candidate beam directions, according to a cost-function that penalises dose to OARs involved in clinically used NTCPs. The method was dosimetrically validated by comparisons with fixed 4- and 6-field clinical beam-angle templates and equiangular configurations, including 72-field equiangular. The latter served as dosimetric 'Utopia' benchmark for the other evaluated beam configurations.

MAIN RESULT

Using 4 optimised patient-specific fields instead of the clinical 4-field beam-angle template resulted in (xerostomia NTCP + dysphagia NTCP)-reductions for all patients, with averages of 3.0 %-point (range: 1.1-5.8) for grade 2 toxicity and 1.2 %-point (range: 0.3-2.8) for grade 3. For 6 fields these reductions were 2.4 %-point (range: 0.0-5.0) and 0.8 %-point (range: -0.1-2.1). Xerostomia NTCPs significantly reduced with increasing numbers of patient-specific fields with a levelling off at 10-12 fields with NTCP values that closely approached those for Utopia 72-field equiangular plans. Beam angle optimisation took 52 min.

CONCLUSION

Automated, patient-tailored beam-angle optimisation could enhance IMPT plans at acceptable optimisation times. Improvements compared to the clinical beam-angle templates were highly patient-specific.

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.

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.

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.

Robust IMPT and follow-up toxicity in skull base chordoma and chondrosarcoma-a single-institution clinical experience

BACKGROUND

Chordomas and chondrosarcomas of the skull base are rare, slowly growing malignant bone neoplasms. Despite their radioresistant properties, proton therapy has been successfully used as an adjunct to resection or as a definitive treatment. Herewith, we present our experience with robustly optimized intensity-modulated proton therapy (IMPT) and related toxicities in skull base chordoma and chondrosarcoma patients treated at HollandPTC, Delft, the Netherlands.

METHODS

Clinical data, treatment plans, and acute toxicities of patients treated between July 2019 and August 2021 were reviewed. CT and 3.0T MRI scans for treatment planning were performed in supine position in a thermoplastic mold. In total, 21 dose optimization and 28 dose evaluation scenarios were simulated. Acute toxicity was scored weekly before and during the treatment according to the CTCAE v4.0. Median follow-up was 35 months (range 12-36 months).

RESULTS

Overall, 9 chordoma and 3 chondrosarcoma patients with 1-3 resections prior to IMPT were included; 4 patients had titanium implants. Brainstem core and surface and spinal cord core and surface were used for nominal plan robust optimization in 11, 10, 8, and 7 patients, respectively. Middle ear inflammation, dry mouth, radiation dermatitis, taste disorder, and/or alopecia of grades 1-3 were noted at the end of treatment among 6 patients without similar complaints at inclusion; symptoms disappeared 3 months following the treatment.

CONCLUSION

Robustly optimized IMPT is clinically feasible as a postoperative treatment for skull base chordoma and chondrosarcoma patients. We observed acceptable early toxicities (grade 1-3) that disappeared within the first 3 months after irradiation.

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.

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.

Proton Pencil-Beam Scanning Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies: Patterns of Practice Survey and Recommendations for Future Development from NRG Oncology and PTCOG

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 due to concerns of amplified uncertainties at the larger dose per fraction. NRG Oncology and Particle Therapy Cooperative Group (PTCOG) Thoracic Subcommittee surveyed US proton centers 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. Amongst other points, the recommendations highlight the need for volumetric image guidance and multiple CT-based robust optimization and robustness tools to minimize further the impact of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.

Robust Beam Selection Based on Water Equivalent Thickness Analysis in Passive Scattering Carbon-Ion Radiotherapy for Pancreatic Cancer

Carbon-ion radiotherapy (CIRT) is one of the most effective radiotherapeutic modalities. This study aimed to select robust-beam configurations (BC) by water equivalent thickness (WET) analysis in passive CIRT for pancreatic cancer. The study analyzed 110 computed tomography (CT) images and 600 dose distributions of eight patients with pancreatic cancer. The robustness in the beam range was evaluated using both planning and daily CT images, and two robust BCs for the rotating gantry and fixed port were selected. The planned, daily, and accumulated doses were calculated and compared after bone matching (BM) and tumor matching (TM). The dose-volume parameters for the target and organs at risk (OARs) were evaluated. Posterior oblique beams (120-240°) in the supine position and anteroposterior beams (0° and 180°) in the prone position were the most robust to WET changes. The mean CTV V95% reductions with TM were -3.8% and -5.2% with the BC for gantry and the BC for fixed ports, respectively. Despite ensuring robustness, the dose to the OARs increased slightly with WET-based BCs but remained below the dose constraint. The robustness of dose distribution can be improved by BCs that are robust to ΔWET. Robust BC with TM improves the accuracy of passive CIRT for pancreatic cancer.

An uncertainty-incorporated method for fast beam angle selection in intensity-modulated proton therapy

Aim

We propose a novel metric called ψ - score to rank the Intensity Modulated Proton Therapy (IMPT) beams in the order of their optimality and robustness. The beams ranked based on this metric were accordingly chosen for IMPT optimization. The objective of this work is to study the effectiveness of the proposed method in various clinical cases.

Methods and Materials

We have used Pinnacle TPS (Philips Medical System V 16.2) for performing the optimization. To validate our approach, we have applied it in four clinical cases: Lung, Pancreas, Prostate+Node and Prostate. Basically, for all clinical cases, four set of plans were created using Multi field optimization (MFO) and Robust Optimization (RO) with same clinical objectives, namely (1) Conventional angle plan without Robust Optimization (CA Plan), (2) Suitable angle Plan without Robust Optimization (SA Plan), (3) Conventional angle plan with Robust Optimization (CA-RO Plan), (4) Suitable angle Plan with Robust Optimization (SA-RO Plan). Initial plan was generated with 20 equiangular beams starting from the gantry angle of 0°. In the corresponding SA Plan and SA-RO Plan, the beam angles were obtained using the guidance provided by ψ - score.

Results

All CA plans were compared against the SA plans in terms of Dose distribution, Dose volume histogram (DVH) and percentage of dose difference. The results obtained from the clinical cases indicate that the plan quality is considerably improved without significantly compromising the robustness when the beam angles are optimized using the proposed method. It takes approximately 10-15 min to find the suitable beam angles without Robust Optimization (RO), while it takes approximately 20-30 min to find the suitable beam angles with RO. However, the inclusion of RO in BAO did not result in a change in the final beam angles for anatomies other than lung.

Conclusion

The results obtained in different anatomic sites demonstrate the usefulness of our approach in improving the plan quality by determining optimal beam angles in IMPT.

Interobserver Variability Prediction of Primary Gross Tumor in a Patient with Non-Small Cell Lung Cancer

This research addresses the problem of interobserver variability (IOV), in which different oncologists manually delineate varying primary gross tumor volume (pGTV) contours, adding risk to targeted radiation treatments. Thus, a method of IOV reduction is urgently needed. Hypothesizing that the radiation oncologist’s IOV may shrink with the aid of IOV maps, we propose IOV prediction network (IOV-Net), a deep-learning model that uses the fuzzy membership function to produce high-quality maps based on computed tomography (CT) images. To test the prediction accuracy, a ground-truth pGTV IOV map was created using the manual contour delineations of radiation therapy structures provided by five expert oncologists. Then, we tasked IOV-Net with producing a map of its own. The mean squared error (prediction vs. ground truth) and its standard deviation were 0.0038 and 0.0005, respectively. To test the clinical feasibility of our method, CT images were divided into two groups, and oncologists from our institution created manual contours with and without IOV map guidance. The Dice similarity coefficient and Jaccard index increased by ~6 and 7%, respectively, and the Hausdorff distance decreased by 2.5 mm, indicating a statistically significant IOV reduction (p < 0.05). Hence, IOV-net and its resultant IOV maps have the potential to improve radiation therapy efficacy worldwide.

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.

Toward automatic beam angle selection for pencil-beam scanning proton liver Treatments: A deep learning-based approach

BACKGROUND

Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning however poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam angles, and the quality of a proton treatment plan is largely determined by the beam angles employed. Finding the optimal beam angles for a proton treatment plan requires time and experience, motivating the investigation of automatic beam angle selection methods.

PURPOSE

A deep learning-based approach to automatic beam angle selection is proposed for proton pencil-beam scanning treatment planning of liver lesions.

METHODS

We cast beam-angle selection as a multi-label classification problem. To account for angular boundary discontinuity, the underlying convolution neural network is trained with the proposed Circular Earth Mover's Distance based regularization and multi-label circular-smooth label technique. Furthermore, an analytical algorithm emulating proton treatment planners' clinical practice is employed in post-processing to improve the output of the model. Forty-nine patients that received proton liver treatments between 2017 and 2020 were randomly divided into training (n = 31), validation (n = 7), and test sets (n = 11). AI-selected beam angles were compared with those angles selected by human planners, and the dosimetric outcome was investigated by creating plans using knowledge-based treatment planning.

RESULTS

For 7 of the 11 cases in the test set, AI-selected beam angles agreed with those chosen by human planners to within 20 degrees (median angle difference = 10°; mean = 18.6°). Moreover, out of the total 22 beam angles predicted by the model, 15 (68%) were within 10 degrees of the human-selected angles. The high correlation in beam angles resulted in comparable dosimetric statistics between proton treatment plans generated using AI- and human-selected angles. For the cases with beam angle differences exceeding 20°, the dosimetric analysis showed similar plan quality although with different emphases on organ-at-risk sparing.

CONCLUSIONS

This pilot study demonstrated the feasibility of a novel deep learning-based beam angle selection technique. Testing on liver cancer patients showed that the resulting plans were clinically viable with comparable dosimetric quality to those using human-selected beam angles. In tandem with auto-contouring and knowledge-based treatment planning tools, the proposed model could represent a pathway for nearly fully automated treatment planning in proton therapy. This article is protected by copyright. All rights reserved.

Technical Note: Does the greater power of pencil beam scanning reduce the need for a proton gantry? A study of head-and-neck and brain tumors

PURPOSE

Proton therapy systems without a gantry can be more compact and less expensive in terms of capital cost, and therefore more available to a larger patient population. Would the advances in pencil beam scanning and robotics make gantry-less treatment possible? In this study, we explore if high-quality treatment plans can be obtained without a gantry.

METHODS AND MATERIALS

We recently showed that proton treatments with the patient in an upright position may be feasible with a new soft robotic immobilization device and imaging which enables multiple possible patient orientations during a treatment. In this study, we evaluate if this new treatment geometry could enable high quality treatment plans without a gantry. We created pencil beam scanning (PBS) treatment plans for seven patients with head-and-neck or brain tumors. Each patient was planned with two scenarios: one with a gantry with the patient in supine position and the other with a gantry-less fixed horizontal beam-line with the patient sitting upright. For the treatment plans, dose-volume-histograms (DVHs), target homogeneity index (HI), mean dose, are reported. A robustness analysis of one plan was performed with 2.5 mm setup errors and 3.5% range uncertainties with nine scenarios.

RESULTS

Most of the PBS-gantry-less plans had similar target HI and OAR mean dose as compared to PBS-gantry plans, and similar robustness with respect to range uncertainties and setup errors.

CONCLUSIONS

Pencil beam scanning provides sufficient power to deliver high quality treatment plans without requiring a gantry for head-and-neck or brain tumors. In combination with the development of the new positioning and immobilization methods required to support this treatment geometry, this work suggests the feasibility of further development of a compact proton therapy system with a fixed horizontal beam-line to treat patients in sitting and reclined positions. This article is protected by copyright. All rights reserved.

Optimization of Field Design in the Treatment of Rectal Cancer with Intensity Modulated Proton Beam Radiation Therapy: How Many Fields Are Needed to Account for Rectal Distension Uncertainty?

Purpose

Preoperative chemoradiation represents the standard of care in patients with locally advanced rectal cancer. Robustness is often compromised in the setting of proton beam therapy owing to the sensitivity of proton particles to tissue heterogeneity, such as with intestinal gas. The ideal beam arrangement to mitigate the anatomic uncertainty caused by intestinal gas is not well defined.

Methods and Materials

We developed pencil beam scanning plans using (1) 1-beam posteroanterior (PA) plans, (2) 2-beam with right and left posterior oblique (RPO and LPO) plans, (3) 3-beam with PA and opposed lateral plans, and (4) 5-beam with PA, RPO, LPO, and opposed lateral plans. We created 12 plans with robustness optimization and ran a total of 60 plan evaluations for varying degrees of intestinal gas distension to evaluate which plans would maintain clinical goals to the greatest degree.

Results

A single PA beam resulted in considerable loss of target coverage to the clinical target volume prescribed 50 Gy (volume receiving 100% of the prescribed dose [V_100%] < 90%) with rectal distension ≥3 cm in diameter in the short axis. In contrast, the other field designs maintained coverage with up to 5 cm of distension. On plans generated based on a 5-cm distended rectum with air medium, the 1-beam, 3-beam, and 5-beam arrangements resulted in loss of target coverage (V_100% < 90%) with rectal contraction ≤3 cm, whereas the 2-beam arrangement maintained coverage to as low as 2 cm. On plans generated based on a 3-cm distension of the rectum, both the 2-beam and 3-beam arrangements maintained V_100% > 90% even with collapsed rectum to as low as 1 cm, simulating a patient treatment scenario without any rectal gas.

Conclusions

A single PA beam should be avoided when using proton beam therapy for rectal cancer. RPO/LPO and PA/opposed lateral arrangements may both be considered; RPO/LPO is favored to reduce integral dose and avoid beams traversing the hips. In patients for whom the plan CT has rectal distension of ≥3 cm, resimulation or strategies to reduce intestinal gas should be strongly considered.

Robust Angle Selection in Particle Therapy

The popularity of particle radiotherapy has grown exponentially over recent years owing to the marked advantage of the depth–dose curve and its unique biological property. However, particle therapy is sensitive to changes in anatomical structure, and the dose distribution may deteriorate. In particle therapy, robust beam angle selection plays a crucial role in mitigating inter- and intrafractional variation, including daily patient setup uncertainties and tumor motion. With the development of a rotating gantry, angle optimization has gained increasing attention. Currently, several studies use the variation in the water equivalent thickness to quantify anatomical changes during treatment. This method seems helpful in determining better beam angles and improving the robustness of planning. Therefore, this review will discuss and summarize the robust beam angles at different tumor sites in particle radiotherapy.

Beam Angle Optimization for Double-Scattering Proton Delivery Technique Using an Eclipse Application Programming Interface and Convolutional Neural Network

To automatically identify optimal beam angles for proton therapy configured with the double-scattering delivery technique, a beam angle optimization method based on a convolutional neural network (BAODS-Net) is proposed. Fifty liver plans were used for training in BAODS-Net. To generate a sequence of input data, 25 rays on the eye view of the beam were determined per angle. Each ray collects nine features, including the normalized Hounsfield unit and the position information of eight structures per 2° of gantry angle. The outputs are a set of beam angle ranking scores (S_beam) ranging from 0° to 359°, with a step size of 1°. Based on these input and output designs, BAODS-Net consists of eight convolution layers and four fully connected layers. To evaluate the plan qualities of deep-learning, equi-spaced, and clinical plans, we compared the performances of three types of loss functions and performed K-fold cross-validation (K = 5). For statistical analysis, the volumes V_27Gy and V_30Gy as well as the mean, minimum, and maximum doses were calculated for organs-at-risk by using a paired-samples t-test. As a result, smooth-L1 loss showed the best optimization performance. At the end of the training procedure, the mean squared errors between the reference and predicted S_beam were 0.031, 0.011, and 0.004 for L1, L2, and smooth-L1 loss, respectively. In terms of the plan quality, statistically, Plan_BAO has no significant difference from Plan_Clinic (P >.05). In our test, a deep-learning based beam angle optimization method for proton double-scattering treatments was developed and verified. Using Eclipse API and BAODS-Net, a plan with clinically acceptable quality was created within 5 min.

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.

SDDRO-joint: simultaneous dose and dose rate optimization with the joint use of transmission beams and Bragg peaks for FLASH proton therapy

Cancer radiotherapy (RT) with the irradiation at ultra-high dose rates, namely FLASH-RT, can substantially reduce radiation-induced normal tissue toxicities while maintaining tumor response. Currently, clinical FLASH-RT on deep-seated tumors can only be performed with proton beams. One way to achieve ultra-high dose rates at depth is through the use of high-energy transmission beams (TB), where the Bragg peaks (BP) fall outside the body. However, planning with TB alone does not fully leverage the degrees of freedom for dose shaping as traditional intensity modulated proton therapy (IMPT) which uses the BP of multi-energy proton beams at the tumor target. This work will develop a simultaneous dose and dose rate optimization (SDDRO) method with the joint use of TB and BP, namely SDDRO-Joint. Specifically, BP are placed inside tumor targets to improve the target dose conformality and sparse the normal-tissue dose, while TB primarily cover the tumor boundary to achieve ultra-high dose rate coverage of organs-at-risk (OAR) close to tumor targets. The sparing of OAR and other normal tissues via SDDRO-Joint is jointly by TB and BP, i.e. the FLASH sparing by TB and the dose sparing by BP. The results suggest that the addition of BP substantially increased the target dose conformality for SDDRO. Noticeably SDDRO-Joint also provided slightly higher conformal index values than the conventional IMPT method with BP alone.

Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network

This study proposes a near-real-time spot-scanning proton dose calculation method with probabilistic uncertainty estimation using a three-dimensional convolutional neural network (3D-CNN). CT images and clinical target volume contours of 215 head and neck cancer patients were collected from a public database. 1484 and 488 plans were extracted for training and testing the 3D-CNN model, respectively. Spot beam data and single-field uniform dose (SFUD) labels were calculated for each plan using an open-source dose calculation toolkit. Variable spot data were converted into a fixed-size volume hereby called a 'peak map' (PM). 300 epochs of end-to-end training was implemented using sets of stopping power ratio and PM as input. Moreover, transfer learning techniques were used to adjust the trained model to SFUD doses calculated with different beam parameters and calculation algorithm using only 7.95% of training data used for the base model. Finally, accuracy of the 3D-CNN-calculated doses and model uncertainty was reviewed with several evaluation metrics. The 3D-CNN model calculates 3D proton dose distributions accurately with a mean absolute error of 0.778 cGyE. The predicted uncertainty is correlated with dose errors at high contrast edges. Averaged Sørensen-Dice similarity coefficients between binarized outputs and ground truths are mostly above 80%. Once the 3D-CNN model was well-trained, it can be efficiently fine-tuned for different proton doses by transfer learning techniques. Inference time for calculating one dose distribution is around 0.8 s for a plan using 1500 spot beams with a consumer grade GPU. A novel spot-scanning proton dose calculation method using 3D-CNN was developed. The 3D-CNN model is able to calculate 3D doses and uncertainty with any SFUD spot data and beam irradiation angles. Our proposed method should be readily extendable to other setups and plans and be useful for dose verification, image-guided proton therapy, or other applications.

Fraction-variant beam orientation optimization for intensity-modulated proton therapy

PURPOSE

To achieve a superior balance between dosimetry and the delivery efficiency of intensity-modulated proton therapy (IMPT) using as few beams as possible in a single fraction, we optimally vary beams in different fractions.

METHODS

In the optimization, 400~800 feasible noncoplanar beams were included in the candidate pool. For each beam, the doses of all scanning spots covering the target volume and a margin were calculated. The fraction-variant beam orientation optimization (FVBOO) problem was formulated to include three terms: two quadratic dose fidelity terms to penalize the deviation of planning target volume fractional dose and organs at risk (OAR) cumulative doses from prescription, respectively; an L2,1/2-norm group sparsity term to control the number of active beams per fraction to between 1 and 4. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) was applied to solve this problem. FVBOO was tested on a patient with base-of-skull (BOS) tumor of 5 fractions (5f) and 30 fractions (30f) with an average number of active beams per fraction varying between 4 and 1. In addition, one bilateral head-and-neck (H&N) patient, and one esophageal cancer (ESG) patient of 30f were tested with about three active beams per fraction. The results were compared with IMPT plans that use fixed beams in each fraction. The fixed beams were selected using the group sparsity term with a fraction-invariant BOO (FIBOO) constraint.

RESULTS

Varying beams were chosen in either the 5f or 30f FVBOO plans. While similar number of beams per fraction was selected as the FIBOO plan, the FVBOO plans were able to spare the OARs better, with an average reduction of [Dmean, Dmax] from the FIBOO plans by [0.85, 2.08] Relative Biological Effective Gy (GyRBE) in the 5f plan and [1.87, 4.06] GyRBE in the 30f plans. While reducing the number of beams per fraction in the BOS patient, a three-beam/fraction 5f FVBOO plan performs comparably as the four-beam FIBOO plan and a two-beam/fraction 30f FVBOO plan still provides superior dosimetry.

CONCLUSION

Fraction-variant beam orientation optimization allows the utilization of a larger beam solution space for superior dose distribution in IMPT while maintaining a practical number of beams in each fraction.

Automating proton treatment planning with beam angle selection using Bayesian optimization

PURPOSE

To present a fully automated treatment planning process for proton therapy including beam angle selection using a novel Bayesian optimization approach and previously developed constrained hierarchical fluence optimization method.

METHODS

We adapted our in-house automated intensity modulated radiation therapy (IMRT) treatment planning system, which is based on constrained hierarchical optimization and referred to as ECHO (expedited constrained hierarchical optimization), for proton therapy. To couple this to beam angle selection, we propose using a novel Bayesian approach. By integrating ECHO with this Bayesian beam selection approach, we obtain a fully automated treatment planning framework including beam angle selection. Bayesian optimization is a global optimization technique which only needs to search a small fraction of the search space for slowly varying objective functions (i.e., smooth functions). Expedited constrained hierarchical optimization is run for some initial beam angle candidates and the resultant treatment plan for each beam configuration is rated using a clinically relevant treatment score function. Bayesian optimization iteratively predicts the treatment score for not-yet-evaluated candidates to find the best candidate to be optimized next with ECHO. We tested this technique on five head-and-neck (HN) patients with two coplanar beams. In addition, tests were performed with two noncoplanar and three coplanar beams for two patients.

RESULTS

For the two coplanar configurations, the Bayesian optimization found the optimal beam configuration after running ECHO for, at most, 4% of all potential configurations (23 iterations) for all patients (range: 2%-4%). Compared with the beam configurations chosen by the planner, the optimal configurations reduced the mandible maximum dose by 6.6 Gy and high dose to the unspecified normal tissues by 3.8 Gy, on average. For the two noncoplanar and three coplanar beam configurations, the algorithm converged after 45 iterations (examining <1% of all potential configurations).

CONCLUSIONS

A fully automated and efficient treatment planning process for proton therapy, including beam angle optimization was developed. The algorithm automatically generates high-quality plans with optimal beam angle configuration by combining Bayesian optimization and ECHO. As the Bayesian optimization is capable of handling complex nonconvex functions, the treatment score function which is used in the algorithm to evaluate the dose distribution corresponding to each beam configuration can contain any clinically relevant metric.

A Pareto-based beam orientation optimization method for spot scanning intensity-modulated proton therapy

PURPOSE

To provide a proof of principle of a Pareto-based method to automatically generate optimal intensity-modulated proton therapy (IMPT) plans for various noncoplanar beam orientations.

METHODS

A novel multicriteria beam orientation optimization (MCBOO) method was developed to generate Pareto database of optimal plans. The MCBOO method automatically explores the beam orientations and the scalarization parameters of the IMPT plans simultaneously. The MCBOO method is based on multicriteria bilevel optimization (i.e., hierarchical optimization with two nested levels, named the upper and lower level optimization). In MCBOO, the upper level optimization explores the noncoplanar beam orientation space, while the lower level explores the scalarization parameters for a given beam orientation. Differential evolution method was used in both levels, and the Pareto optimal plans were aggregated from the bilevel optimizations to construct the Pareto database. The MCBOO method was implemented on a multinode multi-GPU cluster, and it was tested on three brain tumor patient cases. The Pareto database of the three patients was generated for a set of DVH-based objectives. A statistical analysis was performed between a selected set of MCBOO plans and the manual plan (plan with manually selected beam orientation based on the clinical experience and optimized with the same single plan iterative optimizer used in the MCBOO). The selected set of MCBOO plans consisted of plans that matched the performance of the manual plan [i.e., MCBOO plans that have the same target coverage (within 2%) as the manual plan or better and achieved the same dose (within 2%) or lower to all of the organs at risks (OARs) but one OAR]. Additionally, a dosimetric comparison between of one of the selected MCBOO plans vs the manual plan was conducted.

RESULTS

The multicriteria beam orientation optimization algorithm automatically generated Pareto plans for the three noncoplanar brain tumor cases. The MCBOO plans provided an alternative objective trade-offs to the manual plan. The selected MCBOO plans showed a reduction in dose to multiple organs at risk vs the manual plan with a maximum value which ranged between 10.8 and 12.9 Gy for the three patients. The trade-off of the OAR dose reduction resulted in higher dose to no more than one OAR for each of the selected MCBOO plans vs the manual plan. The maximum dose increase in the MCBOO plans over the manual plan ranged from 7.8 to 11.8 Gy.

CONCLUSIONS

A novel multicriteria beam orientation optimization method was developed and tested on three IMPT patient cases. The method automatically generates Pareto plans database by exploring the noncoplanar beam orientations. The method was able to identify beam orientations with Pareto optimal plans that are comparable to the manually created plans with varying objective trade-offs.

Exploring the advantages of intensity-modulated proton therapy: experimental validation of biological effects using two different beam intensity-modulation patterns

In current treatment plans of intensity-modulated proton therapy, high-energy beams are usually assigned larger weights than low-energy beams. Using this form of beam delivery strategy cannot effectively use the biological advantages of low-energy and high-linear energy transfer (LET) protons present within the Bragg peak. However, the planning optimizer can be adjusted to alter the intensity of each beamlet, thus maintaining an identical target dose while increasing the weights of low-energy beams to elevate the LET therein. The objective of this study was to experimentally validate the enhanced biological effects using a novel beam delivery strategy with elevated LET. We used Monte Carlo and optimization algorithms to generate two different intensity-modulation patterns, namely to form a downslope and a flat dose field in the target. We spatially mapped the biological effects using high-content automated assays by employing an upgraded biophysical system with improved accuracy and precision of collected data. In vitro results in cancer cells show that using two opposed downslope fields results in a more biologically effective dose, which may have the clinical potential to increase the therapeutic index of proton therapy.

Beam angle optimization using angular dependency of range variation assessed via water equivalent path length (WEPL) calculation for head and neck proton therapy

PURPOSE

To investigate angular sensitivity of proton range variation due to anatomic change in patients and patient setup error via water equivalent path length (WEPL) calculations.

METHODS

Proton range was estimated by calculating WEPL to the distal edge of target volume using planning CT (pCT) and weekly scatter-corrected cone-beam CT (CBCT) images of 11 head and neck patients. Range variation was estimated as the difference between the distal WEPLs calculated on pCT and scatter-corrected CBCT (cCBCT). This WEPL analysis was performed every five degrees ipsilaterally to the target. Statistics of the distal WEPL difference were calculated over the distal area to compare between different beam angles. Physician-defined contours were used for the WEPL calculation on both pCT and cCBCT, not considering local deformation of target volume. It was also tested if a couch kick (10°) can mitigate the range variation due to anatomic change and patient setup error.

RESULTS

For most of the patients considered, median, 75% quantile, and 95% quantile of the distal WEPL difference were largest for posterior oblique angles, indicating a higher chance of overdosing normal tissues at distal edge with these angles. Using a couch kick resulted in decrease in the WEPL difference for some posterior oblique angles.

CONCLUSIONS

It was demonstrated that the WEPL change has angular dependency for the cohort of head and neck cancer patients. Selecting beam configuration robust to anatomic change in patient and patient setup error may improve the treatment outcome of head and neck proton therapy.

[Proton therapy for head and neck squamous cell carcinomas: From physics to clinic]

Intensity-modulated radiation therapy (IMRT) is presently the recommended technique for the treatment of locally advanced head and neck carcinomas. Proton therapy would allow to reduce the volume of irradiated normal tissue and, thus, to decrease the risk of late dysphagia, xerostomia, dysgeusia and hypothyroidism. An exhaustive research was performed with the search engine PubMed by focusing on the papers about the physical difficulties that slow down use of proton therapy for head and neck carcinomas. Range uncertainties in proton therapy (±3 %) paradoxically limit the use of the steep dose gradient in distality. Calibration uncertainties can be important in the treatment of head and neck cancer in the presence of materials of uncertain stoichiometric composition (such as with metal implants, dental filling, etc.) and complex heterogeneities. Dental management for example may be different with IMRT or proton therapy. Some uncertainties can be somewhat minimized at the time of optimization. Inter- and intrafractional variations and uncertainties in Hounsfield units/stopping power can be integrated in a robust optimization process. Additional changes in patient's anatomy (tumour shrinkage, changes in skin folds in the beam patch, large weight loss or gain) require rescanning. Dosimetric and small clinical studies comparing photon and proton therapy have well shown the interest of proton therapy for head and neck cancers. Intensity-modulated proton therapy is a promising treatment as it can reduce the substantial toxicity burden of patients with head and neck squamous cell carcinoma compared to IMRT. Robust optimization will allow to perform an optimal treatment and to use proton therapy in current clinical practice.

Robust beam orientation optimization for intensity-modulated proton therapy

PURPOSE

Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework.

METHODS

The unified framework is formulated to include a dose fidelity term, a heterogeneity-weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2-norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO-FMO) was tested on two skull-base tumor (SBT) patients and two bilateral head-and-neck (H&N) patients. The conventional CTV-based optimized plans (Conv) with SHBOO-FMO beams (SHBOO-Conv) and manual beams (MAN-Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO-FMO plan were compared against the manual beam plan with CTV-based voxel-wise worst-case scenario approach (MAN-WC).

RESULTS

With SHBOO-FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN-Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO-Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN-Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO-Conv plans. Compared with the MAN-WC plans, the final SHBOO-FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN-WC plans.

CONCLUSION

We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.

On-line dose-guidance to account for inter-fractional motion during proton therapy

Background and purpose

Proton therapy (PT) of extra-cranial tumour sites is challenged by density changes caused by inter-fractional organ motion. In this study we investigate on-line dose-guided PT (DGPT) to account inter-fractional target motion, exemplified by internal motion in the pelvis.

Materials and methods

On-line DGPT involved re-calculating dose distributions with the isocenter shifted up to 15 mm from the position corresponding to conventional soft-tissue based image-guided PT (IGPT). The method was applied to patient models with simulated prostate/seminal vesicle target motion of ±3, ±5 and ±10 mm along the three cardinal axes. Treatment plans were created using either two lateral (gantry angles of 90°/270°) or two lateral oblique fields (gantry angles of 35°/325°). Target coverage and normal tissue doses from DGPT were compared to both soft-tissue and bony anatomy based IGPT.

Results

DGPT improved the dose distributions relative to soft-tissue based IGPT for 39 of 90 simulation scenarios using lateral fields and for 50 of 90 scenarios using lateral oblique fields. The greatest benefits of DGPT were seen for large motion, e.g. a median target coverage improvement of 13% was found for 10 mm anterior motion with lateral fields. DGPT also improved the dose distribution in comparison to bony anatomy IGPT in all cases. The best strategy was often to move the fields back towards the original target position prior to the simulated target motion.

Conclusion

DGPT has the potential to better account for large inter-fractional organ motion in the pelvis than IGPT.

Integrated beam orientation and scanning-spot optimization in intensity-modulated proton therapy for brain and unilateral head and neck tumors

PURPOSE

Intensity-Modulated Proton Therapy (IMPT) is the state-of-the-art method of delivering proton radiotherapy. Previous research has been mainly focused on optimization of scanning spots with manually selected beam angles. Due to the computational complexity, the potential benefit of simultaneously optimizing beam orientations and spot pattern could not be realized. In this study, we developed a novel integrated beam orientation optimization (BOO) and scanning-spot optimization algorithm for intensity-modulated proton therapy (IMPT).

METHODS

A brain chordoma and three unilateral head-and-neck patients with a maximal target size of 112.49 cm³ were included in this study. A total number of 1162 noncoplanar candidate beams evenly distributed across 4π steradians were included in the optimization. For each candidate beam, the pencil-beam doses of all scanning spots covering the PTV and a margin were calculated. The beam angle selection and spot intensity optimization problem was formulated to include three terms: a dose fidelity term to penalize the deviation of PTV and OAR doses from ideal dose distribution; an L1-norm sparsity term to reduce the number of active spots and improve delivery efficiency; a group sparsity term to control the number of active beams between 2 and 4. For the group sparsity term, convex L2,1-norm and nonconvex L2,1/2-norm were tested. For the dose fidelity term, both quadratic function and linearized equivalent uniform dose (LEUD) cost function were implemented. The optimization problem was solved using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The IMPT BOO method was tested on three head-and-neck patients and one skull base chordoma patient. The results were compared with IMPT plans created using column generation selected beams or manually selected beams.

RESULTS

The L2,1-norm plan selected spatially aggregated beams, indicating potential degeneracy using this norm. L2,1/2-norm was able to select spatially separated beams and achieve smaller deviation from the ideal dose. In the L2,1/2-norm plans, the [mean dose, maximum dose] of OAR were reduced by an average of [2.38%, 4.24%] and[2.32%, 3.76%] of the prescription dose for the quadratic and LEUD cost function, respectively, compared with the IMPT plan using manual beam selection while maintaining the same PTV coverage. The L2,1/2 group sparsity plans were dosimetrically superior to the column generation plans as well. Besides beam orientation selection, spot sparsification was observed. Generally, with the quadratic cost function, 30%~60% spots in the selected beams remained active. With the LEUD cost function, the percentages of active spots were in the range of 35%~85%.The BOO-IMPT run time was approximately 20 min.

CONCLUSION

This work shows the first IMPT approach integrating noncoplanar BOO and scanning-spot optimization in a single mathematical framework. This method is computationally efficient, dosimetrically superior and produces delivery-friendly IMPT plans.

Anatomical robust optimization to account for nasal cavity filling variation during intensity-modulated proton therapy: a comparison with conventional and adaptive planning strategies

The aim of this study is to develop an anatomical robust optimization method for intensity-modulated proton therapy (IMPT) that accounts for interfraction variations in nasal cavity filling, and to compare it with conventional single-field uniform dose (SFUD) optimization and online plan adaptation. We included CT data of five patients with tumors in the sinonasal region. Using the planning CT, we generated for each patient 25 'synthetic' CTs with varying nasal cavity filling. The robust optimization method available in our treatment planning system 'Erasmus-iCycle' was extended to also account for anatomical uncertainties by including (synthetic) CTs with varying patient anatomy as error scenarios in the inverse optimization. For each patient, we generated treatment plans using anatomical robust optimization and, for benchmarking, using SFUD optimization and online plan adaptation. Clinical target volume (CTV) and organ-at-risk (OAR) doses were assessed by recalculating the treatment plans on the synthetic CTs, evaluating dose distributions individually and accumulated over an entire fractionated 50 Gy_RBE treatment, assuming each synthetic CT to correspond to a 2 Gy_RBE fraction. Treatment plans were also evaluated using actual repeat CTs. Anatomical robust optimization resulted in adequate CTV doses (V_95%  ⩾  98% and V_107%  ⩽  2%) if at least three synthetic CTs were included in addition to the planning CT. These CTV requirements were also fulfilled for online plan adaptation, but not for the SFUD approach, even when applying a margin of 5 mm. Compared with anatomical robust optimization, OAR dose parameters for the accumulated dose distributions were on average 5.9 Gy_RBE (20%) higher when using SFUD optimization and on average 3.6 Gy_RBE (18%) lower for online plan adaptation. In conclusion, anatomical robust optimization effectively accounted for changes in nasal cavity filling during IMPT, providing substantially improved CTV and OAR doses compared with conventional SFUD optimization. OAR doses can be further reduced by using online plan adaptation.

Mixed integer programming with dose-volume constraints in intensity-modulated proton therapy

Background

In treatment planning for intensity‐modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed‐integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose‐volume constraints. In this study, we incorporated dose‐volume constraints in an MIP model to generate treatment plans for IMPT.

Methods

We created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited‐memory Broyden–Fletcher–Goldfarb–Shanno (L‐BFGS) method while the other plan was derived with our MIP model with dose‐volume constraints. We then compared these two plans by dose‐volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose‐volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection.

Results

The MIP model with dose‐volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial‐and‐error process. Some notable reduction in the mean doses of OARs is observed.

Conclusions

The treatment plans from our MIP model with dose‐volume constraints can meet all dose‐volume constraints for OARs and targets without any tedious trial‐and‐error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.

Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions

Robust optimization of intensity‐modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so‐called “worst case dose” and “minmax” robust optimization approaches and conventional planning target volume (PTV)‐based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull‐based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP‐PTV‐based, NLP‐PTV‐based, LP‐worst case dose, NLP‐worst case dose, LP‐minmax, and NLP‐minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP‐based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP‐based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP‐based methods was superior for the skull‐based and head and neck cancer patients. Overall, LP‐based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull‐based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV‐based optimization, and the NLP‐PTV‐based optimization outperformed the LP‐PTV‐based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP‐based methods had notably fewer scanning spots than did those generated using NLP‐based methods.

Robust treatment planning with conditional value at risk chance constraints in intensity-modulated proton therapy

BACKGROUND AND PURPOSE

Intensity-modulated proton therapy (IMPT) is highly sensitive to range uncertainties and uncertainties caused by setup variation. The conventional inverse treatment planning of IMPT based on the planning target volume (PTV) is not often sufficient to ensure robustness of treatment plans. We applied a probabilistic framework (chance-constrained optimization) in IMPT planning to hedge against the influence of uncertainties.

MATERIAL AND METHODS

We retrospectively selected one patient with lung cancer, one patient with head and neck (H&N) cancer, and one with prostate cancer for this analysis. Using their original images and prescriptions, we created new IMPT plans using two methods: (1) a robust chance-constrained treatment planning method with the clinical target volume (CTV) as the target; (2) the margin-based method with PTV as the target, which was solved by commercial software, CPLEX, using linear programming. For the first method, we reformulated the model into a tractable mixed-integer programming problem and sped up the calculation using Benders decomposition. The dose-volume histograms (DVHs) from the nominal and perturbed dose distributions were used to assess and compare plan quality. DVHs for all uncertain scenarios along with the nominal DVH were plotted. The width of the "bands" of DVHs was used to quantify the plan sensitivity to uncertainty. The newly developed Benders decomposition method was compared with a commercial solution to demonstrate its computational efficiency. The trade-off between nominal plan quality and plan robustness was investigated.

RESULTS

Our chance-constrained model outperformed the PTV method in terms of tumor coverage, tumor dose homogeneity, and plan robustness. Our model was shown to produce IMPT plans to meet the dose-volume constraints of organs at risk (OARs) and had better sparing of OARs than the PTV method in the three clinical cases included in this study. The chance-constrained model provided a flexible tool for users to balance between plan robustness and plan quality. In addition, our in-house developed method was found to be much faster than the commercial solution.

CONCLUSION

With explicit control of plan robustness, the chance-constrained robust optimization model generated superior IMPT plans compared to the PTV-based method.

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

A Retrospective Evaluation of the Benefit of Referring Pediatric Cancer Patients to an External Proton Therapy Center

BACKGROUND

Pediatric cancer patients requiring radiation therapy (RT) have been routinely assessed and referred to proton therapy (PT) at an external institution. The benefit of the delivered PT compared to the state-of-the-art intensity modulated x-ray RT (XT) at the home institution was evaluated.

PROCEDURE

Twenty-four consecutive children referred for PT during 2010-2013 for craniospinal (CSI, n = 10), localized intracranial (IC, n = 7), head/neck (HN, n = 4) or parameningeal (PM, n = 3) lesions were included. The median age was 8 years (2-16 years). XT plans were generated for each patient, blinded to the PT delivered. Dosimetry, estimated growth hormone deficiency (GHD), and neurocognitive dysfunction (NCD) risks were compared for PT and XT (Wilcoxon).

RESULTS

PT started (median) 5 weeks (± 1.3 weeks, 95% CI) after referral. For CSI patients, PT was clearly superior to XT plans with median dose reductions for the heart, lungs and thyroid of 17, 2.5 and 18 Gy, respectively (P = 0.005). The median estimated NCD and GHD risks were 1-3 (max 16) and 2 (max 61) percentage points, respectively, lower for PT compared to XT. The median of the mean doses to the brain, cochleae and pituitary gland was lower with PT than XT for the IC, H/N and PM patients (P < 0.039). For a single IC patient, the dose to hippocampi and optic chiasm was higher for PT compared to XT.

CONCLUSIONS

PT clearly benefitted the patients studied, except for IC disease where differences between PT and XT were modest, and comparative PT and XT treatment planning is warranted prior to referral.

Risk-optimized proton therapy to minimize radiogenic second cancers

Proton therapy confers substantially lower predicted risk of second cancer compared with photon therapy. However, no previous studies have used an algorithmic approach to optimize beam angle or fluence-modulation for proton therapy to minimize those risks. The objectives of this study were to demonstrate the feasibility of risk-optimized proton therapy and to determine the combination of beam angles and fluence weights that minimizes the risk of second cancer in the bladder and rectum for a prostate cancer patient. We used 6 risk models to predict excess relative risk of second cancer. Treatment planning utilized a combination of a commercial treatment planning system and an in-house risk-optimization algorithm. When normal-tissue dose constraints were incorporated in treatment planning, the risk model that incorporated the effects of fractionation, initiation, inactivation, repopulation and promotion selected a combination of anterior and lateral beams, which lowered the relative risk by 21% for the bladder and 30% for the rectum compared to the lateral-opposed beam arrangement. Other results were found for other risk models.

Adaptive and robust radiation therapy in the presence of drift

Combining adaptive and robust optimization in radiation therapy has the potential to mitigate the negative effects of both intrafraction and interfraction uncertainty over a fractionated treatment course. A previously developed adaptive and robust radiation therapy (ARRT) method for lung cancer was demonstrated to be effective when the sequence of breathing patterns was well-behaved. In this paper, we examine the applicability of the ARRT method to less well-behaved breathing patterns. We develop a novel method to generate sequences of probability mass functions that represent different types of drift in the underlying breathing pattern. Computational results derived from applying the ARRT method to these sequences demonstrate that the ARRT method is effective for a much broader class of breathing patterns than previously demonstrated.

Improved beam angle arrangement in intensity modulated proton therapy treatment planning for localized prostate cancer

Purpose: This study investigates potential gains of an improved beam angle arrangement compared to a conventional fixed gantry setup in intensity modulated proton therapy (IMPT) treatment for localized prostate cancer patients based on a proof of principle study. Materials and Methods: Three patients with localized prostate cancer retrospectively selected from our institution were studied. For each patient, IMPT plans were designed using two, three and four beam angles, respectively, obtained from a beam angle optimization algorithm. Those plans were then compared with ones using two lateral parallel-opposed beams according to the conventional planning protocol for localized prostate cancer adopted at our institution. Results: IMPT plans with two optimized angles achieved significant improvements in rectum sparing and moderate improvements in bladder sparing against those with two lateral angles. Plans with three optimized angles further improved rectum sparing significantly over those two-angle plans, whereas four-angle plans found no advantage over three-angle plans. A possible three-beam class solution for localized prostate patients was suggested and demonstrated with preserved dosimetric benefits because individually optimized three-angle solutions were found sharing a very similar pattern. Conclusions: This study has demonstrated the potential of using an improved beam angle arrangement to better exploit the theoretical dosimetric benefits of proton therapy and provided insights of selecting quality beam angles for localized prostate cancer treatment.

Proton energy optimization and reduction for intensity-modulated proton therapy

Intensity-modulated proton therapy (IMPT) is commonly delivered via the spot-scanning technique. To 'scan' the target volume, the proton beam is controlled by varying its energy to penetrate the patient's body at different depths. Although scanning the proton beamlets or spots with the same energy can be as fast as 10-20 m s(-1), changing from one proton energy to another requires approximately two additional seconds. The total IMPT delivery time thus depends mainly on the number of proton energies used in a treatment. Current treatment planning systems typically use all proton energies that are required for the proton beam to penetrate in a range from the distal edge to the proximal edge of the target. The optimal selection of proton energies has not been well studied. In this study, we sought to determine the feasibility of optimizing and reducing the number of proton energies in IMPT planning. We proposed an iterative mixed-integer programming optimization method to select a subset of all available proton energies while satisfying dosimetric criteria. We applied our proposed method to six patient datasets: four cases of prostate cancer, one case of lung cancer, and one case of mesothelioma. The numbers of energies were reduced by 14.3%-18.9% for the prostate cancer cases, 11.0% for the lung cancer cases and 26.5% for the mesothelioma case. The results indicate that the number of proton energies used in conventionally designed IMPT plans can be reduced without degrading dosimetric performance. The IMPT delivery efficiency could be improved by energy layer optimization leading to increased throughput for a busy proton center in which a delivery system with slow energy switch is employed.

Multifield optimization intensity modulated proton therapy for head and neck tumors: a translation to practice

BACKGROUND

We report the first clinical experience and toxicity of multifield optimization (MFO) intensity modulated proton therapy (IMPT) for patients with head and neck tumors.

METHODS AND MATERIALS

Fifteen consecutive patients with head and neck cancer underwent MFO-IMPT with active scanning beam proton therapy. Patients with squamous cell carcinoma (SCC) had comprehensive treatment extending from the base of the skull to the clavicle. The doses for chemoradiation therapy and radiation therapy alone were 70 Gy and 66 Gy, respectively. The robustness of each treatment plan was also analyzed to evaluate sensitivity to uncertainties associated with variations in patient setup and the effect of uncertainties with proton beam range in patients. Proton beam energies during treatment ranged from 72.5 to 221.8 MeV. Spot sizes varied depending on the beam energy and depth of the target, and the scanning nozzle delivered the spot scanning treatment "spot by spot" and "layer by layer."

RESULTS

Ten patients presented with SCC and 5 with adenoid cystic carcinoma. All 15 patients were able to complete treatment with MFO-IMPT, with no need for treatment breaks and no hospitalizations. There were no treatment-related deaths, and with a median follow-up time of 28 months (range, 20-35 months), the overall clinical complete response rate was 93.3% (95% confidence interval, 68.1%-99.8%). Xerostomia occurred in all 15 patients as follows: grade 1 in 10 patients, grade 2 in 4 patients, and grade 3 in 1 patient. Mucositis within the planning target volumes was seen during the treatment of all patients: grade 1 in 1 patient, grade 2 in 8 patients, and grade 3 in 6 patients. No patient experienced grade 2 or higher anterior oral mucositis.

CONCLUSIONS

To our knowledge, this is the first clinical report of MFO-IMPT for head and neck tumors. Early clinical outcomes are encouraging and warrant further investigation of proton therapy in prospective clinical trials.

Dose uncertainties in IMPT for oropharyngeal cancer in the presence of anatomical, range, and setup errors

PURPOSE

Setup, range, and anatomical uncertainties influence the dose delivered with intensity modulated proton therapy (IMPT), but clinical quantification of these errors for oropharyngeal cancer is lacking. We quantified these factors and investigated treatment fidelity, that is, robustness, as influenced by adaptive planning and by applying more beam directions.

METHODS AND MATERIALS

We used an in-house treatment planning system with multicriteria optimization of pencil beam energies, directions, and weights to create treatment plans for 3-, 5-, and 7-beam directions for 10 oropharyngeal cancer patients. The dose prescription was a simultaneously integrated boost scheme, prescribing 66 Gy to primary tumor and positive neck levels (clinical target volume-66 Gy; CTV-66 Gy) and 54 Gy to elective neck levels (CTV-54 Gy). Doses were recalculated in 3700 simulations of setup, range, and anatomical uncertainties. Repeat computed tomography (CT) scans were used to evaluate an adaptive planning strategy using nonrigid registration for dose accumulation.

RESULTS

For the recalculated 3-beam plans including all treatment uncertainty sources, only 69% (CTV-66 Gy) and 88% (CTV-54 Gy) of the simulations had a dose received by 98% of the target volume (D98%) >95% of the prescription dose. Doses to organs at risk (OARs) showed considerable spread around planned values. Causes for major deviations were mixed. Adaptive planning based on repeat imaging positively affected dose delivery accuracy: in the presence of the other errors, percentages of treatments with D98% >95% increased to 96% (CTV-66 Gy) and 100% (CTV-54 Gy). Plans with more beam directions were not more robust.

CONCLUSIONS

For oropharyngeal cancer patients, treatment uncertainties can result in significant differences between planned and delivered IMPT doses. Given the mixed causes for major deviations, we advise repeat diagnostic CT scans during treatment, recalculation of the dose, and if required, adaptive planning to improve adequate IMPT dose delivery.

Incorporating deliverable monitor unit constraints into spot intensity optimization in intensity-modulated proton therapy treatment planning

The purpose of this study is to investigate the feasibility and impact of incorporating deliverable monitor unit (MU) constraints into spot intensity optimization (SIO) in intensity-modulated proton therapy (IMPT) treatment planning. The current treatment planning system (TPS) for IMPT disregards deliverable MU constraints in the SIO routine. It performs a post-processing procedure on an optimized plan to enforce deliverable MU values that are required by the spot scanning proton delivery system. This procedure can create a significant dose distribution deviation between the optimized and post-processed deliverable plans, especially when small spot spacings are used. In this study, we introduce a two-stage linear programming approach to optimize spot intensities and constrain deliverable MU values simultaneously, i.e., a deliverable SIO (DSIO) model. Thus, the post-processing procedure is eliminated and the associated optimized plan deterioration can be avoided. Four prostate cancer cases at our institution were selected for study and two parallel opposed beam angles were planned for all cases. A quadratic programming based model without MU constraints, i.e., a conventional SIO (CSIO) model, was also implemented to emulate commercial TPS. Plans optimized by both the DSIO and CSIO models were evaluated for five different settings of spot spacing from 3 to 7 mm. For all spot spacings, the DSIO-optimized plans yielded better uniformity for the target dose coverage and critical structure sparing than did the CSIO-optimized plans. With reduced spot spacings, more significant improvements in target dose uniformity and critical structure sparing were observed in the DSIO than in the CSIO-optimized plans. Additionally, better sparing of the rectum and bladder was achieved when reduced spacings were used for the DSIO-optimized plans. The proposed DSIO approach ensures the deliverability of optimized IMPT plans that take into account MU constraints. This eliminates the post-processing procedure required by the TPS as well as the resultant deteriorating effect on ultimate dose distributions. This approach therefore allows IMPT plans to adopt all possible spot spacings optimally. Moreover, dosimetric benefits can be achieved using smaller spot spacings.