title: 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.
authors.affiliation: Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Korea. Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea.
authors.forename: Joel N K
authors.initials: JN
authors.lastname: Carlson
authors.forename: Jong Min
authors.initials: JM
authors.lastname: Park
authors.forename: So-Yeon
authors.initials: SY
authors.lastname: Park
authors.forename: Jong In
authors.initials: JI
authors.lastname: Park
authors.forename: Yunseok
authors.initials: Y
authors.lastname: Choi
authors.forename: Sung-Joon
authors.initials: SJ
authors.lastname: Ye
mesh_terms: D006801:Humans; D000069550:Machine Learning; D056888:Patient Positioning / Q000009:adverse effects / Q000379:methods / Q000592:standards*; D011829:Radiation Dosage; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods* / Q000592:standards