Quality assurance of beam base data in modern radiotherapy: a Monte Carlo simulation approach

Le résumé fourni par la source

Accurate dose computation in radiotherapy is critical due to the complexity of modern treatment modalities. Beam base data (BBD) underpin the precision of dose calculations in techniques such as Volumetric Modulated Arc Therapy (VMAT), Intensity-Modulated Radiation Therapy (IMRT), Stereotactic Radiosurgery (SRS), and Stereotactic Body Radiation Therapy (SBRT). Even minor discrepancies in BBD can compromise the accuracy of dose computations, necessitating quality assurance (QA). This study investigates the application of Monte Carlo (MC) simulations, considered the 'gold standard' in dose calculations, for BBD QA using SciMoCa. SciMoCa is a Monte Carlo dose engine which shares its concepts with the VMC family of codes. A total of 87 BBD sets, from 39 datasets, representing diverse linacs, were analyzed, provided by the vendor of the MC-system. Systematic errors (e.g., dose, point dose, spectrum, output errors) were categorized into error classes: severe (Type 1), moderate (Type 2), minor (Type 3). Measurements were conducted using ionization chambers and diodes, and results were compared to MC simulations. The virtual source model was tested against measurements as a proof of concept, showing an overall deviation of less than 1%, with output factors differing by less than 0.3%. The analysis of the 87 BBD sets presented that 86% of BBD sets passed the criterions of Type 1 errors, 60% for Type 2 and 28% for Type 3 criteria. In absolute terms, 24 of the 87 BBD sets met the minimum criteria and would not compromise dose calculation in TPS. The study highlights the potential of MC simulations in establishing a standardized approach to BBD QA. This approach allows for robust validation of BBD quality and self-consistency, achieving typical precision within ±0.5%. In more challenging cases, the precision may expand to ±1.0%.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.