A novel approach to SBRT patient quality assurance using EPID-based real-time transit dosimetry

Moustakis, Christos; Tazehmahalleh, Fatemeh Ebrahimi; Elsayad, Khaled; Fezeu, Francis; Scobioala, Sergiu

doi:10.1007/s00066-019-01549-z

Cited by 12 publications

(5 citation statements)

References 41 publications

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“…investigated the feasibility of EPID for online verification of lung SBRT treatment. The method has the potential for in vivo EPID dosimetry in the near future [ 30 ]. The prediction is represented in the modeling of this study by including the classification of error types and the prediction of error magnitude, while the “prediction” during treatment means dose distribution prediction based on an algorithm.…”

Section: Discussionmentioning

confidence: 99%

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

Huang

Ma³

et al. 2023

Strahlenther Onkol

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Objective To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML). Methods In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose image prediction (PDIP). Besides, 14 image-based features were extracted from RDD and EDD of portal dose predictions to obtain the feature vectors. In addition, a random forest was adopted for the multiclass classification task, and regression prediction for error magnitude. Results The top five features extracted with the highest weight included 1) the relative displacement in the x direction, 2) the ratio of the absolute minimum residual error to the maximal RDD value, 3) the product of the maximum and minimum residuals, 4) the ratio of the absolute maximum residual error to the maximal RDD value, and 5) the ratio of the absolute mean residual value to the maximal RDD value. The relative displacement in the x direction had the highest weight. The overall accuracy of the five-class classification model was 99.85% for the validation set and 99.30% for the testing set. This model could be applied to the classification of the error-free plan, COLL, MU, MLCS, and MLCR with an accuracy of 100%, 98.4%, 99.9%, 98.0%, and 98.3%, respectively. MLCR had the worst performance in error magnitude prediction (70.1–96.6%), while others had better performance in error magnitude prediction (higher than 93%). In the error magnitude prediction, the mean absolute error (MAE) between predicted error magnitude and actual error ranged from 0.03 to 0.33, with the root mean squared error (RMSE) varying from 0.17 to 0.56 for the validation set. The MAE and RMSE ranged from 0.03 to 0.50 and 0.44 to 0.59 for the test set, respectively. Conclusion It could be demonstrated in this study that the image-based features extracted from RDD and EDD can be employed to identify different types of delivery errors and accurately predict error magnitude with the assistance of ML techniques. They can be used to associate traditional gamma analysis with clinically based analysis for error classification and magnitude prediction in patient-specific IMRT quality assurance.

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Section: Discussionmentioning

confidence: 99%

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

Huang

Ma³

et al. 2023

Strahlenther Onkol

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“…In this paper, we have examined the effect of metal implants on dose distribution for the VMAT-SBRT applications with in vivo EPID dosimetry. The performance of in vivo EPID dosimetry for SBRT irradiations has been previously studied (17,(22)(23)(24). It has been suggested that EPID can be used for the prostate, lung, and spine VMAT-SBRT applications and seems to be an innovative approach for in vivo dosimetry (17).…”

Section: Discussionmentioning

confidence: 99%

Transit dosimetry of stereotactic body radiotherapy treatments with electronic portal dosimetry device in patient with spinal implant

et al. 2022

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In recent years, the use of the Electronic Portal Imaging Device (EPID) as an in vivo dosimeter has become widespread. However, reports of EPID for stereotactic body radiotherapy (SBRT) applications is scarce. There is no data on this topic especially when there are high-density materials in the radiation eld. In this study, we aimed to investigate the dose distributions of SBRT treatment plans in patients with spinal implants by in vivo EPID dosimetry.Material Methods:Implants were inserted in phantoms that mimic the vertebrae, and VMAT plans were created on the phantoms to deliver 16 Gy radiation doses to the target in 1 fraction. In vivo EPID measurements were performed for each irradiation. The results were compared with the treatment planning system using the gamma analysis method. ResultsAccording to the gamma analysis results, while the non-implant model met the acceptance criteria with a rate of 95.4%, the implanted models did not pass the test with results between the rates of 70-73%. In addition, while the dose difference in the isocenter was 1.3% for the non-implanted model, this difference was observed to be between 7% and 8% in the implanted models. ConclusionOur study revealed that EPID can be used as in vivo dosimetry for the VMAT-SBRT applications. However, unacceptable dose differences were obtained by in vivo EPID dosimetry in the VMAT-SBRT applications of patients with an implant. In the treatment of such patients, alternative treatment methods should be preferred in which the interaction of the implants with radiation can be prevented.

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“…In terms of survey content and building upon the analysis and discussions around the quality control of the MLC of a LINAC from 2019, we formed a quality improvement team consisting of eight engineers. Each engineer was proficient in the QCC activities, grounded in QCC methodology [ [19] , [20] , [21] , [22] , [23] , [24] , [25] ]. The QCC method is a participative approach to quality management involving a group of workers who voluntarily identify, analyse, and solve work-related problems.…”

Section: Methodsmentioning

confidence: 99%

Continuous quality improvement project to reduce the downtime of medical linear accelerators: A case study at Zhejiang Cancer Hospital

Lu,

Wu,

Mao

et al. 2024

Heliyon

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A novel approach to SBRT patient quality assurance using EPID-based real-time transit dosimetry

Cited by 12 publications

References 41 publications

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

Transit dosimetry of stereotactic body radiotherapy treatments with electronic portal dosimetry device in patient with spinal implant

Continuous quality improvement project to reduce the downtime of medical linear accelerators: A case study at Zhejiang Cancer Hospital

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