Program Information
Catastrophic Error Metrics for Radiation Therapy
S Murphy*, J Molloy , University of Kentucky, Lexington, KY
Presentations
WE-H-BRC-5 (Wednesday, August 3, 2016) 4:30 PM - 6:00 PM Room: Ballroom C
Purpose:Intuitive evaluation of complex radiotherapy treatments is impractical, while data transfer anomalies create the potential for catastrophic treatment delivery errors. Contrary to prevailing wisdom, logical scrutiny can be applied to patient-specific machine settings. Such tests can be automated, applied at the point of treatment delivery and can be dissociated from prior states of the treatment plan, potentially revealing errors introduced early in the process.
Methods:Analytical metrics were formulated for conventional and intensity modulated RT (IMRT) treatments. These were designed to assess consistency between monitor unit settings, wedge values, prescription dose and leaf positioning (IMRT). Institutional metric averages for 218 clinical plans were stratified over multiple anatomical sites. Treatment delivery errors were simulated using a commercial treatment planning system and metric behavior assessed via receiver-operator-characteristic (ROC) analysis. A positive result was returned if the erred plan metric value exceeded a given number of standard deviations, e.g. 2. The finding was declared true positive if the dosimetric impact exceeded 25%. ROC curves were generated over a range of metric standard deviations.
Results:Data for the conventional treatment metric indicated standard deviations of 3%, 12%, 11%, 8%, and 5 % for brain, pelvis, abdomen, lung and breast sites, respectively. Optimum error declaration thresholds yielded true positive rates (TPR) between 0.7 and 1, and false positive rates (FPR) between 0 and 0.2. Two proposed IMRT metrics possessed standard deviations of 23% and 37%. The superior metric returned TPR and FPR of 0.7 and 0.2, respectively, when both leaf position and MUs were modelled. Isolation to only leaf position errors yielded TPR and FPR values of 0.9 and 0.1.
Conclusion:Logical tests can reveal treatment delivery errors and prevent large, catastrophic errors. Analytical metrics are able to identify errors in monitor units, wedging and leaf positions with favorable sensitivity and specificity.
Funding Support, Disclosures, and Conflict of Interest: In part by Varian
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