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》

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》

Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution.

We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom. A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics.

Ishizaka N ，Kinoshita T ，Sakai M ，Tanabe S ，Nakano H ，Tanabe S ，Nakamura S ，Mayumi K ，Akamatsu S ，Nishikata T ，Takizawa T ，Yamada T ，Sakai H ，Kaidu M ，Sasamoto R ，Ishikawa H ，Utsunomiya S ... -  《Journal of Applied Clinical Medical Physics》

Impact of plan parameters on the dosimetric accuracy of volumetric modulated arc therapy.

Masi L ，Doro R ，Favuzza V ，Cipressi S ，Livi L ... -  《-》

Machine learning-generated decision boundaries for prediction and exploration of patient-specific quality assurance failures in stereotactic radiosurgery plans.

Stereotactic radiosurgery (SRS) is a form of radiotherapy treatment during which high radiation dose is delivered in a single or few fractions. These treatments require highly conformal plans with steep dose gradients, which can result in an increase in plan complexity prompting the need for stringent pretreatment patient-specific quality assurance (QA) measurements to ensure the planned and measured dose distributions agree within clinical standards. Complexity scores and machine learning (ML) techniques may help with prediction of QA outcomes; however interpretability and usability of those results continues to be an area of study. This study investigates the use of plan complexity metrics as input for an ML model to allow for prediction of QA outcomes for SRS plans as measured via three-dimension (3D) phantom dose verification. Explorations into interpretability and predictive ability, as well as a prospective in-clinic implementation using the resulting model were performed. Four hundred ninety-eight plans (1571 volumetric modulated arc therapy arcs) were processed via in-house script to generate several complexity scores. 3D phantom dose verification measurement results were extracted and classified as pass or failure (with failures defined as below 95% voxel agreement passing 3%/1-mm gamma criteria with 10% threshold,) and 1472 of the arcs were split into training and testing sets, with 99 arcs as a sequential holdout set. A z-score scaler was trained on the training set and used to scale all other sets. Variations of multi-leaf collimator (MLC) leaf movement variability, aperture complexity, and leaf size, and monitor unit (MU) at control point weighted target area scores were used as input to a support vector classifier to generate a series of 1D, 2D, and 5D decision boundaries. The best performing 5D model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements for 38 patient plans (112 arcs). The decision to order 3D phantom dose verification measurements was recorded before and after prediction. Best performing 1D threshold and 2D prediction models with best performance produced a QA failure recall and QA passing recall of 1.00 and 0.55, and 0.82 and 0.82, respectively. Best performing 5D prediction model produced a QA failure recall (sensitivity) of 1.00 and QA passing recall (specificity) of 0.72. This model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements and achieved a QA failure recall of 1.00 and QA passing recall of 0.58. The decision to order 3D phantom dose verification measurements was recorded before and after measurement. A single initially unidentified failing plan of the prospective cohort was successfully predicted to fail by the model. Implementation of complexity score-based prediction models for SRS would allow for support of a clinician's decision to reduce time spent performing QA measurements and avoid patient treatment delays (i.e., in case of QA failure).

Braun J ，Quirk S ，Tchistiakova E 《-》