| Literature DB >> 31238764 |
Nadja Van Camp1, Yaël Balbastre1, Anne-Sophie Herard1, Sonia Lavisse1, Clovis Tauber2, Catriona Wimberley3, Martine Guillermier1, Aurélie Berniard1, Pauline Gipchtein1, Caroline Jan1, Romina Aron Badin1, Thierry Delzescaux1, Philippe Hantraye1, Gilles Bonvento1.
Abstract
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.Entities:
Keywords: 18 kDa translocator protein; multimodality; non-human primate; positron emission tomography; quantification; reference methods; supervised cluster analysis; three-dimensional immunohistochemistry
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Year: 2019 PMID: 31238764 PMCID: PMC7181080 DOI: 10.1177/0271678X19859034
Source DB: PubMed Journal: J Cereb Blood Flow Metab ISSN: 0271-678X Impact factor: 6.200