The importance of collimator angle error in volumetric-modulated arc therapy

Puchades-Puchades, V.; Serna-Berna, Alfredo; Mata-Colodro, Fernando; Ramos-Amores, David; Baños, Miguel Alcaraz

doi:10.1017/s1460396915000217

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The planned parameters considered to be predictive in determining the delivered MLC leaf positions were extracted or calculated from the log files. Based on what has been reported in literature, the following parameters (Table 1 ) were extracted from the log files: gantry angle, 6 collimator angle, 18 X 1 jaw position, 19 X 2 jaw position, 19 and leaf position. 5 , 12 Gravity vector, 13 leaf gap, 3 , 5 leaf velocity, 3 , 5 , 8 , 9 , 12 , 20 and leaf acceleration 3 , 9 were calculated from the extracted parameters according to the formulas shown in the second column of Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy

Sivabhaskar

Roy

et al. 2022

J Applied Clin Med Phys

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Purpose Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural network (ANN) for predicting the delivered leaf positions for VMAT plans. Methods For this study, 160 MLC log files from 80 VMAT plans were obtained from a single institution treated on 3 Elekta Versa HD linear accelerators. The gravity vector, X1 and X2 jaw positions, leaf gap, leaf position, leaf velocity, and leaf acceleration were extracted and used as model inputs. The models were trained using 70% of the log files and tested on the remaining 30%. Mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination R2, and fitted line plots showing the relationship between delivered and predicted leaf positions were used to evaluate model performance. Results The models achieved the following errors: linear regression (MAE = 0.158 mm, RMSE = 0.225 mm), support vector machine (MAE = 0.141 mm, RMSE = 0.199 mm), random forest (MAE = 0.161 mm, RMSE = 0.229 mm), XGBoost (MAE = 0.185 mm, RMSE = 0.273 mm), and ANN (MAE = 0.361 mm, RMSE = 0.521 mm). A significant correlation between a plan's gamma passing rate (GPR) and the prediction errors of linear regression, support vector machine, and random forest is seen (p < 0.045). Conclusions We examined various models to predict the delivered MLC positions for VMAT plans treated with Elekta linacs. Linear regression, support vector machine, random forest, and XGBoost achieved lower errors than ANN. Models that can accurately predict the individual leaf positions during treatment can help identify leaves that are deviating from the planned position, which can improve a plan's GPR.

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