Automated interpretation of ANCA patterns - a new approach in the serology of ANCA-associated vasculitis
- Equal contributors
1 Research and Development Department, GA Generic Assays GmbH, Ludwig-Erhard-Ring 3, 15827 Dahlewitz/Berlin, Germany
2 Faculty of Science, Lausitz University of Applied Sciences, Großenhainer Str. 57, 01968 Senftenberg, Germany
3 Department of Dermatology, Fondazione Ca Granda Ospedale Maggiore Policlinico, via S. Barnaba 8, 20122 Milan, Italy
4 IRCCS Istituto Auxologico Italiano, Immune research laboratory and Department of Clinical Science and Community, University of Milan, via Spagnoletto 3, 20149 Milan, Italy
5 Institute of Immunology, Technical University Dresden, Fiedler Str. 42, 01307 Dresden, Germany
6 Institute of Molecular and Clinical Immunology, Otto-von-Guericke-University, Leipziger Str. 44, 39120 Magdeburg, Germany
7 Department of Rheumatology, University of Schleswig-Holstein Campus Lübeck and Rheumaklinik Bad Bramstedt, Oskar-Alexander-Straße 26, 24576 Bad Bramstedt, Germany
Arthritis Research & Therapy 2012, 14:R271 doi:10.1186/ar4119Published: 14 December 2012
Indirect immunofluorescence (IIF) employing ethanol-fixed neutrophils (ethN) is still the method of choice for assessing antineutrophil cytoplasmic antibodies (ANCA) in ANCA-associated vasculitides (AAV). However, conventional fluorescence microscopy is subjective and prone to high variability. The objective of this study was to evaluate novel pattern recognition algorithms for the standardized automated interpretation of ANCA patterns.
Seventy ANCA-positive samples (20 antimyeloperoxidase ANCA, 50 antiproteinase3 ANCA) and 100 controls from healthy individuals analyzed on ethN and formalin-fixed neutrophils (formN) by IIF were used as a 'training set' for the development of pattern recognition algorithms. Sera from 342 patients ('test set') with AAV and other systemic rheumatic and infectious diseases were tested for ANCA patterns using the novel pattern recognition algorithms and conventional fluorescence microscopy.
Interpretation software employing pattern recognition algorithms was developed enabling positive/negative discrimination and classification of cytoplasmic ANCA (C-ANCA) and perinuclear ANCA (P-ANCA). Comparison of visual reading of the 'test set' samples with automated interpretation revealed Cohen's kappa (κ) values of 0.955 on ethN and 0.929 on formN for positive/negative discrimination. Analysis of the 'test set' with regard to the discrimination between C-ANCA and P-ANCA patterns showed a high agreement for ethN (κ = 0.746) and formN (κ = 0.847). There was no significant difference between visual and automated interpretation regarding positive/negative discrimination on ethN and formN, as well as ANCA pattern recognition (P > 0.05, respectively).
Pattern recognition algorithms can assist in the automated interpretation of ANCA IIF. Automated reading of ethN and formN IIF patterns demonstrated high consistency with visual ANCA assessment.