Remote dosimetric auditing of clinical trials: The need for vendor specific models to convert images to dose

INTRODUCTION: A previous pilot study has demonstrated the feasibility of a novel image‐based approach for remote dosimetric auditing of clinical trials. The approach uses a model to convert in‐air acquired intensity modulated radiotherapy (IMRT) images to delivered dose inside a virtual phantom. The model was developed using images from an electronic portal imaging device (EPID) on a Varian linear accelerator. It was tuned using beam profiles and field size factors (FSFs) of a series of square fields measured in water tank. This work investigates the need for vendor specific conversion models for image‐based auditing. The EPID measured profile and FSF data for Varian (vendor 1) and Elekta (vendor 2) systems are compared along with the performance of the existing Varian model (VM) and a new Elekta model (EM) for a series of audit IMRT fields measured on vendor 2 systems. MATERIALS AND METHODS: The EPID measured beam profile and FSF data were studied for the two vendors to quantify and understand their relevant dosimetric differences. Then, an EM was developed converting EPID to dose in the virtual water phantom using a vendor 2 water tank data and images from corresponding EPID. The VM and EM were compared for predicting vendor 2 measured dose in water tank. Then, the performance of the new EM was compared to the VM for auditing of 54 IMRT fields from four vendor 2 facilities. Statistical significance of using vendor specific models was determined. RESULTS: Observed dosimetry differences between the two vendors suggested developing an EM would be beneficial. The EM performed better than VM for vendor 2 square and IMRT fields. The IMRT audit gamma pass rates were (99.8 ± 0.5)%, (98.6 ± 2.3)% and (97.0 ± 3.0)% at respectively 3%/3 mm, 3%/2 mm and 2%/2 mm with improvements at most fields compared with using the VM. For the pilot audit, the difference between gamma results of the two vendors was reduced when using vendor specific models (VM: P < 0.0001, vendor specific models: P = 0.0025). CONCLUSION: A new model was derived to convert images from vendor 2 EPIDs to dose for remote auditing vendor 2 deliveries. Using vendor specific models is recommended to remotely audit systems from different vendors, however, the improvements found were not major.