Hardiansyah Deni, Riana Ade, Hänscheid Heribert, Beer Ambros J, Lassmann Michael, Glatting Gerhard
Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany.
EJNMMI Phys. 2025 Apr 8;12(1):37. doi: 10.1186/s40658-025-00735-6.
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.
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.
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.
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.
本研究旨在使用基于群体的模型选择和非线性混合效应（PBMS-NLME）方法，确定一种用于准确计算良性甲状腺疾病碘治疗中靶组织时间积分活度（TIA）的数学模型。
在口服1MBq碘胶囊后2、6、24、48和96小时（N = 53）或120小时（N = 20），收集了73例患者靶组织中碘的生物动力学数据。基于赤池权重，使用PBMS-NLME建模选择描述生物动力学数据的最佳指数和函数（SOEF）。使用了9种具有三到六个参数的SOEF（包括来自欧洲核医学协会标准操作程序（EANM SOP）的函数）。对SOE参数的不同初始值重复拟合1000次以找到最佳拟合。使用赤池权重来识别PBMS-NLME最佳模型以及具有个体拟合的EANM SOP SOE函数的性能。
基于PBMS-NLME分析，SOEF被选为数据最支持的函数。最佳函数的赤池权重约为100%。基于赤池权重，PBMS-NLME方法的最佳SOEF在描述甲状腺中碘的生物动力学数据方面比具有个体拟合的EANM SOP函数表现更好。
PBMS-NLME方法的最佳数学模型比EANM SOP函数多一个自由参数，这可能导致更准确的TIA。
