%0 Journal Article
%A Doering, Elena
%A Hoenig, Merle C.
%A Giehl, Kathrin
%A Dzialas, Verena
%A Andrassy, Grégory
%A Bader, Abdelmajid
%A Bauer, Andreas
%A Elmenhorst, David
%A Ermert, Johannes
%A Frensch, Silke
%A Jäger, Elena
%A Jessen, Frank
%A Krapf, Philipp
%A Kroll, Tina
%A Lerche, Christoph
%A Lothmann, Julia
%A Matusch, Andreas
%A Neumaier, Bernd
%A Onur, Oezguer A.
%A Ramirez, Alfredo
%A Richter, Nils
%A Sand, Frederik
%A Tellmann, Lutz
%A Theis, Hendrik
%A Zeyen, Philip
%A van Eimeren, Thilo
%A Drzezga, Alexander
%A Bischof, Gerard Nisal
%T “Fill States”: PET-derived Markers of the Spatial Extent of Alzheimer Disease Pathology
%J Radiology
%V 314
%N 3
%@ 0033-8419
%C Oak Brook, Ill.
%I Soc.
%M FZJ-2025-02218
%P e241482
%D 2025
%Z Deutsche Forschungsgemeinschaft [DFG] research grant “Brain network dependent propagation of tau-pathology in Alzheimer disease” DR 445/9-1 [AD]). Some co-authors received funding from the DFG (project ID 431549029-SFB 1451). Datacollection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant no. U01 AG024904) and U.S. Department of Defense ADNI (award no. W81XWH-12-2-0012).
%X Background: Alzheimer disease (AD) progression can be monitored by tracking intensity changes in PET standardized uptake value (SUV) ratiosof amyloid, tau, and neurodegeneration. The spatial extent (“fill state”) of these three hallmark pathologic abnormalities may serve as criticalpathophysiologic information, pending further investigation.Purpose: To examine the clinical utility and increase the accessibility of PET-derived fill states.Materials and Methods: This secondary analysis of two prospective studies used data from two independent cohorts: the Alzheimer’s DiseaseNeuroimaging Initiative (ADNI) and the Tau Propagation over Time study (T-POT). Each cohort comprised amyloid-negative cognitively normalindividuals (controls) and patients with subjective cognitive decline, mild cognitive impairment, or probable-AD dementia. Fill states of amyloid, tau,and neurodegeneration were computed as the percentages of significantly abnormal voxels relative to controls across PET scans. Fill states and SUVratios were compared across stages (Kruskal-Wallis H test, area under the receiver operating characteristic curve analysis) and tested for associationwith the severity of cognitive impairment (Spearman correlation, multivariate regression analysis). Additionally, a convolutional neural network(CNN) was developed to estimate fill states from patients’ PET scans without requiring controls.Results: The ADNI cohort included 324 individuals (mean age, 72 years ± 6.8 [SD]; 173 [53%] female), and the T-POT cohort comprised 99individuals (mean age, 66 years ± 8.7; 63 [64%] female). Higher fill states were associated with higher stages of cognitive impairment (P < .001),and tau and neurodegeneration fill states showed higher diagnostic performance for cognitive impairment compared with SUV ratio (P < .05) acrosscohorts. Similarly, all fill states were negatively correlated with cognitive performance (P < .001) and uniquely characterized the degree of cognitiveimpairment even after adjustment for SUV ratio (P < .05). The CNN estimated amyloid and tau accurately, but not neurodegeneration fill states.Conclusion: Fill states provided reliable markers of AD progression, potentially improving early detection, staging, and monitoring of AD in clinicalpractice and trials beyond SUV ratio.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 40131110
%U <Go to ISI:>//WOS:001464548700015
%R 10.1148/radiol.241482
%U https://juser.fz-juelich.de/record/1041319