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Classification of three-dimensional thoracic deformities in adolescent idiopathic scoliosis from a multivariate analysis

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Abstract

Purpose

Understanding how to classify and quantify three-dimensional (3D) spinal deformities remains an open question in adolescent idiopathic scoliosis. The objective of this study was to perform a 3D manifold characterization of scoliotic spines demonstrating thoracic deformations using a novel geometric and intuitive statistical tool to determine patterns in pathological cases.

Methods

Personalized 3D reconstructions of thoracic (T)/lumbar (L) spines from a cohort of 170 Lenke Type-1 patients were analyzed with a non-linear manifold embedding algorithm in order to reduce the high-dimensionality of the data, using statistical properties of neighbouring spine models. We extracted sub-groups of the data from the underlying manifold structure using an unsupervised clustering algorithm to understand the inherent distribution and determine classes of pathologies which appear from the low-dimensional space.

Results

For Lenke Type-1 patients, four clusters were detected from the low-dimensional manifold of 3D models: (1) normal kyphosis (T) with hyper-lordosis (L) and high Cobb angles (37 cases), (2) low kyphosis (T) and normal lordosis (L), with high rotation of plane of maximum curvature (55 cases), (3) hypo-kyphotic (T) and hyper-lordosis (L) (21 cases) and (4) hyper-kyphotic (T) with strong vertebral rotation (57 cases). Results show the manifold representation can potentially be useful for classification of 3D spinal pathologies such as idiopathic scoliosis and serve as a tool for understanding the progression of deformities in longitudinal studies.

Conclusions

Quantitative evaluation illustrates that the complex space of spine variability can be modeled by a low-dimensional manifold and shows the existence of an additional hyper-kyphotic subgroup from the cohort of 3D spine reconstructions of Lenke Type-1 patients when compared with previous findings on the 3D classification of spinal deformities.

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References

  1. Clin J, Aubin CE, Parent S et al (2010) Comparison of the biomechanical 3D efficiency of different brace designs for the treatment of scoliosis using a finite element model. Eur Spine J 19:1169–1178

    Article  PubMed  Google Scholar 

  2. Kadoury S, Cheriet F, Beauséjour M et al (2009) A three-dimensional retrospective analysis of the evolution of spinal instrumentation for the correction of adolescent idiopathic scoliosis. Eur Spine J 18:23–37

    Article  PubMed  Google Scholar 

  3. Villemure I, Aubin CE, Grimard G et al. (2001) Progression of vertebral and spinal three-dimensional deformities in adolescent idiopathic scoliosis: a longitudinal study. Spine 2226–2244

  4. Kadoury S, Cheriet F, Laporte C et al (2007) A versatile 3D reconstruction system of the spine and pelvis for clinical assessment of spinal deformities. Med Biol Eng Comput 45:591–602

    Article  PubMed  Google Scholar 

  5. Carpineta L, Labelle H (2003) Evidence of three-dimensional variability in scoliotic curves. Clin Orthop Relat Res 412:139–148

    Article  PubMed  Google Scholar 

  6. King HA, Moe JH, Bradford DS et al (1983) The selection of fusion levels in thoracic idiopathic scoliosis. J Bone Joint Surg 65:1302–1313

    PubMed  CAS  Google Scholar 

  7. Lenke LG, Betz RR, Bridwell KH et al (1998) Intraobserver and interobserver reliability of the classification of thoracic adolescent idiopathic scoliois. J Bone Joint Surg 80A:1097–1106

    Google Scholar 

  8. Lenke LG, Betz RR, Harms J et al (2001) Adolescent idiopathic scoliosis: a new classification to determine extent of spinal arthrodesis. J Bone Joint Surg 83:1169–1181

    PubMed  Google Scholar 

  9. Cil A, Pekmezci M, Yazici M et al (2005) The validity of Lenke criteria for defining structural proximal thoracic curves in patients with adolescent idiopathic scoliosis. Spine 30:2550–2555

    Article  PubMed  Google Scholar 

  10. D’Andrea LP, Betz RR, Lenke LG et al (2000) Do radiographic parameters correlate with clinical outcomes in adolescent idiopathic scoliosis. Spine 25:1795–1802

    Article  PubMed  Google Scholar 

  11. Stokes IA, Bigalow LC, Moreland MS (1987) Three-dimensional spinal curvature in idiopathic scoliosis. J Orthop Res 5:102–113

    Article  PubMed  CAS  Google Scholar 

  12. Poncet P, Dansereau J, Labelle H (2001) Geometric torsion in idiopathic scoliosis: three-dimensional analysis and proposal for a new classification. Spine 26:2235–2243

    Article  PubMed  CAS  Google Scholar 

  13. Duong L, Cheriet F, Labelle H (2006) Three-dimensional classification of spinal deformities using fuzzy clustering. Spine 31:923–930

    Article  PubMed  Google Scholar 

  14. Sangole A, Aubin CE, Labelle H et al (2009) 3D classification of thoracic scoliotic curves. Spine 34:91–99

    Article  PubMed  Google Scholar 

  15. Cootes T, Edwards G, Taylor C (2001) Active appearance models. IEEE Trans Patt Anal Mach Intel 23:681–685

    Article  Google Scholar 

  16. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326

    Article  PubMed  CAS  Google Scholar 

  17. Kadoury S, Cheriet F (2006) X-ray image restoration with adaptive PDE filter for an accurate 3D reconstruction of the human spine. In: Proceedings of International Conference on Computer Assisted Radiology and Surgery 470

  18. Kadoury S, Cheriet F, Labelle H (2009) Personalized X-ray 3D re construction of the scoliotic spine from statistical and image models. IEEE Trans Med Imag 28:1422–1435

    Article  Google Scholar 

  19. Boisvert J, Cheriet F, Pennec X et al (2008) Geometric variability of the scoliotic spine using statistics on articulated shape models. IEEE Trans Med Imag 27:557–568

    Article  CAS  Google Scholar 

  20. Ray S, Turi RH (1999) Determination of number of clusters in K-means clustering and application in colour image segmentation. In: Proceedings of Conference on Advances in Pattern Recognition and Digital Techniques 137–143

  21. Stokes IA, Bigalow LC, Moreland MS (1986) Measurement of axial rotation of vertebrae in scoliosis. Spine 11:213–218

    Article  PubMed  CAS  Google Scholar 

  22. Stokes IA (1994) Three-dimensional terminology of spinal deformity. A report presented to the Scoliosis Research Society by the Scoliosis Research Society Working Group on 3D terminology of spinal deformity. Spine 19:236–248

    Article  PubMed  CAS  Google Scholar 

  23. Perdriolle R, Le Borgne P, Dansereau J et al (2001) Idiopathic scoliosis in three dimensions: a succession of two-dimensional deformities? Spine 26:2719–2726

    Article  PubMed  CAS  Google Scholar 

  24. Vrtovec T, Vengust R, Likar B et al (2010) Analysis of four manual and a computerized method for measuring axial vertebral rotation in computed tomography images. Spine 20:E535–E541

    Article  Google Scholar 

  25. Vrtovec T, Ourselin S, Gomes L et al (2007) Automated generation of curved planar reformations from MR images of the spine. Phy Med Biol 52:2865–2878

    Article  Google Scholar 

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Correspondence to Samuel Kadoury.

Additional information

This paper was supported in part by a grant from the Fonds Québecois de la Recherche sur la Nature et les Technologies (FQRNT) and funds from the Scoliosis Research Society.

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Kadoury, S., Labelle, H. Classification of three-dimensional thoracic deformities in adolescent idiopathic scoliosis from a multivariate analysis. Eur Spine J 21, 40–49 (2012). https://doi.org/10.1007/s00586-011-2004-2

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  • DOI: https://doi.org/10.1007/s00586-011-2004-2

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