Abstract ID: 222 Clinical implementation of a Monte Carlo based QA platform for validation of Tomotherapy and Cyberknife treatment plans

Introduction This work describes the clinical implementation of a Monte Carlo based platform for treatment plan validation for Tomotherapy and Cyberknife, including a semi-automatic plan evaluation module based on dose constraints for organs-at-risk (OAR). Methods The Monte Carlo-based platform Moderato [1] is based on BEAMnrc/DOSXYZnrc and allows for automated re-calculation of doses planned with Tomotherapy and Cyberknife techniques. The Prescription/Validation module generates a set of dose constraints based on the anatomical region and fractionation scheme considered. Upon achievement of the planning, dose results are displayed with visual warnings in case of constraint violation. The system was tested on 83 patient cases in order to evaluate the influence of difference in calculation algorithms on OAR constraints. Results The first results with the Tomotherapy plans allowed for detecting and correcting a problem with the CT Hounsfield units when using a large reconstruction diameter (a CT artifact that lead to air voxels with an overestimated density). The Cyberknife results also showed some dose differences associated with different energy thresholds between Moderato and the Monte Carlo algorithm used in the Treatment Planning Station. Regarding OAR constraints, re-calculation generated few violations in thoracic, pelvic and abdominal cases. However, in spinal and head cases, significant differences can appear (−11% to +6%) on optic pathways and spinal cord, leading to doses above the limits. Conclusions The Moderato platform constitutes a promising tool for the validation of plan quality, offering both dose re-calculation and OAR constraints evaluation. First results show the importance of this verification for some specific regions. Further work is ongoing to optimize the quantity and relevance of the information displayed, before fully introducing the system in clinical routine.