Kathryn S Stok1,2, Andrew J Burghardt3, Stephanie Boutroy4, Michiel P H Peters5,6,7, Sarah L Manske8,9, Vincent Stadelmann2,10, Nicolas Vilayphiou2, Joop P van den Bergh5,7,11,12, Piet Geusens5,6,10, Xiaojuan Li13, Hubert Marotte14,15,16, Bert van Rietbergen17,18, Steven K Boyd8,9, Cheryl Barnabe8,19. 1. Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia. 2. SCANCO Medical AG, Brüttisellen, Switzerland. 3. Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA. 4. INSERM 1033, Hôpital Edouard Herriot, Lyon Cedex, Lyon, France. 5. Department of Internal Medicine, Division of Rheumatology, Maastricht University Medical Centre, Maastricht, The Netherlands. 6. Research School CAPHRI, School for Public Health and Primary Care, Maastricht, The Netherlands. 7. NUTRIM School of Nutrition & Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands. 8. McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Canada. 9. Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada. 10. Department of Research and Development, Schulthess Klinik, Zürich, Switzerland. 11. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium. 12. VieCuri Medical Centre, Venlo, The Netherlands. 13. Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA. 14. SAINBIOSE, INSERM U1059, University of Lyon, Saint-Etienne, France. 15. Department of Rheumatology, University Hospital of Saint-Etienne, Saint-Etienne, France. 16. INSERM CIE3 1408, University Hospital of Saint-Etienne, Saint-Etienne, France. 17. Orthopaedic Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. 18. Department of Orthopaedic Surgery, Research School CAPHRI, Maastricht University Medical Center, Maastricht, The Netherlands. 19. Department of Medicine and Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.
Abstract
BACKGROUND: Joint space assessment for rheumatoid arthritis (RA) by ordinal conventional radiographic scales is susceptible to floor and ceiling effects. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides superior resolution, and may detect earlier changes. The goal of this work was to compare existing 3D methods to calculate joint space width (JSW) metrics in human metacarpophalangeal (MCP) joints with HR-pQCT and reach consensus for future studies. Using the consensus method, we established reproducibility with repositioning as well as feasibility for use in second-generation HR-pQCT scanners. METHODS: Three published JSW methods were compared using datasets from individuals with RA from three research centers. A SPECTRA consensus method was developed to take advantage of strengths of the individual methods. Using the SPECTRA method, reproducibility after repositioning was tested and agreement between scanner generations was also established. RESULTS: When comparing existing JSW methods, excellent agreement was shown for JSW minimum and mean (ICC 0.987-0.996) but not maximum and volume (ICC 0.000-0.897). Differences were identified as variations in volume definitions and algorithmic differences that generated high sensitivity to boundary conditions. The SPECTRA consensus method reduced this sensitivity, demonstrating good scan-rescan reliability (ICC >0.911) except for minimum JSW (ICC 0.656). There was strong agreement between results from first- and second-generation HR-pQCT (ICC >0.833). CONCLUSIONS: The SPECTRA consensus method combines unique strengths of three independently-developed algorithms and leverages underlying software updates to provide a mature analysis to measure 3D JSW. This method is robust with respect to repositioning and scanner generations, suggesting its suitability for detecting change. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: Joint space assessment for rheumatoid arthritis (RA) by ordinal conventional radiographic scales is susceptible to floor and ceiling effects. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides superior resolution, and may detect earlier changes. The goal of this work was to compare existing 3D methods to calculate joint space width (JSW) metrics in human metacarpophalangeal (MCP) joints with HR-pQCT and reach consensus for future studies. Using the consensus method, we established reproducibility with repositioning as well as feasibility for use in second-generation HR-pQCT scanners. METHODS: Three published JSW methods were compared using datasets from individuals with RA from three research centers. A SPECTRA consensus method was developed to take advantage of strengths of the individual methods. Using the SPECTRA method, reproducibility after repositioning was tested and agreement between scanner generations was also established. RESULTS: When comparing existing JSW methods, excellent agreement was shown for JSW minimum and mean (ICC 0.987-0.996) but not maximum and volume (ICC 0.000-0.897). Differences were identified as variations in volume definitions and algorithmic differences that generated high sensitivity to boundary conditions. The SPECTRA consensus method reduced this sensitivity, demonstrating good scan-rescan reliability (ICC >0.911) except for minimum JSW (ICC 0.656). There was strong agreement between results from first- and second-generation HR-pQCT (ICC >0.833). CONCLUSIONS: The SPECTRA consensus method combines unique strengths of three independently-developed algorithms and leverages underlying software updates to provide a mature analysis to measure 3D JSW. This method is robust with respect to repositioning and scanner generations, suggesting its suitability for detecting change. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Entities:
Keywords:
3D imaging; OMERACT; X-ray computed tomography; arthritis; high-resolution peripheral quantitative computed tomography (HR-pQCT); in vivo; joint space width (JSW); metacarpophalangeal joint; peripheral quantitative CT; rheumatoid; three-dimensional
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