Survey of patient‐specific quality assurance practice for IMRT and VMAT

Chan, Grace; Chin, Lee; Abdellatif, Ady; Bissonnette, Jean‐Pierre; Buckley, Lesley; Comsa, D; Granville, D; King, Jenna; Rapley, P.; Vandermeer, Aaron

doi:10.1002/acm2.13294

Cited by 17 publications

(13 citation statements)

References 31 publications

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“…Patient-specific QA of treatment plans has been recommended for all IMRT or VMAT plans. Practically, patient-specific QA can be implemented for all plans or sampled for selected plans ( 37 , 38 ). For the sampling method, the selection criteria were either totally random or based on plenty of selection criteria, such as machine availability, plan complexity, economic factors, physicist time or preference, etc.…”

Section: Discussionmentioning

confidence: 99%

“…For the sampling method, the selection criteria were either totally random or based on plenty of selection criteria, such as machine availability, plan complexity, economic factors, physicist time or preference, etc. ( 37 ). So far, there is no uniform selection criterion or explicit guidelines for radiation therapy centers to screen patients for patient-specific QA ( 39 , 40 ).…”

Section: Discussionmentioning

confidence: 99%

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Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

Zhang

Liu

et al. 2022

Front. Oncol.

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PurposeThis study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations.Materials and methodsPatient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA.ResultsThe thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively.ConclusionsIndependent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

Zhang

Liu

et al. 2022

Front. Oncol.

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“…For our ultimate goal of PSQA failure prediction, we consider the same data in the classification context by thresholding the regression labels and converting them into binary classification labels. We defined the action threshold level in the gamma analysis to be at 95 % as is common in clinical practice 39,40,41 and obtained binary classification labels (pass or fail) based on this threshold. We reserved 65% of the samples for the training set, 15% for the validation set and 20% for the test set.…”

Section: Methodsmentioning

confidence: 99%

Patient-specific Quality Assurance Failure Prediction with Deep Tabular Models

Levin

Aravkin

Kim

2022

Preprint

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Background: Patient-specific quality assurance (PSQA) is part of the standard practice to ensure that a patient receives the dose from intensity-modulated radiotherapy (IMRT) beams as planned in the treatment planning system (TPS). PSQA failures can cause a delay in patient care and increase workload and stress of staff members. A large body of previous work for PSQA failure prediction focuses on non-learned plan complexity measures. Another prominent line of work uses machine learning methods, often in conjunction with feature engineering. Currently, there are no machine learning solutions which work directly with multi-leaf collimator (MLC) leaf positions, providing an opportunity to improve leaf sequencing algorithms using these techniques. Purpose: To improve patient safety and work efficiency, we develop a tabular transformer model based directly on the MLC leaf positions (without any feature engineering) to predict IMRT PSQA failure. This neural model provides an end-to-end differentiable map from MLC leaf positions to the probability of PSQA plan failure, which could be useful for regularizing gradient-based leaf sequencing optimization algorithms and generating a plan that is more likely to pass PSQA. Method: We retrospectively collected DICOM RT PLAN files of 968 patient plans treated with volumetric arc therapy. We construct a beam-level tabular dataset with 1873 beams as samples and MLC leaf positions as features. We train an attention-based neural network FT-Transformer to predict the ArcCheck-based PSQA gamma pass rates. In addition to the regression task, we evaluate the model in the binary classification context predicting the pass or fail of PSQA. The performance was compared to the results of the two leading tree ensemble methods (CatBoost and XGBoost) and a non-learned method based on mean MLC gap. Results: The FT-Transformer model achieves 1.44% Mean Absolute Error (MAE) in the regression task of the gamma pass rate prediction and performs on par with XGBoost (1.53 % MAE) and CatBoost (1.40 % MAE). In the binary classification task of PSQA failure prediction, FT-Transformer achieves 0.85 ROC AUC (with CatBoost and XGBoost achieving 0.87 ROC AUC and the mean-MLC-gap complexity metric achieving 0.72 ROC AUC). %While AUC summarizes the performance of binary classification models for all prediction thresholds, the performance at specific thresholds that maximize the true positive rate of PSQA failure identification while maintaining low false positive rate is of interest for clinical applications. Moreover, FT-Transformer, CatBoost, and XGBoost all achieve 80\% true positive rate while keeping the false positive rate under 20%. Conclusions: We demonstrate that reliable PSQA failure predictors can be successfully developed based solely on MLC leaf positions. Our FT-Transformer neural network can reduce the need for patient rescheduling due to PSQA failures by 80% while sending only 20% of plans that would not have failed the PSQA for replanning. FT-Transformer achieves comparable performance with the leading tree ensemble methods while having an additional benefit of providing an end-to-end differentiable map from MLC leaf positions to the probability of PSQA failure.

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“…This re search was ac tu ated by changes in in terna tional rec om men da tions re gard ing tol er ance lev els [7]. Be sides this, the re cent stud ies [8,9] have shown that com mon tol er ance cri te ria used is still 3 % dose dif fer ence and 3 mm dis tance-to-agree ment. Thus, the first part of our re search in ves ti gates how changes in tol er ance in flu ence gamma pass ing rates.…”

Section: Introductionmentioning

confidence: 99%

Impact of stringent tolerance criteria on verification of absorbed dose distributions and evaluation through inhomogeneous media

et al. 2022

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Advances of radiation delivery devices have increased the complexity of the radiation oncology treatments. Herewith, outcome of the treatment, as well as patient safety, strongly depend on the consistency of absorbed dose delivery. Both can be ensured by comprehensive system of verification of calculated absorbed dose distributions. Standard method is evaluation of calculated absorbed dose distribution according to gamma method, using a 2-D detector and a homogeneous phantom, to obtain measured dose distribution. Purpose of this research was to investigate the influence of tolerance criteria on gamma passing rate. Additionally, the agreement in heterogeneous phantom was analysed. Absorbed dose calculations were performed using systems Monaco and XiO. Detector with 1020 ionization chambers in homogeneous phantom and semi-anthropomorphic phantom was used for measurements. Absorbed dose distributions of around 3500 patients were analysed using gamma method. In homogeneous phantom, average gamma passing rates were within tolerance for 3 %/2 mm. For measurements in heterogeneous media, the highest average gamma passing rate was obtained for small volumes of medium treatment complexity (??=93.84%), while large volumes of treatment with low complexity yielded the lowest gamma passing rates (??= 83.22%).

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Survey of patient‐specific quality assurance practice for IMRT and VMAT

Cited by 17 publications

References 31 publications

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

Patient-specific Quality Assurance Failure Prediction with Deep Tabular Models

Impact of stringent tolerance criteria on verification of absorbed dose distributions and evaluation through inhomogeneous media

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