Validation of the usefulness of dose calculation algorithms in radiotherapy planning system

One of the main goals of radiotherapy is to achieve tumor control and minimize probability of normal-tissue complications. For this reason radiation oncology requires high accuracy, which implies no more than 2-3% uncertainty levels in the treatment planning calculations. That is challenging, when heterogeneous tissues such as lungs and bones are involved. To verify the accuracy of the dose calculation algorithms numerous approaches might be performed. The most common are point dose, one-dimensional profile and two-dimensional isodose line comparison with experimental measurements. In presented study, results of transport modeling and the deposited spatial distribution of the dose, obtained by anisotropic analytical algorithm and pencil beam convolution algorithm, were compared to measurements recorded during the experiment. To achieve meaningful conclusions, three parameters: dose difference, distance to agreement and gamma parameter (γ) were taken into consideration and examined. The irradiation was performed using CIRS anthropomorphic phantom. For dose detection gafchromic EBT films were used and scanned after exposure using Epson Scanner. Measured and planned dose distributions were analyzed via FilmQA software. Preliminary results showed that the anisotropic analytical algorithm, with its complex accounting of heterogeneities, provides more accurate dose calculation within an area of a high density gradient, than pencil beam convolution does. The level of the data accuracy derived from the experiment was: dose difference (5%) - 83.4% and 68% pixels passing, distance to agreement (3 mm) - 99.0% and 96.7%, gamma parameter (for dose difference (3%), distance to agreement (3 mm)) - 90% and 75.5%, respectively, for anisotropic analytical algorithm and pencil beam convolution algorithms. The comparison between studied parameters dose difference, distance to agreement and γ for both algorithms implicated anisotropic analytical algorithm as an appropriate approach in radiotherapy treatment planning.