Tuning and validation of the new RadCalc 3D Monte Carlo based pre-treatment plan verification tool
Authors
Giada Sceni, Andrea Botti, Matteo Orlandi, Daniele Lambertini, Domenico Fincchiaro, Adriana Barani, Valentina Trojani, Roberto Sghedoni, Mauro Iori
Source
Journal of Mechanics in Medicine and Biology. doi: 10.1142/S021951942340047X
The results of RadCalc’s 3D Monte Carlo algorithm secondary check on the patient’s heterogeneous CT datasets were compared against on-couch homogeneous phantom measurements after fine tuning the models in RadCalc with a Gamma criteria of 2%/2mm and low dose thresholds of 50%. 70 VMAT plans were used for clinical validation of RadCalc’s 3D Monte Carlo algorithm for 6x, 10x, 6FFF and 10FFF against Eclipse v13.7 for both AAA and Acuros XB. Of the 70 plans, 20 were used for tuning and the other 50 were utilized as a validation set.
The RadCalc MC modeling process allows the user to choose the spot size and mean energy that best fits 3 open fields. From this spot size and mean energy combination, a BEAMnrc modeled machine is loaded, with every physical component modeled. This unique auto modeling method provides near-instantaneous beams with only one parameter that needs to be fine-tuned: the additional Radiation Light Field Offset (ARLF), better known in the Varian world as the Dosimetric Leaf Gap (DLG).
The authors quote their DLG for each energy, as well as the resulting ARLF from the model tuning performed. The authors demonstrate the accuracy of RadCalc’s Monte Carlo against the Eclipse algorithms and the on-couch homogenous phantom measurements against the Eclipse algorithms as well. As is the topic of the up-and-coming TG 360, the authors performed statistical methods on the comparison of the gamma passing rates and utilized ROC curve analysis to set the acceptable plans for the on-couch measurements and the RadCalc Monte Carlo calculations as the 95th and 90th percentile respectively. The confusion matrix, including the number of True Positives/Negatives and False Positives/Negatives is demonstrated, as well as Gamma Passing Rate comparisons against AAA/Acuros XB, and a box chart that also includes the on-couch measurements. In summary, these data show a high degree of agreement between the RadCalc MC and Acuros XB calculations, especially for the lung subset used.
As the authors conclude, after tuning, the RadCalc’s 3D Monte Carlo Algorithm provides a solution to independently verify treatment plans directly on the patient’s CT with sensitivities and specificities similar to on-couch phantom solutions, with the added benefit of detecting inaccuracies in tissue inhomogeneities that homogeneous on-couch phantoms are unable to detect.