Prediction of VMAT delivery accuracy with textural features calculated from fluence maps

Park, Jong Min; Kim, Jung-In; Park, So‐Yeon

doi:10.1186/s13014-019-1441-7

Cited by 18 publications

(27 citation statements)

References 32 publications

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“…Volumetric‐modulated arc therapy (VMAT) for stereotactic body radiation therapy (SBRT) can deliver high doses to target volumes while sparing proximal organs at risk (OARs) by generating a rapid fall‐off of dose outside the target in a hypofractionated regimen 1 . The steep dose gradients are enabled by simultaneous modulation of multileaf collimator (MLC) positions, gantry rotation speeds, and dose rates during single or multiple gantry rotations around a patient 2 …”

Section: Introductionmentioning

confidence: 99%

“…However, extensive modulations utilized in SBRT can lead to deviation of delivered dose distribution from the planned one. This can be due to delivery system uncertainties such as MLC leaf position errors and gantry rotational instability 1 . Therefore, pretreatment quality assurance (QA) of modulated arc therapy is necessary for patient safety.…”

Section: Introductionmentioning

confidence: 99%

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Patient‐specific quality assurance using machine log files analysis for stereotactic body radiation therapy (SBRT)

Chow

Kan

Chan

2020

J Applied Clin Med Phys

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An in-house trajectory log analysis program (LOGQA) was developed to evaluate the delivery accuracy of volumetric-modulated arc therapy (VMAT) for stereotactic body radiation therapy (SBRT). Methods have been established in LOGQA to provide analysis on dose indices, gantry angles, and multi-leaf collimator (MLC) positions. Between March 2019 and May 2020, 120 VMAT SBRT plans of various treatment sites using flattening filter-free (FFF) mode were evaluated using both LOGQA and phantom measurements. Gantry angles, dose indices, and MLC positions were extracted from log and compared with each plan. Integrated transient fluence map (ITFM) was reconstructed from log to examine the deviation of delivered fluence against the planned one. Average correlation coefficient of dose index versus gantry angle and ITFM for all patients were 1.0000, indicating that the delivered beam parameters were in good agreement with planned values. Maximum deviation of gantry angles and monitor units (MU) of all patients were less than 0.2 degree and 0.03 % respectively. Regarding MLC positions, maximum and rootmean-square (RMS) deviations from planned values were less than 0.6 mm and 0.3 mm respectively, indicating that MLC positions during delivery followed planned values in precise manner. Results of LOGQA were consistent with measurement, where all gamma-index passing rates were larger than 95 %, with 2 %/2 mm criteria. Three types of intentional errors were introduced to patient plan for software validation. LOGQA was found to recognize the introduced errors of MLC positions, gantry angles, and dose indices with magnitudes of 1 mm, 1 degree, and 5 %, respectively, which were masked in phantom measurement. LOGQA was demonstrated to have the potential to reduce or even replace patient-specific QA measurements for SBRT plan delivery provided that the frequency and amount of measurement-based machine-specific QA can be increased to ensure the log files record real values of machine parameters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Patient‐specific quality assurance using machine log files analysis for stereotactic body radiation therapy (SBRT)

Chow

Kan

Chan

2020

J Applied Clin Med Phys

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“…Park et al 28 determined a new metric of MLC speeds and accelerations to predict the plan delivery accuracy of VMAT, with strong correlations to VMAT delivery accuracy. Park et al 29 also studied the correlation between image textures of planned fluences and GPR measured from MapCHECK2 and ArcCHECK dosimeters, demonstrating that image textures strongly correlated with the global GPR. Thus, image textures from planned fluence can estimate the VMAT delivery accuracy without processing the patient-specific QA.…”

Section: Introductionmentioning

confidence: 99%

Predicting gamma evaluation results of patient‐specific head and neck volumetric‐modulated arc therapy quality assurance based on multileaf collimator patterns and fluence map features: A feasibility study

Thongsawad

Srisatit

Fuangrod

2022

J Applied Clin Med Phys

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The purpose of this study was to develop a predictive model for patient‐specific VMAT QA results using multileaf collimator (MLC) effect and texture analysis. The MLC speed, acceleration and texture analysis features were extracted from 106 VMAT plans as predictors. Gamma passing rate (GPR) was collected as a response class with gamma criteria of 2%/2 mm and 3%/2 mm. The model was trained using two machine learning methods: AdaBoost classification and bagged regression trees model. GPR was classified into the “PASS” and “FAIL” for the classification model using the institutional warning level. The accuracy of the model was assessed using sensitivity and specificity. In addition, the accuracy of the regression model was determined using the difference between predicted and measured GPR. For the AdaBoost classification model, the sensitivity/specificity was 94.12%/100% and 63.63%/53.13% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. For the bagged regression trees model, the sensitivity/specificity was 94.12%/91.89% and 61.18%/68.75% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The root mean square error (RMSE) of difference between predicted and measured GPR was found at 2.44 and 1.22 for gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The promising result was found at tighter gamma criteria 2%/2 mm with 94.12% sensitivity (both bagged regression trees and AdaBoost classification model) and 100% specificity (AdaBoost classification model).

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“…Interian et al (2018) predicted the GPR of the IMRT plan using fluence maps-based features. Park et al (2019) performed the texture analysis method on fluence maps of the VMAT plan. The fluence map for a VMAT plan is generated by superposing each fluence map of the control point.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the limitation of using an overall feature, more attention should be paid to the information from each control point (Shiba et al, 2020). Inspired from the fluence map-based method (Park et al, 2019), the aperture-based metrics at each control point can be calculated based on the RT plan DICOM file, and then a series of statistical histogram data using metrics of all control points, noted as planomics, can be extracted as plan features.…”

Section: Introductionmentioning

confidence: 99%

Improvement Using Planomics Features on Prediction and Classification of Patient-Specific Quality Assurance Using Head and Neck Volumetric Modulated Arc Therapy Plan

Chen

Guo

et al. 2021

Front. Neurosci.

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Purpose: This study aimed to evaluate the utility of a new plan feature (planomics feature) for predicting the results of patient-specific quality assurance using the head and neck (H&N) volumetric modulated arc therapy (VMAT) plan.Methods: One hundred and thirty-one H&N VMAT plans in our institution from 2019 to 2021 were retrospectively collected. Dosimetric verification for all plans was carried out using the portal dosimetry system integrated into the Eclipse treatment planning system based on the electronic portal imaging devices. Gamma passing rates (GPR) were analyzed using three gamma indices of 3%/3 mm, 3%/2 mm, and 2%/2 mm with a 10% dose threshold. Forty-eight conventional features affecting the dose delivery accuracy were used in the study, and 2,476 planomics features were extracted based on the radiotherapy plan file. Three prediction and classification models using conventional features (CF), planomics features (PF), and hybrid features (HF) combining two sets of features were constructed by the gradient boosting regressor (GBR) and Ridge classifier for each GPR of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. The absolute prediction error (APE) and the area under the curve (AUC) were adopted for assessing the performance of prediction and classification models.Results: In the GPR prediction, the average APE of the models using CF, PF, and HF was 1.3 ± 1.2%/3.6 ± 3.0%, 1.7 ± 1.5%/3.8 ± 3.5%, and 1.1 ± 1.0%/4.1 ± 3.1% for 2%/2 mm; 0.7 ± 0.6%/2.0 ± 2.0%, 1.0±1.1%/2.2 ± 1.8%, and 0.6 ± 0.6%/2.2 ± 1.9% for 3%/2 mm; and 0.4 ± 0.3%/1.2 ± 1.2%, 0.4±0.5%/1.3 ± 1.0%, and 0.3±0.3%/1.2 ± 1.1% for 3%/3 mm, respectively. In the regression prediction, three models give a similar modeling performance for predicting the GPR. The classification results were 0.67 ± 0.03/0.66 ± 0.07, 0.77 ± 0.03/0.73 ± 0.06, and 0.78 ± 0.02/0.75 ± 0.04 for 3%/3 mm, respectively. For 3%/2 mm, the AUCs of the training and testing cohorts were 0.64 ± 0.03/0.62 ± 0.07, 0.70 ± 0.03/0.67 ± 0.06, and 0.75 ± 0.03/0.71 ± 0.07, respectively, and for 2%/2 mm, the average AUCs of the training and testing cohorts were 0.72 ± 0.03/0.72 ± 0.06, 0.78 ± 0.04/0.73 ± 0.07, and 0.81 ± 0.03/0.75 ± 0.06, respectively. In the classification, the PF model has a better classification performance than the CF model. Moreover, the HF model provides the best result among the three classifications models.Conclusions: The planomics features can be used for predicting and classifying the GPR results and for improving the model performance after combining the conventional features for the GPR classification.

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Prediction of VMAT delivery accuracy with textural features calculated from fluence maps

Cited by 18 publications

References 32 publications

Patient‐specific quality assurance using machine log files analysis for stereotactic body radiation therapy (SBRT)

Patient‐specific quality assurance using machine log files analysis for stereotactic body radiation therapy (SBRT)

Predicting gamma evaluation results of patient‐specific head and neck volumetric‐modulated arc therapy quality assurance based on multileaf collimator patterns and fluence map features: A feasibility study

Improvement Using Planomics Features on Prediction and Classification of Patient-Specific Quality Assurance Using Head and Neck Volumetric Modulated Arc Therapy Plan

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