[18F]fluorodeprenyl-D2 PET can detect and monitor astrogliosis in anti-LGI1-IgG autoimmune encephalitis
Year: 2025
Type: article
Study design: Observational Study
Abstract: PURPOSE: To explore whether detecting local astrogliosis using [18F]fluorodeprenyl-D2 ([18F]F-DED) positron-emission-tomography (PET) uncovers and monitors inflammatory lesions in patients with anti-leucine-rich glioma-inactivated 1 antibody (LGI1-ab) associated autoimmune encephalitis (AE). METHODS: Dynamic [18F]F-DED PET scans (0–60 min post-injection) were obtained from a cohort of 15 LGI1-AE patients, 9 of whom were re-examined during the disease course, and from 15 controls. PET quantification was performed by kinetic modelling with an image derived input function and calculation of simplified standardized uptake values (SUV). [18F]F-DED SUVr (referenced to the straight gyrus) were analyzed for LGl1-AE target regions, compared between baseline and follow-up PET scans, used to investigate asymmetry of the mesial temporal lobe (MTL), and correlated to routine clinical data. RESULTS: Simplified [18F]F-DED PET quantification of the 30–60 min time-frame showed excellent agreement with kinetic modelling of the full dynamic imaging protocol. In LGI1-AE patients, [18F]F-DED SUVr values were significantly increased by 16% in the MTL (p = .002) and by 12% in the cerebellum (p = .014). [18F]F-DED signals in the MTL significantly declined during the course of the disease (mean follow-up time: 15 months, p = .006). MTL asymmetry (> 5%) of [18F]F-DED SUVr was present in 8/15 of LGI1-AE patients compared to 1/15 controls (p < .001) and showed marked decline between baseline and follow-up in LGI1-AE patients. CONCLUSION: This pilot study shows that [18F]F-DED PET is a promising tool to monitor regional astrogliosis in LGI1-AE patients and may provide a direct read-out of this important aspect of inflammatory disease activity.
Authors: J. A. Gernert, L. Sanzo, H. Zimmermann, F. S. Thaler, L. Vogler, J. S. Gnörich, L. Tagnin, S. Lindner, B. Kugelmann, R. Schaefer, G. N. Bischof, S. Katzdobler, R. A. Werner, G. U. Höglinger, J. Levin, N. Franzmeier, B. Rauchmann, R. Perneczky, M. Kerschensteiner, T. Kümpfel, M. Brendel
Institutions: Institut für Klinische Neuroimmunologie, Ludwig-Maximilians-Universität München, LMU Klinikum, Institut für Diagnostische und Interventionelle Neuroradiologie, German Center for Neurodegenerative Diseases, Johns Hopkins University, Johns Hopkins Medicine, Munich Cluster for Systems Neurology, Institut für Schlaganfall- und Demenzforschung, University of Gothenburg, Imperial College London, University of Sheffield
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,490
Awards: G1789099517, G1993314343, G2588154941, PREDICTION OF ALZHEIMER’S DISEASE USING AN AI DRIVEN SCREENING PLATFORM, EXC 2145: Munich Cluster for Systems Neurology (SyNergy), G609929020, Imaging of Astrogliosis and Functional Connectivity in Alzheimer's Disease, TRR 274: Kontrollpunkte der ZNS Reparatur
OpenAlex ID: W4414524946
