A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

Abstract

Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD = 1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be delivered to the patient.

A deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance

“…In addition, we implemented a machine learning model. Several prior studies have reported that machine learning models, using information from TPS, were useful for prediction of various factors . In this study, we also implemented a machine learning‐based linear regression model to predict GPR.…”

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning

“…In pathognomy, several studies have addressed the prediction of clinical outcome of stereotactic body radiation therapy for lung cancer using radiomic features based on CT images . In medical physics, Carlson et al reported the prediction of multi‐leaf collimator positional errors using machine learning . Sum et al reported the prediction of beam output and derived MUs using machine learning .…”

IMRT QA using machine learning: A multi‐institutional validation

“…Machine learning is a subfield of data science that focuses on designing algorithms that can learn from and make predictions on data. Machine learning applications in radiotherapy have emerged increasingly in recent years, with applications including predictive modeling of treatment outcome in radiation oncology,1, 2, 3, 4, 5, 6, 7 treatment optimization,8, 9, 10, 11 error detection and prevention,12, 13, 14, 15 and treatment machine quality assurance (QA) 16, 17, 18, 19. These machine learning techniques have provided physicians and physicists information for more effective and accurate treatment delivery as well as the ability to achieve personalized treatment.…”