A decision tool to adjust the prescribed dose after change in the dose calculation algorithm

Abstract

Purpose: This work aims to introduce a method to quantify and assess the differences in monitor unites MUs when changing to new dose calculation software that uses a different algorithm, and to evaluate the need and extent of adjustment of the prescribed dose to maintain the same clinical results. Methods: Doses were calculated using two classical algorithms based on the Pencil Beam Convolution PBC model, using 6 patients presenting lung cancers. For each patient, 3 treatment plans were generated: Plan 1 was calculated using reference algorithm PBC without heterogeneity correction, Plan 2 was calculated using test algorithm with heterogeneity correction, and in plan 3 the dose was recalculated using test algorithm and monitor unites MUs obtained from plan 1 as input. To assess the differences in the calculated MUs, isocenter dose, and spatial dose distributions using a gamma index were compared. Statistical analysis was based on a Wilcoxon signed rank test. Results: The test algorithm in plan 2 calculated significantly less MUs than reference algorithm in plan 1 by on average 5%, (p < 0.001). We also found underestimating dose for target volumes using 3D gamma index analysis. In this example, in order to obtain the same clinical outcomes with the two algorithms the prescribed dose should be adjusted by 5%. Conclusion: This method provides a quantitative evaluation of the differences between two dose calculation algorithms and the consequences on the prescribed dose. It could be used to adjust the prescribed dose when changing calculation software to maintain the same clinical results as obtained with the former software. In particular, the gamma evaluation could be applied to any situation where changes in the dose calculation occur in radiotherapy.

Statistical methods to evaluate the correlation between measured and calculated dose using quality assurance method in IMRT

“…It is interesting to note that the γ evaluation is not able to predict the change in the dose volume histogram either for the target volume or OAR. [24][25][26][27][28] Chaikh et al, 2014, showed for a specific patient, that 95% of pixels had γ < 1 with a γ average value less than unity 24 . In this case, we might conclude that there is no difference between the reference plan and the tested plan, but the real changes of dose distribution were not taken into consideration to protect the OAR.…”

“…It was observed that a γ evaluation with (3%, 3 mm) under-estimated the dose in a small fraction of the target volume and overestimated the dose for OAR. 24 Zhen et al 2011, showed a significant correlation between 3D dose volumes in a patient geometry for QA comparison. 25 In this study, the dose difference reached up to 15%, while γ evaluation showed 95% of pixels as having γ < 1.…”