Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy

Kimura, Yuto; Tomori, Seiji; Oku, Yohei; Jingu, Keiichi

doi:10.1016/j.ejmp.2020.03.022

Cited by 46 publications

(80 citation statements)

References 21 publications

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“…In addition to gamma maps, the DD maps were created from volumetric modulated arc therapy (VMAT) QA and analyzed using CNN models. 12 Higher accuracy was found on the classification of MLC positional errors using the DD maps comparing to that of the gamma maps in VMAT QA.…”

Section: Introductionmentioning

confidence: 90%

“…The applications of radiomics and CNN on the analysis of gamma maps in IMRT QA have been demonstrated to provide complementary information to traditional gamma analysis. In addition to gamma maps, the DD maps were created from volumetric modulated arc therapy (VMAT) QA and analyzed using CNN models 12 . Higher accuracy was found on the classification of MLC positional errors using the DD maps comparing to that of the gamma maps in VMAT QA.…”

Section: Introductionmentioning

confidence: 99%

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The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Wang

Zhou

et al. 2020

Medical Physics

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Purpose The implementation of radiomics and machine learning (ML) techniques on analyzing two‐dimensional gamma maps has been demonstrated superior to the conventional gamma analysis for error identification in intensity modulated radiotherapy (IMRT) quality assurance (QA). Recently, the Structural SIMilarity (SSIM) sub‐index maps were shown to be able to reveal the error types of the dose distributions. In this study, we aimed to apply radiomics analysis on SSIM sub‐index maps and develop ML models to classify delivery errors in patient‐specific dynamic IMRT QA. Methods Twenty‐one sliding‐window IMRT plans of 180 beams for three treatment sites were involved in this study. Four types of machine‐related errors of various magnitudes were simulated for each beam at each control point, including the monitor unit (MU) variations, same‐directional and opposite‐directional shifts of the multileaf collimators (MLCs) and random mispositioning of the MLCs. In the QA process, a total of 1620 portal dose (PD) images were acquired for the beams with and without errors. The predicted PD images of the original beams were set as references. To quantify the agreement between a measured PD image and the corresponding predicted PD image, four difference maps including three SSIM sub‐index maps, and one dose difference‐derived map were calculated. Then, radiomic features were extracted from the four difference maps of each measured PD image. We tested four typical classifiers including linear discriminant classifier (LDC), two supporting vector machine (SVM) classifiers, and random forest (RF) for this multiclass classification task. A nested cross‐validation scheme was used for model evaluations, where the SVM recursive feature elimination method was applied for feature selection. Finally, the performance of the ML model on identifying the error‐free and the erroneous cases was compared to that of the conventional gamma analysis. Results The statistics of the selected features showed that all of the difference maps and the feature categories made balanced contributions to solve this classification task. Best performance was achieved by the Linear‐SVM model with average overall classification accuracy of 0.86. Specifically, the average classification accuracies of the shift, opening, and the random errors were around 0.9. Moreover, ~80% of error‐free and MU errors were correctly classified. Using gamma analysis, the 3 mm/3% criterion was found insensitive to errors (sensitivity was only 0.33). Although the sensitivity to errors with the 2 mm/2% criterion increased to 0.79, still 8% worse than that of the ML model. Conclusions We proposed an ML‐based method for machine‐related error identification in patient‐specific dynamic IMRT QA, where radiomic analysis on SSIM sub‐index maps were used for feature extraction. With extensive validation to select the best features and classifiers, high accuracies in error classification were achieved. Compared with the conventional gamma threshold method, this approach has great potential in error...

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Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Wang

Zhou

et al. 2020

Medical Physics

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“…In 2020, Osman et al developed an artificial neural network model with log file for predicting individual MLC positioning deviations 23 . Kimura et al reported a CNN model with dose difference map for classifying the presence or absence of MLC positioning errors 24 …”

Section: Introductionmentioning

confidence: 99%

Systematic method for a deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance of volumetric modulated arc therapy

et al. 2021

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Purpose This study aimed to develop and evaluate a novel strategy for establishing a deep learning‐based gamma passing rate (GPR) prediction model for volumetric modulated arc therapy (VMAT) using dummy target plan data, one measurement process, and a multicriteria prediction method. Methods A total of 147 VMAT plans were used for the training set (two sets of 48 dummy target plans) and test set (51 clinical target plans). The dummy plans were measured using a diode array detector. We developed an original convolutional neural network that accepts coronal and sagittal dose distributions to predict the GPRs of 36 pairs of gamma criteria from 0.5%/0.5 mm to 3%/3 mm. Sixfold cross‐validation and model averaging were performed, and the mean training result and mean test result were derived from six trained models that were produced during cross‐validation. Results Strong or moderate correlations were observed between the measured and predicted GPRs in all criteria. The mean absolute errors and root mean squared errors of the test set (clinical target plan) were 0.63 and 1.11 in 3%/3 mm, 1.16 and 1.73 in 3%/2 mm, 1.96 and 2.66 in 2%/2 mm, 5.00 and 6.35 in 1%/1 mm, and 5.42 and 6.78 in 0.5%/1 mm, respectively. The Pearson correlation coefficients were 0.80 in the training set and 0.68 in the test set at the 0.5%/1 mm criterion. Conclusion Our results suggest that the training of the deep learning‐based quality assurance model can be performed using a dummy target plan.

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“…ML models based on hand-crafted features like plan complexity metrics (PQM) and/or machine parameters have been demonstrated to predict GPR with high accuracy [125][126][127][128][129]. In addition, CNN approaches based on fluence maps can achieve similar prediction capabilities as ML methods [130][131][132][133][134]. These tools can be used as feedback into the treatment planning process.…”

Section: Patient-specific Treatment Verificationmentioning

confidence: 99%

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Vandewinckele

Claessens

Dinkla

et al. 2020

Radiotherapy and Oncology

185

116

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This work commenced during the 3rd European Society for Therapeutic Radiology and Oncology (ESTRO) physics workshop on 'Implementation/commissioning/QA of artificial intelligence techniques' in Budapest (2019) Radiotherapy and Oncology xxx (xxxx) xxx Contents lists available at ScienceDirect Radiotherapy and Oncology j o u r n a l h o m e p a g e : w w w. t h e g r e e n j o u r n a l. c o m Please cite this article as: L. Vandewinckele, M. Claessens, A. Dinkla et al., Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance, Radiotherapy and Oncology,

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Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy

Cited by 46 publications

References 21 publications

The structural similarity index for IMRT quality assurance: radiomics‐based error classification

The structural similarity index for IMRT quality assurance: radiomics‐based error classification

Systematic method for a deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance of volumetric modulated arc therapy

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

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