Transverse plane 3D analysis of mild scoliosis
To demonstrate the reality of a transverse plane pattern independent of the scoliotic curve location and to show the importance of the transverse plane pattern in the assessment of the progression risk in a population of mild scoliosis.
Spines of 111 patients with adolescent idiopathic mild scoliosis were reconstructed using biplanar stereoradiography. The apical axial rotation, the intervertebral axial rotation at junctions and the torsion index were computed. Mean values of each parameter were compared between thoracic, thoracolumbar and lumbar curves. Then a cluster analysis was performed using these parameters on 78 patients with effective outcomes at skeletal maturity. The effective outcomes and the results reached with the statistical analysis were compared and analyzed (ROC and logistic regression).
No statistical difference was observed when considering each parameter between the different types of curves. Two clusters independent of the curve type were identified. The mean values of transverse plane parameters were significantly higher in Cluster 1 than in Cluster 2. 91 % of patients classified in Cluster 1 had progressive curve and 73 % of patients classified in Cluster 2 remained stable at skeletal maturity. All parameters were good predictors but the best was the torsion index.
This study demonstrated that a transverse plane pattern combining apical axial rotation, the intervertebral axial rotation at junctions and the torsion index is independent of the scoliotic curve location and significant in the determination of the progression risk of mild scoliosis.
KeywordsScoliosis Progression risk Transverse plane 3D Biplanar stereoradiography
- 11.Dubousset J, Charpak G, Dorion I, Skalli W, Lavaste F, Deguise J, Kalifa G, Ferey S (2005) A new 2D and 3D imaging approach to musculoskeletal physiology and pathology with low-dose radiation and the standing position: the EOS system. Bull Acad Natl Med 189:287–297; discussion 297–300Google Scholar
- 17.MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol 1, pp 281–297Google Scholar