TY - JOUR
T1 - Treatment planning in prrt based on simulated pet data and a PBPK model
T2 - determination of accuracy using a pet noise model
AU - Hardiansyah, Deni
AU - Guo, Wei
AU - Attarwala, Ali Asgar
AU - Kletting, Peter
AU - Mottaghy, Felix M.
AU - Glatting, Gerhard
N1 - Funding Information:
Funding The authors gratefully acknowledge grants by ?Direktorat Jendral Pendidikan Tinggi? (Directorate General of Higher Education DIKTI of Ministry for Research, Technology and Higher Education, Republic Indonesia. Grant Number: 2644/E4.4/K/2013) for DH. The authors also gratefully acknowledge the ?Deutsche Forschungsgemein-schaft? (DFG) for support (GL 236/11?1 and KL 2742/2?1), funding received for MITIGATE from the European Community?s Seventh Framework Programme (FP7/2007?20013) under grant agreement no 602306, for M?OLIE (Research Campus funded by the German Federal Ministry of Education and Research (BMBF) within the Framework ?Forschungscampus: public-private partnership for Innovations?) and for Perspektivf?rderung ?Translationale Radiochemie und Radiopharmazie? (Land Baden-W?rttemberg).
Publisher Copyright:
© Schattauer 2017.
PY - 2017
Y1 - 2017
N2 - Aim: To investigate the accuracy of treatment planning in peptide-receptor radionuclide therapy (PRRT) based on simulated PET data (using a PET noise model) and a physiologically based pharmacokinetic (PBPK) model. Methods: The parameters of a PBPK model were fitted to the biokinetic data of 15 patients. True mathematical phantoms of patients (MPPs) were the PBPK model with the fitted parameters. PET measurements after bolus injection of 150 MBq 68Ga-DOTATATE were simulated for the true MPPs. PET noise with typical noise levels was added to the data (i.e. c = 0.3 [low], 3, 30 and 300 [high]). Organ activity data in the kidneys, tumour, liver and spleen were simulated at 0.5, 1 and 4 h p.i. PBPK model parameters were fitted to the simulated noisy PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 3.3 GBq of90Y-DOTATATE over 30 min. Time-integrated activity coefficients (TIACs) of simulated therapy in tumour, kidneys, liver, spleen and remainder were calculated from both, true MPPs (true TIACs) and predicted MPPs (predicted TIACs). Variability v between true TIACs and predicted TIACs were calculated and analysed. Variability ≤ 10 % was considered to be an accurate prediction. Results: For all noise level, variabilities for the kidneys, liver, and spleen showed an accurate prediction for TIACs, e.g. c = 300: vkidney = (5 ± 2)%, vliver = (5 ± 2)%, vspleen = (4 ± 2)%. However, tumour TIAC predictions were not accurate for all noise levels, e.g. c = 0.3: vtumour = (8 ± 5)%. Conclusion: PET- based treatment planning with kidneys as the dose limiting organ seems possible for all reported noise levels using an adequate PBPK model and previous knowledge about the individual patient.
AB - Aim: To investigate the accuracy of treatment planning in peptide-receptor radionuclide therapy (PRRT) based on simulated PET data (using a PET noise model) and a physiologically based pharmacokinetic (PBPK) model. Methods: The parameters of a PBPK model were fitted to the biokinetic data of 15 patients. True mathematical phantoms of patients (MPPs) were the PBPK model with the fitted parameters. PET measurements after bolus injection of 150 MBq 68Ga-DOTATATE were simulated for the true MPPs. PET noise with typical noise levels was added to the data (i.e. c = 0.3 [low], 3, 30 and 300 [high]). Organ activity data in the kidneys, tumour, liver and spleen were simulated at 0.5, 1 and 4 h p.i. PBPK model parameters were fitted to the simulated noisy PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 3.3 GBq of90Y-DOTATATE over 30 min. Time-integrated activity coefficients (TIACs) of simulated therapy in tumour, kidneys, liver, spleen and remainder were calculated from both, true MPPs (true TIACs) and predicted MPPs (predicted TIACs). Variability v between true TIACs and predicted TIACs were calculated and analysed. Variability ≤ 10 % was considered to be an accurate prediction. Results: For all noise level, variabilities for the kidneys, liver, and spleen showed an accurate prediction for TIACs, e.g. c = 300: vkidney = (5 ± 2)%, vliver = (5 ± 2)%, vspleen = (4 ± 2)%. However, tumour TIAC predictions were not accurate for all noise levels, e.g. c = 0.3: vtumour = (8 ± 5)%. Conclusion: PET- based treatment planning with kidneys as the dose limiting organ seems possible for all reported noise levels using an adequate PBPK model and previous knowledge about the individual patient.
KW - PBPK Model
KW - PET
KW - PET noise model
KW - PRRT
UR - https://www.scopus.com/pages/publications/85012868144
U2 - 10.3413/Nukmed-0819-16-04
DO - 10.3413/Nukmed-0819-16-04
M3 - Article
C2 - 27885372
AN - SCOPUS:85012868144
SN - 0029-5566
VL - 56
SP - 23
EP - 30
JO - Nuklearmedizin
JF - Nuklearmedizin
IS - 1
ER -