Investigation of a real-time EPID-based patient dose monitoring safety system using site-specific control limits. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Fuangrod, Todsaporn; Greer, Peter; Woodruff, Henry; Simpson, John; Bhatia, Shashank; Zwan, Benjamin; vanBeek, Timothy A.; McCurdy, Boyd M.C.; Middleton, Richard
• Is Part Of: Radiation oncology. Volume 11:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Statistical process control -- Real-time patient dose monitoring safety system -- EPID; Cancer -- Radiotherapy -- Periodicals; 616.9940642
• DOI: 10.1186/s13014-016-0682-y
• Languages: English
• ISSNs: 1748-717X

Abstract

Abstract Purpose The aim of this study is to investigate the performance and limitations of a real-time transit electronic portal imaging device (EPID) dosimetry system for error detection during dynamic intensity modulated radiation therapy (IMRT) treatment delivery. Sites studied are prostate, head and neck (HN), and rectal cancer treatments. Methods The system compares measured cumulative transit EPID image frames with predicted cumulative image frames in real-time during treatment using a χ comparison with 4 %, 4 mm criteria. The treatment site-specific thresholds (prostate, HN and rectum IMRT) were determined using initial data collected from 137 patients (274 measured treatment fractions) and a statistical process control methodology. These thresholds were then applied to data from 15 selected patients including 5 prostate, 5 HN, and 5 rectum IMRT treatments for system evaluation and classification of error sources. Results Clinical demonstration of real-time transit EPID dosimetry in IMRT was presented. For error simulation, the system could detect gross errors (i.e. wrong patient, wrong plan, wrong gantry angle) immediately after EPID stabilisation; 2 seconds after the start of treatment. The average rate of error detection was 7.0 % (prostate = 5.6 %, HN= 8.7 % and rectum = 6.7 %). The detected errors were classified as either clinical in origin (e.g. patient anatomical changes), or non-clinical in origin (e.g. detection system errors). Classified errors were 3.2 % clinical and 3.9 % non-clinical. Conclusion An EPID-based real-time error detection method for treatment verification during dynamic IMRT has been developed and tested for its performance and limitations. The system is able to detect gross errors in real-time, however improvement in system robustness is required to reduce the non-clinical sources of error detection.