A proposal for minimum detectable compartment in MIRD dosimetry modelling

Abstract

The accuracy of radiation dose estimates from radiopharmaceutical administrations has recently become more important for three main reasons: (i) clinical providers are demanding more information on diagnostic procedures; (ii) regulatory groups are scrutinizing dosimetry for research subjects; and (iii) accurate organ doses are crucial in therapeutic administrations. These dose estimates are a sensitive function of the residence times. Because most clinical data acquisition protocols are limited to the first 24 h after dose administration, the area under the remainder of the time - activity curve (TAC) must be estimated. Estimation methods range from assuming physical decay only (overly conservative) to extrapolating end point physiological kinetics (overly liberal). This study demonstrates how much the results from these two methods vary and develops an alternative method which more accurately estimates this remainder term. A method, called the minimum detectable compartment (MDC), is constructed so that an accurate dose estimate can be made with a realistic measure of the remainder term. The method for determining MDC uses standard hypothesis testing. Using an analogue of the traditional minimal detectable activity calculation, a model with and without constant compartments is fitted to the TAC. The size of the constant compartment is varied until the relative likelihood of the two models meets the desired measure of power and sensitivity. Computer simulations of a simple mono-exponential are used to demonstrate the MDC as a function of the model, the number of data points, the range of the data and the noise in the data. The MDC is a very sensitive function of the data range. It falls by more than 50% when the data range is increased from two to three half-lives. In addition, the MDC is moderately sensitive to the noise in the data and relatively insensitive to the number of data points. These findings suggest that the MDC method can also be uses a priori to indicate what type of data collection regimen is necessary to achieve a certain accuracy.