Pretreatment Patient-specific Quality Assurance Research Articles

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.

Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction.

Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

Pretreatment patient-specific quality assurance prediction based on 1D complexity metrics and 3D planning dose: classification, gamma passing rates, and DVH metrics

PurposeHighly modulated radiotherapy plans aim to achieve target conformality and spare organs at risk, but the high complexity of the plan may increase the uncertainty of treatment. Thus, patient-specific quality assurance (PSQA) plays a crucial role in ensuring treatment accuracy and providing clinical guidance. This study aims to propose a prediction model based on complexity metrics and patient planning dose for PSQA results.Materials and methodsPlanning dose, measurement-based reconstructed dose and plan complexity metrics of the 687 radiotherapy plans of patients treated in our institution were collected for model establishing. Global gamma passing rate (GPR, 3%/2mm,10% threshold) of 90% was used as QA criterion. Neural architecture models based on Swin-transformer were adapted to process 3D dose and incorporate 1D metrics to predict QA results. The dataset was divided into training (447), validation (90), and testing (150) sets. Evaluation of predictions was performed using mean absolute error (MAE) for GPR, planning target volume (PTV) HI and PTV CI, mean absolute percentage error (MAPE) for PTV D95, PTV D2 and PTV Dmean, and the area under the receiver operating characteristic (ROC) curve (AUC) for classification. Furthermore, we also compare the prediction results with other models based on either only 1D or 3D inputs.ResultsIn this dataset, 72.8% (500/687) plans passed the pretreatment QA under the criterion. On the testing set, our model achieves the highest performance, with the 1D model slightly surpassing the 3D model. The performance results are as follows (combine, 1D, and 3D transformer): The AUCs are 0.92, 0.88 and 0.86 for QA classification. The MAEs of prediction are 0.039, 0.046, and 0.040 for 3D GPR, 0.018, 0.021, and 0.019 for PTV HI, and 0.075, 0.078, and 0.084 for PTV CI. Specifically, for cases with 3D GPRs greater than 90%, the MAE could achieve 0.020 (combine). The MAPE of prediction is 1.23%, 1.52%, and 1.66% for PTV D95, 2.36%, 2.67%, and 2.45% for PTV D2, and 1.46%, 1.70%, and 1.71% for PTV Dmean.ConclusionThe model based on 1D complexity metrics and 3D planning dose could predict pretreatment PSQA results with high accuracy and the complexity metrics play a leading role in the model. Furthermore, dose-volume metric deviations of PTV could be predicted and more clinically valuable information could be provided.

Is it still necessary to perform measured based pre-treatment patient-specific QA for SRS HyperArc treatments?

The Mobius3D system was validated as a modern secondary check dosimetry system. In particular, our objective has been to assess the suitability of the M3D as pre-treatment patient-specific Quality Assurance (QA) tool for Stereotactic Radiosurgery (SRS) HyperArc (HA) treatments. We aimed to determine whether Mobius3D could safely replace the measurements-based patient-specific QA for this type of treatment. 30 SRS HA treatment plans for brain were selected. The dose distributions, calculated by Mobius and our routinely used algorithm (AcurosXB v.15.6), were compared using gamma analysis index and DVH parameters based on the patient's CT dataset. All 30 plans were then delivered across the ionization chamber in a homogeneous phantom and the measured dose was compared with both M3D and TPS calculated one. The plans were delivered and verified in terms of PSQA using the electronic portal imaging device (EPID) with Portal Dosimetry (PD) and myQA SRS (IBA Dosimetry) detector. Plans that achieved a global gamma passing rate (GPR) ≥ 97% based on 2%/2mm criteria, with both Mobius3D and the conventional methods were evaluated acceptable. Finally, we assessed the capability of the M3D system to detect errors related to the position of the Multi-Leaf Collimator (MLC) in comparison to the analyzed measurement-based systems. No relevant differences were observed in the comparison between the dose calculated on the CT-dataset by M3D and the TPS. Observed discrepancies are imputable to different used algorithms, but no discrepancies related to goodness of plans have been found. Average differences between calculated (M3D and TPS) vs measured dose with ionization chamber were 2.5% (from 0.41% to 3.2%) and 1.81% (from 0.66% to 2.65%), for M3D and TPS, respectively. All plans passed with a gamma passing rate>97% using conventional PSQA methods with a gamma criterion of 2% dose difference and 2mm distance-to-agreement. The average gamma passing rate for the M3D system was determined to be 99.4% (from 97.3% to 100%). Results from this study also demonstrated Mobius has better error detectability than conventional measurement-based systems. Our study shows Mobius3D could be a suitable alternative to conventional measured based QA methods for SRS HyperArc treatments.

TransQA: deep hybrid transformer network for measurement-guided volumetric dose prediction of pre-treatment patient-specific quality assurance

Objective. Performing pre-treatment patient-specific quality assurance (prePSQA) is considered an essential, time-consuming, and resource-intensive task for volumetric modulated arc radiotherapy (VMAT) which confirms the dose accuracy and ensure patient safety. Most current machine learning and deep learning approaches stack excessive convolutional/pooling operations (CPs) to predict prePSQA with two-dimensional or one-dimensional information input. However, these models generally present limitations in explicitly modeling long-range dependency for volumetric dose prediction due to the loss of spatial dose features and the inherent locality of CPs. The purpose of this work is to construct a deep hybrid network by combining the self-attention mechanism-based Transformer with modified U-Net for predicting measurement-guided volumetric dose (MDose) of prePSQA. Approach. The enrolled 307 cancer patients underwent VMAT were randomly divided into 246 and 61 cases for training and testing the model. The input data included computed tomography images, radiotherapy dose images exported from the treatment planning system, as well as the MDose distribution from the verification system. The output was the predicted high-quality voxel-wise prePSQA dose distribution. Main results: qualitative and quantitative experimental results show that the proposed prediction method could achieve comparable or better performance on MDose prediction over other approaches in terms of spatial dose distribution, dose–volume histogram metrics, gamma passing rates, mean absolute error, root mean square error, and structural similarity. Significance. The preliminary results on multiple cancer sites show that our approach can be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

Complexity Metrics and Planning Dose-Based Pretreatment Patient-Specific Quality Assurance Prediction: Classification, Gamma Passing Rates, and DVH Deviation

Patient-specific quality assurance (QA) prediction before treatment is beneficial to the clinical resource allocation and the dosimetric monitoring of the patient plans. The aim of this study is to investigate the potential of complexity metrics of radiotherapy plan and patient planning dose to predict QA result, gamma passing rates and dose-volume indices deviation. Planning dose from treatment planning system (TPS), reconstructed dose from a vendor provided QA phantom and complexity metrics of the 499 radiotherapy plans of patients in our institution from March 2022 to September 2022 were used for methodology verification. Gamma passing rate (3%/2mm,10% threshold) 90% was regarded as criterion of QA pass or fail. A deep learning model ResNet-50 was modified to 3D dose processing and a multilayer perceptron (MLP) with three layers were adopted to extract features from 3D dose and 1D metrics in two parallel ways, then, the features were concatenate together to predict QA results. The dataset was split into 349 for train, 50 for validation and 100 for testing. Evaluation of predictions was based on absolute value deviation and area under the curves (AUC) of receiver operator characteristic (ROC) curve. In this dataset, 71% (355/499) plans pass the pretreatment QA test. For QA passing prediction in 100 testing cases, the AUC of ROC could achieve 0.92. For gamma passing rates prediction, a mean absolute error (MAE) of 1.8% could be observed for cases with gamma passing rates bigger than 90%, and a MAE of 4.5% deviation could be observed for cases with gamma passing rates from 80% to 90%. For PTV ΔD95 (%) and PTV ΔHI (%), the MAE of prediction and ground truth is 1%. The model with only complexity metrics and only 3D dose could achieve the AUC of ROC 0.91 and 0.84, respectively. The complexity metrics and 3D planning dose-based model could predict pretreatment patient specific QA results with high accuracy and the complexity metrics play a leading role in the model. Dose-volume metrics deviations of PTV could be predicted and more clinically useful information could be provided.

Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance.

Tolerance limit is defined on pre-treatment patient specific quality assurance results to identify "out of the norm" dose discrepancy in plan. An out-of-tolerance plan during measurement can often cause treatment delays especially if replanning is required. In this study, we aim to develop an outlier detection model to identify out-of-tolerance plan early during treatment planning phase to mitigate the above-mentioned risks. Patient-specific quality assurance results with portal dosimetry for stereotactic body radiotherapy measured between January 2020 and December 2021 were used in this study. Data were divided into thorax and pelvis sites and gamma passing rates were recorded using 2%/2mm, 2%/1mm, and 1%/1mm gamma criteria. Statistical process control method was used to determine six different site and criterion-specific tolerance and action limits. Using only the inliers identified with our determined tolerance limits, we trained three different outlier detection models using the plan complexity metrics extracted from each treatment field-robust covariance, isolation forest, and one class support vector machine. The hyperparameters were optimized using the F1-score calculated from both the inliers and validation outliers' data. 308 pelvis and 200 thorax fields were used in this study. The tolerance (action) limits for 2%/2mm, 2%/1mm, and 1%/1mm gamma criteria in the pelvis site are 99.1% (98.1%), 95.8% (91.1%), and 91.7% (86.1%), respectively. The tolerance (action) limits in the thorax site are 99.0% (98.7%), 97.0% (96.2%), and 91.5% (87.2%). One class support vector machine performs the best among all the algorithms. The best performing model in the thorax (pelvis) site achieves a precision of 0.56 (0.54), recall of 1.0 (1.0), and F1-score of 0.72 (0.70) when using the 2%/2mm (2%/1mm) criterion. The model will help the planner to identify an out-of-tolerance plan early so that they can refine the plan further during the planning stage without risking late discovery during measurement.

Piloted Pretreatment Patient-Specific Quality Assurance in Preparedness for Clinical Implementation of Intensity Modulated Radiotherapy

Piloted Pretreatment Patient-Specific Quality Assurance in Preparedness for Clinical Implementation of Intensity Modulated Radiotherapy

Virtual pretreatment patient-specific quality assurance of volumetric modulated arc therapy using deep learning.

Automatic patient-specific quality assurance (PSQA) is recently explored using artificial intelligence approaches, and several studies reported the development of machine learning models for predicting the gamma pass rate (GPR) index only. To develop a novel deep learning approach using a generative adversarial network (GAN) to predict the synthetic measured fluence. A novel training method called "dual training," which involves the training of the encoder and decoder separately, was proposed and evaluated for cycle GAN (cycle-GAN) and conditional GAN (c-GAN). A total of 164 VMAT treatment plans, including 344 arcs (training data: 262, validation data: 30, and testing data: 52) from various treatment sites, were selected for prediction model development. For each patient, portal-dose-image-prediction fluence from TPS was used as input, and measured fluence from EPID was used as output/response for model training. Predicted GPR was derived by comparing the TPS fluence with the synthetic measured fluence generated by the DL models using gamma evaluation of criteria 2%/2 mm. The performance of dual training was compared against the traditional single-training approach. In addition, we also developed a separate classification model specifically designed to detect automatically three types of errors (rotational, translational, and MU-scale) in the synthetic EPID-measured fluence. Overall, the dual training improved the prediction accuracy of both cycle-GAN and c-GAN. Predicted GPR results of single training were within 3% for 71.2% and 78.8% of test cases forcycle-GAN and c-GAN, respectively. Moreover, similar results for dual training were 82.7% and 88.5% for cycle-GAN and c-GAN, respectively. The error detection model showed high classification accuracy (>98%) for detecting errors related to rotational and translational errors. However, it struggled to differentiate the fluences with "MU scale error" from "error-free" fluences. We developed a method to automatically generate the synthetic measured fluence and identify errors within them. The proposed dual training improved the PSQA prediction accuracy of both the GAN models, with c-GAN demonstrating superior performance over the cycle-GAN. Our results indicate that the c-GAN with dual training approach combined with error detection model, can accurately generate the synthetic measured fluence for VMAT PSQA and identify the errors. This approach has the potential to pave the way for virtual patient-specific QA of VMAT treatments.

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine

Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload. Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance. For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%. The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.

Electronic Portal Imaging Device in Pre-Treatment Patient-Specific Quality Assurance of volumetric-modulated arc therapy delivery

Abstract Background: Radiotherapy treatment delivery is evaluated by a pre-treatment patient-specific quality assurance (PSQA) procedure to ensure the patient receives an accurate radiation dose. The current PSQA practice by using conventional phantoms requires more set-up time and cost of purchasing the tools. Therefore, this study aimed to investigate the efficiency of an electronic portal imaging device (EPID) of linear accelerator (LINAC) as a PSQA tool for volumetric-modulated arc therapy (VMAT) planning technique for nasopharyngeal carcinoma (NPC) treatment delivery. Methods: A NPC VMAT plan on a Rando phantom was performed by following the Radiation Therapy Oncology Group (RTOG) 0615 protocol. The gamma passing rate of the EPID and PSQA phantom (ArcCHECK) were compared among the gamma criteria of 3%/3 mm, 2%/2 mm and 1%/1 mm, respectively. Results: Both EPID and ArcCHECK phantom had distinguishable gamma passing rates in 3%/3 mm and 2%/2 mm with a difference of 0·87% and 0·30%, respectively. Meanwhile, the EPID system had a lower gamma passing rate than the ArcCHECK phantom in 1%/1 mm (21·23% difference). Furthermore, the sensitivity of the EPID system was evaluated and had the largest deviation in gamma passing rate from the reference position in gamma criteria of 2%/2 mm (41·14%) compared to the 3%/3 mm (25·45%) and 1%/1 mm (31·78%), discretely. The best fit line of the linear regression model for EPID was steeper than the ArcCHECK phantom in 3%/3 mm and 2%/2 mm, and vice versa in gamma criteria of 1%/1 mm. This indicates that the EPID had a higher sensitivity than the ArcCHECK phantom in 3%/3 mm and 2%/2 mm but less sensitivity in 1%/1 mm. Conclusions: The EPID system was efficient in performing the PSQA test of VMAT treatment in HUSM with the gamma criteria of 3%/3 mm and 2%/2 mm.

Clinical Efficacy of an Electronic Portal Imaging Device versus a Physical Phantom Tool for Patient-Specific Quality Assurance.

Pre-treatment patient-specific quality assurance (QA) is critical to prevent radiation accidents. The electronic portal imaging device (EPID) is a dose measurement tool with good resolution and a low volume-averaging effect. EPIbeam-an EPID-based portal dosimetry software-has been newly installed in three institutions in Korea. This study evaluated the efficacy of the EPID-based patient-specific QA tool versus the PTW729 detector (a previously used QA tool) based on gamma criteria and planning target volume (PTV). A significant difference was confirmed through the R statistical analysis software. The average gamma passing rates of PTW729 and EPIbeam were 98.73% and 99.60% on 3 mm/3% (local), 96.66% and 97.91% on 2 mm/2% (local), and 88.41% and 74.87% on 1 mm/1% (local), respectively. The p-values between them were 0.015 (3 mm/3%, local), 0.084 (2 mm/2%, local), and less than 0.01 (1 mm/1%, local). Further, the average gamma passing rates of PTW 729 and EPIbeam according to PTV size were 99.55% and 99.91% (PTV < 150 cm3) and 97.91% and 99.28% (PTV > 150 cm3), respectively. The p-values between them were 0.087 (PTV < 150 cm3) and 0.036 (PTV > 150 cm3). These results confirm that EPIbeam can be an effective patient-specific QA tool.

Comprehensive clinical evaluation of TomoEQA for patient-specific pre-treatment quality assurance in helical tomotherapy

BackgroundBased on a previous study on the feasibility of TomoEQA, an exit detector-based patient-specific pre-treatment quality assurance (QA) method for helical tomotherapy, an in-depth clinical evaluation was conducted.MethodsData of one hundred patients were analyzed to evaluate the clinical usefulness of TomoEQA for patient-specific pre-treatment QA in comparison with the conventional phantom-based method. Additional investigations were also performed under unusual measurement conditions to validate the off-axis region. In addition to the clinical evaluation of TomoEQA, a statistical analysis was conducted to determine the plan parameters that affect the pass/failure results of pre-treatment QA.ResultsThe average and standard deviations of the gamma passing rate and point dose error for TomoEQA were comparable to those of the conventional QA method. For TomoEQA, the average values of the gamma passing rate and point dose error were 96.32% (standard deviation (1 sigma) = 3.94; 95% confidence interval (CI), 95.55 to 97.09) and − 1.12% (standard deviation (1 sigma) = 1.04; CI, − 1.32 to − 0.92), respectively. For the conventional QA method, the average values of the gamma passing rate and point dose error were 95.95% (standard deviation (1 sigma) = 4.35; 95% confidence interval (CI), 95.10 to 96.80) and − 1.20% (standard deviation (1 sigma) = 1.61; CI, − 1.52 to − 0.88), respectively. Further experiments on the off-axis region demonstrated that TomoEQA can provide accurate results for 3D dose analysis, which is inherently difficult in the conventional QA method. Through a statistical analysis based on the results of TomoEQA, it was validated that the total fraction (Total Fx), monitor units, beam-on-time, leaf-of-time below 100 ms, and planning target volume diameter were statistically significant for the pass/failure of the pre-treatment QA results.ConclusionsTomoEQA is a clinically beneficial alternative to the conventional phantom-based QA method.

Development and dosimetric evaluation of the IMRT prostate at outside-the-irradiated field in a heterogeneity male pelvis phantom

Abstract Background: Intensity-modulated radiation therapy (IMRT) treatment delivery requires pre-treatment patient-specific quality assurance (QA) for the dosimetry verification due to its complex multileaf-collimator movement. The prostate target close position between the bladder and rectum requires a tight margin during planning, and mistreatment would have a huge impact on the patient. A commercially available QA tool consists of a homogeneous medium and does not represent an exact photon interaction on the tumour and also on the nearby healthy organ. Objective: A heterogeneous male pelvis phantom was developed and investigated the efficiency of the treatment planning system (TPS) calculation on the off-axis region. Methods: Polymethyl methacrylate was used for the phantom housing, and the material closed to the bladder, rectum and prostate density was chosen to construct the organ models. The phantom was scanned and validated by the computed tomography number and density. An IMRT treatment was planned in the Monaco TPS, and a thermoluminescent dosimeter (TLD-100) was used to validate the point dosimetry. In addition, an EGSnrc Monte Carlo simulation was carried out to validate the phantom dosimetry. Results & Discussion: The dose measurement between TLD-100, TPS, and EGSnrc was compared and validated in the pelvis phantom. In the prostate region, the dose difference was within ± 5%, and the maximum dose difference outside-the-irradiated field was up to 20·07 % and 47·31 % in TPS and TLD-100, respectively. Meanwhile, the measured dose was lower than the calculated dose, and it was apparent for the dose outside-the-irradiated field. Conclusion: The developed heterogeneity male pelvis phantom was validated and verified to be an important QA device for validating radiation dosimetry in the pelvis region. The dose outside-the-irradiated field was underestimated by both TPS and TLD, respectively.

Optimizing the Region for Evaluation of Global Gamma Analysis for Nasopharyngeal Cancer (NPC) Pretreatment IMRT QA by COMPASS: A Retrospective Study.

BackgroundThe global gamma passing rate is the most commonly used metric for patient-specific pretreatment quality assurance in radiation therapy. However, the optimal region for evaluation and specific action limits (ALs) need to be explored. Therefore, this study was carried out to explore the optimal region for evaluation of the global gamma passing rate and define ALs by using the COMPASS software.MethodsA total of 93 intensity-modulated radiation therapy (IMRT) plans for nasopharyngeal cancer (NPC) patients, including 61 original plans and 32 multileaf collimator (MLC) error-introduced test plans, were selected for retrospective analysis. Firstly, the dose distribution was divided into six isodose regions (“≥10%”, “≥20%”, “≥30%”, “≥40%”, “≥50%”, and “≥60%”) based on the prescribed dose and one clinically oriented region for evaluation (“whole”) to perform the three-dimensional (3D) global gamma reanalysis. Meanwhile, the percentage gamma passing rate (%GP), mean gamma index (μGI) based on 3%/2 mm criteria, and percentage dose error (%DE) of the dose–volume histogram (DVH) metrics were recorded by COMPASS application. Secondly, the Pearson’s correlation coefficient was used to analyze the correlation between %GP and %DE and between μGI and %DE in different regions. Additionally, receiver operating characteristic (ROC) methodology was applied to quantify the fraction of patient-specific plans evaluated as “fail” and “pass”. In order to improve the correlation between gamma analysis result and clinical criteria, ROC analysis was carried out in accordance with hybridization analysis criteria (%DE ≤3%, %GP ≥90% and %DE ≤3%, μGI ≤0.6). ROC was performed for two purposes: 1) to analyze the sensitivity and specificity of %GP and μGI in different regions for evaluation and 2) to define the ALs of %GP and μGI in the optimal region for evaluation. Finally, the plans introduced with MLC errors were prepared for validation. Moreover, we also compared the positive rate of ALs of both %GP and μGI in detecting MLC error-introduced plans in different regions.Results1) In our study, a number of DVH-based metrics were found to be correlated with the evaluation parameters. The corresponding number was 4, 2, 1, 1, 1, 1, and 3 in γwhole, γ10%, γ20%, γ30%, γ40%, γ50%, and γ60%, respectively, and 5, 3, 0, 1, 1, 4, and 2 in μGIwhole, μGI10%, μGI20%, μGI30%, μGI40%, μGI50%, and μGI60%, respectively. The results by COMPASS have revealed that the %DE of specific structures has a slightly higher correlation with both %GP and μGI of the “whole” region compared with that of any other region. However, it is a moderate correlation (0.5 ≤ |r| < 0.8). 2) The areas under the curves (AUCs) of γwhole, μGIwhole, μGI40%, μGI50%, and μGI60% were >0.7 based on 3%/2 mm criteria. According to the Youden coefficient, we defined the ALs of γwhole ≥92%, μGIwhole ≤0.36, μGI40% ≤0.43, and μGI60% ≤0.40 based on 3%/2 mm criteria. 3) In the validation, for original plans, the accuracy of ALγwhole, ALγ10%, ALμGIwhole, ALμGI40%, ALμGI50%, and ALμGI60% was 23%, 9.8%, 90%, 80.3%, 9.8%, and 88.5%, respectively. For test plans with systematic MLC errors smaller than 0.8 mm, the positive rates of ALγwhole, ALγ10%, ALμGIwhole, ALμGI40%, ALμGI50%, and ALμGI60% were 25%, 58%, 92%, 92%, 42%, and 100%, respectively. For the plans with systematic MLC errors higher than 0.8 mm, the positive rates of all AL%GP and ALμGI were 100%. From the COMPASS validation results, the accuracy of γwhole, μGIwhole, μGI40%, and μGI60% was higher than that of the conventional γ10% and commonly used μGI50%.ConclusionsCompared with the traditional evaluation region (i.e., the criteria with a threshold of 10% or a threshold of 50%, it was the same with the isodose regions of “≥10%”, “≥50%” based on the prescribed dose in our study), the “whole” region is more meaningful to the clinic by COMPASS. The accuracy of μGIwhole is higher than that of the conventional γ10% and the commonly used μGI50%.

Clinical implementation of PerFRACTION™ for pre-treatment patient-specific quality assurance

Clinical implementation of PerFRACTION™ for pre-treatment patient-specific quality assurance

Machine learning-generated decision boundaries for prediction and exploration of patient-specific quality assurance failures in stereotactic radiosurgery plans.

Stereotactic radiosurgery (SRS) is a form of radiotherapy treatment during which high radiation dose is delivered in a single or few fractions. These treatments require highly conformal plans with steep dose gradients, which can result in an increase in plan complexity prompting the need for stringent pretreatment patient-specific quality assurance (QA) measurements to ensure the planned and measured dose distributions agree within clinical standards. Complexity scores and machine learning (ML) techniques may help with prediction of QA outcomes; however interpretability and usability of those results continues to be an area of study. This study investigates the use of plan complexity metrics as input for an ML model to allow for prediction of QA outcomes for SRS plans as measured via three-dimension (3D) phantom dose verification. Explorations into interpretability and predictive ability, as well as a prospective in-clinic implementation using the resulting model were performed. Four hundred ninety-eight plans (1571 volumetric modulated arc therapy arcs) were processed via in-house script to generate several complexity scores. 3D phantom dose verification measurement results were extracted and classified as pass or failure (with failures defined as below 95% voxel agreement passing 3%/1-mm gamma criteria with 10% threshold,) and 1472 of the arcs were split into training and testing sets, with 99 arcs as a sequential holdout set. A z-score scaler was trained on the training set and used to scale all other sets. Variations of multi-leaf collimator (MLC) leaf movement variability, aperture complexity, and leaf size, and monitor unit (MU) at control point weighted target area scores were used as input to a support vector classifier to generate a series of 1D, 2D, and 5D decision boundaries. The best performing 5D model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements for 38 patient plans (112 arcs). The decision to order 3D phantom dose verification measurements was recorded before and after prediction. Best performing 1D threshold and 2D prediction models with best performance produced a QA failure recall and QA passing recall of 1.00 and 0.55, and 0.82 and 0.82, respectively. Best performing 5D prediction model produced a QA failure recall (sensitivity) of 1.00 and QA passing recall (specificity) of 0.72. This model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements and achieved a QA failure recall of 1.00 and QA passing recall of 0.58. The decision to order 3D phantom dose verification measurements was recorded before and after measurement. A single initially unidentified failing plan of the prospective cohort was successfully predicted to fail by the model. Implementation of complexity score-based prediction models for SRS would allow for support of a clinician's decision to reduce time spent performing QA measurements and avoid patient treatment delays (i.e., in case of QA failure).

Study of the use of gel dosimetry in combination with 3D printing phantom for personalized pretreatment QA in radiotherapy

In modern radiotherapy, pretreatment patient-specific quality assurance (PSQA) generally consists in delivering the treatment plan to a phantom equipped with a detector and in comparing the measured dose and the dose calculated by the treatment planning system (TPS) in order to detect any gap between both dose distributions. Dosimetric gels have interesting properties for QA. In this work, the use of gel dosimetry together with a patient-based 3D printed phantom for personalized PSQA is investigated. CT images of a patient with a right mesencephalic brain tumor were used to generate a 3D printed phantom. Then it was filled with water and a radiochromic gel jar and irradiated according to the patient intracranial stereotactic plan using a Novalis TrueBeam STX accelerator. Measured dose distributions agree well with the calculated ones. Regarding 3D gamma-index (1 mm – 2%) estimated within the central 85% of the jar volume, 96.3% of points pass the test. In addition, 86.5% of the points pass the local 2D 3 mm-3% gamma-index. Results are promising but further work is needed to improve the protocol and investigate the possibility to extend it to end-to-end tests.

Analysis of the planned, delivered dose distributions and quality assurance for helical tomotherapy and volumetric modulated arc therapy in locally advanced non-small cell lung cancer.

BackgroundWith full access to both helical tomotherapy (HT) and volumetric modulated arc therapy (VMAT), we compared locally advanced non-small cell lung cancer (LA-NSCLC) treatment plans and verified the plans using patient-specific pretreatment quality assurance (PSQA).Materials and methodsFor each of the seventeen patients included in the study, two treatment plans (i.e. HT and VMAT) were created. Optimized plans were evaluated following the ICRU 83 criteria. Planned quality indexes and dosimetric parameters were compared. Lastly, all plans were subjected to PSQA assessment by determining the gamma passing rate (GPR).ResultsAll dosimetry results obtained from the planning target volume passed the ICRU 83 criteria. With regard to similar homogeneity indices, VMAT produced better conformity number values than HT (0.78 vs. 0.64), but differences in the values were insignificant. Furthermore, VMAT was associated with a significantly shorter mean treatment time (1.91 minutes vs. 6.66 minutes). For PSQA assessment, both techniques resulted in adequate GPR values (> 90% at the 3%/3 mm criteria).ConclusionBoth HT and VMAT techniques led to the generation of clinically satisfactory and reliable radiotherapy plans. However, the VMAT plan was associated with a non-significantly better degree of conformity and a significantly shorter treatment time. Thus, VMAT was determined to be a better choice for LA-NSCLC.

Analysis of clinical patient-specific pre-treatment quality assurance with the new helical tomotherapy platform, following the AAPM TG-218 report

PurposeThis study presents patient-specific quality assurance (QA) results from the first 395 clinical cases for the new helical TomoTherapy® platform (Radixact) coupled with dedicated Precision TPS.MethodsThe passing rate of the Gamma Index (GP%) of 395 helical QA of patient-specific tomotherapy, acquired with ArcCHECK, is presented, analysed and correlated to various parameters of the plan. Following TG-218 recommendations, the clinic specific action limit (ALcs) and tolerance limit (TLcs) were calculated for our clinic and monitored during the analysed period.ResultsThe mean values ​​(± 1 standard deviation) of GP% (3%/2 mm) (both global and local normalization) are: 97.6% and 90.9%, respectively. The proposed ALcs and TLcs, after a period of two years’ process monitoring are 89.4% and 91.1% respectively.ConclusionsThe phantom measurements closely match the planned dose distributions, demonstrating that the calculation accuracy of the new Precision TPS and the delivery accuracy of the Radixact unit are adequate, with respect to international guidelines and reports. Furthermore, a first correlation with the planning parameters was made. Action and tolerance limits have been set for the new Radixact Linac.