Cargando…
Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept
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. It performs a model selection (MS) within the framework of the nonlinear mixed-effects (NLME) model (MS–NLME). MET...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911583/ https://www.ncbi.nlm.nih.gov/pubmed/36759362 http://dx.doi.org/10.1186/s40658-023-00530-1 |
_version_ | 1784885018245464064 |
---|---|
author | Hardiansyah, Deni Riana, Ade Beer, Ambros J. Glatting, Gerhard |
author_facet | Hardiansyah, Deni Riana, Ade Beer, Ambros J. Glatting, Gerhard |
author_sort | Hardiansyah, Deni |
collection | PubMed |
description | 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. It performs a model selection (MS) within the framework of the nonlinear mixed-effects (NLME) model (MS–NLME). METHODS: Biokinetic data of [(111)In]In-DOTATATE activity 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-square 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 for all functions passing the goodness-of-fit test. RESULTS: The function [Formula: see text] with four adjustable parameters and [Formula: see text] was selected as the function most supported by the data with an Akaike weight of (45 ± 6) %. RD and RMSE values show that the MS–NLME method performs better than functions with three or five adjustable parameters. The RMSEs of TIA(NLME–PBMS) and TIA(3-parameters) 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. The proof of concept was demonstrated for biokinetic (111)In-DOTATATE data, showing that four-parameter functions perform better than three- and five-parameter functions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-023-00530-1. |
format | Online Article Text |
id | pubmed-9911583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-99115832023-02-11 Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept Hardiansyah, Deni Riana, Ade Beer, Ambros J. Glatting, Gerhard EJNMMI Phys Original Research 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. It performs a model selection (MS) within the framework of the nonlinear mixed-effects (NLME) model (MS–NLME). METHODS: Biokinetic data of [(111)In]In-DOTATATE activity 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-square 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 for all functions passing the goodness-of-fit test. RESULTS: The function [Formula: see text] with four adjustable parameters and [Formula: see text] was selected as the function most supported by the data with an Akaike weight of (45 ± 6) %. RD and RMSE values show that the MS–NLME method performs better than functions with three or five adjustable parameters. The RMSEs of TIA(NLME–PBMS) and TIA(3-parameters) 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. The proof of concept was demonstrated for biokinetic (111)In-DOTATATE data, showing that four-parameter functions perform better than three- and five-parameter functions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-023-00530-1. Springer International Publishing 2023-02-10 /pmc/articles/PMC9911583/ /pubmed/36759362 http://dx.doi.org/10.1186/s40658-023-00530-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Hardiansyah, Deni Riana, Ade Beer, Ambros J. Glatting, Gerhard Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept |
title | Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept |
title_full | Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept |
title_fullStr | Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept |
title_full_unstemmed | Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept |
title_short | Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept |
title_sort | single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911583/ https://www.ncbi.nlm.nih.gov/pubmed/36759362 http://dx.doi.org/10.1186/s40658-023-00530-1 |
work_keys_str_mv | AT hardiansyahdeni singletimepointdosimetryusingmodelselectionandnonlinearmixedeffectsmodellingaproofofconcept AT rianaade singletimepointdosimetryusingmodelselectionandnonlinearmixedeffectsmodellingaproofofconcept AT beerambrosj singletimepointdosimetryusingmodelselectionandnonlinearmixedeffectsmodellingaproofofconcept AT glattinggerhard singletimepointdosimetryusingmodelselectionandnonlinearmixedeffectsmodellingaproofofconcept |