Treatment planning in PRRT based on simulated PET data and a PBPK model

Author:

Hardiansyah Deni,Guo Wei,Attarwala Ali Asgar,Kletting Peter,Mottaghy Felix M.,Glatting Gerhard

Abstract

SummaryAim: 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 of 90Y-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.

Funder

Direktorat Jendral Pendidikan Tinggi

Publisher

Georg Thieme Verlag KG

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

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