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DOI: 10.3413/Nukmed-0819-16-04
Treatment planning in PRRT based on simulated PET data and a PBPK model
Determination of accuracy using a PET noise modelBehandlungsplanung in der PRRT basierend auf simulierten PET-Daten und einem PBPK-ModellBestimmung der Genauigkeit unter Verwendung eines Modells für das PET-RauschenFunding 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 Forschungsgemeinschaft“ (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 M2OLIE (Research Campus funded by the German Federal Ministry of Education and Research (BMBF) within the Framework “Forschungscampus: public-private partnership for Innovations”) and for Perspektivförderung „Translationale Radiochemie und Radiopharmazie“ (Land Baden-Württemberg).
Publication History
received:
06 April 2016
accepted in revised form:
07 November 2016
Publication Date:
02 January 2018 (online)
Summary
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 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.
Zusammenfassung
Ziel: Untersuchung der Genauigkeit der Behandlungsplanung in der Peptidrezeptor-Ra- dionuklid-Therapie (PRRT) basierend auf simulierten PET-Daten und einem physiologisch ba- siertenpharmakokinetischen (PBPK) Modell. Methoden: Die Parameter eines PBPK-Modells wurden für die biokinetischen Daten von 15 Patienten bestimmt. Mathematische Patien- ten-Phantome (wahre MPP) wurden als PBPK- Modell mit den geschätzten Parametern definiert. PET-Messungen nach Bolus-Injektion von 150 MBq 68Ga-DOTATATE wurden mittels der wahren MPP simuliert. Rauschen wurde für typische Rauschpegel zu den Daten hinzugefügt: c=0.3 (niedrig), 3, 30 und 300 (hoch). Organ-Aktivitätsdaten für Nieren, Tumor, Leber und Milz wurden 0,5, 1und 4 h nach Injektion simuliert. Die PBPK-Modellparameter wurden an die simulierten PET-Daten angepasst, um die PET-vorhergesagten MPP herzuleiten. Die Therapie wurde für beide (wahre und vorhergesagte) MPP simuliert für den Fall einer Infusion von 3,3 GBq von 90Y-DOTATATE über 30 min. Die Zeit-integrierten AktivitätsKoeffizienten (TIACs) der simulierten Therapie wurden für Tumor, Nieren, Leber, Milz und Restkörper für beide (wahre und vorhergesagte) MPP berechnet. Die Variabilität v zwischen wahren und vorhergesagten TIAC wurde berechnet und analysiert. Eine Variabilität < 10 % wurde als genaue Vorhersage angesehen. Ergebnisse: Für alle Rauschpegel zeigten die Variabilitäten für Leber, Nieren, und Milz eine genaue Vorhersage für die TI- ACs, z.B. c=300: vNiere=(5 ± 2)%, vLeber- = (5 ± 2)%, vMilz=(4 ± 2)%. Jedoch sind die Vorhersagen für die Tumor-TIACs für alle Geräuschpegel ungenau, z.B. c=0,3: vTumor=- (8 ± 5)%. Schlussfolgerung: Eine PET-basier- te Behandlungsplanung erscheint für alle untersuchten Rauschpegel für die Nieren als dosislimitierendes Organ möglich bei Verwendung eines geeigneten PBPK-Modells zusammen mit individuellem Vorwissen.
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