Full-field optical coherence tomography for the diagnosis of giant cell arteritis

Thomas Maldiney, Hélène Greigert, Laurent Martin, Emilie Benoit, Catherine Creuzot-Garcher, Pierre-Henry Gabrielle, Jean-Marie Chassot, Claude Boccara, Daniel Balvay, Bertrand Tavitian, Olivier Clément, Sylvain Audia, Bernard Bonnotte, Maxime Samson, Thomas Maldiney, Hélène Greigert, Laurent Martin, Emilie Benoit, Catherine Creuzot-Garcher, Pierre-Henry Gabrielle, Jean-Marie Chassot, Claude Boccara, Daniel Balvay, Bertrand Tavitian, Olivier Clément, Sylvain Audia, Bernard Bonnotte, Maxime Samson

Abstract

Histopathological examination of temporal artery biopsy (TAB) remains the gold standard for the diagnosis of giant cell arteritis (GCA) but is associated with essential limitations that emphasize the need for an upgraded pathological process. This study pioneered the use of full-field optical coherence tomography (FF-OCT) for rapid and automated on-site pathological diagnosis of GCA. Sixteen TABs (12 negative and 4 positive for GCA) were selected according to major histopathological criteria of GCA following hematoxylin-eosin-saffron-staining for subsequent acquisition with FF-OCT to compare structural modifications of the artery cell wall and thickness of each tunica. Gabor filtering of FF-OCT images was then used to compute TAB orientation maps and validate a potential automated analysis of TAB sections. FF-OCT allowed both qualitative and quantitative visualization of the main structures of the temporal artery wall, from the internal elastic lamina to the vasa vasorum and red blood cells, unveiling a significant correlation with conventional histology. FF-OCT imaging of GCA TABs revealed destruction of the media with distinct remodeling of the whole arterial wall into a denser reticular fibrous neo-intima, which is distinctive of GCA pathogenesis and accessible through automated Gabor filtering. Rapid on-site FF-OCT TAB acquisition makes it possible to identify some characteristic pathological lesions of GCA within a few minutes, paving the way for potential machine intelligence-based or even non-invasive diagnosis of GCA.

Trial registration: ClinicalTrials.gov NCT02158208 NCT02857192.

Conflict of interest statement

We declare the paid employment of LLTech SAS (E. B.) to provide additional FF-OCT images acquisition and analysis. Besides, the commercial affiliation with LLTech SAS does not alter our adherence to PLOS ONE policies on sharing data and materials.

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