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Performance of Deauville Criteria in [18F]FDG-PET/CT Diagnostics of Giant Cell Arteritis

Authors :
Jeffrey Siefert
Jonas Kaufmann
Felix Thiele
Thula Walter-Rittel
Julian Rogasch
Robert Biesen
Gerd R. Burmester
Holger Amthauer
Udo Schneider
Christian Furth
Source :
Diagnostics, Vol 13, Iss 1, p 157 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

In this retrospective study, PET/CT data from 59 patients with suspected giant cell arteritis (GCA) were reviewed using the Deauville criteria to determine an optimal cut-off between PET positivity and negativity. Seventeen standardised vascular regions were analysed per patient by three investigators blinded to clinical information. Statistical analysis included ROC curves with areas under the curve (AUC), Cohen’s and Fleiss’ kappa (κ) to calculate sensitivity, specificity, accuracy, and agreement. According to final clinician’s diagnosis and the revised 2017 ACR criteria GCA was confirmed in 29 of 59 (49.2 %) patients. With a diagnostic cut-off ≥ 4 (highest tracer uptake of a vessel wall exceeds liver uptake) for PET positivity, all investigators achieved high accuracy (range, 89.8–93.2%) and AUC (range, 0.94–0.97). Sensitivity and specificity ranged from 89.7–96.6% and 83.3–96.7%, respectively. Agreement between the three investigators suggested ‘almost perfect agreement’ (Fleiss’ κ = 0.84) A Deauville score of ≥4 as threshold for PET positivity yielded excellent results with high accuracy and almost perfect inter-rater agreement, suggesting a standardized, reproducible, and reliable score in diagnosing GCA. However, the small sample size and reference standard could lead to biases. Therefore, verification in a multicentre study with a larger patient cohort and prospective setting is needed.

Details

Language :
English
ISSN :
20754418
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.9f6939240ed24098bd9a993ff141fa16
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics13010157