Dosimetric validation of an Intrabeam™ GATE model, based on Monte Carlo GEANT4 toolkit, for IORT applications

International audiencePurpose: Intraoperative RadioTherapy (IORT) with Intrabeam™ is delivered using a miniaturized radiation device with applicators generating the prescribed dose directed to the tumor bed. However, the dose delivered to the patient is not well known. In this context, a Monte Carlo (MC) calculation can accurately assess the dose delivered to the patient by simulating the particle transport. The aim of this study is to create and validate an Intrabeam model on the GATE platform, a MC code based on Geant4. Methods: A model of the Intrabeam™ was created on GATE taking into account dimensions and materials of each element of the system as well as physical processes. This model was extended to include simulations based on patient CT images including the presence of applicators within the same simulation framework allowing their inclusion in the dose calculation. For breast cancer simple simulations in a water phantom were performed including the bare probe and the spherical applicators, using soft X-Ray PTW ionization chambers. For spinal metastases, the RANDO phantom was used to simulate a patient and realistic treatment conditions allowing dose delivery verifications using thermoluminescent dosimeters (TLD). Finally a breast cancer patient CT acquisition, carried out during the operation with the applicator in place, was also used in the validation of the Intrabeam™ GATE model and associated MC based dosimetry using TLDs. Results: Depth dose curve and isotropy were compared showing a good agreement in water. For breast application, the comparison between TLD measurements and GATE simulations on patient and phantom CT images showed a good agreement with less than 1% of deviation (one billion simulated particles). Conclusion: The Monte Carlo GATE platform can simulate accurately relative dose distributions delivered by the Intrabeam system. The proposed model has been validated and will be used for personalized 3D dose prediction in IORT