The aim of this study is to present a modulation index (MI) for volumetric modulated arc therapy (VMAT) based on the speed and acceleration analysis of modulating-parameters such as multi-leaf collimator (MLC) movements, gantry rotation and dose-rate, comprehensively. The performance of the presented MI (MIt) was evaluated with correlation analyses to the pre-treatment quality assurance (QA) results, differences in modulating-parameters between VMAT plans versus dynamic log files, and differences in dose-volumetric parameters between VMAT plans versus reconstructed plans using dynamic log files. For comparison, the same correlation analyses were performed for the previously suggested modulation complexity score (MCS(v)), leaf travel modulation complexity score (LTMCS) and MI by Li and Xing (MI Li&Xing). In the two-tailed unpaired parameter condition, p values were acquired. The Spearman's rho (r(s)) values of MIt, MCSv, LTMCS and MI Li&Xing to the local gamma passing rate with 2%/2 mm criterion were -0.658 (p < 0.001), 0.186 (p = 0.251), 0.312 (p = 0.05) and -0.455 (p = 0.003), respectively. The values of rs to the modulating-parameter (MLC positions) differences were 0.917, -0.635, -0.857 and 0.795, respectively (p < 0.001). For dose-volumetric parameters, MIt showed higher statistically significant correlations than the conventional MIs. The MIt showed good performance for the evaluation of the modulation-degree of VMAT plans.
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.
The contrast (d = 1) and variance (d = 1) calculated from fluence maps of VMAT plans showed considerable correlations with the plan deliverability, indicating their potential use as indicators for assessing the degree of modulation of VMAT plans. Both contrast and variance consistently showed better performance than the conventional modulation indices for VMAT.
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