Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a pharmacokinetic model. (October 2017)

• Record Type: Journal Article
• Authors: Hardiansyah, Deni; Attarwala, Ali Asgar; Kletting, Peter; Mottaghy, Felix M.; Glatting, Gerhard
• Is Part Of: Physica medica. Volume 42(2017)
• Page Start: 298
• Page End: 304
• Publication Date: 2017-10
• Subjects: PBPK model -- PRRT treatment planning -- Dynamic PET -- PET noise model; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2017.06.024
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Individual treatment planning for PRRT using PET and a PBPK model is investigated. Dynamic 68 Ga-DOTATATE PET data are used to predict the therapy with 90 Y-DOTATATE. This may lead to simplified individual treatment planning in molecular radiotherapy. Abstract: 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 150 MBq 68 Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35 min p.i. (P1 ), 4 h p.i. (P2 ) and the combination of P1 and P2 (P3 ) were simulated. Each measurement was simulated with four frames of 5 min 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 5.1 GBq of 90 Y-DOTATATE over 30 min 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 P1 and P2 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 P3, 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.