Non-linear mixed-effects modelling and population-based model selection for I kinetics in benign thyroid disease.

PURPOSE

This study aimed to determine a mathematical model for accurately calculating time-integrated activities (TIAs) of target tissue in I therapy for benign thyroid disease using the population-based model selection and non-linear mixed-effects (PBMS-NLME) method.

METHODS

Biokinetic data of I in target tissue were collected from seventy-three patients at 2, 6, 24, 48, and 96 (N = 53) or 120 (N = 20) h after oral capsule administration with 1 MBq I. Based on the Akaike weight, the best sum-of-exponential function (SOEF) describing the biokinetic data was selected using PBMS-NLME modelling. Nine SOEF with three to six parameters (including the function from the European Association of Nuclear Medicine Standard Operational Procedure (EANM SOP)) were used. The fittings were repeated 1000 times with different starting values of the SOE parameters to find the optimal fit. Akaike weight was used to identify the performance of the best model from PBMS-NLME and the EANM SOP SOE function with individual fitting.

RESULTS

Based on the PBMS-NLME analysis, the SOEF was selected as the function most supported by the data. The Akaike weight of the best function was approximately 100%. The best SOEF from the PBMS-NLME approach shows a better performance in describing the biokinetic data of I in the thyroid gland than the function from the EANM SOP with individual fitting, based on the Akaike weight.

CONCLUSIONS

The best mathematical model from the PBMS-NLME approach has one more free parameter than the EANM SOP function, which could lead to more accurate TIAs.