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Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
Published on: August 8, 2019
Data-driven definitions for active and structural MRI lesions in the sacroiliac joint in spondyloarthritis and their
Walter P Maksymowych1,2, Robert G Lambert3,4, Xenofon Baraliakos5
1Department of Medicine, University of Alberta, Edmonton.
Objectives:
To determine quantitative SI joint MRI lesion cut-offs that optimally define a positive MRI for inflammatory and structural lesions typical of axial SpA (axSpA) and that predict clinical diagnosis.
Methods:
The Assessment of SpondyloArthritis international Society (ASAS) MRI group assessed MRIs from the ASAS Classification Cohort in two reading exercises where (A) 169 cases and 7 central readers; (B) 107 cases and 8 central readers. We calculated sensitivity/specificity for the number of SI joint quadrants or slices with bone marrow oedema (BME), erosion, fat lesion, where a majority of central readers had high confidence there was a definite active or structural lesion. Cut-offs with ≥95% specificity were analysed for their predictive utility for follow-up rheumatologist diagnosis of axSpA by calculating positive/negative predictive values (PPVs/NPVs) and selecting cut-offs with PPV ≥ 95%.
Results:
Active or structural lesions typical of axSpA on MRI had PPVs ≥ 95% for clinical diagnosis of axSpA. Cut-offs that best reflected a definite active lesion typical of axSpA were either ≥4 SI joint quadrants with BME at any location or at the same location in ≥3 consecutive slices. For definite structural lesion, the optimal cut-offs were any one of ≥3 SI joint quadrants with erosion or ≥5 with fat lesions, erosion at the same location for ≥2 consecutive slices, fat lesions at the same location for ≥3 consecutive slices, or presence of a deep (i.e. >1 cm depth) fat lesion.
Conclusion:
We propose cut-offs for definite active and structural lesions typical of axSpA that have high PPVs for a long-term clinical diagnosis of axSpA for application in disease classification and clinical research.

