Advertisement

Rheumatology International

, Volume 38, Issue 10, pp 1919–1926 | Cite as

Development and validation of SCAISS, a tool for semi-automated quantification of sacroilitis by magnetic resonance in spondyloarthritis

  • Pedro Zarco
  • Raquel Almodóvar
  • Ángel Bueno
  • Luis Miguel Molinero
  • SCAISS Study Group
Validation Studies

Abstract

To develop a semi-automated method to quantify inflammation in sacroiliac (SI) joints by measuring bone marrow edema (BME) on MRI. The SCAISS was designed as an image-processing software. Validation followed: (1) three readers evaluated SI images of 23 patients with axial SpA and various levels of BME severity with the SCAISS, and two non-automated methods, SPARCC and Berlin; (2) 20 readers evaluated 12 patients images, also with the three methods; (3) 203 readers evaluated 12 patient images with the Berlin and the SCAISS. Convergent validity, reliability and feasibility were estimated in the first two steps and reliability was confirmed with the third. The interobserver reliability (ICC and 95% CI) in the three observers’ study was similar across methods: SCAISS = 0.770 (0.580–0.889); Berlin = 0.725 (0.537–0.860); and SPARCC = 0.824 (0.671–0.916). In the 20 observers’ study, ICC was: SCAISS = 0.801 (0.653–0.927); Berlin = 0.702 (0.518–0.882); and SPARCC = 0.790 (0.623–0.923). In the 203 observers’ study, ICC were: SCAISS = 0.810 (0.675–0.930), and Berlin = 0.636 (0.458–0.843). SCAISS showed good convergent validity (r with SPARCC = 0.760). Median time (interquartile range) employed in the reading procedure was 28 (27) seconds for the SCAISS, 14 (9) for the Berlin score, and 94 (68) for the SPARCC. The SCAISS permits a valid, reliable, and fast calculation of overall BME lesion at the SI joint on MRI images not dependent on readers’ experience.

Keywords

Magnetic resonance imaging Spondylarthritis Spondylitis Ankylosing Sacroiliitis Validation studies 

Notes

Acknowledgements

Mireia Moreno (Rheumatology, H. Parc Taulí), Xavier Juanola (Rheumatology, H. Bellvitge), Maite Ventemillas (Radiology, H. Parc Taulí), Victoria Navarro (Rheumatology, H. La Paz), Daniel Bernabeu (Radiology, H. La Paz), Rafael Montero Perez-Barquero (Radiology, H. Reina Sofía de Córdoba), Concha Crespo (Radiology, H. de San Juan de Alicante), Enrique Batlle (Rheumatology, H. de San Juan de Alicante), Carmen Castro Copete (Radiology, H. H. de San Juan de Alicante), Carlos Quiles (Radiology, H. General Universitario de Valencia), Emma Beltrán (Rheumatology, H. del Mar), Fran García Lorente (Rheumatology, H. Universitario de Basurto), Fernando Díez (Radiology, H. Universitario de Basurto), Luis Linares (Rheumatology, H. Virgen de la Arrixaca), Manuel José Moreno Ramos (Rheumatology, H. Virgen de la Arrixaca), Angela Cepero (Radiology, H. H. Virgen de la Arrixaca), Cristina Fernández Carballido (Rheumatology, H. de Elda), Christopher Pack (Radiology, H. de Elda). In addition, a list of the participants of the workshops is presented in Suppl. Material.

Author contributions

All authors contributed to the design and conception of study. AB, RA and PZ prepared the selection of patients and images, read the images and commented on problems of data collection, LMM performed the software adaptation and statistical analysis. RA and PZ wrote the draft of the manuscript. The components of the SCAISS Group voluntarily participated in the validation studies by providing the readings. All authors contributed to the manuscript and revised the last version.

Compliance with ethical standards

Conflict of interest

The study was not funded and the software does not have a commercial use. Therefore, the authors declare no competing interests.

Research involving human participants

The study protocol was approved by the hospital Universitario Fundación Alcorcón Ethic Committee (number 16/112, date January 12, 2017) and the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained for using patient images.

Supplementary material

296_2018_4104_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 23 KB)

References

  1. 1.
    Rudwaleit M, Landewe R, van der Heijde D, Listing J, Brandt J, Braun J, Burgos-Vargas R, Collantes-Estevez E, Davis J, Dijkmans B, Dougados M, Emery P, van der Horst-Bruinsma IE, Inman R, Khan MA, Leirisalo-Repo M, van der Linden S, Maksymowych WP, Mielants H, Olivieri I, Sturrock R, de Vlam K, Sieper J (2009) The development of assessment of Spondyloarthritis International Society classification criteria for axial spondyloarthritis (part I): classification of paper patients by expert opinion including uncertainty appraisal. Ann Rheum Dis 68(6):770–776.  https://doi.org/10.1136/ard.2009.108217 CrossRefPubMedGoogle Scholar
  2. 2.
    Rudwaleit M, van der Heijde D, Landewe R, Listing J, Akkoc N, Brandt J, Braun J, Chou CT, Collantes-Estevez E, Dougados M, Huang F, Gu J, Khan MA, Kirazli Y, Maksymowych WP, Mielants H, Sorensen IJ, Ozgocmen S, Roussou E, Valle-Onate R, Weber U, Wei J, Sieper J (2009) The development of Assessment of Spondyloarthritis international Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann Rheum Dis 68(6):777–783.  https://doi.org/10.1136/ard.2009.108233 CrossRefPubMedGoogle Scholar
  3. 3.
    Ahlstrom H, Feltelius N, Nyman R, Hallgren R (1990) Magnetic resonance imaging of sacroiliac joint inflammation. Arthritis Rheum 33(12):1763–1769CrossRefPubMedGoogle Scholar
  4. 4.
    Braun J, Bollow M, Eggens U, Konig H, Distler A, Sieper J (1994) Use of dynamic magnetic resonance imaging with fast imaging in the detection of early and advanced sacroiliitis in spondylarthropathy patients. Arthritis Rheum 37(7):1039–1045CrossRefPubMedGoogle Scholar
  5. 5.
    Bollow M, Braun J, Hamm B, Eggens U, Schilling A, Konig H, Wolf KJ (1995) Early sacroiliitis in patients with spondyloarthropathy: evaluation with dynamic gadolinium-enhanced MR imaging. Radiology 194(2):529–536.  https://doi.org/10.1148/radiology.194.2.7824736 CrossRefPubMedGoogle Scholar
  6. 6.
    Puhakka KB, Jurik AG, Egund N, Schiottz-Christensen B, Stengaard-Pedersen K, van Overeem Hansen G, Christiansen JV (2003) Imaging of sacroiliitis in early seronegative spondylarthropathy. Assessment of abnormalities by MR in comparison with radiography and CT. Acta Radiol 44(2):218–229CrossRefPubMedGoogle Scholar
  7. 7.
    Lambert RG, Bakker PA, van der Heijde D, Weber U, Rudwaleit M, Hermann KG, Sieper J, Baraliakos X, Bennett A, Braun J, Burgos-Vargas R, Dougados M, Pedersen SJ, Jurik AG, Maksymowych WP, Marzo-Ortega H, Ostergaard M, Poddubnyy D, Reijnierse M, van den Bosch F, van der Horst-Bruinsma I, Landewe R (2016) Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group. Ann Rheum Dis 75(11):1958–1963.  https://doi.org/10.1136/annrheumdis-2015-208642 CrossRefPubMedGoogle Scholar
  8. 8.
    Weber U, Lambert RG, Ostergaard M, Hodler J, Pedersen SJ, Maksymowych WP (2010) The diagnostic utility of magnetic resonance imaging in spondylarthritis: an international multicenter evaluation of one hundred eighty-seven subjects. Arthritis Rheum 62(10):3048–3058.  https://doi.org/10.1002/art.27571 CrossRefPubMedGoogle Scholar
  9. 9.
    Weber U, Ostergaard M, Lambert RG, Pedersen SJ, Chan SM, Zubler V, Rufibach K, Zhao Z, Maksymowych WP (2015) Candidate lesion-based criteria for defining a positive sacroiliac joint MRI in two cohorts of patients with axial spondyloarthritis. Ann Rheum Dis 74(11):1976–1982.  https://doi.org/10.1136/annrheumdis-2014-205408 CrossRefPubMedGoogle Scholar
  10. 10.
    Bennett AN, McGonagle D, O’Connor P, Hensor EM, Sivera F, Coates LC, Emery P, Marzo-Ortega H (2008) Severity of baseline magnetic resonance imaging-evident sacroiliitis and HLA-B27 status in early inflammatory back pain predict radiographically evident ankylosing spondylitis at eight years. Arthritis Rheum 58(11):3413–3418.  https://doi.org/10.1002/art.24024 CrossRefPubMedGoogle Scholar
  11. 11.
    van der Heijde DM, Landewe RB, Hermann KG, Jurik AG, Maksymowych WP, Rudwaleit M, O’Connor PJ, Braun J (2005) Application of the OMERACT filter to scoring methods for magnetic resonance imaging of the sacroiliac joints and the spine. Recommendations for a research agenda at OMERACT 7. J Rheumatol 32(10):2042–2047PubMedGoogle Scholar
  12. 12.
    Ostergaard M, Poggenborg RP, Axelsen MB, Pedersen SJ (2010) Magnetic resonance imaging in spondyloarthritis—how to quantify findings and measure response. Best Pract Res Clin Rheumatol 24(5):637–657.  https://doi.org/10.1016/j.berh.2010.06.001 CrossRefPubMedGoogle Scholar
  13. 13.
    Bollow M, Braun J, Taupitz M, Haberle J, Reibhauer BH, Paris S, Mutze S, Seyrekbasan F, Wolf KJ, Hamm B (1996) CT-guided intraarticular corticosteroid injection into the sacroiliac joints in patients with spondyloarthropathy: indication and follow-up with contrast-enhanced MRI. J Comput Assist Tomogr 20(4):512–521CrossRefPubMedGoogle Scholar
  14. 14.
    Hermann KG, Bollow M (2004) Magnetic resonance imaging of the axial skeleton in rheumatoid disease. Best Pract Res Clin Rheumatol 18(6):881–907.  https://doi.org/10.1016/j.berh.2004.06.005 CrossRefPubMedGoogle Scholar
  15. 15.
    Madsen KB, Egund N, Jurik AG (2010) Grading of inflammatory disease activity in the sacroiliac joints with magnetic resonance imaging: comparison between short-tau inversion recovery and gadolinium contrast-enhanced sequences. J Rheumatol 37(2):393–400.  https://doi.org/10.3899/jrheum.090519 CrossRefPubMedGoogle Scholar
  16. 16.
    Maksymowych WP, Inman RD, Salonen D, Dhillon SS, Williams M, Stone M, Conner-Spady B, Palsat J, Lambert RG (2005) Spondyloarthritis Research Consortium of Canada magnetic resonance imaging index for assessment of sacroiliac joint inflammation in ankylosing spondylitis. Arthritis Rheum 53(5):703–709.  https://doi.org/10.1002/art.21445 CrossRefPubMedGoogle Scholar
  17. 17.
    Marzo-Ortega H, Braun J, Maksymowych WP (2002) Interreader agreement in the assessment of magnetic resonance imaging of the sacroiliac joints in spondyloarthropathy—the 1st MISS study. Arthritis Rheum 46(Suppl):S428Google Scholar
  18. 18.
    Landewe RB, Hermann KG, van der Heijde DM, Baraliakos X, Jurik AG, Lambert RG, Ostergaard M, Rudwaleit M, Salonen DC, Braun J (2005) Scoring sacroiliac joints by magnetic resonance imaging. A multiple-reader reliability experiment. J Rheumatol 32(10):2050–2055PubMedGoogle Scholar
  19. 19.
    Lambert RG, Salonen D, Rahman P, Inman RD, Wong RL, Einstein SG, Thomson GT, Beaulieu A, Choquette D, Maksymowych WP (2007) Adalimumab significantly reduces both spinal and sacroiliac joint inflammation in patients with ankylosing spondylitis: a multicenter, randomized, double-blind, placebo-controlled study. Arthritis Rheum 56(12):4005–4014.  https://doi.org/10.1002/art.23044 CrossRefPubMedGoogle Scholar
  20. 20.
    Schneider CA, Rasband WS, Eliceiri KW (2012) NIH image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Rudwaleit M, Schwarzlose S, Hilgert ES, Listing J, Braun J, Sieper J (2008) MRI in predicting a major clinical response to anti-tumour necrosis factor treatment in ankylosing spondylitis. Ann Rheum Dis 67(9):1276–1281.  https://doi.org/10.1136/ard.2007.073098 CrossRefPubMedGoogle Scholar
  22. 22.
    Team RC (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
  23. 23.
    Carmona L, Sellas A, Rodriguez-Lozano C, Juanola X, Garcia Llorente JF, Fernandez Sueiro JL, Linares LF, de Castro MC, Moreno M, Zarco P, Ariza R, Baraliakos X, de Miguel E (2013) Scoring with the Berlin MRI method for assessment of spinal inflammatory activity in patients with ankylosing spondylitis: a calibration exercise among rheumatologists. Clin Exp Rheumatol 31(6):883–888PubMedGoogle Scholar
  24. 24.
    Hermann KG, Bollow M (2014) Magnetic resonance imaging of sacroiliitis in patients with spondyloarthritis: correlation with anatomy and histology. Rofo 186(3):230–237.  https://doi.org/10.1055/s-0033-1350411 PubMedCrossRefGoogle Scholar
  25. 25.
    Gong Y, Zheng N, Chen SB, Xiao ZY, Wu MY, Liu Y, Zeng QY (2012) Ten years’ experience with needle biopsy in the early diagnosis of sacroiliitis. Arthritis Rheum 64(5):1399–1406.  https://doi.org/10.1002/art.33453 CrossRefPubMedGoogle Scholar
  26. 26.
    Hededal P, Ostergaard M, Sorensen IJ, Loft AG, Hindrup JS, Thamsborg G, Asmussen K, Hendricks O, Norregaard J, Moller JM, Jurik AG, Morsel L, Balding L, Pedersen SJ (2018) Development and validation of MRI sacroiliac joint scoring methods for the semiaxial scan plane corresponding to the Berlin and SPARCC MRI scoring methods, and of a new global MRI sacroiliac joint method. J Rheumatol 45(1):70–77.  https://doi.org/10.3899/jrheum.161583 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Rheumatology DepartmentHospital Universitario Fundación AlcorcónMadridSpain
  2. 2.Radiology DepartmentHospital Universitario Fundación AlcorcónMadridSpain
  3. 3.Alce IngenieríaMadridSpain

Personalised recommendations