Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a pharmacokinetic model.

Autor: Hardiansyah D; Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Electrical Engineering, Universitas Padjadjaran, Bandung, Indonesia., Attarwala AA; Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany., Kletting P; Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany., Mottaghy FM; Klinik für Nuklearmedizin, University Hospital, RWTH Aachen University, Aachen, Germany; Department of Nuclear Medicine, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands., Glatting G; Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany. Electronic address: gerhard.glatting@uni-ulm.de.
Jazyk: angličtina
Zdroj: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2017 Oct; Vol. 42, pp. 298-304. Date of Electronic Publication: 2017 Jul 22.
DOI: 10.1016/j.ejmp.2017.06.024
Abstrakt: Purpose: To investigate the accuracy of predicted time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using simulated dynamic PET data and a physiologically based pharmacokinetic (PBPK) model.
Methods: PBPK parameters were estimated using biokinetic data of 15 patients after injection of (152±15)MBq of 111 In-DTPAOC (total peptide amount (5.78±0.25)nmol). True mathematical phantoms of patients (MPPs) were the PBPK model with the estimated parameters. Dynamic PET measurements were simulated as being done after bolus injection of 150MBq 68 Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35min p.i. (P 1 ), 4h p.i. (P 2 ) and the combination of P 1 and P 2 (P 3 ) were simulated. Each measurement was simulated with four frames of 5min each and 2 bed positions. PBPK parameters were fitted to the PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 5.1GBq of 90 Y-DOTATATE over 30min in both true and PET-predicted MPPs. TIACs of simulated therapy were calculated, true MPPs (true TIACs) and predicted MPPs (predicted TIACs) followed by the calculation of variabilities v.
Results: For P 1 and P 2 the population variabilities of kidneys, liver and spleen were acceptable (v<10%). For the tumours and the remainders, the values were large (up to 25%). For P 3 , population variabilities for all organs including the remainder further improved, except that of the tumour (v>10%).
Conclusion: Treatment planning of PRRT based on dynamic PET data seems possible for the kidneys, liver and spleen using a PBPK model and patient specific information.
(Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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