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An analysis of confidence limit calculations used in AAPM Task Group No. 119
1Department of Radiation Oncology, Karmanos Cancer Center, Detroit, Michigan 48201, USA. knillcor@gmail.com
Purpose:
The report issued by AAPM Task Group No. 119 outlined a procedure for evaluating the effectiveness of IMRT commissioning. The procedure involves measuring gamma pass-rate indices for IMRT plans of standard phantoms and determining if the results fall within a confidence limit set by assuming normally distributed data. As stated in the TG report, the assumption of normally distributed gamma pass rates is a convenient approximation for commissioning purposes, but may not accurately describe the data. Here the authors attempt to better describe gamma pass-rate data by fitting it to different distributions. The authors then calculate updated confidence limits using those distributions and compare them to those derived using TG No. 119 method.
Methods:
Gamma pass-rate data from 111 head and neck patients are fitted using the TG No. 119 normal distribution, a truncated normal distribution, and a Weibull distribution. Confidence limits to 95% are calculated for each and compared. A more general analysis of the expected differences between the TG No. 119 method of determining confidence limits and a more time-consuming curve fitting method is performed.
Results:
The TG No. 119 standard normal distribution does not fit the measured data. However, due to the small range of measured data points, the inaccuracy of the fit has only a small effect on the final value of the confidence limits. The confidence limits for the 111 patient plans are within 0.1% of each other for all distributions. The maximum expected difference in confidence limits, calculated using TG No. 119's approximation and a truncated distribution, is 1.2%.
Conclusions:
A three-parameter Weibull probability distribution more accurately fits the clinical gamma index pass-rate data than the normal distribution adopted by TG No. 119. However, the sensitivity of the confidence limit on distribution fit is low outside of exceptional circumstances.
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