Quantification of Treatment Plan Deliverability in Breast Volumetric-modulated Arc Therapy With Agility Multi-leaf Collimator.

The aim was to assess the complexity of breast volumetric-modulated arc therapy (VMAT) plans using various indices and to evaluate their performance through gamma analysis in predicting plan deliverability. A total of 285 VMAT plans for 260 patients were created using the VersaHD™ linear accelerator with a Monaco treatment planning system. Corresponding verification plans were generated using the ArcCHECK® detector, and gamma analysis was conducted employing various criteria. Twenty-eight plan complexity metrics were computed, and Pearson's correlation coefficients were determined between the gamma passing rate (GPR) and these metrics. The average GPR values for all plans were 97.7%, 89.9%, and 78.0% for the 2 mm/2%, 1 mm/2%, and 1 mm/1% criteria, respectively. While most complexity metrics exhibited weak correlations with GPRs under the 2 mm/2% criterion, leaf sequence variability (LSV), plan-averaged beam area (PA), converted area metric (CAM), and edge area metric (EAM) demonstrated the most robust performance, with Pearson's correlation coefficients of 0.57, 0.50, -0.70, and -0.56, respectively. Metrics related to beam aperture size and irregularity, such as LSV, PA, CAM and EAM, proved to be reasonable predictors of plan deliverability in breast VMAT.

Oh DH ，Choi JH ，Ryu H ，Chun M ... -  《-》

Texture analysis on the fluence map to evaluate the degree of modulation for volumetric modulated arc therapy.

Park SY ，Kim IH ，Ye SJ ，Carlson J ，Park JM ... -  《-》

Modulation indices and plan delivery accuracy of volumetric modulated arc therapy.

We evaluated the performance of various modulation indices (MI) for volumetric modulated arc therapy (VMAT) to predict plan delivery accuracy. The specific indices evaluated were MI quantifying the mechanical uncertainty (MIt ), MI quantifying the mechanical and dose calculation uncertainties (MIc ), MI for station parameter optimized radiation therapy (MISPORT ), modulation complexity score for VMAT (MCSv ), leaf travel modulation complexity score (LTMCS), plan averaged beam area (PA), plan averaged beam irregularity (PI), plan averaged beam modulation (PM), and plan normalized monitor unit (PMU) to predict VMAT delivery accuracy. By utilizing 240 VMAT plans generated with the Trilogy and TrueBeam STx, Spearman's rank correlation coefficients (r) were calculated between the MIs and measures of conventional methods. For the Trilogy system, MIc showed the highest r values with gamma passing rates (GPRs) (r = -0.624 with P < 0.001 for MapCHECK2 and r = -0.655 with P < 0.001 for ArcCHECK). For TrueBeam STx, MIc also showed the highest r values with GPRs (r = -0.625 with P < 0.001 for the MapCHECK2 and r = -0.561 with P < 0.001 for the ArcCHECK). The MIt and MIc showed the highest r values to the MLC position errors for the Trilogy and TrueBeam STx systems (r = 0.770 with P < 0.001 and r = 0.712 with P < 0.001, respectively). The PA showed the highest percent of r values (P < 0.05) to differences in the dose-volume parameters between original VMAT plans and actual deliveries for the Trilogy systems (30.9%). Both the MIt and MIc showed the highest percent of r values (P < 0.05) to differences in the dose-volume parameters between original VMAT plans and actual deliveries for the TrueBeam STx systems (31.8%). To comprehensively review the results, the MIc showed the best performance to predict the VMAT delivery accuracy.

Park JM ，Kim JI ，Park SY 《Journal of Applied Clinical Medical Physics》

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy.

To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis. The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365. The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

Xue X ，Luan S ，Ding Y ，Li X ，Li D ，Wang J ，Ma C ，Jiang M ，Wei W ，Wang X ... -  《Journal of Applied Clinical Medical Physics》

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Cho H ，Lee JS ，Kim JS ，Kim D ，Chang JS ，Byun HK ，Lee IJ ，Kim YB ，Kim C ，Lee H ，Kim H ... -  《-》