Volumetric Modulated Arc Therapy Quality Assurance Research Articles

A StarGAN and transformer-based hybrid classification-regression model for multi-institution VMAT patient-specific quality assurance.

The field of artificial intelligence (AI)-based patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi-institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi-institution data is critical for enabling large-scale integration and analysis. This study aims to develop a star generative adversarial network (StarGAN) and transformer-based hybrid classification-regression PSQA framework to address unification of heterogeneous data from different institutions. A StarGAN and transformer-based hybrid classification-regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as "Pass" or "Fail" at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation. For the 3%/3, 3%/2, and 2%/2mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root-mean-square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework. The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA.

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

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. 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. 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. 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.

Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based on dose distribution analysis using unsupervised deep learning approach and analyzed 161 prostate VMAT beams measured with a cylindrical detector. For performing error simulation, in addition to error-free dose distribution, dose distributions containing nine types of error, including multileaf collimator (MLC) positional errors, gantry rotation errors, radiation output errors and phantom setup errors, were generated. Only error-free data were employed for the model training, and error-free and error data were employed for the tests. As a deep learning model, the variational autoencoder (VAE) was adopted. The anomaly of test data was quantified by calculating Mahalanobis distance based on the feature vectors acquired from a trained encoder. Based on this anomaly, test data were classified as 'error-free' or 'any-error.' For comparison with conventional approaches, gamma (γ)-analysis was performed, and supervised learning convolutional neural network (S-CNN) was constructed. Receiver operating characteristic curves were obtained to evaluate their performance with the area under the curve (AUC). For all error types, except systematic MLC positional and radiation output errors, the performance of the methods was in the order of S-CNN ˃ VAE-based ˃ γ-analysis (only S-CNN required error data for model training). For example, in random MLC positional error simulation, the AUC of our method, S-CNN and γ-analysis were 0.699, 0.921 and 0.669, respectively. Our results showed that the VAE-based method has the potential to detect errors in patient-specific VMAT QA.

Patient-Specific Pre-Treatment VMAT Plan Verification Using Gamma Passing Rates

Continuous gantry motion, continuous beam modulation, and variable dose rate are used in volumetric modulated arc therapy (VMAT) to obtain highly conformal radiation therapy dose distributions. Several errors during daily radiation therapy treatment can be sources of uncertainties in dose delivery. These errors include monitor unit calculation errors and other human mistakes. Due to the uncertainties in the excessively modulated VMAT plan, the intended dose distribution is not delivered perfectly, leading to a mismatch between the measured and planned dose distributions. This necessitates an extensive and effective quality assurance (QA) program for both machine and patient. In this study, VMAT QA plan verification of 62 head and neck (HN) and 19 prostate cases was done using Octavius 4D setup with its associating VeriSoft gamma analysis software. The plans showed a maximum 3D gamma passing rate with 4 mm/3 % gamma acceptance criteria, i.e., 99.7 % for the HN cancer cases and 99.5 % for the prostate cancer cases. Local gamma analysis was also performed for both regions. Furthermore, 2D and volumetric gamma analyses were also carried out. Gamma analysis with respect to different axis was also carried out. It was known that the transversal axis showed the highest gamma passing rate in both HN and prostate cases, i.e., 99.17 % and 98.3 %, respectively. The transverse axis came to be a better fit for the planned dose distribution.

A novel and clinically useful weight-optimized dynamic conformal arc in stereotactic radiation therapy of non-small cell lung cancer: Dosimetric comparison of treatment plans with volumetric‐modulated arc therapy

PurposeThis study constitutes a feasibility assessment of dynamic conformal arc (DCA) therapy as an alternative to volumetric‐modulated arc therapy (VMAT) for stereotactic body radiation therapy (SBRT) of lung cancer with the free-breathing technique using four-dimensional computed tomography. MethodsA total of 25 patients who received 50 Gy in four fractions treated using VMAT technique having two partial coplanar arcs with 6 MV beams for non-small cell lung cancer (NSCLC) were included. Plans were re‐planned using a novel and clinically technique of weight optimized based on dynamic conformal Arcs with two coplanar partial dynamic conformal arcs (WO-DCA). For the two partial arcs, MLC aperture around the PTV was automatically generated at different margins for both arcs and maintained dynamically around the target during arc rotation. Weight of the two arcs using optimization method was adjusted between the arcs to maximize tumor coverage and protect organs at risk (OAR). The clinical VMAT and WO-DCA plans were compared via the RTOG-0915 protocol for conformity and dose to the organs at risk (OAR). Additionally, delivery efficiency, quality assurance pass rate, monitor unit and beam treatment time were recorded. ResultsThe mean value of quality assurance (QA) pass rate 98.16 ± 1.27 in WO-DCA and 92.87 ± 1.56 in VMAT. The rate was higher in WO-DCA (p < 0.001 and t = 8.75). The values of beam‐on time (BOT) and monitor units (MU) in the VMAT technique were 4.20 (3.45–4.95) and 3155 (2279–4867) and they were 3.10 (2.85–3.35) and 2167 (1702–2948) in WO-DCA. These values were significantly improved with WO-DCA (p < 0.001 and p < 0.001) ConclusionsAs there is, no beam modulation through the target, WO‐DCA plans could potentially minimize small‐field dosimetry error without MLC interplay effects via respiratory motion and provide similar doses to OAR and the tumor while providing faster treatment delivery by significantly reducing MU and BOT in lung cancer for tumors of appropriate localization. Additionally, providing WO‐DCA eliminate patient‐specific VMAT quality assurance; potentially offering cost‐effective, same day SBRT treatments.

Predictive gamma passing rate of 3D detector array-based volumetric modulated arc therapy quality assurance for prostate cancer via deep learning.

To predict the gamma passing rate (GPR) of the three-dimensional (3D) detector array-based volumetric modulated arc therapy (VMAT) quality assurance (QA) for prostate cancer using a convolutional neural network (CNN) with the 3D dose distribution. One hundred thirty-five VMAT plans for prostate cancer were selected: 110 plans were used for training and validation, and 25 plans were used for testing. Verification plans were measured using a helical 3D diode array (ArcCHECK). The dose distribution on the detector element plane of these verification plans was used as input data for the CNN model. The measured GPR (mGPR) values were used as the training data. The CNN model comprises eighteen layers and predicted GPR (pGPR) values. The mGPR and pGPR values were compared, and a cumulative frequency histogram of the prediction error was created to clarify the prediction error tendency. The correlation coefficients of pGPR and mGPR were 0.67, 0.69, 0.66, and 0.73 for 3%/3-mm, 3%/2-mm, 2%/3-mm, and 2%/2-mm gamma criteria, respectively. The respective mean±standard deviations of pGPR-mGPR were -0.87±2.18%, -0.65±2.93%, -0.44±2.53%, and -0.71±3.33%. The probabilities of false positive error cases (pGPR < mGPR) were 72%, 60%, 68%, and 56% for each gamma criterion. We developed a deep learning-based prediction model of the 3D detector array-based VMAT QA for prostate cancer, and evaluated the accuracy and tendency of prediction GPR. This model can provide a proactive estimation for the results of the patient-specific QA before the verification measurement.

Retrospective analysis of portal dosimetry pre-treatment quality assurance of intracranial SRS/SRT VMAT treatment plans

AbstractBackground:The complexity associated with the treatment planning and delivery of stereotactic radiosurgery (SRS) or stereotactic radiotherapy (SRT) volumetric modulated arc therapy (VMAT) plans which employs continuous dynamic modulation of dose rate, field aperture and gantry speed necessitates diligent pre-treatment patient-specific quality assurance (QA). Numerous techniques for pre-treatment VMAT treatment plans QA are currently available with the aid of several different devices including the electronic portal imager (EPID). Although several studies have provided recommendations for gamma criteria for VMAT pre-treatment QA, there are no specifics for SRS/SRT VMAT QA. Thus, we conducted a study to evaluate intracranial SRS/SRT VMAT QA to determine clinical action levels for gamma criteria based on the institutional estimated means and standard deviations.Materials and methods:We conducted a retrospective analysis of 118 EPID patient-specific pre-treatment QA dosimetric measurements of 47 brain SRS/SRT VMAT treatment plans using the integrated Varian solution (RapidArcTM planning, EPID and Portal dosimetry system) for planning, delivery and EPID QA analysis. We evaluated the maximum gamma (γmax), average gamma (γave) and percentage gamma passing rate (%GP) for different distance-to-agreement/dose difference (DTA/DD) criteria and low-dose thresholds.Results:The gamma index analysis shows that for patient-specific SRS/SRT VMAT QA with the portal dosimetry, the mean %GP is ≥98% for 2–3 mm/1–3% and Field+0%, +5% and +10% low-dose thresholds. When applying stricter spatial criteria of 1 mm, the mean %GP is &gt;90% for DD of 2–3% and ≥88% for DD of 1%. The mean γmax ranges: 1·32 ± 1·33–2·63 ± 2·35 for 3 mm/1–3%, 1·57 ± 1·36–2·87 ± 2·29 for 2 mm/1–3% and 2·36 ± 1·83–3·58 ± 2·23 for 1 mm/1–3%. Similarly the mean γave ranges: 0·16 ± 0·06–0·19 ± 0·07 for 3 mm/1–3%, 0·21 ± 0·08–0·27 ± 0·10 for 2 mm/1–3% and 0·34 ± 0·14–0·49 ± 0·17 for 1 mm/1–3%. The mean γmax and mean γave increase with increased DTA and increased DD for all low-dose thresholds.Conclusions:The establishment of gamma criteria local action levels for SRS/SRT VMAT pre-treatment QA based on institutional resources is imperative as a useful tool for standardising the evaluation of EPID-based patient-specific SRS/SRT VMAT QA. Our data suggest that for intracranial SRS/SRT VMAT QA measured with the EPID, a stricter gamma criterion of 1 mm/2% or 1 mm/3% with ≥90% %GP could be used while still maintaining an in-control QA process with no extra burden on resources and time constraints.

Novel Clinically Weight-Optimized Dynamic Conformal Arcs (WO-DCA) for Liver SBRT: A Comparison with Volumetric Modulated Arc Therapy (VMAT).

PurposeTo evaluate the feasibility of shortening the duration of liver stereotactic radiotherapy (SBRT) without jeopardizing dosimetry or conformity by utilizing weight-optimized dynamic conformal arcs (WO-DCA) as opposed to volumetric modulated arc therapy (VMAT) for tumors away from critical structures.MethodsNineteen patients with liver metastasis were included, previously treated with 50 Gy in 4 fractions with VMAT technique using two partial coplanar arcs of 6 MV beams delivered in high-definition multi-leaf collimator (HD-MLC). Two coplanar partial WO-DCA were generated on Pinnacle treatment planning system (TPS) for each patient; and MLC aperture around the planning target volume (PTV) was automatically generated at different margins for both arcs and maintained dynamically around the target during arc rotation. Weight of the two arcs using optimization method was adjusted between the arcs to maximize tumor coverage and protect organs at risk (OAR) based on the RTOG‐0438 protocol.ResultsThe WO-DCA plans successfully “agreed” with the standard VMAT for OAR (liver, spinal cord, stomach, duodenum, small bowel, and heart) and PTV (Dmean, D98%, D2%, CI, and GI), with superior mean quality assurance (QA) pass rate (97.06 vs 93.00 for VMAT; P < 0.001 and t = 8.87). Similarly, the WO-DCA technique additionally reduced the beam‐on time (3.26 vs 4.43; P < 0.001) and monitor unit (1860 vs 2705 for VMAT; P < 0.001) values significantly.ConclusionThe WO‐DCA plans might minimize small‐field dosimetry errors and defeat patient‐specific VMAT QA requirements due to the omission of MLC beam modulation through the target volume. The WO‐DCA plans may additionally enable faster treatment delivery times and lower OAR without sacrificing target doses in SBRT of liver tumors away from critical structures.

Error detection model developed using a multi‐task convolutional neural network in patient‐specific quality assurance for volumetric‐modulated arc therapy

In patient-specific quality assurance (QA) for static beam intensity-modulated radiation therapy (IMRT), machine-learning-based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two-dimensional detector, they have not been extended to the analysis of volumetric-modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient-specific VMAT QA. A total of 161 beams from 104 prostate VMAT plans were analyzed. All beams were measured using a cylindrical detector (Delta4; ScandiDos, Uppsala, Sweden), and predicted dose distributions in a cylindrical phantom were calculated using a treatment planning system (TPS). In addition to the error-free plan, we simulated 12 types of errors: two types of multileaf collimator positional errors (systematic or random leaf offset of 2mm), two types of monitor unit (MU) scaling errors (±3%), two types of gantry rotation errors (±2° in clockwise and counterclockwise direction), and six types of phantom setup errors (±1mm in lateral, longitudinal, and vertical directions). The error-introduced predicted dose distributions were created by editing the calculated dose distributions using a TPS with in-house software. Those 13 types of dose difference maps, consisting of an error-free map and 12 error maps, were created from the measured and predicted dose distributions and were used to train the convolutional neural network (CNN) model. Our model was a multi-task model that individually detected each of the 12 types of errors. Two datasets, Test sets 1 and 2, were prepared to evaluate the performance of the model. Test set 1 consisted of 13 types of dose maps used for training, whereas Test set 2 included the dose maps with 25 types of errors in addition to the error-free dose map. The dose map, which introduced 25 types of errors, was generated by combining two of the 12 types of simulated errors. For comparison with the performance of our model, gamma analysis was performed for Test sets 1 and 2 with the criteria set to 3%/2mm and 2%/1mm (dose difference/distance to agreement). For Test set 1, the overall accuracy of our CNN model, gamma analysis with the criteria set to 3%/2mm, and gamma analysis with the criteria set to 2%/1mm was 0.92, 0.19, and 0.81, respectively. Similarly, for Test set 2, the overall accuracy was 0.44, 0.42, and 0.95, respectively. Our model outperformed gamma analysis in the classification of dose maps containing a single type error, and the performance of our model was inferior in the classification of dose maps containing compound errors. A multi-task CNN model for detecting errors in patient-specific VMAT QA using a cylindrical measuring device was constructed, and its performance was evaluated. Our results demonstrate that our model was effective in identifying the error type in the dose map for VMAT QA.

Application programming interface guided QA plan generation and analysis automation.

PurposeLinear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process.MethodsAutomating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light‐radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6‐month period.ResultsThe AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field‐matched junction and ball‐bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%.ConclusionThe automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.

Comparison of pretreatment VMAT quality assurance with the integral quality monitor (IQM) and electronic portal imaging device (EPID).

The purpose of this study was to compare pretreatment volumetric modulated arc therapy (VMAT) quality assurance (QA) measurements and evaluate the multileaf collimator (MLC) error sensitivity of two detectors: the integral quality monitor (IQM) system (iRT systems IQM) and the electronic portal imaging device (EPID) (Varian PortalVision aS1200). Pretreatment QA measurements were performed for 20 retrospective VMAT plans (53 arcs). A subset of ten plans (23 arcs) was used to investigate MLC error sensitivity of each device. Eight MLC error plans were created for each VMAT plan. The errors included systematic opening/closing (±0.25, ±0.50, ±0.75 mm) of the MLC and random positional errors (1 mm) for individual/groups of leaves. The IQM was evaluated using the percent error of the measured cumulative signal relative to the calculated signal. The EPID was evaluated using two methods: a novel percent error of the measured relative to the predicted cumulative signals, and gamma (γ) analysis (1%/1 mm, 2%/2 mm, 3%/3 mm and 3%/1 mm for Stereotactic Body Radiation Therapy plans). The average change in maximum dose obtained from dose‐volume histogram (DVH) data and change in detector signals for different systematic MLC shifts was also compared. Cumulative signal differences showed similar levels of agreement between measured and expected detector signals (IQM: 1.00 ± 0.55%; EPID: 1.22 ± 0.92%). Results from γ analysis lacked specificity. Only the 1%/1 mm criteria produced data with remarkable differences. A strong linear correlation was observed between IQM and EPID cumulative signal differences with MLC error magnitude (R = 0.99). Likewise, results indicate a strong correlation between the cumulative signal for both detectors and DVH dose (RIQM = 0.99; REPID = 0.97). In conclusion, use of cumulative signal differences could be more useful for detecting errors in treatment delivery in EPID than γ analysis.

Preliminary study on monitoring patient-specific volumetric modulated arc therapy quality assurance process with statistical process control methodology on the basis of TG-218 report

Patient-specific volumetric modulated arc therapy (VMAT) quality assurance (QA) process is an important component of the implementation process of clinical radiotherapy. The tolerance limit and action limit of discrepancies between the calculated dose and the delivered radiation dose are the key parts of the VMAT QA processes as recognized by the AAPM TG-218 report, however, there is no unified standard for these two values among radiotherapy centers. In this study, based on the operational recommendations given in the AAPM TG-218 report, treatment site-specific tolerance limits and action limits of gamma pass rate in VMAT QA processes when using ArcCHECK for dose verification were established by statistical process control (SPC) methodology. The tolerance limit and action limit were calculated based on the first 25 in-control VMAT QA for each site. The individual control charts were drawn to continuously monitor the VMAT QA process with 287 VMAT plans and analyze the causes of VMAT QA out of control. The tolerance limits for brain, head and neck, abdomen and pelvic VMAT QA processes were 94.56%, 94.68%, 94.34%, and 92.97%, respectively, and the action limits were 93.82%, 92.54%, 93.23%, and 90.29%, respectively. Except for pelvic, the tolerance limits for the brain, head and neck, and abdomen were close to the universal tolerance limit of TG-218 (95%), and the action limits for all sites were higher than the universal action limit of TG-218 (90%). The out-of-control VMAT QAs were detected by the individual control chart, including one case of head and neck, two of the abdomen and two of the pelvic site. Four of them were affected by the setup error, and one was affected by the calibration of ArcCHECK. The results show that the SPC methodology can effectively monitor the IMRT/VMAT QA processes. Setting treatment site-specific tolerance limits is helpful to investigate the cause of out-of-control VMAT QA.

Quality assurance of VMAT on flattened and flattening filter-free accelerators using a high spatial resolution detector.

PurposeThis study investigated the use of high spatial resolution solid‐state detectors (DUO and Octa) combined with an inclinometer for machine‐based quality assurance (QA) of Volumetric Modulated Arc Therapy (VMAT) with flattened and flattening filter‐free beams.MethodThe proposed system was inserted in the accessory tray of the gantry head of a Varian 21iX Clinac and a Truebeam linear accelerator. Mutual dependence of the dose rate (DR) and gantry speed (GS) was assessed using the standard Varian customer acceptance plan (CAP). The multi‐leaf collimator (MLC) leaf speed was evaluated under static gantry conditions in directions parallel and orthogonal to gravity as well as under dynamic gantry conditions. Measurements were compared to machine log files.ResultsDR and GS as a function of gantry angle were reconstructed using the DUO/inclinometer and in agreement to within 1% with the machine log files in the sectors of constant DR and GS. The MLC leaf speeds agreed with the nominal speeds and those extracted from the machine log files to within 0.03 cm s−1. The effect of gravity on the leaf motion was only observed when the leaves traveled faster than the nominal maximum velocity stated by the vendor. Under dynamic gantry conditions, MLC leaf speeds ranging between 0.33 and 1.42 cm s−1 were evaluated. Comparing the average MLC leaf speeds with the machine log files found differences between 0.9% and 5.7%, with the largest discrepancy occurring under conditions of fastest leaf velocity, lowest DR and lowest detector signal.ConclusionsThe investigation on the use of solid‐state detectors in combination with an inclinometer has demonstrated the capability to provide efficient and independent verification of DR, GS, and MLC leaf speed during dynamic VMAT delivery. Good agreement with machine log files suggests the detector/inclinometer system is a useful tool for machine‐specific VMAT QA.

VMAT treatment plan acceptability and quality assurance study for prostate cancer in radiotherapy

AbstractAim:The aim of this work was to study the acceptability of plans prepared for prostate patients treated by volumetric modulated arc therapy (VMAT) with the vision to evaluate the quality of plans and test pre-treatment quality assurance (QA).Material methods:VMAT plans of 35 patients, planned on the Eclipse Treatment Planning System (Aria 15), were included in the study. Plan acceptability was checked using statistical analysis, which includes homogeneity index, radical and median homogeneity index, coverage and uniformity index. Dose–volume histograms (DVH) of the plans were also studied to check prescribed dose (PD), Dmax, Dmin, D5 and D95. Portal dosimetry was also done by gamma analysis using 3%/3 mm criterion. SD and mean SD error were also calculated and analysed.Results:Statistical analysis showed a mean HI of 1·054, coverage 0·959, UI 1·055, mDHI 0·962 and rDHI 0·866. SD of HI, coverage, UI, mDHI and rDHI was 0·019, 0·019, 0·014, 0·013 and 0·030, respectively. From the DVHs, mean of D5, D95, Dmin and Dmax was calculated at 6,252·9, 5,757·4, 6,413·3 and 5,657·7 cGy, respectively, with a prescribed dose of 6,000 cGy. According to gamma analysis, area gamma &lt; 1 was 99·12% with a tolerance limit of 95%, maximum gamma was 1·466 with a tolerance limit of 3·5, average gamma was 0·388 with a tolerance limit of 0·5, area gamma &gt; 1·2 was 0·242% with a tolerance limit of 0·5%, maximum dose difference was 0·6 with a tolerance limit of 1·0 and average dose difference was 0·029 with a tolerance limit of 0·2.Conclusion:All three computations showed the results to be within acceptable limits. VMAT possesses a unique feature of delivering the whole treatment with only two rotations of the gantry. VMAT has an improved efficiency of delivery for equivalent dosimetric quality.

Considerations for the verification of volumetric modulated arc therapy-planned dose distributions

Background: Volumetric modulated arc therapy (VMAT) is the standard of care for many clinical indications, but should only be considered with proper technical support and quality assurance (QA) in place. Despite the high accuracy of VMAT systems, errors can be present and adequate verification is required. Dosimetric VMAT verification systems have a broadly similar analysis philosophy. However, many factors influence the analyses and the subsequent QA outcome, based on which the plan will pass or fail.Aim: This study investigated various factors that influence the dosimetric impact and detectability of known linac component deviations on VMAT QA, including geometries, tissue densities, gamma criteria and dose–volume differences.Setting: Universitas Hospital (Annex), Bloemfontein, South Africa.Methods: Deliberate multi-leaf collimator (MLC)-bank offsets were introduced on four different VMAT plans of the prostate, nasopharynx and brain. Measured reference dose sets were compared to measured QA results, using the IBA Dolphin© detector and Compass© software for three dosimetric scenarios. Gamma pass rates over a range of criteria from 1%/2-mm to 4%/4-mm in the total volumes and per structure, as well as dose–volume differences were studied.Results: Gamma tests in the total patient/phantom did not sufficiently detect errors. The calculation media did not influence the QA outcome greatly. However, the detection geometry affected the results. Per structure gamma analyses provided superior error detection, although still missed some clinically relevant differences. The addition of dose–volume analyses highlighted several important errors.Conclusion: Volumetric modulated arc therapy using only total volume gamma analyses can easily overlook clinically relevant errors. The choice of gamma criterion is crucial. Verification with at least a per structure gamma test in combination with dose–volume checks is recommended, especially in small target volume cases.

Error detection sensitivity test using complex errors on three patient-specific VMAT QA systems

The purpose of this study was to investigate error detection sensitivity for three patient-specific volumetric modulated arc therapy (VMAT) quality assurance (QA) systems (Delta4, EPID-based dosimetry, and log file) with three possible scenarios. Ten patient-specific VMAT QA were randomly selected to test their error detection sensitivities. Artificial complex errors were introduced to the original plans then the QA tests were repeated. These errors were simulated into three possible scenarios: uncertainty, miss-calibration, and worst-case scenario. For uncertainty scenario, the random errors (σ) of multi-leaf collimators (MLC) at ± 2.0 mm and gantry angle at ± 2.0 degree were introduced. The systematic errors of +2MU, and the random errors of MLC and gantry angle at ± 2.0 mm and ± 2.0 degree were applied as a miss-calibration scenario. For worst case scenario, errors were integrated between systematic and random variation of MLC and gantry angle at 2±0.5 mm and 2±0.5 degree, respectively. The dosimetric agreements between QA tests on original versus artificial error plans were determined to investigate error detection sensitivity used gamma analysis with 3%, 3 mm criteria. EPID-based dosimetry showed the most sensitive QA tool to detect three possible scenarios. Log file was the second best method, whereas Delta4 was the worst method to detect three possible scenario errors.

Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy

To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans. Three hundred three VMAT plans, including 176 gynecologic cancer and 127 head and neck cancer plans, were chosen in this study. Fifty-four complexity metrics were extracted from the QA plans and considered as inputs. Patient-specific QA was performed, and gamma passing rates (GPRs) were used as outputs. One Poisson lasso (PL) regression model was developed, aiming to predict individual GPR, and 1 random forest (RF) classification model was developed to classify QA results as "pass" or "fail." Both technical validation (TV) and clinical validation (CV) were used to evaluate the model reliability. GPR prediction accuracy of PL and classification performance of PL and RF were evaluated. In TV, the mean prediction error of PL was 1.81%, 2.39%, and 4.18% at 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. No significant differences in prediction errors between TV and CV were observed. In QA results classification, PL had a higher specificity (accurately identifying plans that can pass QA), whereas RF had a higher sensitivity (accurately identifying plans that may fail QA). By using 90% as the action limit at a 3%/2 mm criterion, the specificity of PL and RF was 97.5% and 87.7% in TV and 100% and 71.4% in CV, respectively. The sensitivity of PL and RF was 31.6% and 100% in TV and 33.3% and 100% in CV, respectively. With 100% sensitivity, the QA workload of 81.2% of plans in TV and 62.5% of plans in CV could be reduced by RF. The PL model could accurately predict GPR for most VMAT plans. The RF model with 100% sensitivity was preferred for QA results classification. Machine learning can be a useful tool to assist VMAT QA and reduce QA workload.

Computational model of a 3D dosimetry system - ArcCHECK®

Ionizing radiation therapies have been improving over the years, becoming more specific for each patient. Thereby, as the treatment planning system (TPS) complexities increases, the quality assurance (QA) methods have to be in constant evolution. One of the techniques that demand great complexity is the Volumetric Modulated Arc Therapy (VMAT), and one possible way to VMAT commissioning is using 3D dosimetry systems. Recently a new 3D dosimetry system called ArcCHECK had been developed and commercialized mainly for VMAT quality assurance. It is water-equivalent and composed by an array of 1386 diodes arranged in a helical pattern. Since simulation methods, like Monte Carlo method, ensure highly accurate results, MCNP (A General Monte Carlo N-Particle Transport Code System) is totally reliable for problems that involve radiation transport. This work presents two computational models for Monte Carlo simulations to predict detection responses in the QA procedure using the ArcCHECK. The computational models were validated comparing the system responses obtained from MCNP6 simulations against measured responses due to a 6 MeV clinical photon beam from a Linear Accelerator. 2D dose maps were reported here with good agreement between simulated and measured values.

Characterization of EPID software for VMAT transit dosimetry.

Dosimetry check (DC) is a commercial software that allows reconstruction of 3D dose distributions using transit electronic portal imaging device (EPID) images. In this work, we evaluated the suitability of DC software for volumetric modulated arc therapy (VMAT) transit dosimetry. The volumetric gamma agreement index 3%/3mm between twenty VMAT dose distributions reconstructed by DC and calculated with treatment planning system (TPS) were compared to those obtained using PTW OCTAVIUS®4D to assess DC accuracy in VMAT quality assurance (QA). The sensitivity of DC in detecting VMAT delivery and set-up errors and anatomical variations has been investigated by measuring the variation of the gamma agreement index before and after the introduction of specific errors in four VMAT plans related to different anatomical sites. The influence of dose computation algorithm in presence of density inhomogeneity was also assessed. The assessment of VMAT QA shows agreements with TPS maps comparable to OCTAVIUS® 4D (OCT) in homogeneous phantom (p < 0.001). DC mean gamma agreement index was 94.2%±3.4, versus 95.6%±2.5 of OCT, lower dose threshold was set to 10%. Introduction of deliberate errors resulted in lower gamma agreement index and in 38/56 cases the gamma agreement index was over the detection threshold. The dose computation algorithm of DC is accurate in all anatomical sites except lung. However in lung cases, the aqua vivo approach used in this work reduced the algorithm dependence of DC results. DC accurately reproduced VMAT 3D dose distributions in phantom and is sensitive to detect errors caused by delivery inaccuracy and anatomical variations of patients.

Daily Based Quality Assurance of Volumetric Modulated Arc Therapy for the Full Session of Treatment

Volumetric modulated arc therapy (VMAT) with highly complex advanced delivering technique, is widely practiced in radiation therapy and requires a quality assurance for treatment plan at least once a treatment session. In this study we collected six VMAT patients with different treatment sites. The VMAT plans and their quality assurance (QA) plans were performed on a daily basis before treatment for their full period of treatment. We tried to evaluate the accuracy of their delivering through analyses of first mechanical error of multileaf collimator (MLC) movement and second dosimetric error of beam delivery. We used a MATLAB program and portal dosimetry to estimate a maximum root mean square (RMS) error from dynalog files for mechanical analysis and a gamma index from portal dosimetry for dosimetric analysis, respectively. We introduced newly in this study a representative VMAT QA to be used to perform a routine VMAT QA. The daily variation of maximum error RMSs for all treatment sites were all below maximum 0.3503 ± 0.0002 mm for treatment plans and QA plans and they all showed the gamma pass rate of more than 98%. Mean of inter difference between treatment and QA plans stayed below 0.04 mm, showing that QA deliveries agree well with treatment deliveries. The Linac used kept a good performance within the tolerances in American Association of Physicists in Medicine (AAPM) TG142 guideline for the period of research. We suggest that the representative VMAT QA can be a good surrogate for real VMAT plan and “whole time” plan QA for a patient selected randomly at least once a year be performed to confirm the long-term stability of Linac performance.