Inter-unit variability of multi-leaf collimator parameters for IMRT and VMAT treatment planning: a multi-institutional survey

Isono, Motohide; Akino, Yuichi; Mizuno, Hirokazu; Tanaka, Yoshihiro; Masai, Norihisa; Yamamoto, Toshijiro

doi:10.1093/jrr/rrz082

Cited by 10 publications

(13 citation statements)

References 17 publications

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“…Glenn et al reported institutional variation up to ±25% from the mean value of the TF and DLG for Varian linacs and the Eclipse TPS 3 . Isono et al showed that the variations in the measured DLG were up to ±18% from the mean value for the Varian TrueBeam with the Millennium 120 MLC 4 . For example, for a IMRT plan we examined in this study, the maximal change in dose profile was about 7% in the area of high dose gradient for the 25% change of the DLG, and the maximal change was about 7% in the area just outside of the field edge formed by MLCs for the 25% change of the TF.…”

Section: Introductionmentioning

confidence: 99%

“…The accuracy of MLC modeling and the detectability of its errors at each institution have recently been questioned from various viewpoints. Some institutional surveys revealed that MLC modeling parameters such as the TF and DLG showed considerable variation even among the same types of linacs and TPSs 3,4 . Glenn et al reported institutional variation up to ±25% from the mean value of the TF and DLG for Varian linacs and the Eclipse TPS 3 .…”

Section: Introductionmentioning

confidence: 99%

“…For example, for a IMRT plan we examined in this study, the maximal change in dose profile was about 7% in the area of high dose gradient for the 25% change of the DLG, and the maximal change was about 7% in the area just outside of the field edge formed by MLCs for the 25% change of the TF. The variation in MLC modeling parameters can be attributed to differences in dose measurement tools and/or interinstitutional differences among the methods used to determine the parameters 3‐6 . The problem seems to be that it may be difficult to establish a consensus about the accuracy in determining the MLC modeling parameters in TPS commissioning in the community of radiation therapy 3 …”

Section: Introductionmentioning

confidence: 99%

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Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

et al. 2021

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Purpose We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient‐specific quality assurance (QA) for intensity‐modulated radiation therapy (IMRT) with an electric portal imaging device (EPID). Methods Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k‐nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error‐free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross‐validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2 mm for all of the types of error. Results The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection. Conclusions Compared to the conventional gamma analysis, the radiomics‐based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics‐based IMRT QA was shown to be a promising approach for detecting the MLC modeli...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

et al. 2021

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“…Modern day radiotherapy frequently utilizes modulated treatment, either in form of static gantry dynamic multileaf collimator (MLC) treatments or volumetric modulated treatment (VMAT), 1 for which the gantry, MLC leaves, and dose rate constantly change during beam delivery 2 . Such advanced treatment techniques require a well‐controlled beam delivery during all stages of treatment, a stable beam, and a robust MLC model implemented in the treatment planning system (TPS), especially when consideration is given to reduction of standard planning target volume (PTV) margins in stereotactic body radiotherapy (SBRT) 3 …”

Section: Introductionmentioning

confidence: 99%

Identification of a potential source of error for 6FFF beams delivered on an Agility^TM multileaf collimator

Lorenz

Paris

2021

J Applied Clin Med Phys

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Purpose: The performance of the Agility TM multileaf collimator was investigated with a focus on dynamic, small fields for flattening filter free (FFF) beams. Methods: In this study we have developed a simple tool to test the robustness of the control mechanisms during dynamic beam delivery for Elekta's VersaHD linear accelerator with Integrity 4.0.4 control software. We have programed the planning system to calculate dose for delivery of sweeping gaps. These sweeping gaps have a constant speed, constant size, and are delivered at a constant dose rate. Therefore they specifically identify delivery problems in dynamic mode. Results: The Elekta Agility TM control mechanism fails to maintain accurate delivery for small, dynamic sweeping gaps. For small gap sizes, the Agility TM control mechanism delivers a field that is more than four times the size of the planned field width without generating an interlock. This has dosimetric implications: The discrepancy between calculated and measured doses increases with decreasing gap size and exceeds 10% and 60% at isocenter for a 3.5 mm and 1 mm gap size, respectively. Conclusion: A deficiency of the Agility TM control system was identified in this study. This deficiency is a potential source of error for volumetric modulated arc therapy fields and could therefore contribute to relatively high failure rates in quality assurance measurements, especially for FFF beams.

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“…QA tools (16). As the number of verification plans and parameters increases, the necessity to establish suitable and common MLC parameters increases accordingly.…”

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confidence: 99%

Evaluation of the Differences Between Measurements in Multiple Institutions and Calculation Modeled by Representative Beam Data in Prostate VMAT Plan

GOTO

Mizuno

Akino

et al. 2020

In Vivo

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Background/Aim: This study aimed to investigate the potential differences between multi-institutional measurements and treatment planning system (TPS) calculation modeled by representative beam data for patient-specific quality assurance (QA), including multi-leaf collimator (MLC) parameters. Materials and Methods: Eleven TrueBeam from nine institutions were used in this study. Volumetric arc therapy (VMAT) plan for verification was created using Eclipse. The point dose of the CC13 ionization chamber and the dose distribution of the GAFCHROMIC EBT3 film were measured and analyzed. Results: Point dose differences in patient-specific QA provided a mean±standard deviation of 1.0%±0.6%. Mean gamma pass rates of dose distribution were in excess of 99% and 96% for 3%/2 mm and 2%/2 mm gamma criteria, respectively. Conclusion: There was good agreement between measurements and calculations, indicating the small influence of complex VMAT in the underlying processes. Therefore, implementation of the same MLC parameters on TPS among different institutions with the same planning policy should be considered to ensure consistency and efficiency in radiation treatment processes.Novel radiotherapy devices are becoming increasingly accurate, and a prominent example involves TrueBeam (Varian Medical Systems, Inc., Palo Alto, CA) where the respective variance has become very narrow (1-4). Varian Medical Systems provide representative beam data (RBD) that is averaged beam data measured by three TrueBeam at the Duke University (1). It has been reported that the measured TrueBeam and the RBD data were very similar. Tanaka et al. (5) have reported that the averaged measurement data for treatment planning system (TPS) modeling indicated a small variance. More specifically, the percentage depth dose (PDD) difference between the RBD and the averaged measurement data was within 0.5%, whereas the off-center ratio (OCR) for a flat 10×10, 20×20, and 30×30 cm 2 region between the RBD and the averaged measurement data was within 1%.Multi-leaf collimator (MLC) systems enable the precise delivery of the intended dose according to planned dose constraints by complex MLC motion (6-9). The accuracy of the MLC position influences the intensity-modulated radiation therapy (IMRT) and the volumetric modulated arc therapy (VMAT) (10-12). Simultaneously, MLC parameters in TPS need to be modified to maintain consistency with patient-specific quality assurance (QA) measured data (11,13). In Eclipse (Varian Medical Systems), MLC parameters are controlled by the dosimetric leaf gap (DLG) and the transmission factor, which are not provided as RBD (14). Moreover, the optimum MLC parameters depend on the plan, delivery system, TPS, and tools used in patient-specific QA (12,13,15). Our previous report suggests that the variation between the optimum and the measured MLC parameters was significantly large because of the different clinical conditions in each institution, different types of cases, systems, TPS, and 1503 This article is freely accessible online.

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Inter-unit variability of multi-leaf collimator parameters for IMRT and VMAT treatment planning: a multi-institutional survey

Cited by 10 publications

References 17 publications

Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

Identification of a potential source of error for 6FFF beams delivered on an Agility^TM multileaf collimator

Evaluation of the Differences Between Measurements in Multiple Institutions and Calculation Modeled by Representative Beam Data in Prostate VMAT Plan

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Inter-unit variability of multi-leaf collimator parameters for IMRT and VMAT treatment planning: a multi-institutional survey

Cited by 10 publications

References 17 publications

Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

Detecting MLC modeling errors using radiomics‐based machine learning in patient‐specific QA with an EPID for intensity‐modulated radiation therapy

Identification of a potential source of error for 6FFF beams delivered on an AgilityTM multileaf collimator

Evaluation of the Differences Between Measurements in Multiple Institutions and Calculation Modeled by Representative Beam Data in Prostate VMAT Plan

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Identification of a potential source of error for 6FFF beams delivered on an Agility^TM multileaf collimator