title: The role of patient-based treatment planning in peptide receptor radionuclide therapy.
abstract: BACKGROUND
  Accurate treatment planning is recommended in peptide-receptor radionuclide therapy (PRRT) to minimize the toxicity to organs at risk while maximizing tumor cell sterilization. The aim of this study was to quantify the effect of different degrees of individualization on the prediction accuracy of individual therapeutic biodistributions in patients with neuroendocrine tumors (NETs).
  
  
  METHODS
  A recently developed physiologically based pharmacokinetic (PBPK) model was fitted to the biokinetic data of 15 patients with NETs after pre-therapeutic injection of (111)In-DTPAOC. Mathematical phantom patients (MPP) were defined using the assumed true (true MPP), mean (MPP 1A) and median (MPP 1B) parameter values of the patient group. Alterations of the degree of individualization were introduced to both mean and median patients by including patient-specific information as a priori knowledge: physical parameters and hematocrit (MPP 2A/2B). Successively, measurable individual biokinetic parameters were added: tumor volume V tu (MPP 3A/3B), glomerular filtration rate GFR (MPP 4A/4B), and tumor perfusion f tu (MPP 5A/5B). Furthermore, parameters of MPP 5A/5B and a simulated (68)Ga-DOTATATE PET measurement 60 min p.i. were used together with the population values used as Bayesian parameters (MPP 6A/6B). Therapeutic biodistributions were simulated assuming an infusion of (90)Y-DOTATATE (3.3 GBq) over 30 min to all MPPs. Time-integrated activity coefficients were predicted for all MPPs and compared to the true MPPs for each patient in tumor, kidneys, spleen, liver, remainder, and whole body to obtain the relative differences RD.
  
  
  RESULTS
  The large RD values of MPP 1A [RDtumor = (625 ± 1266)%, RDkidneys = (11 ± 38)%], and MPP 1B [RDtumor = (197 ± 505)%, RDkidneys = (11 ± 39)%] demonstrate that individual treatment planning is needed due to large physiological differences between patients. Although addition of individual patient parameters reduced the deviations considerably [MPP 5A: RDtumor = (-2 ± 27)% and RDkidneys = (16 ± 43)%; MPP 5B: RDtumor = (2 ± 28)% and RDkidneys = (7 ± 40)%] errors were still large. For the kidneys, prediction accuracy was considerably improved by including the PET measurement [MPP 6A/MPP 6B: RDtumor = (-2 ± 22)% and RDkidneys = (-0.1 ± 0.5)%].
  
  
  CONCLUSIONS
  Individualized treatment planning is needed in the investigated patient group. The use of a PBPK model and the inclusion of patient specific data, e.g., weight, tumor volume, and glomerular filtration rate, do not suffice to predict the therapeutic biodistribution. Integrating all available a priori information in the PBPK model and using additionally PET data measured at one time point for tumor, kidneys, spleen, and liver could possibly be sufficient to perform an individualized treatment planning.
authors.affiliation: 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.
authors.forename: Deni
authors.initials: D
authors.lastname: Hardiansyah
authors.affiliation: Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Maass
authors.affiliation: 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.
authors.forename: Ali Asgar
authors.initials: AA
authors.lastname: Attarwala
authors.affiliation: Klinik für Nuklearmedizin, University Hospital, RWTH Aachen University, Aachen, Germany.
authors.forename: Berthold
authors.initials: B
authors.lastname: Müller
authors.affiliation: Klinik für Nuklearmedizin, Universität Ulm, Ulm, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Kletting
authors.affiliation: Klinik für Nuklearmedizin, University Hospital, RWTH Aachen University, Aachen, Germany.|Department of Nuclear Medicine, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands.
authors.forename: Felix M
authors.initials: FM
authors.lastname: Mottaghy
authors.affiliation: Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. gerhard.glatting@medma.uni-heidelberg.de.
authors.forename: Gerhard
authors.initials: G
authors.lastname: Glatting
mesh_terms: D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018358:Neuroendocrine Tumors / Q000532:radiotherapy*; D015282:Octreotide / Q000031:analogs & derivatives* / Q000493:pharmacokinetics / Q000627:therapeutic use; D066230:Patient-Specific Modeling; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics / Q000627:therapeutic use*; D011880:Radiotherapy Planning, Computer-Assisted; D018000:Receptors, Peptide / Q000378:metabolism
chemical_list: D019275:Radiopharmaceuticals; D018000:Receptors, Peptide; C496730:90Y-octreotide, DOTA-Tyr(3)-; D015282:Octreotide
keywords: PBPK modeling; PET; PRRT; Treatment planning