Cross verification of independent dose recalculation, log files based, and phantom measurement‐based pretreatment quality assurance for volumetric modulated arc therapy

Han, Ce; Jin, Yi; Zhu, Kecheng; Zhou, Yanfang; Ai, Yao; Zheng, Xiaomin; Xie, Conghua; Jin, Xiance

doi:10.1002/acm2.13036

Cited by 12 publications

(15 citation statements)

References 27 publications

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“…In addition, cross verification investigated the dosimetric agreement among independent TPS dose recalculation, log file-based, and phantom measurement-based QA imposed that care must be taken when choosing the ways of patient-specific QA. 28 Here, it is important to highlight that the deep learning-based prediction model is not intended to replace measurement-based QA but rather to complement the measurement-based QA and provide a more comprehensive view. The prediction model could improve the efficiency of IMRT QA and the safety of treatment delivery.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files

Huang

Ma³

et al. 2022

Technol Cancer Res Treat

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Objectives: In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. Methods: A total of 112 IMRT plans for chest cancers were planned and measured by portal dosimetry equipped on TrueBeam linac. The convolutional neural network (CNN) based learning model was trained using delivery fluence as inputs and gamma passing rates (GPRs) of 4 different criteria (3%/3 mm, 2%/3 mm, 3%/2 mm, and 2%/2 mm) as outputs. Model performance for both validation and test sets was assessed using mean absolute error (MAE), mean squared error (MSE), root MSE (RMSE), Spearman rank correlation coefficients (Sr), and Determination coefficient ( R2) between the measured and predicted GPR values. Results: In the test set, the MAE of the prediction model were 0.402, 0.511, 1.724, and 2.530, the MSE were 0.640, 0.986, 6.654, and 9.508, the RMSE were 0.800, 0.993, 2.580, and 3.083, the Sr were 0.643, 0.684, 0.821, and 0.824 ( P < .001) and the R2 were 0.4110, 0.4666, 0.6677, and 0.6769 for 3%/3 mm, 3%/2 mm, 2%/3 mm, and 2%/2 mm, respectively. The MAE and RMSE of the prediction model decreased with stricter gamma criteria while the Sr and R2 between measured and predicted GPR values increased. Conclusions: The CNN prediction model based on delivery fluence informed by log files could accurately predict IMRT QA passing rates for different gamma criteria. It could reduce QA workload and improve efficiency in pretreatment QA. Our results suggest that the CNN prediction model based on delivery fluence informed by log files may be a promising tool for the gamma evaluation of IMRT QA.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files

Huang

Ma³

et al. 2022

Technol Cancer Res Treat

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“…Various methods have been introduced for the implementation of this comparison, involving the measurement of the calculated dose distribution with one-, two-or threedimensional (3D) geometry systems or further evaluation of the distributions with the utilization of 3D reconstruction algorithms or machine log files. [3][4][5][6][7] A number of different metrics have been developed regarding the quantification of the distributions' differences 8 in both the dose and space domains. The one most widely used is the gamma index, introduced by Low et al, 9 which merges the dose difference (DD) and distance-to-agreement (DTA) metrics into a unitless quantity.…”

Section: Introductionmentioning

confidence: 99%

“…This procedure consists of the comparison of the machine delivered with the TPS calculated dose distribution, the first one being the reference and the second being the evaluated one. Various methods have been introduced for the implementation of this comparison, involving the measurement of the calculated dose distribution with one‐, two‐ or three‐dimensional (3D) geometry systems or further evaluation of the distributions with the utilization of 3D reconstruction algorithms or machine log files 3–7 …”

Section: Introductionmentioning

confidence: 99%

Implementation of TG‐218 for patient‐specific quality assurance tolerance and action limits determination: Gamma passing rate evaluation using 3DVH software

et al. 2022

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Purpose To determine the tolerance limit (TL) and action limit (AL) of gamma passing rates (%GP) for volumetric‐modulated arc therapy (VMAT) patient‐specific quality assurance (PSQA) according to the American Association of Physicists in Medicine (AAPM) Task Group (TG)‐218 recommendations, and to comparatively evaluate the clinical relevance of 2D %GP and 3D %GP. Methods PSQA was performed for 100 head and neck (H&N) and 73 prostate cancer VMAT treatment plans. Measurements were acquired using a cylindrical water equivalent phantom, hollow in the center, allowing measurements with homogeneous or heterogeneous inserts. The LINAC‐delivered dose distributions were compared to those calculated from the treatment planning system through the gamma index. TL and AL were determined through the computation of two‐dimensional (2D) %GP using the recommended acceptance criteria. Dose–volume histograms were reconstructed from the measurements using a commercially available software to detect the dosimetric errors (%DE) between the compared dose distributions. Utilizing the estimated dose on the patient anatomy, structure‐specific %GP (3D %GP) were calculated. The 3D %GP were compared to the 2D %GP ones based on their correlation with the %DE. Each metric's sensitivity was determined through receiver operator characteristic analysis. Results TL and AL were in concordance with the universal ones, regarding the prostate cancer cases, but were lower for the H&N cases. Evaluation of %DE did not deem the plans unacceptable. The 2D %GP and the 3D %GP did not differ significantly regarding their correlation with %DE. For prostate plans, %GP sensitivity was higher than for H&N cases. Conclusions Determination of institutional‐specific TL and AL allows the monitoring of the PSQA procedure, yet for plans close to the limits, clinically relevant metrics should be used before they are deemed unacceptable for the process to be of higher sensitivity and efficiency.

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“…Due to their convenience and high temporal resolution, the use of machine log files as input data for linac and patient‐specific QA has been heavily investigated 19–55 . Log file–based patient‐specific QA usually involves the recalculation of the treatment plan with control points modified based upon log file data used as a representation of the “actual” delivery to be compared to the planned delivery 23–28,32,34–36,41–43,45–47,50–52,55 . A number of authors have cautioned about the nonindependence of log files from the systems under investigation and potential insensitivity of log files to MLC mis‐calibration or to faults in the MLC drive train and hence have suggested a need for separate MLC QA to assure log file accuracy 20,22,23,32,37,38,43,45,50 .…”

Section: Introductionmentioning

confidence: 99%

Insensitivity of machine log files to MLC leaf backlash and effect of MLC backlash on clinical dynamic MLC motion: An experimental investigation

Barnes

Pomare

Doebrich

et al. 2022

J Applied Clin Med Phys

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Multi-leaf -collimator (MLC) leaf position accuracy is important for accurate dynamic radiotherapy treatment plan delivery. Machine log files have become widely utilized for quality assurance (QA) of such dynamic treatments. The primary aim is to test the sensitivity of machine log files in comparison to electronic portal imaging device (EPID)-based measurements to MLC position errors caused by leaf backlash. The secondary aim is to investigate the effect of MLC leaf backlash on MLC leaf motion during clinical dynamic plan delivery. Methods: The sensitivity of machine log files and two EPID-based measurements were assessed via a controlled experiment, whereby the length of the "T" section of a series of 12 MLC leaf T-nuts in a Varian Millennium MLC for a Trilogy C-series type linac was reduced by sandpapering the top of the "T" to introduce backlash. The built-in machine MLC leaf backlash test as well as measurements for two EPID-based dynamic MLC positional tests along with log files were recorded pre-and post-T-nut modification. All methods were investigated for sensitivity to the T-nut change by assessing the effect on measured MLC leaf positions. A reduced version of the experiment was repeated on a TrueBeam type linac with Millennium MLC. Results: No significant differences before and after T-nut modification were detected in any of the log file data. Both EPID methods demonstrated sensitivity to the introduced change at approximately the expected magnitude with a strong dependence observed with gantry angle. EPID-based data showed MLC positional error in agreement with the micrometer measured T-nut length change to 0.07 ± 0.05 mm (1 SD) using the departmental routine QA test. Backlash results were consistent between linac types. Conclusion: Machine log files appear insensitive to MLC position errors caused by MLC leaf backlash introduced via the T-nut. The effect of backlash on clinical MLC motions is heavily gantry angle dependent.

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Cross verification of independent dose recalculation, log files based, and phantom measurement‐based pretreatment quality assurance for volumetric modulated arc therapy

Cited by 12 publications

References 27 publications

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files

Implementation of TG‐218 for patient‐specific quality assurance tolerance and action limits determination: Gamma passing rate evaluation using 3DVH software

Insensitivity of machine log files to MLC leaf backlash and effect of MLC backlash on clinical dynamic MLC motion: An experimental investigation

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