Article Abstract

Quantitative comparison of dose distribution in radiotherapy plans using 2D gamma maps and X-ray computed tomography

Authors: Abdulhamid Chaikh, Jacques Balosso


Background: The advanced dose calculation algorithms implemented in treatment planning system (TPS) have remarkably improved the accuracy of dose calculation especially the modeling of electrons transport in the low density medium. The purpose of this study is to evaluate the use of 2D gamma (γ) index to quantify and evaluate the impact of the calculation of electrons transport on dose distribution for lung radiotherapy.
Methods: X-ray computed tomography images were used to calculate the dose for twelve radiotherapy treatment plans. The doses were originally calculated with Modified Batho (MB) 1D density correction method, and recalculated with anisotropic analytical algorithm (AAA), using the same prescribed dose. Dose parameters derived from dose volume histograms (DVH) and target coverage indices were compared. To compare dose distribution, 2D γ-index was applied, ranging from 1%/1 mm to 6%/6 mm. The results were displayed using γ-maps in 2D. Correlation between DVH metrics and γ passing rates was tested using Spearman’s rank test and Wilcoxon paired test to calculate P values.
Results: the plans generated with AAA predicted more heterogeneous dose distribution inside the target, with P<0.05. However, MB overestimated the dose predicting more coverage of the target by the prescribed dose. The γ analysis showed that the difference between MB and AAA could reach up to ±10%. The 2D γ-maps illustrated that AAA predicted more dose to organs at risks, as well as lower dose to the target compared to MB.
Conclusions: Taking into account of the electrons transport on radiotherapy plans showed a significant impact on delivered dose and dose distribution. When considering the AAA represent the true cumulative dose, a readjusting of the prescribed dose and an optimization to protect the organs at risks should be taken in consideration in order to obtain the better clinical outcome.