Évaluation de méthodes simplifiées pour la quantification de l'imagerie PET [18F]-DPA-714

  • Recherche,
  • Santé-Sciences-Technologie,

le 11 septembre 2019

Projet de recherche collaborative dirigé par le Prof. G. Bonvento (CEA, Institut François Jacob, MIRCen, CNRS UMR 9199, Université Paris-Sud, Université Paris-Saclay, Fontenay-aux-Roses, France) auquel le Prof. C. Tauber a participé

Assessment of simplified methods for quantification of [18F]-DPA-714 using 3D whole-brain TSPO immunohistochemistry in a non-human primate


The 18 kDa translocator protein (TSPO) is the main molecular target to image neuroinflammation by positron emission tomography (PET). However, TSPO-PET quantification is complex and none of the kinetic modelling approaches has been validated using a voxel-by-voxel comparison of TSPO-PET data with the actual TSPO levels of expression. Here, we present a single case study of binary classification of in vivo PET data to evaluate the statistical performance of different TSPO-PET quantification methods. To that end, we induced a localized and adjustable increase of TSPO levels in a non-human primate brain through a viral-vector strategy. We then performed a voxel-wise comparison of the different TSPO-PET quantification approaches providing parametric [18F]-DPA-714 PET images, with co-registered in vitro three-dimensional TSPO immunohistochemistry (3D-IHC) data. A data matrix was extracted from each brain hemisphere, containing the TSPO-IHC and TSPO-PET data for each voxel position. Each voxel was then classified as false or true, positive or negative after comparison of the TSPO-PET measure to the reference 3D-IHC method. Finally, receiver operating characteristic curves (ROC) were calculated for each TSPO-PET quantification method. Our results show that standard uptake value ratios using cerebellum as a reference region (SUVCBL) has the most optimal ROC score amongst all non-invasive approaches.


18 kDa #translocator protein; #multimodality; non-human primate; #PET; quantification; reference #methods; supervised cluster analysis; 3D #immunohistochemistry

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