Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM Task Group No. 218

Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools

Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Opportunities and challenges of upright patient positioning in radiotherapy

Objective.Upright positioning has seen a surge in interest as a means to reduce radiotherapy (RT) cost, improve patient comfort, and, in selected cases, benefit treatment quality. In particle therapy (PT) in particular, eliminating the need for a gantry can present massive cost and facility footprint reduction. This review discusses the opportunities of upright RT in perspective of the open challenges.Approach.The clinical, technical, and workflow challenges that come with the upright posture have been extracted from an extensive literature review, and the current state of the art was collected in a synergistic perspective from photon and particle therapy. Considerations on future developments and opportunities are provided.Main results.Modern image guidance is paramount to upright RT, but it is not clear which modalities are essential to acquire in upright posture. Using upright MRI or upright CT, anatomical differences between upright/recumbent postures have been observed for nearly all body sites. Patient alignment similar to recumbent positioning was achieved in small patient/volunteer cohorts with prototype upright positioning systems. Possible clinical advantages, such as reduced breathing motion in upright position, have been reported, but limited cohort sizes prevent resilient conclusions on the treatment impact. Redesign of RT equipment for upright positioning, such as immobilization accessories for various body regions, is necessary, where several innovations were recently presented. Few clinical studies in upright PT have already reported promising outcomes for head&neck patients.Significance.With more evidence for benefits of upright RT emerging, several centers worldwide, particularly in PT, are installing upright positioning devices or have commenced upright treatment. Still, many challenges and open questions remain to be addressed to embed upright positioning firmly in the modern RT landscape. Guidelines, professionals trained in upright patient positioning, and large-scale clinical studies are required to bring upright RT to fruition.

Investigation of the linear accelerator low dose rate mode for pulsed low-dose-rate radiotherapy delivery

Purpose. Pulsed volumetric modulated arc therapy (VMAT) was proposed as an advanced treatment that combines the biological benefits of pulsed low dose rate (PLDR) and the dosimetric benefits of the intensity-modulated beams. In our conventional pulsed VMAT technique, a daily fractional dose of 200 cGy is delivered in 10 arcs with 3 min intervals between the arcs. In this study, we are testing the feasibility of pulsed VMAT that omits the need to split into ten arcs and excludes any beam-off gaps.Methods. The study was conducted using computed tomographic images of 24 patients previously treated at our institution with the conventional PLDR technique. Our newly installed Elekta machine has a low dose rate option on the order of 25 MU min-1. PLDR requires an effective dose rate of 6.7 cGy min-1with attention being paid to the maximum dose received within any point within the target not to exceed 13 cGy min-1. The quality of treatment plans was judged based on dose-volume histograms, isodose distribution, dose conformality to the target, and target dose homogeneity. The dose delivery accuracy was assessed by measurements using theMatriXXEvolution2D array system.Results. All cases were normalized to cover 95% of the target volume with 100% of the prescription dose. The average conformity index was 1.03 ± 0.08 while the average homogeneity index was 1.05 ± 0.02. The maximum reported dose rate at any point within the target was 10.44 cGy min-1. The mean dose rate for all pulsed VMAT plans was 6.88 ± 0.1 cGy min-1. All cases passed our gamma analysis with an average passing rate of 99.00% ± 0.48%.Conclusion. The study showed the applicability of planning pulsed VMAT using Eclipse and its successful delivery on our Elekta linac. Pulsed VMAT using the machine's low dose rate mode is more efficient than our previous pulsed VMAT delivery.

Portability of IMRT QA between matched linear accelerators

PURPOSE

To determine if patient-specific IMRT quality assurance can be measured on any matched treatment delivery system (TDS) for patient treatment delivery on another.

METHODS

Three VMAT plans of varying complexity were created for each available energy for head and neck, SBRT lung, and right chestwall anatomical sites. Each plan was delivered on three matched Varian TrueBeam TDSs to the same Scandidos Delta4 Phantom+ diode array with only energy-specific device calibrations. Dose distributions were corrected for TDS output and then compared to TPS calculations using gamma analysis. Round-robin comparisons between measurements from each TDS were also performed using point-by-point dose difference, median dose difference, and the percent of point dose differences within 2% of the mean metrics.

RESULTS

All plans had more than 95% of points passing a gamma analysis using 3%/3 mm criteria with global normalization and a 20% threshold when comparing measurements to calculations. The tightest gamma analysis criteria where a plan still passed > 95% were similar across delivery systems-within 0.5%/0.5 mm for all but three plan/energy combinations. Median dose deviations in measurement-to-measurement comparisons were within 0.7% and 1.0% for global and local normalization, respectively. More than 90% of the point differences were within 2%.

CONCLUSION

A set of plans spanning available energies and complexity levels were delivered by three matched TDSs. Comparisons to calculations and between measurements showed dose distributions delivered by each TDS using the same DICOM RT-plan file meet tolerances much smaller than typical clinical IMRT QA criteria. This demonstrates each TDS is modeled to a similar accuracy by a common class (shared) beam model. Additionally, it demonstrates that dose distributions from one TDS show small differences in median dose to the others. This is an important validation component of the common beam model approach, allowing for operational improvements in the clinic.

Adapting outside the box: Simulation-free MR-guided stereotactic ablative radiotherapy for prostate cancer

BACKGROUND AND PURPOSE

Magnetic resonance (MR)-guided radiotherapy (MRgRT) enhances treatment precision and adaptive capabilities, potentially supporting a simulation-free (sim-free) workflow. This work reports the first clinical implementation of a sim-free workflow using the MR-Linac for prostate cancer patients treated with stereotactic ablative radiotherapy (SABR).

MATERIALS AND METHODS

Fifteen patients who had undergone a prostate-specific membrane antigen positron emission tomography/CT (PSMA-PET/CT) scan as part of diagnostic workup were included in this work. Two reference plans were generated per patient: one using PSMA-PET/CT (sim-free plan) and the other using standard simulation CT (simCT plan). Dosimetric evaluations included comparisons between simCT, sim-free, and first fraction plans. Timing measurements were conducted to assess durations for both simCT and sim-free pre-treatment workflows.

RESULTS

All 15 patients underwent successful treatment using a sim-free workflow. Dosimetric differences between simCT, sim-free, and first fraction plans were minor and within acceptable clinical limits, with no major violations of standardised criteria. The sim-free workflow took on average 130 min, while the simCT workflow took 103 min.

CONCLUSION

This work demonstrates the feasibility and benefits of sim-free MR-guided adaptive radiotherapy for prostate SABR, representing the first reported clinical experience in an ablative setting. By eliminating traditional simulation scans, this approach reduces patient burden by minimising hospital visits and enhances treatment accessibility.

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

PURPOSE

To 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.

METHODS

A 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.

RESULTS

The 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.

CONCLUSIONS

The 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.

Dosimetric effects of small field size, dose grid size, and variable split-arc methods on gamma pass rates in radiation therapy

This study investigates the influence of calculation accuracy in peripheral low-dose regions on the gamma pass rate (GPR), utilizing the Acuros XB (AXB) algorithm and ArcCHECK™ measurement. The effects of varying small field sizes, dose grid sizes, and split-arc techniques on GPR were analyzed. Various small field sizes were employed. Thirty-two single-arc plans with dose grid sizes of 2 mm and 1 mm and prescribed doses of 2, 5, 10, and 20 Gy were calculated using the AXB algorithm. In total, 128 GPR plans were examined. These plans were categorized into three sub-fields (3SF), four sub-fields (4SF), and six sub-fields (6SF). The GPR results deteriorated with smaller target sizes and a 2 mm dose grid size in a single arc. A similar degradation in GPR was observed with smaller target sizes and a 1 mm dose grid size. However, the 1 mm dose grid size generally resulted in better GPR compared with the 2 mm dose grid size for the same target sizes. The GPR improved with finer split angles and a 2 mm dose grid size in the split-arc method. However, no statistically significant improvement was observed with finer split angles and a 1 mm dose grid size. This study demonstrates that coarser dose grid sizes result in lower GPRs in peripheral low-dose regions as calculated by AXB with ArcCHECK™ measurement. To enhance GPR, employing split-arc methods and finer dose grid sizes could be beneficial.

Multi-granularity prior networks for uncertainty-informed patient-specific quality assurance

Deep Learning Automated Patient-Specific Quality Assurance (PSQA) aims to reduce clinical resource requirements. It is vital to ensure the safety and effectiveness of radiation therapy by predicting the dose difference metric (Gamma passing rate) and its distribution. However, current research overlooks uncertainty quantification in model predictions, limiting their trustworthiness in real clinical environments. This paper proposes a Multi-granularity Uncertainty Quantification (MGUQ) framework. A Bayesian framework that quantifies uncertainties at multiple granularities for multi-task PSQA, specifically Gamma Passing Rate (GPR) prediction and Dose Difference Prediction (DDP), integrates visualization-based interactive components. Using Bayesian theory, we derive a comprehensive multi-granularity loss function that comprises granularity-specific loss and coherence loss components. Additionally, we proposed Multi-granularity Prior Networks, a dual-stream network architecture, to infer the distributions of DDP (modeled as t-distributions) and GPR (modeled as Gaussian distributions) under specific statistical assumptions. Comprehensive evaluations are conducted on a dataset from ''Peeking Union Medical College Hospital'', and results show that our proposed method achieves a minimum MAE loss of 0.864 with a 2%/3 mm criterion and realizes the uncertainty visualization of dose difference. Further, it also achieves 100% Clinical Accuracy (CA) with a workload of 67.2%. Experiments demonstrate that the proposed framework can enhance the trustworthiness of deep learning applications in PSQA.

In-beam PET treatment monitoring of carbon therapy patients: Results of a clinical trial at CNAO

PURPOSE

Carbon ion therapy treatments can be monitored non-invasively with in-beam Positron Emission Tomography (PET). At CNAO the INSIDE in-beam PET scanner has been used in a clinical trial (NCT03662373) to monitor cancer treatments with proton and carbon therapy. In this work we present the analysis results of carbon therapy data, acquired during the first phase of the clinical trial, analyzing data of nine patients treated at CNAO for various malignant tumors in the head-and-neck region.

MATERIALS AND METHODS

The patient group contained two patients requiring replanning, and seven patients without replanning, based on established protocols. For each patient the PET images acquired along the course of treatment were compared with a reference, applying two analysis methods: the beam-eye-view (BEV) method and the γ-index analysis. Time trends in several parameters were investigated, as well as the agreement with control CTs, if available.

RESULTS

Regarding the BEV-method, the average sigma value σ was 3.7 mm of range difference distributions for patients without changes (sensitivity of the INSIDE detector). The 3D-information obtained from the BEV analysis was partly in agreement with what was observed in the control CT. The data quality and quantity was insufficient for a definite interpretation of the time trends.

CONCLUSION

We analyzed carbon therapy data acquired with the INSIDE in-beam PET detector using two analysis methods. The data allowed to evaluate sensitivity of the INSIDE detector for carbon therapy and to make several recommendations for the future.

3D small-scale dosimetry and tumor control of 225Ac radiopharmaceuticals for prostate cancer

Radiopharmaceutical therapy using α -emitting225 Ac is an emerging treatment for patients with advanced metastatic cancers. Measurement of the spatial dose distribution in organs and tumors is needed to inform treatment dose prescription and reduce off-target toxicity, at not only organ but also sub-organ scales. Digital autoradiography with α -sensitive detection devices can measure radioactivity distributions at 20-40 μ m resolution, but anatomical characterization is typically limited to 2D. We collected digital autoradiographs across whole tissues to generate 3D dose volumes and used them to evaluate the simultaneous tumor control and regional kidney dosimetry of a novel therapeutic radiopharmaceutical for prostate cancer, [225Ac]Ac-Macropa-PEG4-YS5, in mice. 22Rv1 xenograft-bearing mice treated with 18.5 kBq of [225Ac]Ac-Macropa-PEG4-YS5 were sacrificed at 24 h and 168 h post-injection for quantitative α -particle digital autoradiography and hematoxylin and eosin staining. Gamma-ray spectroscopy of biodistribution data was used to determine temporal dynamics and213 Bi redistribution. Tumor control probability and sub-kidney dosimetry were assessed. Heterogeneous225 Ac spatial distribution was observed in both tumors and kidneys. Tumor control was maintained despite heterogeneity if cold spots coincided with necrotic regions.225 Ac dose-rate was highest in the cortex and renal vasculature. Extrapolation of tumor control suggested that kidney absorbed dose could be reduced by 41% while maintaining 90% TCP. The 3D dosimetry methods described allow for whole tumor and organ dose measurements following225 Ac radiopharmaceutical therapy, which correlate to tumor control and toxicity outcomes.

Evaluation of the conformity of intensity-modulated radiation therapy and volumetric modulated arc therapy using AAPM TG 119 protocol

The aim of this work was to evaluate the conformity of intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and verify the accuracy of the planning and delivery system used in this work based on the AAPM TG-119 protocol. The Eclipse 13.6 treatment planning system (TPS) was used to plan the TG-119 test suite, which included four test cases: MultiTarget, Prostate, Head/Neck, and C-Shape for IMRT and VMAT techniques with 6 MV and 10 MV acceleration voltages. The results were assessed and discussed in terms of the TG-119 protocol and the results of previous studies. In addition, point dose and planar dose measurements were done using a semiflex ion chamber and an electronic portal imaging device (EPID), respectively. The planned doses of all test cases met the criteria of the TG-119 protocol, except those for the spinal cord of the C-Shape hard case. There were no significant differences between the treatment planning doses and the doses given in the TG-119 report, with p-values ranging from 0.974 to 1 (p > 0.05). Doses to the target volumes were similar in the IMRT and VMAT plans, but the organs at risk (OARs) doses were different depending on the test case. The planning results showed that IMRT is more conformal than VMAT in certain cases. For the point dose measurements, the confidence limit (CLpoint) of 0.030 and 0.021 were better than the corresponding values of 0.045 and 0.047 given in the TG-119 report for high-dose and low-dose areas, respectively. Regarding the planar dose measurements, the CLplanar value of 0.38 obtained in this work was lower than that given in the TG-119 report (12.4). It is concluded that the dosimetry measurements performed in this study showed better confidence limits than those provided in the TG 119 report. IMRT remains more conformal in certain circumstances than the more progressive VMAT. When selecting the method of delivering a dose to the patient, several factors must be considered, including the radiotherapy technique, energy, treatment site, and tumour geometry.

Machine learning and deep learning prediction of patient specific quality assurance in breast IMRT radiotherapy plans using Halcyon specific complexity indices

INTRODUCTION

New radiotherapy machines such as Halcyon are capable of delivering dose-rate of 600 monitor-units per minute, allowing large numbers of patients treated per day. However, patient-specific quality assurance (QA) is still required, which dramatically decrease machine availability. Innovative artificial intelligence (AI) algorithms could predict QA result based on complexity metrics. However, no AI solution exists for Halcyon machines and the complexity metrics to be used have not been definitively determined. The aim of this study was to develop an AI solution capable of firstly determining the complexity indices to be obtained and secondly predicting patient-specific QA in a routine clinical setting.

METHODS

Three hundred and eighteen beams from 56 patients with breast cancer were used. The seven complexity indices named Modulation-Complexity-Score (MCS), Small-Aperture-Score (SAS10), Beam-Area (BA), Beam-Irregularity (BI), Beam-Modulation (BM), Gantry and Collimator angles were used as input to the AI model. Machine learning (ML) and deep learning (DL) models using tensorflow were set up to predict DreamDose QA conformance.

RESULTS

MCS, BI, gantry and collimator angle are not correlated with QA compliance. Therefore, ML and DL models were trained using SAS10, BA and BM complexity indices. ROC analyses enabled to find best predicted probability threshold to increase specificity and sensitivity. ML models did not show satisfactory performance with an area under-the-curve (AUC) of 0.75 and specificity and sensitivity of 0.88 and 0.86. However, optimised DL model showed better performance with an AUC of 0.95 and specificity and sensitivity of 0.98 and 0.97.

CONCLUSION

The DL model demonstrated a high degree of accuracy in its predictions of the quality assurance (QA) results. Our online predictive QA-platform offers significant time savings in terms of accelerator occupancy and working time.

MR-OCTAVIUS 4D with 1500 MR and 1600 MR arrays is suitable for plan QA in a 1.5 T MRI-linac

To ensure the accuracy of radiation delivery to patients in a 1.5 T MRI-linac, the implementation of quality assurance (QA) devices compatible with MR technology is essential. The OCTAVIUS 4D MR, made by PTW (Freiburg, Germany) is designed to ensure consistent and ideal alignment of its detectors with the direction of each beam segment. This study focuses on investigating the fundamental characteristics of the detector response for the OCTAVIUS Detector (OD) 1500 MR and OCTAVIUS 1600 MR when used in the MR-compatible OCTAVIUS 4D. Characteristics examined included short-term reproducibility, dose linearity, field size dependency, monitor unit (MU) rate dependency, dose-per-pulse dependency, and angular dependency. The evaluation of OD 1500 MR also involved measuring 25 clinical treatment plans across diverse target sizes and anatomical sites, including the liver/pancreas, rectum, prostate, lungs, and lymph nodes. One plan was measured with the standard setup and with a 5 cm left offset. The OD 1600 MR was not available for these measurements. The capability of the OD 1500 MR to identify potential errors was assessed by introducing a MU and positional shift within the software. The results demonstrated no significant differences in short-term reproducibility (<0.2%), dose linearity (<1%), field size dependency (<0.7%for field sizes larger than 5 cm × 5 cm), MU rate dependency (<0.8%), dose-per-pulse dependency (<0.4%) and angular dependency (standard deviation<0.5%). All tests of clinical plans were successfully completed. The OD 1500 MR demonstrated compatibility with the standard 95% pass rate when employing a global 3%/3 mm gamma criterion, and a 90% pass rate using a global 2%/2 mm gamma criterion. The detector demonstrated the capacity to measure treatment plans with a 5 cm left offset. With the standard parameters, the gamma test was sensitive to position errors but required an addition tests of mean/median dose or point dose in order to detect small dose difference.

Translating contrast enhanced computed tomography images to liver radioembolization dose distribution for more comprehensively indicating patients

Objective.Contrast-enhanced computed tomography (CECT) is commonly used in the pre-treatment evaluation of liver Y-90 radioembolization feasibility. CECT provides detailed imaging of the liver and surrounding structures, allowing healthcare providers to assess the size, location, and characteristics of liver tumors prior to the treatment. Here we propose a method for translating CECT images to an expected dose distribution for tumor(s) and normal liver tissue.Approach.A pre-procedure CECT is used to obtain an iodine arterial-phase distribution by subtracting the non-contrast CT from the late arterial phase. The liver segments surrounding the targeted tumor are selected using Couinaud's method. The resolution of the resulting images is then degraded to match the resolution of the positron emission tomography (PET) images, which can image the Y-90 activity distribution post-treatment. The resulting images are then used in the same way as PET images to compute doses using the local deposition method. CECT images from three patients were used to test this method retrospectively and were compared with Y-90 PET-based dose distributions through dose volume histograms.Main results.Results show a concordance between predicted and delivered Y-90 dose distributions with less than 10% difference in terms of mean dose, for doses greater than 10% of the 98th percentile (D2%).Significance.CECT-derived predictions of Y-90 radioembolization dose distributions seem promising as a supplementary tool for physicians when assessing treatment feasibility. This dosimetry prediction method could provide a more comprehensive pre-treatment evaluation-offering greater insights than a basic assessment of tumor opacification on CT images.

Quantified difference of the collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms based on DVH and gamma analysis for cervical cancer radiation therapy

OBJECTIVE

To quantify the difference between the (collapsed cone convolution) CCC algorithm and the (Monte Carlo) MC algorithm and remind that the planners should pay attention to some possible uncertainties of the two algorithms when employing the two algorithms.

METHODS

Thirty patients' cervical cancer VMAT plans were designed with a Pinnacle TPS (Philips) and divided equally into two groups: the simple group (SG, target volume was only the PTV) and the complex group (CG, target volume included the PTV and PGTV). The plans from the Pinnacle TPS were transferred to the Monaco TPS (Elekta). The plans' parameters all remained unchanged, and the dose was recalculated. Gamma passing rates (GPRs) obtained from dose distribution from Pinnacle TPS compared with that from Monaco TPS with SNC software based on three triaxial planes (transverse, sagittal and coronal). GPRs and DVH were used to quantify the difference between the CCC algorithm in pinnacle TPS and the MC algorithm in Monaco TPS.

RESULTS

Among the statistical dose indexes in DVHs from the Pinnacle and Monaco TPSs, there were 7(7/15) dose indexes difference with statistically significant differences in the SG, and 10(10/18) dose indexes difference with statistically significant differences in the CG. With 3%/3 mm criterion, the most (5/6) GPRs were greater than 95% from the SG and CG. But with 2%/2 mm criterion, the most (5/6) GPRs were less than 90% from the two groups. In addition, we found that GPRs were also related to the selected triaxial planes and the complexity of the plan (GPRs varied with the SG and CG).

CONCLUSIONS

Obvious difference between the CCC and MC algorithms from Pinnacle and Monaco TPS. DVH maybe better than 2D gamma analysis on quantifying difference of the CCC and MC algorithms. Some attention should be paid to the uncertainty of the TPS algorithm, especially when the indicator on the DVH is at the critical point of the threshold value, because the algorithm used may overestimate or underestimate the DVH indicator.

Multi-institution single geometry plan complexity characteristics based on IROC phantoms

BACKGROUND

Clinical intensity modulated radiation therapy plans have been described using various complexity metrics to help identify problematic radiotherapy plans. Most previous studies related to the quantification of plan complexity and their utility have relied on institution-specific plans which can be highly variable depending on the machines, planning techniques, delivery modalities, and measurement devices used. In this work, 1723 plans treating one of only four standardized geometries were simultaneously analyzed to investigate how radiation plan complexity metrics vary across four different sets of common objectives.

PURPOSE

To assess the treatment plan complexity characteristics of plans developed for Imaging and Radiation Oncology Core (IROC) phantoms. Specifically, to understand the variability in plan complexity between institutions for a common plan objective, and to evaluate how various complexity metrics differentiate relevant groups of plans.

METHODS

1723 plans treating one of four standardized IROC phantom geometries representing four different anatomical sites of treatment were analyzed. For each plan, 22 MLC-descriptive plan complexity metrics were calculated, and principal component analysis (PCA) was applied to the 22 metrics in order to evaluate differences in plan complexity between groups. Across all metrics, pairwise comparisons of the IROC phantom data were made for the following classifications of the data: anatomical phantom treated, treatment planning system (TPS), and the combination of MLC model and treatment planning system. An objective k-means clustering algorithm was also applied to the data to determine if any meaningful distinctions could be made between different subgroups. The IROC phantom database was also compared to a clinical database from the University of Wisconsin-Madison (UW) which included plans treating the same four anatomical sites as the IROC phantoms using a TrueBeam™ STx and Pinnacle3 TPS.

RESULTS

The IROC head and neck and spine plans were distinct from the prostate and lung plans based on comparison of the 22 metrics. All IROC phantom plan group complexity metric distributions were highly variable despite all plans being designed for identical geometries and plan objectives. The clusters determined by the k-means algorithm further supported that the IROC head and neck and spine plans involved similar amounts of complexity and were largely distinct from the prostate and lung plans, but no further distinctions could be made. Plan complexity in the head and neck and spine IROC phantom plans were similar to the complexity encountered in the UW clinical plans.

CONCLUSIONS

There is substantial variability in plan complexity between institutions when planning for the same objective. For each IROC anatomical phantom treated, the magnitude of variability in plan complexity between institutions is similar to the variability in plan complexity encountered within a single institution database containing several hundred unique clinical plans treating corresponding anatomies in actual patients.

Error detection for radiotherapy planning validation based on deep learning networks

BACKGROUND

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks.

PURPOSE

The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations.

METHOD

We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators.

RESULTS

The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors.

CONCLUSION

When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Feasibility of patient-specific quality assurance (PSQA) for real-time robotic stereotactic body radiotherapy (SBRT) based on tumor motion traces

PURPOSE

To design a patient specific quality assurance (PSQA) process for the CyberKnife Synchrony system and quantify its dosimetric accuracy using a motion platform driven by patient tumor traces with rotation.

METHODS

The CyberKnife Synchrony system was evaluated using a motion platform (MODUSQA) and a SRS MapCHECK phantom. The platform was programed to move in the superior-inferior (SI) direction based on tumor traces. The detector array housed by the StereoPhan was placed on the platform. Extra rotational angles in pitch (head down, 4.0° ± 0.15° or 1.2° ± 0.1°) were added to the moving phantom to examine robot capability of angle correction during delivery. A total of 15 Synchrony patients were performed SBRT PSQA on the moving phantom. All the results were benchmarked by the PSQA results based on static phantom.

RESULTS

For smaller pitch angles, the mean gamma passing rates were 99.75% ± 0.87%, 98.63% ± 2.05%, and 93.11% ± 5.52%, for 3%/1 mm, 2%/1 mm, and 1%/1 mm, respectively. Large discrepancy in the passing rates was observed for different pitch angles due to limited angle correction by the robot. For larger pitch angles, the corresponding mean passing rates were dropped to 93.00% ± 10.91%, 88.05% ± 14.93%, and 80.38% ± 17.40%. When comparing with the static phantom, no significant statistic difference was observed for smaller pitch angles (p = 0.1 for 3%/1 mm), whereas a larger statistic difference was observed for larger pitch angles (p < 0.02 for all criteria). All the gamma passing rates were improved, if applying shift and rotation correction.

CONCLUSIONS

The significance of this work is that it is the first study to benchmark PSQA for the CyberKnife Synchrony system using realistically moving phantoms with rotation. With reasonable delivery time, we found it may be feasible to perform PSQA for Synchrony patients with a realistic breathing pattern.

Technical recommendations for implementation of Volumetric Modulated Arc Therapy and Helical Tomotherapy Total Body Irradiation

As a component of myeloablative conditioning before allogeneic hematopoietic stem cell transplantation (HSCT), Total Body Irradiation (TBI) is employed in radiotherapy centers all over the world. In recent and coming years, many centers are changing their technical setup from a conventional TBI technique to multi-isocenter conformal arc therapy techniques such as Volumetric Modulated Arc Therapy (VMAT) or Helical Tomotherapy (HT). These techniques allow better homogeneity and control of the target prescription dose, and provide more freedom for individualized organ-at-risk sparing. The technical design of multi-isocenter/multi-plan conformal TBI is complex and should be developed carefully. A group of early adopters with conformal TBI experience using different treatment machines and treatment planning systems came together to develop technical recommendations and share experiences, in order to assist departments wishing to implement conformal TBI, and to provide ideas for standardization of practices.

Improving the performance of deep learning models in predicting and classifying gamma passing rates with discriminative features and a class balancing technique: a retrospective cohort study

BACKGROUND

The purpose of this study was to improve the deep learning (DL) model performance in predicting and classifying IMRT gamma passing rate (GPR) by using input features related to machine parameters and a class balancing technique.

METHODS

A total of 2348 fields from 204 IMRT plans for patients with nasopharyngeal carcinoma were retrospectively collected to form a dataset. Input feature maps, including fluence, leaf gap, leaf speed of both banks, and corresponding errors, were constructed from the dynamic log files. The SHAP framework was employed to compute the impact of each feature on the model output for recursive feature elimination. A series of UNet++ based models were trained on the obtained eight feature sets with three fine-tuning methods including the standard mean squared error (MSE) loss, a re-sampling technique, and a proposed weighted MSE loss (WMSE). Differences in mean absolute error, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between the different models.

RESULTS

The models trained with feature sets including leaf speed and leaf gap features predicted GPR for failed fields more accurately than the other models (F(7, 147) = 5.378, p < 0.001). The WMSE loss had the highest accuracy in predicting GPR for failed fields among the three fine-tuning methods (F(2, 42) = 14.149, p < 0.001), while an opposite trend was observed in predicting GPR for passed fields (F(2, 730) = 9.907, p < 0.001). The WMSE_FS5 model achieved a superior AUC (0.92) and more balanced sensitivity (0.77) and specificity (0.89) compared to the other models.

CONCLUSIONS

Machine parameters can provide discriminative input features for GPR prediction in DL. The novel weighted loss function demonstrates the ability to balance the prediction and classification accuracy between the passed and failed fields. The proposed approach is able to improve the DL model performance in predicting and classifying GPR, and can potentially be integrated into the plan optimization process to generate higher deliverability plans.

TRIAL REGISTRATION

This clinical trial was registered in the Chinese Clinical Trial Registry on March 26th, 2020 (registration number: ChiCTR2000031276). https://clinicaltrials.gov/ct2/show/ChiCTR2000031276.

Applying the six-sigma methodology to determine the limits of quality control (QC) tests for a specific linear accelerator

PURPOSE

We aimed to show the framework of the six-sigma methodology (SSM) that can be used to determine the limits of QC tests for the linear accelerator (Linac). Limits for QC tests are individually determined using the SSM.

METHODS AND MATERIALS

The SSM is based on the define-measure-analyze-improve-control (DMAIC) stages to improve the process. In the "define" stage, the limits of QC tests were determined. In the "measure" stage, a retrospective collection of daily QC data using a Machine Performance Check platform was performed from January 2020 to December 2022. In the "analyze" stage, the process of determining the limits was proposed using statistical analyses and process capability indices. In the "improve" stage, the capability index was used to calculate the action limits. The tolerance limit was established using the larger one of the control limits in the individual control chart (I-chart). In the "control" stage, daily QC data were collected prospectively from January 2023 to May 2023 to monitor the effect of action limits and tolerance limits.

RESULTS

A total of 798 sets of QC data including beam, isocenter, collimation, couch, and gantry tests were collected and analyzed. The Collimation Rotation offset test had the min-Cp, min-Cpk, min-Pp, and min-Ppk at 2.53, 1.99, 1.59, and 1.25, respectively. The Couch Rtn test had the max-Cp, max-Cpk, max-Pp, and max-Ppk at 31.5, 29.9, 23.4, and 22.2, respectively. There are three QC tests with higher action limits than the original tolerance. Some data on the I-chart of the beam output change, isocenter KV offset, and jaw X1 exceeded the lower tolerance and action limit, which indicated that a system deviation occurred and reminded the physicist to take action to improve the process.

CONCLUSIONS

The SSM is an excellent framework to use in determining the limits of QC tests. The process capability index is an important parameter that provides quantitative information on determining the limits of QC tests.

Effect of modulation factor and low dose threshold level on gamma pass rates of single isocenter multi-target SRT treatment plans

PURPOSE

SRS MapCHECK (SMC) is a commercially available patient-specific quality assurance (PSQA) tool for stereotactic radiosurgery (SRS) applications. This study investigates the effects of degree of modulation, location off-axis, and low dose threshold (LDT) selection on gamma pass rates (GPRs) between SMC and treatment planning system, Analytical Anisotropic Algorithm (AAA), or Vancouver Island Monte Carlo (VMC++ algorithm) system calculated dose distributions.

METHODS

Volumetric-modulated arc therapy (VMAT) plans with modulation factors (MFs) ranging from 2.7 to 10.2 MU/cGy were delivered to SMC at isocenter and 6 cm off-axis. SMC measured dose distributions were compared against AAA and VMC++ via gamma analysis (3%/1 mm) with LDT of 10% to 80% using SNC Patient software.

RESULTS

Comparing on-axis SMC dose against AAA and VMC++ with LDT of 10%, all AAA-calculated plans met the acceptance criteria of GPR ≥ 90%, and only one VMC++ calculated plan was marginally outside the acceptance criteria with pass rate of 89.1%. Using LDT of 80% revealed decreasing GPR with increasing MF. For AAA, GPRs reduced from 100% at MF of 2.7 MU/cGy to 57% at MF of 10.2 MU/cGy, and for VMC++ calculated plans, the GPRs reduced from 89% to 60% in the same MF range. Comparison of SMC dose off-axis against AAA and VMC++ showed more pronounced reduction of GPR with increasing MF. For LDT of 10%, AAA GPRs reduced from 100% to 83% in the MF range of 2.7 to 9.8 MU/cGy, and VMC++ GPR reduced from 100% to 91% in the same range. With 80% LDT, GPRs dropped from 100% to 42% for both algorithms.

CONCLUSIONS

MF, dose calculation algorithm, and LDT selections are vital in VMAT-based SRT PSQA. LDT of 80% enhances sensitivity of gamma analysis for detecting dose differences compared to 10% LDT. To achieve better agreement between calculated and SMC dose, it is recommended to limit the MF to 4.6 MU/cGy on-axis and 3.6 MU/cGy off-axis.

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA.

Forecasting model for qualitative prediction of the results of patient-specific quality assurance based on planning and complexity metrics and their interrelations. Pilot study

Background

The purpose was to analyse the interrelations between planning and complexity metrics and gamma passing rates (GPRs) obtained from VMAT treatments and build the forecasting models for qualitative prediction (QD) of GPRs results.

Materials and method

802 treatment arcs from the plans prepared for the head and neck, thorax, abdomen, and pelvic cancers were analysed. The plans were verified by portal dosimetry and analysed twice using the gamma method with 3%|2mm and 2%|2mm acceptance criteria. The tolerance limit of GPR was 95%. Red, yellow, and green QDs were established for GPR examination. The interrelations were examined, as well as the analysis of effective differentiation of QD. Three models for QD forecasting based on discriminant analysis (DA), random decision forest (RDF) methods, and the hybrid model (HM) were built and evaluated.

Results

Most of the interrelations were small or moderate. The exception is correlations of the join function with the average number of monitor units per control point (R = 0.893) and the beam aperture with planning target volume (R = 0.897). While many metrics allow for the effective separation of the QDs from each other, the study shows that predicting the values of the QD is possible only through multi-component forecasting models, of which the HM is the most accurate (0.894).

Conclusion

Of the three models explored in this study, the HM, which uses DA methods to predict red QD and RDF methods to predict green and yellow QDs, is the most promising one.

Improvement of treatment plan quality with modified fixed field volumetric modulated arc therapy in cervical cancer

PURPOSE

This study aims to introduce modified fixed field volumetric modulated arc therapy (MF-VMAT) which manually opened the field size by fixing the jaws and comparing it to the typical planning technique, auto field volumetric modulated arc therapy (AF-VMAT) in cervical cancer treatment planning.

METHODS AND MATERIALS

Previously treated twenty-eight cervical cancer plans were retrospectively randomly selected and replanned in this study using two different planning techniques: AF-VMAT and MF-VMAT, resulting in a total of fifty-six treatment plans. In this study, we compared both planning techniques in three parts: (1) Organ at Risk (OARs) and whole-body dose, (2) Treatment plan efficiency, and (3) Treatment plan accuracy.

RESULTS

For OARs dose, bowel bag (p-value = 0.001), rectum (p-value = 0.002), and left femoral head (p-value = 0.001) and whole-body (p-value = 0.000) received a statistically significant dose reduction when using the MF-VMAT plan. Regarding plan efficiency, MF-VMAT exhibited a statistically significant increase in both number of monitor units (MUs) and control points (p-values = 0.000), while beam-on time, maximum leaf travel, average maximum leaf travel, and maximum leaf travel per gantry rotation were statistically significant decreased (p-values = 0.000). In terms of plan accuracy, the average gamma passing rate was higher in the MF-VMAT plan for both absolute dose (AD) (p-value = 0.001, 0.004) and relative dose (RD) (p-value = 0.000, 0.000) for 3%/3 and 3%/2 mm gamma criteria, respectively.

CONCLUSION

The MF-VMAT planning technique significantly reduces OAR doses and decreases the spread of low doses to normal tissues in cervical cancer patients. Additionally, this planning approach demonstrates efficient plans with lower beam-on time and reduced maximum leaf travel. Furthermore, it indicates higher plan accuracy through an increase in the average gamma passing rate compared to the AF-VMAT plan. Consequently, MF-VMAT offers an effective treatment planning technique for cervical cancer patients.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability

BACKGROUND

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures.

PURPOSE

By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability.

METHODS

The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation.

RESULTS

The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific.

CONCLUSION

This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Patient-specific quality assurance of dynamically-collimated proton therapy treatment plans

BACKGROUND

The dynamic collimation system (DCS) provides energy layer-specific collimation for pencil beam scanning (PBS) proton therapy using two pairs of orthogonal nickel trimmer blades. While excellent measurement-to-calculation agreement has been demonstrated for simple cube-shaped DCS-trimmed dose distributions, no comparison of measurement and dose calculation has been made for patient-specific treatment plans.

PURPOSE

To validate a patient-specific quality assurance (PSQA) process for DCS-trimmed PBS treatment plans and evaluate the agreement between measured and calculated dose distributions.

METHODS

Three intracranial patient cases were considered. Standard uncollimated PBS and DCS-collimated treatment plans were generated for each patient using the Astroid treatment planning system (TPS). Plans were recalculated in a water phantom and delivered at the Miami Cancer Institute (MCI) using an Ion Beam Applications (IBA) dedicated nozzle system and prototype DCS. Planar dose measurements were acquired at two depths within low-gradient regions of the target volume using an IBA MatriXX ion chamber array.

RESULTS

Measured and calculated dose distributions were compared using 2D gamma analysis with 3%/3 mm criteria and low dose threshold of 10% of the maximum dose. Median gamma pass rates across all plans and measurement depths were 99.0% (PBS) and 98.3% (DCS), with a minimum gamma pass rate of 88.5% (PBS) and 91.2% (DCS).

CONCLUSIONS

The PSQA process has been validated and experimentally verified for DCS-collimated PBS. Dosimetric agreement between the measured and calculated doses was demonstrated to be similar for DCS-collimated PBS to that achievable with noncollimated PBS.

Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans

Background and purpose

Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity.

Materials and methods

Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013-2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool's impact was assessed after nine months of clinical use.

Results

Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization.

Conclusions

ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.

Multi-centre real-world validation of automated treatment planning for breast radiotherapy

PURPOSE

To present the results of the first multi-centre real-world validation of autoplanning for whole breast irradiation after breast-sparing surgery, encompassing high complexity cases (e.g. with a boost or regional lymph nodes) and a wide range of clinical practices.

METHODS

The 24 participating centers each included 10 IMRT/VMAT/Tomotherapy patients, previously treated with a manually generated plan ('manplan'). There were no restrictions regarding case complexity, planning aims, plan evaluation parameters and criteria, fractionation, treatment planning system or treatment machine/technique. In addition to dosimetric comparisons of autoplans with manplans, blinded plan scoring/ranking was conducted by a clinician from the treating center. Autoplanning was performed using a single configuration for all patients in all centres. Deliverability was verified through measurements at delivery units.

RESULTS

Target dosimetry showed comparability, while reductions in OAR dose parameters were 21.4 % for heart Dmean, 16.7 % for ipsilateral lung Dmean, and 101.9 %, 45.5 %, and 35.7 % for contralateral breast D0.03cc, D5% and Dmean, respectively (all p < 0.001). Among the 240 patients included, the clinicians preferred the autoplan for 119 patients, with manplans preferred for 96 cases (p = 0.01). Per centre there were on average 5.0 ± 2.9 (1SD) patients with a preferred autoplan (range [0-10]), compared to 4.0 ± 2.7 with a preferred manplan ([0,9]). No differences were observed regarding deliverability.

CONCLUSION

The automation significantly reduced the hands-on planning workload compared to manual planning, while also achieving an overall superiority. However, fine-tuning of the autoplanning configuration prior to clinical implementation may be necessary in some centres to enhance clinicians' satisfaction with the generated autoplans.

The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy

Background and purpose

In sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses.

Materials and methods

A total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR).

Results

The correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation rpcm-QA with CQA (max: 0.84) than MQA (max: 0.65). The proportion of rpcm-QA  ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest rpcm-QA occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR's determination coefficients (R2 ) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42).

Conclusions

CQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.

Impact of setup errors on the robustness of linac-based single-isocenter coplanar and non-coplanar VMAT plans for multiple brain metastases

PURPOSE

Patient setup errors have been a primary concern impacting the dose delivery accuracy in radiation therapy. A robust treatment plan might mitigate the effects of patient setup errors. In this reported study, we aimed to evaluate the impact of translational and rotational errors on the robustness of linac-based, single-isocenter, coplanar, and non-coplanar volumetric modulated arc therapy treatment plans for multiple brain metastases.

METHODS

Fifteen patients were retrospectively selected for this study with a combined total of 49 gross tumor volumes (GTVs). Single-isocenter coplanar and non-coplanar plans were generated first with a prescribed dose of 40 Gy in 5 fractions or 42 Gy in 7 fractions to cover 95% of planning target volume (PTV). Next, four setup errors (+1  and +2 mm translation, and +1° and +2° rotation) were applied individually to generate modified plans. Different plan quality evaluation metrics were compared between coplanar and non-coplanar plans. 3D gamma analysis (3%/2 mm) was performed to compare the modified plans (+2 mm and +2° only) and the original plans. Paired t-test was conducted for statistical analysis.

RESULTS

After applying setup errors, variations of all plan evaluation metrics were similar (p > 0.05). The worst case for V100% to GTV was 92.07% ± 6.13% in the case of +2 mm translational error. 3D gamma pass rates were > 90% for both coplanar (+2 mm and +2°) and the +2 mm non-coplanar groups but was 87.40% ± 6.89% for the +2° non-coplanar group.

CONCLUSION

Translational errors have a greater impact on PTV and GTV dose coverage for both planning methods. Rotational errors have a greater negative impact on gamma pass rates of non-coplanar plans. Plan evaluation metrics after applying setup errors showed that both coplanar and non-coplanar plans were robust and clinically acceptable.

Influence of dose calculation algorithms on the helical diode array using volumetric-modulated arc therapy for small targets

BACKGROUND

For patient-specific quality assurance (PSQA) for small targets, the dose resolution can change depending on the characteristics of the dose calculation algorithms.

PURPOSE

This study aimed to evaluate the influence of the dose calculation algorithms Acuros XB (AXB), anisotropic analytical algorithm (AAA), photon Monte Carlo (pMC), and collapsed cone (CC) on a helical diode array using volumetric-modulated arc therapy (VMAT) for small targets.

MATERIALS AND METHODS

ArcCHECK detectors were inserted with a physical depth of 2.9 cm from the surface. To evaluate the influence of the dose calculation algorithms for small targets, rectangular fields of 2×100, 5×100, 10×100, 20×100, 50×100, and 100×100 mm2 were irradiated and measured using ArcCHECK with TrueBeam STx. A total of 20 VMAT plans for small targets, including the clinical sites of 19 brain metastases and one spine, were also evaluated. The gamma passing rates (GPRs) were evaluated for the rectangular fields and the 20 VMAT plans using AXB, AAA, pMC, and CC.

RESULTS

For rectangular fields of 2×100 and 5×100 mm2, the GPR at 3%/2 mm of AXB was < 50% because AXB resulted in a coarser dose resolution with narrow beams. For field sizes > 10×100 mm2, the GPR at 3%/2 mm was > 88.1% and comparable for all dose calculation algorithms. For the 20 VMAT plans, the GPRs at 3%/2 mm were 79.1 ± 15.7%, 93.2 ± 5.8%, 94.9 ± 4.1%, and 94.5 ± 4.1% for AXB, AAA, pMC, and CC, respectively.

CONCLUSION

The behavior of the dose distribution on the helical diode array differed depending on the dose calculation algorithm for small targets. Measurements using ArcCHECK for VMAT with small targets can have lower GPRs owing to the coarse dose resolution of AXB around the detector area.

The effect of Inc parameter on VMAT radiotherapy plans quality for rectal cancer using Monaco TPS

BACKGROUND

To investigate the effect of the Increment of gantry angle (Inc) parameter setting of the Monaco Treatment planning system (Monaco TPS) on the dosimetry and quality parameters of the volumetric modulated arc therapy (VMAT) program for rectal cancer.

METHODS

A retrospective analysis was conducted on 50 patients with rectal cancer who underwent intensity modulated radiation therapy using the Monaco TPS system from 2020 to 2021. Under the same optimization function configuration and other parameter settings, the Inc parameters in the VMAT radiotherapy plan were set to 10°, 20°, 30°, and 40°. The dose-volume histogram (DVH) was used to evaluate the dose distribution of the target area and the radiation dose of the organs at risk (OAR). The differences in the dosimetry of the planning target volume (PTV) and OAR, as well as the gamma pass rate (GPR) were compared.

RESULTS

In terms of target dose, D98, Dmin, HI, and conformity index (CI) of Inc10 group was significantly lower than those of Inc20, 30, and 40 groups (P < 0.05), and D2 of Inc10 group was significantly higher than that of Inc20 group (P = 0.009). We also found CI of Inc20 and 30 were significantly better than that of Inc40 (both P < 0.05). In terms of OAR dose, the study found that the Dmean, Dmin, V50%, V45%, and V40% for the bladder of the Inc10 group were lower than those of the other groups (all P < 0.05), the Dmean for femoral head of the Inc20 group was lower than that of the Inc30 group (P < 0.05), and Inc20 showed a better protective effect on the femoral head. The MUs tend to decrease as the Inc parameter setting is increased. The monitor unit (MU) in Inc10 group were significantly higher than those in Inc20, Inc30, and Inc40 groups, and the MU of Inc20 group was significantly higher than that of Inc40 group (both P < 0.05). We found that for the 3%/3 mm and 2%/2 mm standards, the GPRs of each plan were > 90%, which met clinical requirements.

CONCLUSIONS

Different settings of Inc parameters have varying degrees of impact on target dose, OAR dose, and machine MU. It is important for doctors to choose different Inc parameters according to different clinical needs.

Development of an algorithm for proton dose calculation in magnetic fields

BACKGROUND

The advantages of proton therapy can be further enhanced with online magnetic resonance imaging (MRI) guidance. One of the challenges in the realization of MRI-guided proton therapy (MRPT) is accurately calculating the radiation dose in the presence of magnetic fields.

PURPOSE

This study aims to develop an efficient and accurate proton dose calculation algorithm adapted to the presence of magnetic fields.

METHODS

An analytical-numerical radiation dose calculation algorithm, Proton and Ion Dose Engine (PRIDE), was developed. The algorithm combines the pencil beam algorithm (PBA) with a novel iterative voxel-based ray-tracing algorithm. The new ray-tracing method uses fewer assumptions and ensures broader applicability for proton beam trajectory prediction in magnetic fields, and has been compared to Wolf's method and Schellhammer's method. The accuracy of PRIDE algorithm was validated on three phantoms and two practical plans (one single-field water plan and one prostate tumor plan) in different magnetic field strengths up to 3.0 T. The validation was performed by comparing the results against the Monte Carlo (MC) simulations, using the global gamma index criteria of 2%/2 mm and 3%/3 mm with a 10% threshold.

RESULTS

PRIDE showed good agreement with MC in homogeneous and slab heterogeneous phantom, achieving gamma passing rates (%GPs) above 99% for 2%/2 mm criteria when magnetic field strength is not greater than 1.5 T. Although the agreement decreased for scenarios involving high proton energy (240 MeV) and strong magnetic field (3.0 T), the 2%/2 mm %GPs still remained above 98%. In lateral heterogeneous phantom, the accuracy of PRIDE decreased due to the PBA's limitation. For the two practical plans in different magnetic fields, %GPs exceeded 98% and 99% for 2%/2 mm and 3%/3 mm criteria, respectively.

CONCLUSIONS

PRIDE can perform efficient and accurate proton dose calculation in magnetic fields up to 3.0 T, and is expected to work as a useful tool for proton dose calculation in MRPT.

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy

PURPOSE

The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery.

METHODS

A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database. Multi-correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates.

RESULTS

The MLC positional deviation and effective impact factors show a strong multi-correlation (R = 0.701, p-value < 0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5-fold cross-validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235 cGy, while the relative volume deviation remained small at 0.470%-5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of -0.506 to -0.720 and p-value < 0.05) but marginally with dosage deviations (with correlation coefficient < 0.498 and p-value > 0.05).

CONCLUSIONS

The RF predictive model provides a prior tool for VMAT quality assurance.

Unifying gamma passing rates in patient-specific QA for VMAT lung cancer treatment based on data assimilation

This study aimed to identify systematic errors in measurement-, calculation-, and prediction-based patient-specific quality assurance (PSQA) methods for volumetric modulated arc therapy (VMAT) on lung cancer and to standardize the gamma passing rate (GPR) by considering systematic errors during data assimilation. This study included 150 patients with lung cancer who underwent VMAT. VMAT plans were generated using a collapsed-cone algorithm. For measurement-based PSQA, ArcCHECK was employed. For calculation-based PSQA, Acuros XB was used to recalculate the plans. In prediction-based PSQA, GPR was forecasted using a previously developed GPR prediction model. The representative GPR value was estimated using the least-squares method from the three PSQA methods for each original plan. The unified GPR was computed by adjusting the original GPR to account for systematic errors. The range of limits of agreement (LoA) were assessed for the original and unified GPRs based on the representative GPR using Bland-Altman plots. For GPR (3%/2 mm), original GPRs were 94.4 ± 3.5%, 98.6 ± 2.2% and 93.3 ± 3.4% for measurement-, calculation-, and prediction-based PSQA methods and the representative GPR was 95.5 ± 2.0%. Unified GPRs were 95.3 ± 2.8%, 95.4 ± 3.5% and 95.4 ± 3.1% for measurement-, calculation-, and prediction-based PSQA methods, respectively. The range of LoA decreased from 12.8% for the original GPR to 9.5% for the unified GPR across all three PSQA methods. The study evaluated unified GPRs that corrected for systematic errors. Proposing unified criteria for PSQA can enhance safety regardless of the methods used.

MU variability in CBCT-guided online adaptive radiation therapy

PURPOSE

CBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large.

METHODS

Dosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array.

RESULTS

MU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose.

CONCLUSIONS

MU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy

PURPOSE

To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning.

METHODS

The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis.

RESULTS

The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365.

CONCLUSIONS

The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

The impact of the titanium cranial hardware in proton single-field uniform dose plans

BACKGROUND

Neurosurgical cranial titanium mesh and screws are commonly encountered in postoperative radiation therapy. However, only a limited number of reports are available in the context of proton therapy, resulting in a lack of consensus among the proton centers regarding the protocol for handling the hardware.

PURPOSE

This study is to examine the impact of the hardware in proton plans. The results serve as evidence for proton centers to generate standard operating procedures to manage the hardware in proton treatment.

METHODS

Plans with different gantry angles and material overrides are generated on the CT images of a phantom made of the hardware. The dose distributions of the plans with and without material override, at different depths are compared. Films and ionization chambers are used to measure the plans and the measurements are compared to the treatment planning system (TPS) calculations by gamma analysis.

RESULTS

There are some overdose and underdose regions downstream of the hardware. The overdose and underdose values are within a few percent of the prescribed dose when multiple fields with large hinge angles are used. The gamma analysis results show that the measurements agree with the TPS calculations within limits that are clinically relevant.

CONCLUSION

The study has demonstrated the influence of the hardware on proton plans. Based on the result of this study, a standard operating procedure of managing the hardware has been implemented in our clinic.

Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator

Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a deep learning (DL) system for oMRgRT to predict the gamma passing rate (GPR). This study collected 125 verification plans [reference plan (RP), 100; adapted plan (AP), 25] from patients with prostate cancer treated using Elekta Unity. Based on our previous study, we employed a convolutional neural network that predicted the GPRs of nine pairs of gamma criteria from 1%/1 mm to 3%/3 mm. First, we trained and tested the DL model using RPs (n = 75 and n = 25 for training and testing, respectively) for its optimization. Second, we tested the GPR prediction accuracy using APs to determine whether the DL model could be applied to APs. The mean absolute error (MAE) and correlation coefficient (r) of the RPs were 1.22 ± 0.27% and 0.29 ± 0.10 in 3%/2 mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2 mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1 mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2 mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2 mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1 mm, respectively. The time cost was within 3 s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.

Clinical association between plan complexity and the local-recurrence-free-survival of non-small-cell lung cancer patients receiving stereotactic body radiation therapy

PURPOSE

To investigate the clinical impact of plan complexity on the local recurrence-free survival (LRFS) of non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).

METHODS

Data from 123 treatment plans for 113 NSCLC patients were analyzed. Plan-averaged beam modulation (PM), plan beam irregularity (PI), monitor unit/Gy (MU/Gy) and spherical disproportion (SD) were calculated. The γ passing rates (GPR) were measured using ArcCHECK 3D phantom with 2 %/2mm criteria. High complexity (HC) and low complexity (LC) groups were statistically stratified based on the aforementioned metrics, using cutoffs determined by their significance in correlation with survival time, as calculated using the R-3.6.1 packages. Kaplan-Meier analysis, Cox regression, and Random Survival Forest (RSF) models were employed for the analysis of local recurrence-free survival (LRFS). Propensity-score-matched pairs were generated to minimize bias in the analysis.

RESULTS

The median follow-up time for all patients was 25.5 months (interquartile range 13.4-41.2). The prognostic capacity of PM was suggested using RSF, based on Variable Importance and Minimal Depth methods. The 1-, 2-, and 3-year LRFS rates in the HC group were significantly lower than those in the LC group (p = 0.023), when plan complexity was defined by PM. However, no significant difference was observed between the HC and LC groups when defined by other metrics (p > 0.05). All γ passing rates exceeded 90.5 %.

CONCLUSIONS

This study revealed a significant association between higher PM and worse LRFS in NSCLC patients treated with SBRT. This finding offers additional clinical evidence supporting the potential optimization of pre-treatment quality assurance protocols.

Error detection using a multi-channel hybrid network with a low-resolution detector in patient-specific quality assurance

PURPOSE

This study aimed to develop a hybrid multi-channel network to detect multileaf collimator (MLC) positional errors using dose difference (DD) maps and gamma maps generated from low-resolution detectors in patient-specific quality assurance (QA) for Intensity Modulated Radiation Therapy (IMRT).

METHODS

A total of 68 plans with 358 beams of IMRT were included in this study. The MLC leaf positions of all control points in the original IMRT plans were modified to simulate four types of errors: shift error, opening error, closing error, and random error. These modified plans were imported into the treatment planning system (TPS) to calculate the predicted dose, while the PTW seven29 phantom was utilized to obtain the measured dose distributions. Based on the measured and predicted dose, DD maps and gamma maps, both with and without errors, were generated, resulting in a dataset with 3222 samples. The network's performance was evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1-score, ROC curves, and normalized confusion matrix. Besides, other baseline methods, such as single-channel hybrid network, ResNet-18, and Swin-Transformer, were also evaluated as a comparison.

RESULTS

The experimental results showed that the multi-channel hybrid network outperformed other methods, demonstrating higher average precision, accuracy, sensitivity, specificity, and F1-scores, with values of 0.87, 0.89, 0.85, 0.97, and 0.85, respectively. The multi-channel hybrid network also achieved higher AUC values in the random errors (0.964) and the error-free (0.946) categories. Although the average accuracy of the multi-channel hybrid network was only marginally better than that of ResNet-18 and Swin Transformer, it significantly outperformed them regarding precision in the error-free category.

CONCLUSION

The proposed multi-channel hybrid network exhibits a high level of accuracy in identifying MLC errors using low-resolution detectors. The method offers an effective and reliable solution for promoting quality and safety of IMRT QA.

Characterization of a flexible a-Si:H detector for in vivo dosimetry in therapeutic x-ray beams

BACKGROUND

The increasing use of complex and high dose-rate treatments in radiation therapy necessitates advanced detectors to provide accurate dosimetry. Rather than relying on pre-treatment quality assurance (QA) measurements alone, many countries are now mandating the use of in vivo dosimetry, whereby a dosimeter is placed on the surface of the patient during treatment. Ideally, in vivo detectors should be flexible to conform to a patient's irregular surfaces.

PURPOSE

This study aims to characterize a novel hydrogenated amorphous silicon (a-Si:H) radiation detector for the dosimetry of therapeutic x-ray beams. The detectors are flexible as they are fabricated directly on a flexible polyimide (Kapton) substrate.

METHODS

The potential of this technology for application as a real-time flexible detector is investigated through a combined dosimetric and flexibility study. Measurements of fundamental dosimetric quantities were obtained including output factor (OF), dose rate dependence (DPP), energy dependence, percentage depth dose (PDD), and angular dependence. The response of the a-Si:H detectors investigated in this study are benchmarked directly against commercially available ionization chambers and solid-state diodes currently employed for QA practices.

RESULTS

The a-Si:H detectors exhibit remarkable dose linearities in the direct detection of kV and MV therapeutic x-rays, with calibrated sensitivities ranging from (0.580 ± 0.002) pC/cGy to (19.36 ± 0.10) pC/cGy as a function of detector thickness, area, and applied bias. Regarding dosimetry, the a-Si:H detectors accurately obtained OF measurements that parallel commercially available detector solutions. The PDD response closely matched the expected profile as predicted via Geant4 simulations, a PTW Farmer ionization chamber and a PTW ROOS chamber. The most significant variation in the PDD performance was 5.67%, observed at a depth of 3 mm for detectors operated unbiased. With an external bias, the discrepancy in PDD response from reference data was confined to ± 2.92% for all depths (surface to 250 mm) in water-equivalent plastic. Very little angular dependence is displayed between irradiations at angles of 0° and 180°, with the most significant variation being a 7.71% decrease in collected charge at a 110° relative angle of incidence. Energy dependence and dose per pulse dependence are also reported, with results in agreement with the literature. Most notably, the flexibility of a-Si:H detectors was quantified for sample bending up to a radius of curvature of 7.98 mm, where the recorded photosensitivity degraded by (-4.9 ± 0.6)% of the initial device response when flat. It is essential to mention that this small bending radius is unlikely during in vivo patient dosimetry. In a more realistic scenario, with a bending radius of 15-20 mm, the variation in detector response remained within ± 4%. After substantial bending, the detector's photosensitivity when returned to a flat condition was (99.1 ± 0.5)% of the original response.

CONCLUSIONS

This work successfully characterizes a flexible detector based on thin-film a-Si:H deposited on a Kapton substrate for applications in therapeutic x-ray dosimetry. The detectors exhibit dosimetric performances that parallel commercially available dosimeters, while also demonstrating excellent flexibility results.

Efficacious patient-specific QA for Vertebra SBRT using a high-resolution detector array SRS MapCHECK: AAPM TG-218 analysis

PURPOSE

Patient-specific quality assurance (PSQA) for vertebra stereotactic body radiation therapy (SBRT) presents challenges due to highly modulated small fields with high-dose gradients between the target and spinal cord. This study aims to explore the use of the SRS MapCHECK® (SRSMC) for vertebra SBRT PSQA.

METHODS

Twenty vertebra SBRT treatment plans including prescriptions 20 Gy/1 fraction and 24 Gy/2 fractions were selected for each of Millennium (M)-Multileaf Collimator (MLC), and high-definition (HD)-MLC. All 40 plans were measured using Gafchromic EBT3 film (film) and SRSMC, using the StereoPHAN phantom. Plan complexity was assessed using modulation complexity score (MCS), edge metric (EM) (mm-1), modulation factor (MU/cGy), and average leaf pair opening (ALPO) (mm) and its correlation with gamma-pass rate was investigated. The high dose gradient between the target and the spinal cord was analyzed for film and SRSMC and compared against the treatment planning system (TPS). Applying the methodology proposed by AAPM TG-218, action and tolerance values specific to the SRSMC for vertebra SBRT were determined for β values ranging from 5 to 8.

RESULTS

Film and SRSMC gamma-pass rates showed no correlation (p > 0.05). A moderate negative correlation (R = -0.57, p = 0.01) is present between EM and SRSMC 3%/1 mm gamma-pass rate for HD-MLC plans. Both film and SRSMC accurately measured high dose gradients between the target and the spinal cord (R2 > 0.86, p ≤ 0.05). Notably, dose-gradient of HD-MLC plans is 22% steeper and has a smaller standard deviation to M-MLC plans (p ≤ 0.05). Applying TG-218, the film tolerance limit was 96% with action limit 95% for 5%/1 mm (β = 6) and for the SRSMC tolerance limit was 97% with an action limit of 96% for 4%/1 mm (β = 6).

CONCLUSION

Our findings suggest that universal TG-218 limits may not be suitable for vertebra SBRT PSQA. This study demonstrates that SRSMC is a viable tool for vertebra SBRT PSQA, supported by TG-218 implementation of process-based tolerance and action limits.

Organs-at-risk dose and normal tissue complication probability with dynamic trajectory radiotherapy (DTRT) for head and neck cancer

We compared dynamic trajectory radiotherapy (DTRT) to state-of-the-art volumetric modulated arc therapy (VMAT) for 46 head and neck cancer cases. DTRT had lower dose to salivary glands and swallowing structure, resulting in lower predicted xerostomia and dysphagia compared to VMAT. DTRT is deliverable on C-arm linacs with high dosimetric accuracy.

Independent Monte Carlo dose calculation identifies single isocenter multi-target radiosurgery targets most likely to fail pre-treatment measurement

PURPOSE

For individual targets of single isocenter multi-target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre-treatment Quality Assurance (QA) measurement.

METHODS

Treatment plans from 31 SIMT SRS patients were recalculated in a series of scenarios designed to investigate sources of discrepancy between TPS and independent MC. Targets with > 5% discrepancy in DMean[Gy] after progressing through all scenarios were measured with SRS MapCHECK. A matched pair analysis was performed comparing SRS MapCHECK results for these targets with matched targets having similar characteristics (volume & distance from isocenter) but no such MC dose discrepancy.

RESULTS

Of 217 targets analyzed, individual target mean dose (DMean[Gy]) fell outside a 5% threshold for 28 and 24 targets before and after removing tissue heterogeneity effects, respectively, while only 5 exceeded the threshold after removing effect of patient geometry (via calculation on StereoPHAN geometry). Significant factors affecting agreement between the TPS and MC included target distance from isocenter (0.83% decrease in DMean[Gy] per 2 cm), volume (0.15% increase per cc), and degree of plan modulation (0.37% increase per 0.01 increase in modulation complexity score). SRS MapCHECK measurement had better agreement with MC than with TPS (2%/1 mm / 10% threshold gamma pass rate (GPR) = 99.4 ± 1.9% vs. 93.1 ± 13.9%, respectively). In the matched pair analysis, targets exceeding 5% for MC versus TPS also had larger discrepancies between TPS and measurement with no GPR (2%/1 mm / 10% threshold) exceeding 90% (71.5% ± 16.1%); whereas GPR was high for matched targets with no such MC versus TPS difference (96.5% ± 3.3%, p = 0.01).

CONCLUSIONS

Independent MC complements pre-treatment QA measurement for SIMT SRS by identifying problematic individual targets prior to pre-treatment measurement, thus enabling plan modifications earlier in the planning process and guiding selection of targets for pre-treatment QA measurement.

Incorporating plan complexity into the statistical process control of volumetric modulated arc therapy pre-treatment verifications

BACKGROUND

Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolved challenge.

PURPOSE

The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinical application.

METHODS

A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The 'Identify-Eliminate-Recalculate' method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the control process.

RESULTS

The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highly complex.

CONCLUSIONS

Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plan complexity.

Impact of beam complexity on plan delivery accuracy verification of a transmission detector in volumetric modulated arc therapy

OBJECTIVE

To study the effect of beam complexity on VMAT delivery accuracy evaluated by means of a transmission detector, together with the possibility of scoring plan complexity.

METHODS

43 clinical VMAT plans delivered by a TrueBeam linear accelerator to both Delta4 Discover and Delta4 Phantom+ for patient-specific quality assurance were evaluated. Global Dose-γ analysis, MLC-γ analysis, percentage of leaves with a deviation between planned and measured leaf tip position lower than 1 mm (LD) were computed. Modulation complexity score (MCSv), average leaf travel (LT), a multiplicative combination of LT and MCSv (LTMCS), percentage of leaves with speed lower than 5 mm/s (LS), from 5 to 20 mm/s (MS), higher than 20 mm/s (HS) and the average value of leaf speed (MLCSav) were evaluated by means of an home-made Matlab script.

RESULTS

Dose-γ passing rate showed a moderate correlation with MCSv, LT, MLCSav, LS and HS, while a stronger positive correlation was found with LTMCS. A strong correlation was observed between LD and both LT and leaves speed, while a weak correlation was observed with MCSv. A correlation between MLC-γ pass rate and plan complexity parameters was found except for MCSv; a moderate correlation with LS was observed, while all other parameters showed weak correlations.

CONCLUSIONS

The study confirmed the possibility to establish correlations between plan complexity indices versus dose distribution and MLC parameters measured by a transmissive detector. Further investigation is necessary to define specific values of the complexity indices to evaluate whether a VMAT plan is deliverable as intended.

Commissioning and dosimetric verification of volumetric modulated arc therapy for multiple modalities using electronic portal imaging device-based 3D dosimetry system: a novel approach

The purpose of this study was to validate an electronic portal imaging device (EPID) based 3-dimensional (3D) dosimetry system for the commissioning of volumetric modulated arc therapy (VMAT) delivery for flattening filter (FF) and flattening filter free (FFF) modalities based on test suites developed according to American Association of Physicists in Medicine Task Group 119 (AAPM TG 119) and pre-treatment patient specific quality assurance (PSQA).With ionisation chamber, multiple-point measurement in various planes becomes extremely difficult and time-consuming, necessitating repeated exposure of the plan. The average agreement between measured and planned doses for TG plans is recommended to be within 3%, and both the ionisation chamber and PerFRACTION™ measurement were well within this prescribed limit. Both point dose differences with the planned dose and gamma passing rates are comparable with TG reported multi-institution results. From our study, we found that no significant differences were found between FF and FFF beams for measurements using PerFRACTION™ and ion chamber. Overall, PerFRACTION™ produces acceptable results to be used for commissioning and validating VMAT and for performing PSQA. The findings support the feasibility of integrating PerFRACTION™ into routine quality assurance procedures for VMAT delivery. Further multi-institutional studies are recommended to establish global baseline values and enhance the understanding of PerFRACTION™'s capabilities in diverse clinical settings.

Evaluating AAPM-TG-218 recommendations: Gamma index tolerance and action limits in IMRT and VMAT quality assurance using SunCHECK

PURPOSE

This study aimed to improve the safety and accuracy of radiotherapy by establishing tolerance (TL) and action (AL) limits for the gamma index in patient-specific quality assurance (PSQA) for intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) using SunCHECK software, as per AAPM TG-218 report recommendations.

METHODS

The study included 125 patients divided into six groups by treatment regions (H&N, thoracic and pelvic) and techniques (VMAT, IMRT). SunCHECK was used to calculate the gamma passing rate (%GP) and dose error (%DE) for each patient, for the planning target volume and organs at risk (OARs). The TL and AL were then determined for each group according to TG-218 recommendations. We conducted a comprehensive analysis to compare %DE among different groups and examined the relationship between %GP and %DE.

RESULTS

The TL and AL of all groups were more stringent than the common standard as defined by the TG218 report. The TL and AL values of the groups differed significantly, and the values for the thoracic groups were lower for both VMAT and IMRT. The %DE of the parameters D95%, D90%, and Dmean in the planning target volume, and Dmean and Dmax in OARs were significantly different. The dose deviation of VMAT was larger than IMRT, especially in the thoracic group. A %GP and %DE correlation analysis showed a strong correlation for the planning target volume, but a weak correlation for the OARs. Additionally, a significant correlation existed between %GP of SunCHECK and Delta4.

CONCLUSION

The study established TL and AL values tailored to various anatomical regions and treatment techniques at our institution. Establishing PSQA workflows for VMAT and IMRT offers valuable clinical insights and guidance. We also suggest developing a standard combining clinically relevant metrics with %GP to evaluate PSQA results comprehensively.