Error detection during VMAT delivery using EPID-based 3D transit dosimetry

Mijnheer, Ben J.; Jomehzadeh, Ali; González, P.; Olaciregui-Ruiz, Ígor; Rozendaal, R.; Shokrani, Parvaneh; Spreeuw, H.; Tielenburg, R.; Mans, A.

doi:10.1016/j.ejmp.2018.10.005

Cited by 44 publications

(34 citation statements)

References 26 publications

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“…ROC analysis, however, requires a statistically significant size of error and no-error samples as input. The experimental acquisition of EPID measurements to produce such samples is typically a cumbersome process, which explains why there are only a few studies on the topic in the IVD literature [78] , [79] , [80] , [81] . Recently, use has been made of synthetic EPID images to eliminate the need for phantom error introduction and positioning [82] .…”

Section: Future Directions For Research Development and Clinical Pramentioning

confidence: 99%

In vivo dosimetry in external beam photon radiotherapy: Requirements and future directions for research, development, and clinical practice

Olaciregui-Ruiz

Beddar

Greer

et al. 2020

Physics and Imaging in Radiation Oncology

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External beam radiotherapy with photon beams is a highly accurate treatment modality, but requires extensive quality assurance programs to confirm that radiation therapy will be or was administered appropriately. In vivo dosimetry (IVD) is an essential element of modern radiation therapy because it provides the ability to catch treatment delivery errors, assist in treatment adaptation, and record the actual dose delivered to the patient. However, for various reasons, its clinical implementation has been slow and limited. The purpose of this report is to stimulate the wider use of IVD for external beam radiotherapy, and in particular of systems using electronic portal imaging devices (EPIDs). After documenting the current IVD methods, this report provides detailed software, hardware and system requirements for in vivo EPID dosimetry systems in order to help in bridging the current vendor-user gap. The report also outlines directions for further development and research. In vivo EPID dosimetry vendors, in collaboration with users across multiple institutions, are requested to improve the understanding and reduce the uncertainties of the system and to help in the determination of optimal action limits for error detection. Finally, the report recommends that automation of all aspects of IVD is needed to help facilitate clinical adoption, including automation of image acquisition, analysis, result interpretation, and reporting/documentation. With the guidance of this report, it is hoped that widespread clinical use of IVD will be significantly accelerated.

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Section: Future Directions For Research Development and Clinical Pramentioning

confidence: 99%

In vivo dosimetry in external beam photon radiotherapy: Requirements and future directions for research, development, and clinical practice

Olaciregui-Ruiz

Beddar

Greer

et al. 2020

Physics and Imaging in Radiation Oncology

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“…The insensitivity of patient‐specific IMRT QA to the MLC modeling error may be due to not only smallness of the error but also the use of a gamma analysis in the comparison of the measured and calculated dose maps 12,13 . It has been pointed out that the gamma analysis has failed to detect several types of errors involved in IMRT delivery, such as an MLC positional error or an error in the dose calculation by the TPS (including the MLC modeling parameters) 14‐21 . In addition, since a gamma analysis usually uses only the summarized results, that is, the “gamma passing rate” — which is the rate of the number of points satisfying given criteria — it is usually difficult to obtain a clear insight into the cause of error in the result of the gamma analysis itself.…”

Section: Introductionmentioning

confidence: 99%

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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“…For these reasons it has been recommended by several international organizations and has become mandatory in some western countries . Moreover, it is widely used in Europe to evaluate the interfractional variations in dose delivery and patient setup for many treatment regions; many studies have validated the technique and presented the results obtained in the clinical practice . Mijnheer et al, analyzed with EPID‐based IVD more than 15 000 plans on different treatment sites, and found that more than 30% exceeded the alert criteria, attributing most of the errors to deviations from the routine clinical procedure and to anatomical changes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of interfraction setup variations for postmastectomy radiation therapy using EPID‐based in vivo dosimetry

Kang

et al. 2019

J Applied Clin Med Phys

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Postmastectomy radiation therapy is technically difficult and can be considered one of the most complex techniques concerning patient setup reproducibility. Slight patient setup variationsparticularly when high-conformal treatment techniques are usedcan adversely affect the accuracy of the delivered dose and the patient outcome. This research aims to investigate the inter-fraction setup variations occurring in two different scenarios of clinical practice: at the reference and at the current patient setups, when an image-guided system is used or not used, respectively.The results were used with the secondary aim of assessing the robustness of the patient setup procedure in use. Forty eight patients treated with volumetric modulated arc and intensity modulated therapies were included in this study. EPID-based in vivo dosimetry (IVD) was performed at the reference setup concomitantly with the weekly cone beam computed tomography acquisition and during the daily current setup. Three indices were analyzed: the ratio R between the reconstructed and planned isocenter doses, γ% and the mean value of γ from a transit dosimetry based on a two-dimensional γ-analysis of the electronic portal images using 5% and 5 mm as dose difference and distance to agreement gamma criteria; they were considered in tolerance if R was within 5%, γ% > 90% and γ mean < 0.4. One thousand and sixteen EPID-based IVD were analyzed and 6.3% resulted out of the tolerance level.Setup errors represented the main cause of this off tolerance with an occurrence rate of 72.2%. The percentage of results out of tolerance obtained at the current setup was three times greater (9.5% vs 3.1%) than the one obtained at the reference setup, indicating weaknesses in the setup procedure. This study highlights an EPID-based IVD system's utility in the radiotherapy routine as part of the patient's treatment quality controls and to optimize (or confirm) the performed setup procedures' accuracy. K E Y W O R D Sbreast radiotherapy, in vivo dosimetry, volumetric modulated arc therapy ---

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Error detection during VMAT delivery using EPID-based 3D transit dosimetry

Cited by 44 publications

References 26 publications

In vivo dosimetry in external beam photon radiotherapy: Requirements and future directions for research, development, and clinical practice

In vivo dosimetry in external beam photon radiotherapy: Requirements and future directions for research, development, and clinical practice

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

Evaluation of interfraction setup variations for postmastectomy radiation therapy using EPID‐based in vivo dosimetry

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