Measurement of synovial tissue volume in knee osteoarthritis using a semiautomated MRI‐based quantitative approach. Issue 5 (15th February 2019)

• Record Type: Journal Article
• Authors: Perry, Thomas A.; Gait, Andrew; O'Neill, Terence W.; Parkes, Matthew J.; Hodgson, Richard; Callaghan, Michael J.; Arden, Nigel K.; Felson, David T.; Cootes, Timothy F.
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 5(2019)
• Page Start: 3056
• Page End: 3064
• Publication Date: 2019-02-15
• Subjects: osteoarthritis -- segmentation -- semiautomated -- synovial tissue volume; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27633
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Synovitis is common in knee osteoarthritis and is associated with both knee pain and progression of disease. Semiautomated methods have been developed for quantitative assessment of structure in knee osteoarthritis. Our aims were to apply a novel semiautomated assessment method using 3D active appearance modeling for the quantification of synovial tissue volume (STV) and to compare its performance with conventional manual segmentation. Methods: Thirty‐two sagittal T1 ‐weighted fat‐suppressed contrast‐enhanced MRIs were assessed for STV by a single observer using 1) manual segmentation and 2) a semiautomated approach. We compared the STV analysis using the semiautomated and manual segmentation methods, including the time taken to complete the assessments. We also examined the reliability of STV assessment using the semiautomated method in a subset of 12 patients who had participated in a clinical trial of vitamin D therapy in knee osteoarthritis. Results: There was no significant difference in STV using the semiautomated quantitative method compared to manual segmentation, mean difference = 207.2 mm 3 (95% confidence interval −895.2 to 1309.7). The semiautomated method was significantly quicker than manual segmentation (18 vs. 71 min). For the semiautomated method, intraobserver agreement was excellent (intraclass correlation coefficient (3, 1) = 0.99) and interobserver agreement was very good (intraclass correlation coefficient (3, 1) = 0.83). Conclusion: We describe the application of a semiautomated method that is accurate, reliable, and quicker than manual segmentation for assessment of STV. The method may help increase efficiency of image assessment in large imaging studies and may also assist investigation of treatment efficacy in knee osteoarthritis.