Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept

Area of Science:

• Nuclear medicine
• Medical physics
• Radiopharmaceutical dosimetry

Background:

• Accurate time-integrated activity (TIA) determination is crucial for molecular radiotherapy dosimetry.
• Single-time-point (STP) dosimetry offers a simplified approach but requires robust methods for TIA calculation.
• Existing methods may lack the precision needed for personalized dosimetry.

Purpose of the Study:

• To develop and evaluate a novel model selection within nonlinear mixed-effects (MS-NLME) method for accurate TIA determination in STP dosimetry.
• To compare the performance of different kinetic models in estimating TIAs for molecular radiotherapy.
• To identify the optimal function complexity for reliable dosimetry calculations.

Main Methods:

• Utilized biokinetic data of [111In]In-DOTATATE in kidneys from eight patients.
• Derived eleven mono-, bi-, and tri-exponential functions for kinetic modeling.
• Applied nonlinear mixed-effects (NLME) framework with Akaike weights for model selection (MS-NLME).
• Calculated relative deviations (RD) and root-mean-square errors (RMSE) for TIA estimations at T3 and T4 post-injection.

Main Results:

• The MS-NLME method selected a four-parameter function with an Akaike weight of (45 ± 6)%, indicating strong data support.
• The MS-NLME approach demonstrated superior performance compared to three- or five-parameter functions.
• RMSE values for the MS-NLME method were 7.8% (T3) and 4.9% (T4), outperforming simpler models (10.9% and 10.7% respectively).

Conclusions:

• The developed MS-NLME method effectively determines the optimal kinetic function for TIA calculation in STP dosimetry.
• Four-parameter functions provide more accurate TIAs than three- or five-parameter functions for [111In]In-DOTATATE kidney dosimetry.
• This method offers a validated approach for personalized dosimetry in molecular radiotherapy.