A quantification of the effectiveness of EPID dosimetry and software‐based plan verification systems in detecting incidents in radiotherapy. Issue 9 (19th August 2015) - Bojechko, Casey; Phillps, Mark; Kalet, Alan; Ford, Eric C.
Abstract : Purpose: Complex treatments in radiation therapy require robust verification in order to prevent errors that can adversely affect the patient. For this purpose, the authors estimate the effectiveness of detecting errors with a "defense in depth" system composed of electronic portal imaging device (EPID) based dosimetry and a software‐based system composed of rules‐based and Bayesian network verifications. Methods: The authors analyzed incidents with a high potential severity score, scored as a 3 or 4 on a 4 point scale, recorded in an in‐house voluntary incident reporting system, collected from February 2012 to August 2014. The incidents were categorized into different failure modes. The detectability, defined as the number of incidents that are detectable divided total number of incidents, was calculated for each failure mode. Results: In total, 343 incidents were used in this study. Of the incidents 67% were related to photon external beam therapy (EBRT). The majority of the EBRT incidents were related to patient positioning and only a small number of these could be detected by EPID dosimetry when performed prior to treatment (6%). A large fraction could be detected by in vivo dosimetry performed during the first fraction (74%). Rules‐based and Bayesian network verifications were found to be complimentary to EPID dosimetry, able to detect errors related to patient prescriptions and documentation, and errors unrelated to photon EBRT. Combining all of the verification steps together, 91% of all EBRT incidents could be detected. Conclusions: This study shows that the defense in depth system is potentially able to detect a large majority of incidents. The most effective EPID‐based dosimetry verification is in vivo measurements during the first fraction and is complemented by rules‐based and Bayesian network plan checking.