Abstract
Purpose
This project aims to develop and evaluate a method for accurately determining time-integrated activities (TIAs) in single-time-point (STP) dosimetry for molecular radiotherapy (MRT). It performs a Model Selection (MS) within the framework of the Non-Linear Mixed-Effects (NLME) model (MS-NLME).
Methods
Biokinetic data of 111 In-DOTATATE in kidneys at T1=(2.9±0.6) h, T2=(4.6±0.4) h, T3=(22.8±1.6) h, T4=(46.7±1.7) h, and T5=(70.9±1.0) h post injection were obtained from eight patients using planar imaging. Eleven functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted simultaneously (in the NLME framework) to the biokinetic data of all patients. The Akaike weights were used to select the fit function most supported by the data. The relative deviations (RD) and the Root-Mean-Squared Error (RMSE) of the calculated TIAs for the STP dosimetry at T3=(22.8±1.6) h and T4=(46.7±1.7) h p.i. were determined using the best model of the MS-NLME (TIA MS-NLME ) and the bi-exponential function from the literature (TIA LIT ).
Results
The function [[EQUATION]] was selected as the function most supported by the data with an Akaike weight of (39±6) %. RD and RMSE values show that the MS-NLME method has a better performance than the bi-exponential function from the literature. The RMSEs of TIA NLME-PBMS and TIA LIT were 7.8% and 10.9% (for STP at T3), and 4.9% and 10.7% (for STP at T4), respectively.
Conclusion
An MS-NLME method was developed to determine the best fit function for calculating TIAs in STP dosimetry for a given radiopharmaceutical, organ and patient population. This STP dosimetry based on the MS-NLME method performs better than the
This project aims to develop and evaluate a method for accurately determining time-integrated activities (TIAs) in single-time-point (STP) dosimetry for molecular radiotherapy (MRT). It performs a Model Selection (MS) within the framework of the Non-Linear Mixed-Effects (NLME) model (MS-NLME).
Methods
Biokinetic data of 111 In-DOTATATE in kidneys at T1=(2.9±0.6) h, T2=(4.6±0.4) h, T3=(22.8±1.6) h, T4=(46.7±1.7) h, and T5=(70.9±1.0) h post injection were obtained from eight patients using planar imaging. Eleven functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted simultaneously (in the NLME framework) to the biokinetic data of all patients. The Akaike weights were used to select the fit function most supported by the data. The relative deviations (RD) and the Root-Mean-Squared Error (RMSE) of the calculated TIAs for the STP dosimetry at T3=(22.8±1.6) h and T4=(46.7±1.7) h p.i. were determined using the best model of the MS-NLME (TIA MS-NLME ) and the bi-exponential function from the literature (TIA LIT ).
Results
The function [[EQUATION]] was selected as the function most supported by the data with an Akaike weight of (39±6) %. RD and RMSE values show that the MS-NLME method has a better performance than the bi-exponential function from the literature. The RMSEs of TIA NLME-PBMS and TIA LIT were 7.8% and 10.9% (for STP at T3), and 4.9% and 10.7% (for STP at T4), respectively.
Conclusion
An MS-NLME method was developed to determine the best fit function for calculating TIAs in STP dosimetry for a given radiopharmaceutical, organ and patient population. This STP dosimetry based on the MS-NLME method performs better than the
| Original language | English |
|---|---|
| Publisher | Research Square |
| Publication status | Published - 28 Sept 2022 |
Keywords
- Akaike weight
- Model Selection
- NLME
- PRRT