Multileaf Collimator Research Articles

Which failures do patient-specific quality assurance systems need to catch?

The Joint AAPM-ESTRO TG-360 is developing a quantitative framework to evaluate treatment verification systems used for patient-specific quality assurance (PSQA). A subgroup was commissioned to determine which potential failure modes had the greatest risk to treatment quality and safety, and therefore should be evaluated as part of the PSQA verification. To create an extensive database of potential radiotherapy failure modes that should be detected by PSQA and to determine their relative importance for maximizing treatment quality. The subgroup consisted of eight physicists from seven countries, including representatives from three international quality assurance groups. We collected error reports from RO-ILS, SAFRON, AAPM TG publications, and other literature, including international audits. We focused on the subset of failure modes that impact whether the planned dose matches the dose received by the patient. We performed a failure-mode-and-effects analysis (FMEA), estimating the severity (S), occurrence (O), and detectability (D) of each failure mode. Detectability was scored assuming that PSQA was not done but other routine clinical QA was performed, which allowed us to see the importance of PSQA for detecting each specific failure mode. We analyzed the risk priority number (RPN=O*S*D), O*S, and severity rankings to determine the priority of each failure mode. We collected 394 error reports, which we categorized into 33 failure modes that underwent FMEA. Five failure modes were in the top ranks for both RPN and O*S analysis: four involving treatment planning system (TPS) commissioning and one regarding patient model errors. The highest-ranking RPN failure modes were: TPS algorithm limitations, TPS commissioning errors [multileaf collimator (MLC) modeling, output factor, percent-depth-dose/tissue-maximum-ratio (PDD/TMR), off-axis factor], and patient weight variation. The highest O*S failure modes were similar, with the addition of external patient position variation and incorrect linear accelerator isocenter and cGy/monitor units calibration. RPN and O*S analyses prioritized failure modes that impacted multiple patients with high occurrence and detectability scores, while severity analysis gave higher priority to single-patient modes with high severity scores. The highest-ranking severity modes were MLC sequence deletion, collision, and TPS isocenter incorrect. We have developed a list of failure modes critical to be detected during PSQA and ranked them in order of importance. The top failure modes emphasize the importance of utilizing a variety of treatment verification systems for PSQA, from secondary dose calculation through in-vivo dosimetry, in order to detect all possible errors. For failure modes in the top quartile, PSQA is critical. Without adequate PSQA, these errors may go undetected unless caught by an external audit. This analysis can be useful for optimizing PSQA workflows and for designing evaluations of treatment verification systems, and will be used by the Joint AAPM-ESTRO TG-360 to determine an appropriate validation strategy.

Dosimetric optimization for dynamic mixed beam arc therapy (DYMBARC).

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. 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. 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 80cm 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% / 2mm (global), 10% dose threshold) is performed on a TrueBeam system in developer mode for one case. 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 2.9Gy 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. 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.

Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net

Abstract Background & Purpose: In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algorithms are generally time-consuming, reducing the efficiency of ART workflow. This study aims to develop an ultra-fast deep-learning (DL) based secondary dose verification algorithm to accurately estimate dose distributions using CT and fluence maps (FMs). 
Methods: We integrated FMs into the CT image domain by explicitly resolving the geometry of treatment delivery. For each gantry angle, an FM was constructed based on the optimized multi-leaf collimator apertures and corresponding monitoring units (MUs). To effectively encode treatment beam configuration, the constructed FMs were back-projected to 30 cm away from the isocenter with respect to the exact geometry of the treatment machines. Then, a 3D U-Net was utilized to take the integrated CT and FM volume as input to estimate dose. Training and validation were performed on 381 prostate cancer cases, with an additional 40 testing cases for independent evaluation of model performance.
Results: The proposed model can estimate dose in ~15 ms for each patient. The average γ passing rate (3%/2mm, 10% threshold) for the estimated dose was 99.9% ± 0.15% on testing patients. The mean dose differences for the planning target volume (PTV) and organs at risk (OARs) were 0.07%±0.34% and 0.48%±0.72%, respectively.
Conclusion: We have developed a geometry-resolved DL framework for accurate dose estimation and demonstrated its potential in real-time online ART doses verification.

Development of an EGSnrc multi-leaf collimator component module and treatment head model for a low-field MRI linear accelerator.

Monte Carlo (MC) modeling of MR-guided radiotherapy (MRgRT) treatment machines enables the characterization of photon/electron interactions in the presence of a magnetic field. The EGSnrc MC code system is a well-established system for radiation dose calculations. The multi-leaf collimator (MLC) component modules presently available within the EGSnrc MC code system do not include a model of the double-focused MLC available on a low-field (0.35T) MRI linear accelerator (MR linac). Here we developed and validated a new component module (CM) for the low-field MRgRT MLC using the EGSnrc/BEAMnrc/DOSXYZnrc code system. We performed detailed modeling of the treatment head and validated the model using measurements and calculations from the vendor-specific treatment planning system (TPS). The detailed geometry of the low-field MR linac MLC and other treatment head structures were modeled using BEAMnrc. Comparisons of DOSXYZnrc simulated dose against measurements and the low-field MR linac TPS for a variety of AAPM TG-53 task group report suggested square and shaped fields, as well as a step-and-shoot intensity-modulated radiotherapy (IMRT) plan, are presented. Our model agrees with both measured and TPS calculated data on average within 2%/2mm (dose/DTA) criterion for square field profiles. Output factors agreed within 1% for field sizes down to 2.49×2.49cm2 and within 2% of TPS data for the smallest field size of 0.83×0.83cm2. Shaped field and IMRT MC calculations agreed with measured and TPS data such that the gamma pass rates (3%/2mm) were 99.5% and (3%/3mm) 96.2%, respectively. We developed and validated an MLC CM (SYNCVRMLC) for the low-field MR linac using the EGSnrc MC code systems. This new CM will facilitate MC computation of fluence and dose distributions using BEAMnrc/DOSXYZnrc for patients treated on the low-field MR linac.

A deep learning-based dose calculation method for volumetric modulated arc therapy

BackgroundVolumetric modulated arc therapy (VMAT) planning optimization involves iterative adjustment of numerous parameters, and hence requires repeatedly dose recalculation. In this study, we used the deep learning method to develop a fast and accurate dose calculation method for VMAT.MethodsThe classical 3D UNet was adopted and trained to learn the physics principle of dose calculation. The inputs included the projected fluence map (FM), computed tomography (CT) images, the radiological depth and the source-to-voxel distance (SVD). The projected FM was generated by projecting the accumulated FM between two consecutive control points (CPs) onto the patient’s anatomy. The accumulated FM was calculated by simulating the movement of the multi-leaf collimator (MLC) from one CP to the next. The dose, calculated by the treatment planning system (TPS), was used as ground truth. 51 head and neck VMAT plans were used, with 43, 1 and 7 cases as training, validation, and testing datasets, respectively. Correspondingly, 7182, 180 and 1260 CP samples were included in the training, validation, and testing datasets.ResultsThis presented method was evaluated by comparing the derived dose distribution to the TPS calculated dose distribution. The dose profiles coincided for both the single CP and the entire plan (summation of all CPs). But the network derived dose was smoother than the TPS calculated dose. Gamma analysis was performed between the network derived dose and the TPS calculated dose. The average gamma pass rate was 96.56%, 98.75%, 98.03% and 99.30% under the criteria of 2% (tolerance) -2 mm (distance to agreement, DTA). 2%-3 mm, 3%-2 mm and 3%-3 mm. No significant difference was observed on the critical indices including the max, mean dose, and the relative volume covered by the 2000 cGy, 4000 cGy and the prescription dose. For one CP, the average computational time of the network and TPS was 0.09s and 0.53s. And for one patient, the average time was 16.51s and 95.60s.ConclusionThe dose distribution derived by the network showed good agreement with the TPS calculated dose distribution. The computational time was reduced to approximate one-sixth of its original duration. Therefore the presented deep learning-based dose calculation method has the potential to be used for planning optimization.

Evaluation of patient-specific quality assurance for fractionated stereotactic treatment plans with 6 and 10MV photon beams in beam-matched linacs.

Beam-matched linear accelerators (LA's) require accurate and precise dosimetry for fractionated stereotactic treatment. In this study, the beam data were validated by comparing the three-beam-matched LA's measured data and the vendor reference data. Upon its validation, the accuracy of the volumetric dose delivery for eighty patient-specific fractionated stereotactic treatment plans was evaluated. Measurements of the percentage depth dose (PDD), beam profiles, output factors (OFs), absolute output, and dynamic multi-leaf collimator (MLC) transmission factors for 6MV and 10MV flattening filter (FF) and flattening filter-free (FFF) photon beams were obtained from three-beam-matched LA's. The patient-specific quality assurance evaluation for all eighty plans was performed using PTW Octavius 1000 SRS™ array detectors for two-dimensional (2D) fluence measurement. The following 2D gamma passing criteria were used: 1%/1mm, 2%/1mm, 1%/2mm, 2%/2mm and 3%/2mm. In all three LA's, gamma analysis for PDD and profile were above 97% with gamma criteria of 1%/1mm. The differences OFs, absolute output, and dynamic MLC transmission factors were less than ± 1% of base value. For all eighty cases, the median passing rates on the three LA's were above 76%, 88%, 92%, 96%, and 98% for the above-mentioned gamma criteria of the three LA's. The beam-matched LA's showed good agreement between the measured and treatment planning system (TPS) calculated values for fractionated stereotactic VMAT plans with 6MV and 10MV (FF and FFF) photon beams. Patients can be shifted and treated on any beam-matched linac without the need of re-planning.

Validation of MLC leaf open time calculation methods for PSQA in adaptive radiotherapy with tomotherapy units.

Treatment delivery safety and accuracy are essential to control the disease and protect healthy tissues in radiation therapy. For usual treatment, a phantom-based patient specific quality assurance (PSQA) is performed to verify the delivery prior to the treatment. The emergence of adaptive radiation therapy (ART) adds new complexities to PSQA. In fact, organ at risks and target volume re-contouring as well as plan re-optimization and treatment delivery are performed with the patient immobilized on the treatment couch, making phantom-based pretreatment PSQA impractical. In this case, phantomless PSQA tools based on multileaf collimator (MLC) leaf open times (LOTs) verifications provide alternative approaches for the Radixact® treatment units. However, their validity is compromised by the lack of independent and reliable methods for calculating the LOT performed by the MLC during deliveries. To provide independent and reliable methods of LOT calculation for the Radixact® treatment units. Two methods for calculating the LOTs performed by the MLC during deliveries have been implemented. The first method uses the signal recorded by the build-in detector and the second method uses the signal recorded by optical sensors mounted on the MLC. To calibrate the methods to the ground truth, in-phantom ionization chamber LOT measurements have been conducted on a Radixact® treatment unit. The methods were validated by comparing LOT calculations with in-phantom ionization chamber LOT measurements performed on two Radixact® treatment units. The study shows a good agreement between the two LOT calculation methods and the in-phantom ionization chamber measurements. There are no notable differences between the two methods and the same results were observed on the different treatment units. The two implemented methods have the potential to be part of a PSQA solution for ART in tomotherapy.

Dosimetric evaluation and gradient analysis of various MLC leaf-width effects in external beam radiation therapy: TrueBeam Vs Halcyon

IntroductionThis study investigates dosimetric influence and gradient-analysis of leaf-width of various Multi-leaf-collimators (MLC) from Truebeam and Halcyon linear accelerators. MethodsThe leaf-width effects of Millennium120 and HD120MLC from Truebeam and SX1 and SX2 from Halcyon were studied using virtual phantom in Eclipse16.1.2 TPS. Target structures consist of bore and wave cylinders of equally spaced projected Planning target volume (PTV) of diameters ranging from 1-to-5 cm and 20 cm length. Treatment plans for all PTVs, and four different MLCs configurations from both machines were created utilizing 6MVFFF beam to deliver dose of 50Gy/25# using IMRT and VMAT. Plans were evaluated using plan quality indices including dose conformity, homogeneity, gradient radius, and Monitor-unit (MU). Also, dose gradients were analyzed by estimating distinct integral volume VD% and paired-t-tests were performed to evaluate statistical differences. ResultsAll the plan satisfied the minimum criteria of D95 %≥prescription dose and V107 %≤2cc. Mean conformity and homogeneity indices for SX1MLC(CI = 0.680,HI = 0.022) were found significantly higher and lower than SX2(CI = 0.746,HI = 0.016), Millennium120 (CI = 0.739,HI = 0.012), and HD120MLC(CI = 0.745,HI = 0.017), respectively. However, CI and HI for SX2, Millennium120, and HD120MLC found comparable. Gradient radius enclosing 50 % of isodose observed maximum and minimum for SX1 and HD120MLC, respectively. Plan MUs for Truebeam MLCs were found approximately 25 % higher than Halcyon MLCs. Dose distribution generated using SX2 and Millennium120 found comparable, however p ≤ 0.05 ascertained substantial differences among SX1, Millennium120, and HD120MLC. ConclusionMLC leaf-width influences intensity-modulation and significantly alters dosimetric outcome depending on the magnitude of the leaf-width. HD120MLC does not show much significant advantages over SX2 and Millennium120 except gradient control. Implications for practiceHalcyon SX1MLC produces least effective plan compared to all other MLC types. However, SX2 and Millennium120 can produce plans of comparable quality except plan MUs difference.

Study of Characteristics of Fluorescent Optical Crystals of Multi-Leaf Collimator of Linear Electron Accelerator By Electa Synergy for the Purpose of the Possible Import Substitution

Study of Characteristics of Fluorescent Optical Crystals of Multi-Leaf Collimator of Linear Electron Accelerator By Electa Synergy for the Purpose of the Possible Import Substitution

Possibilities of Forming Fields of Uneven Irradiation Using a Multi-Leaf Collimator of Electron Accelerators During Radiation Therapy

Possibilities of Forming Fields of Uneven Irradiation Using a Multi-Leaf Collimator of Electron Accelerators During Radiation Therapy

A leaf sequencing algorithm for an orthogonal dual-layer multileaf collimator.

Purpose. Dual layer MLC (DMLC) has have been adopted in several commercial products and one major challenge in DMLC usage is leaf sequencing for intensity-modulated radiation therapy (IMRT). In this study we developed a leaf sequencing algorithm for IMRT with an orthogonal DMLC.Methods and Materials. This new algorithm is inspired by the algorithm proposed by Dai and Zhu for IMRT with single layer MLC (SMLC). It iterately determines a delivery segment intensity and corresponding segment shape for a given fluence matrix and leaves residual fluence matrix to following iterations. The segment intensity is determined according to complexities of residual fluence matrix when segment intensity varies from one to highest level in the matrix. The segment intensity and corresponding segment shape that result least complexity was selected. Although the algorithm framework is similar to Dai and Zhu's algorithm, this new algorithm develops complexity algorithms along with rules for determining segment leaf settings when delivered with orthogonal DMLC. This algorithm has been evaluated with 9 groups of randomly generated fluence matrices with various dimensions and intensity levels. Sixteen fluence matrices generated in Pinnacle system for two clinical IMRT examples were also used for evaluation. Statistical information of leaf sequences generated with this algorithm for both the random and clinical matrices were compared to the results of two typical algorithms for SMLC: that proposed by Dai and Zhu and that proposed by Bortfled.Results. Compared to the SMLC delivery sequences generated with Dai and Zhu's algorithm, the proposed algorithm for orthogonal DMLC delivery reduces the average number of segments by 27.7% ∼ 41.8% for 9 groups of randomly generated fluence matrices and 10.5% ∼ 41.7% for clinical ones. When comparing MU efficiency between different algorithms, it is observed that the proposed algorithm performs better than the optimal efficiency of SMLC delivery when dealing with simple fluence matrices, but slightly worse when handling complex ones.Conclusion. This new algorithm generates leaf sequences for orthogonal DMLC delivery with high delivery efficiency in terms of number of leaf segments. This algorithm has potential to work well with orthogonal DMLC for improving efficiency or quality of IMRT.

Open LEARN: Open access linear accelerator education and augmented reality Navigator

PurposeTo create an open-access Linear Accelerator Education and Augmented Reality Navigator (Open LEARN) via 3D printable objects and interactive augmented reality assets. MethodsThis study describes an augmented reality linear accelerator (linac) model accessible through a QR code and a smartphone to address the challenges of medical physics and radiation oncology trainees in low-to-middle-income countries. ResultsMajor components of a generic linear accelerator are modeled as individual objects. These objects can be 3D printed for hands-on learning and used as interactive 3D assets within the augmented reality app. In the AR app, descriptions are displayed to navigate the components spatially and textually. Items modeled include the treatment couch, klystron, circulator, RF waveguides, electron gun, waveguide, beam steering assemblies, target, collimators, multi-leaf collimators, and imaging systems. The linear accelerator is rendered at nearly 100% of its actual size, allowing users to change magnification and view objects from different angles. ConclusionsThe augmented reality linear accelerators and 3D-printed objects make these complex machines easily accessible with smartphones and 3D-printing technologies, facilitating education and training through physical and virtual interaction.

Personalized selection of unequal sub-arc collimator angles in VMAT for multiple brain metastases

PurposeInvestigating the effects of unequal sub-arc personalized collimator angle selection on the quality of Volumetric Modulated Arc Therapy (VMAT) plans for treating multiple brain metastases. MethodsThis study included 21 patients, each with 2–4 target volumes of multiple brain metastases. Two stereotactic radiotherapy (SRT) approaches were utilized: sub-arc collimator VMAT (SAC-VMAT) and fixed collimator VMAT (FC-VMAT). In the SAC-VMAT group, multi-leaf collimators (MLC) shaped the target area, dividing the full arc into four unequal sub-arcs under the beam's eye view (BEV). Each sub-arc had an appropriate collimator angle selected to mitigate ‘island blocking problems'. Conversely, the FC-VMAT group used a fixed collimator angle of 15° or 345°. A comparative analysis of the dosimetric parameters of the target volumes and normal tissues, along with the monitor units (MU), was conducted between the two groups. ResultsThe mean dose and dose-volume to normal brain tissue (2–26 Gy, with a step of 2 Gy) were significantly lower in the SAC-VMAT group (P < 0.01). There was no statistical difference between the two groups in dose to the target volumes, conformity index (CI), homogeneity index (HI), and other normal tissues (P > 0.05). Compared with the FA-VMAT group, the SAC-VMAT group significantly reduced the gradient index (GI) (4.5 ± 0.59 vs 5.2 ± 0.75, P < 0.001) and MU (1774.33 ± 181.77 vs 2001.0 ± 344.86, P < 0.001). Notably, with an increase in the number of PTV, the SAC-VMAT group demonstrated more significant improvements in the dose-volume of normal brain tissue, GI, and MU. ConclusionsIn this study, personalized selection of the unequal sub-arc collimator angle ensured the prescribed dose to the PTV, CI, and HI, while significantly reducing the GI, MU, and the dose to normal brain tissue in the VMAT plan for multi-target brain metastases in the cohort of cases with 2–4 target volumes. Particularly as the number of targets increase, the advantages of this method become more pronounced.

Technical Note: Investigating of dosimetric leaf gap and leaf transmission factor variations across gantry and collimator angles in volumetric modulation arc therapy.

This study investigates the influence of gantry and collimator angles on the dosimetric leaf gap (DLG) and leaf transmission factor (LTF) in a Varian LINAC equipped with rounded-end multi-leaf collimators (MLCs). While Varian guidelines recommend DLG measurements at zero degrees for both gantry and collimator, this research aims to address the knowledge gap by assessing DLG and LTF variations at different gantry and collimator angles. Measurements were conducted using a Varian TrueBeam LINAC with a Millennium 120-leaf MLC and Eclipse TPS version 16.1. The beams utilized in this study had energies of 6 MV, 10 MV, 6 FFF, and 10 FFF. LTF and DLG were determined using ionization chambers in solid water phantoms at various gantry angles (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). For each gantry angle, measurements were also taken at various collimator angles (0°, 45°, 90°, and 315°). Dosimetric impacts were evaluated through VMAT Picket Fence tests and patient-specific verification using portal dosimetry for 10 clinical VMAT plans. LTF values showed no significant variation across gantry and collimator angles. However, DLG values exhibited notable differences depending on the gantry angle and were independent of the collimator angle. The highest DLG value was observed at a gantry angle of 270 degrees, while the lowest was at 90 degrees. The AXB DLGAverage (averaging seven measurements of DLGs at different gantry angles) model demonstrated the best agreement between measured and calculated dose distributions, indicating the importance of considering averaged DLG values across multiple gantry angles for accurate dose calculations. Our study highlights the variability of DLG with gantry angle alterations, contrary to Varian guidelines recommending DLG measurements at zero gantry angle only. We advocate for utilizing an averaged DLG value from measurements across multiple gantry angles, as outlined in our methodology.

Deep learning for patient-specific quality assurance of volumetric modulated arc therapy: Prediction accuracy and cost-sensitive classification performance

PurposeTo evaluate a deep learning model’s performance in predicting and classifying patient-specific quality assurance (PSQA) results for volumetric modulated arc therapy (VMAT), aiming to streamline PSQA workflows and reduce the onsite measurement workload. MethodsA total of 761 VMAT plans were analyzed using 3D-MResNet to process multileaf collimator images and monitor unit data, with the gamma passing rate (GPR) as the output. Thresholds for the predicted GPR (Th-p) and measured GPR (Th-m) were established to aid in PSQA decision-making, using cost curves and error rates to assess classification performance. ResultsThe mean absolute errors of the model for the test set were 1.63 % and 2.38 % at 3 %/2 mm and 2 %/2 mm, respectively. For the classification of the PSQA results, Th-m was 88.3 % at 2 %/2 mm and 93.3 % at 3 %/2 mm. The lowest cost-sensitive error rates of 0.0127 and 0.0925 were obtained when Th-p was set as 91.2 % at 2 %/2 mm and 96.4 % at 3 %/2 mm, respectively. Additionally, the 2 %/2 mm classifier also achieved a lower total expected cost of 0.069 compared with 0.110 for the 3 %/2 mm classifier. The deep learning classifier under the 2 %/2 mm gamma criterion had a sensitivity and specificity of 100 % (10/10) and 83.5 % (167/200), respectively, for the test set. ConclusionsThe developed 3D-MResNet model can accurately predict and classify PSQA results based on VMAT plans. The introduction of a deep learning model into the PSQA workflow has considerable potential for improving the VMAT PSQA process and reducing workloads.

Robust optimization and assessment of dynamic trajectory and mixed-beam arc radiotherapy: a preliminary study

Objective. Dynamic trajectory radiotherapy (DTRT) and dynamic mixed-beam arc therapy (DYMBARC) exploit non-coplanarity and, for DYMBARC, simultaneously optimized photon and electron beams. Margin concepts to account for set-up uncertainties during delivery are ill-defined for electron fields. We develop robust optimization for DTRT&DYMBARC and compare dosimetric plan quality and robustness for both techniques and both optimization strategies for four cases. Approach. Cases for different treatment sites and clinical target volume (CTV) to planning target volume (PTV) margins, m , were investigated. Dynamic gantry-table-collimator photon paths were optimized to minimize PTV/organ-at-risk (OAR) overlap in beam’s-eye-view and minimize potential photon multileaf collimator (MLC) travel. For DYMBARC plans, non-isocentric partial electron arcs or static fields with shortened source-to-surface distance (80 cm) were added. Direct aperture optimization (DAO) was used to simultaneously optimize MLC-based intensity modulation for both photon and electron beams yielding deliverable PTV-based DTRT&DYMBARC plans. Robust-optimized plans used the same paths/arcs/fields. DAO with stochastic programming was used for set-up uncertainties with equal weights in all translational directions and magnitude δ such that m= 0.7δ . Robust analysis considered random errors in all directions with or without an additional systematic error in the worst 3D direction for the adjacent OARs. Main results. Electron contribution was 7%–41% of target dose depending on the case and optimization strategy for DYMBARC. All techniques achieved similar CTV coverage in the nominal (no error) scenario. OAR sparing was overall better in the DYMBARC plans than in the DTRT plans and DYMBARC plans were generally more robust to the considered uncertainties. OAR sparing was better in the PTV-based than in robust-optimized plans for OARs abutting or overlapping with the target volume, but more affected by uncertainties. Significance. Better plan robustness can be achieved with robust optimization than with margins. Combining electron arcs/fields with non-coplanar photon trajectories further improves robustness and OAR sparing.

A mini review of plan quality and secondary cancer risk in CyberKnife M6 radiosurgery for benign intracranial tumors.

With advancements in medical technology, stereotactic radiosurgery (SRS) has become an essential option for treating benign intracranial tumors. Due to its minimal side effects and high local control rate, SRS is widely applied. This paper evaluates the plan quality and secondary cancer risk (SCR) in patients with benign intracranial tumors treated with the CyberKnife M6 system. The CyberKnife M6 robotic radiosurgery system features both multileaf collimator (MLC) and IRIS variable aperture collimator systems, providing different treatment options. The study included 15 patients treated with the CyberKnife M6 system, examining the differences in plan quality and SCR between MLC and IRIS systems. Results showed that MLC and IRIS plans had equal PTV (planning target volume) coverage (98.57% vs. 98.75%). However, MLC plans demonstrated better dose falloff and conformity index (CI: 1.81 ± 0.26 vs. 1.92 ± 0.27, P = 0.025). SCR assessment indicated that MLC plans had lower cancer risk estimates, with IRIS plans having average LAR (lifetime attributable risk) and EAR (excess absolute risk) values approximately 25% higher for cancer induction and 15% higher for sarcoma induction compared to MLC plans. The study showed that increasing tumor volume increases SCR probability, but there was no significant difference between different plans in PTV and brainstem analyses.

A methodology for computationally generating phase space files for Monte Carlo simulations applied to treatment plans for medical linear accelerators

The application of Monte Carlo (MC) simulation in the context of Medical Linear Accelerator (LinAc) treatment planning has increased in recent decades owing to its accuracy in dose calculations. The Multileaf Collimator (MLC), a component of the LinAc heads, plays an essential role by allowing the precise customization of the treatment beam's shape, effectively targeting the tumor area while minimizing radiation exposure to surrounding healthy structures. During a jaws static treatment, this MLC configuration varies for each angular position of the accelerator gantry, while the rest of the LinAc geometry remains static. To address the operation of the accelerator during the treatment planning avoiding the need of computationally intensive and repetitive simulations, phase-space files (PSF) are widely employed in LinAc MC calculations. This study aims to introduce and validate a methodology for the computational generation of PSFs specifically defined just below the MLC. These PSFs will assume the appropriate shape corresponding to the angle at which the accelerator gantry is positioned during treatment. The core of this methodology involves the creation of a comprehensive database containing precalculated probability functions derived from MC. In order to validate the methodology, simulated dose values of a LinAc emitting a 6 MV photon beam have been employed. The results have been tested against experimental data, and compared with the results from complete simulations, yielding PSFs of varying shapes. Finally, this study reveals that the results obtained by comparing organ dose values computed from the generated PSFs to those from the reference PSF are statistically compatible. This innovative method not only offers a valuable tool for optimizing LinAc treatment planning but also underscores the importance of accurate dose calculations in the field of radiation therapy.

Spatially fractionated radiotherapy (Lattice SFRT) in the palliative treatment of locally advanced bulky unresectable head and neck cancer

ObjectivesLocally advanced bulky unresectable head neck cancer causes significant tumor mass effects, leading to severe symptoms. This study aims to report the safety and outcomes in patients undergoing Lattice spatially fractionated radiotherapy (Lattice SFRT) for locally advanced bulky unresectable head and neck cancer. MethodsPatients with bulky head and neck cancer received Lattice SFRT between June 2022 and June 2023. Lattice SFRT was administered in 2–3 fractions of 12 Gy (Gy) using 6-megavolt (MV) photon beams through a multileaf collimator (MLC) based on VMAT technology. The primary endpoints were symptomatic and tumor response rates. Secondary endpoints were overall survival, local control, and acute and late toxicity rates. Results19 consecutive patients meeting the study criteria were identified, predominantly with squamous cell carcinoma histology. The median patient age was 62 years (range 39–79 years), and the median tumor volume was 208 cc (cc) (range 48–701 cc). All patients completed radiotherapy. Among all investigated patients, 16 of 19 (84.2 %) patients achieved an objective response, including 10 individuals achieved a partial response (PR), with 3 of them exhibiting regression exceeding 75 %. 17 patients showed symptom improvement to varying degrees. Acute toxicity of Radiation Therapy Oncology Group (RTOG) grade 1 or higher occurred in 5 patients, while no grade 3 adverse events was observed. ConclusionsLattice SFRT proves to be a viable treatment option for the palliative management of bulky head and neck cancer. In the palliative setting, Lattice SFRT offers timely symptom relief, enhancing patient quality of life. Treatment toxicity remains within an acceptable range. Continued optimization of Lattice SFRT delivery and patient selection can benefit from further data on the feasibility and efficacy of this radiation modality.

Consideration of Optimal Evaluation Metrics for Internal Gross Tumor Dose Relevant to Tumor Response in Multi-fraction Stereotactic Radiosurgery of Brain Metastasis.

Introduction In stereotactic radiosurgery (SRS) for brain metastasis (BM), the target dose inhomogeneity remains highly variable among modalities, irradiation techniques, and facilities, which can affect tumor response during and after multi-fraction SRS. Volumetric-modulated arcs (VMAs) can provide a concentrically-layered steep dose increase inside a gross tumor volume (GTV) boundarycompared to dynamic conformal arcs. This study was conducted to review the optimal evaluation method for the internal GTV doses relevant to maximal response and local control, specifically to examine the significance of the doses 2 mm and 4 mm inside the GTV boundary in VMA-based SRS. Materials and methods This was a planning study for the clinical scenario of a single BM and targeted 25 GTVs of >0.50 cc, including eight spherical models with diameters of 10-45 mm and 17 clinical BMs (GTV: 0.72-44.33 cc). SRS plans were generated for each GTV using VMA with a 5-mm leaf-width multileaf collimator and the optimization that prioritized the steepness of the dose gradient outside the GTV boundary without any internal dose constraints. The dose prescription and evaluation were based on the GTV D V-0.01 cc, a minimum dose of GTV minus 0.01 cc. Two planning systems were compared for the GTV - 2 mm and GTV - 4 mm structures that were generated by equally reducing 2 mm and 4 mm from the GTV surface. The D eIIVs, a minimum dose of the irradiated isodose volume equivalent to the GTV - 2 mm and GTV - 4 mm, were compared to other common metrics. Results The GTV - 2 mm and GTV - 4 mm volumes differed significantly between the systems. In the spherical GTVs, the irradiated isodose surfaces of GTV D 80% and D 50% corresponded to 0.4-1.6 mm (<2 mm) and 1.0-4.6 mm inside the GTV boundary, respectively. In the 25 GTVs, the GTV - 2 mm coverage with the D eIIV varied from 83.7% to 98.2% (95-98% in 68% of the cases), while the GTV coverage with the GTV - 2 mm D eIIV was 20.2-75.9%. In the 23 GTVs of ≥1.26 cc, the GTV coverage with the GTV - 4 mm D eIIVvaried from 1.9% to 55.6% (<50% in 87% of the cases). No significant difference was observed between the GTV D 50% and the GTV - 2 mm D eIIV, while the GTV - 4 mm D eIIV was significantly higher than the GTV D 50%. No significant correlations were observed between the GTV D 50% and the D eIIVs of the GTV - 2 mm and GTV - 4 mm. Conclusions The doses 2 mm and 4 mm inside a GTV have low correlations with the GTV D 50% and may be more relevant tomaximal response and local control for SRS of BM. The D eIIVinstead of the minimum dose of a fixed % coverage (e.g. D 98%) is suitable for reporting the doses 2 mm and 4 mm inside the GTV boundary in terms of avoiding the over- or under-coverage, with consideration tosubstantial variability inminus margin addition functions among planning systems. In VMA-based SRS with a steep dose gradient, the doses 2-4 mm inside a GTV decrease significantly as the GTV increases, which can attenuate the excessive dose exposure to the surrounding brain in a large BM due to the GTV shrinkage during multi-fraction SRS.