Single-time-point renal dosimetry for [¹⁷⁷Lu]Lu-PSMA-617: Incorporating multi-cycle population data using nonlinear mixed-effects models.

Abstract

BackgroundSingle-time-point (STP) dosimetry using nonlinear mixed-effects (NLME) modeling has emerged as a promising approach, with recent studies demonstrating high accuracy. This framework enables estimation of the time-integrated activity coefficient (TIAC) from a single imaging time point and, consequently, prediction of absorbed dose for subsequent therapy cycles. However, the use of multi-cycle population data to support NLME-based STP dosimetry has not yet been systematically evaluated.PurposeThis study aimed to evaluate whether incorporating multi-cycle population data into an NLME framework improves agreement between STP-derived TIACs and all-time-point (ATP)-derived model-based reference TIACs in renal dosimetry.MethodsRenal biokinetic data [¹⁷⁷Lu]Lu-PSMA-617 were collected from 10 patients over two therapy cycles, with both kidneys analyzed, yielding 40 renal datasets. In both cycles, SPECT/CT imaging was performed at five time points. A literature-based structural function was used in the NLME modeling to incorporate inter-individual variability, inter-cycle variability, and variability between left and right kidney positions. Goodness-of-fit was evaluated based on visual inspection, relative standard errors (RSEs) ResultsThe model fits satisfied all predefined goodness-of-fit criteria. STP dosimetry using the multi-cycle dataset offered greater flexibility in time-point combinations, leading to MAPEs ConclusionsIncorporating multi-cycle data into NLME-based STP dosimetry reduced extreme RDs, increased flexibility in feasible time-point combinations, and improved early-time-point STP performance in this small two-cycle cohort.