Jesse Shen1,2, Stefan Parent1,2, James Wu1,2, Carl-Éric Aubin1,3, Jean-Marc Mac-Thiong1,2, Samuel Kadoury1,3, Peter Newton4, Lawrence G Lenke5, Virginie Lafage6, Soraya Barchi1, Hubert Labelle7,8. 1. CHU Sainte-Justine, Montréal, Canada. 2. University of Montreal, Montréal, Canada. 3. Polytechnique Montreal, Montréal, Canada. 4. Rady Children's Hospital, San Diego, USA. 5. NewYork-Presbyterian, New York, USA. 6. Hospital for Special Surgery, New York, USA. 7. CHU Sainte-Justine, Montréal, Canada. Hubert.labelle@umontreal.ca. 8. University of Montreal, Montréal, Canada. Hubert.labelle@umontreal.ca.
Abstract
STUDY DESIGN: Retrospective analysis of consecutive cases. OBJECTIVES: To identify clinically relevant three-dimensional (3D) sub-groups for adolescent idiopathic scoliosis (AIS). Classifications for AIS are developed to assist surgeons in surgical planning and therapeutic management. However, current systems are based on two-dimensional (2D) parameters that do not completely describe the 3D deformity. Hence, variations in surgical results based on pre-operative 2D classifications may be attributed to the lack of 3D description. METHODS: Subjects from a multicenter database of AIS patients were included in this study. All patients had bi-planar radiographs and 3D reconstruction of the entire spine. A clustering algorithm based on fuzzy c-means was utilized to identify sub-groups based on the following ten parameters measured on 3D reconstructions of the spine: Cobb angle, orientation of the plane of maximum curvature of the proximal thoracic, mid-thoracic (MT) and thoracolumbar (TLL) levels, axial rotation of the apical vertebra of the MT and TLL segments, T4-T12 thoracic kyphosis, and L1-S1 lumbar lordosis. Da Vinci views were also generated and analyzed for each patient in the study. A panel of four experienced spine surgeons from the SRS 3D Scoliosis Committee reviewed and evaluated each group to determine if cluster groups were clinically distinct from each other. RESULTS: The clustering algorithm was able to detect 11 sub-groups. The population size for each cluster varied from 11 to 290. Statistically significant differences were seen between the parameters for each group. Four spine surgeons reviewed the three most representative cases of each group and unanimously agreed that each cluster group represents a sub-group that was not defined in current classifications. CONCLUSIONS: This study presents a new method of classifying AIS based on a fuzzy clustering algorithm using parameters describing the 3D characteristics of the deformity. Further clinical validation is needed to confirm the usefulness of this classification system. LEVEL OF EVIDENCE: IV.
STUDY DESIGN: Retrospective analysis of consecutive cases. OBJECTIVES: To identify clinically relevant three-dimensional (3D) sub-groups for adolescent idiopathic scoliosis (AIS). Classifications for AIS are developed to assist surgeons in surgical planning and therapeutic management. However, current systems are based on two-dimensional (2D) parameters that do not completely describe the 3D deformity. Hence, variations in surgical results based on pre-operative 2D classifications may be attributed to the lack of 3D description. METHODS: Subjects from a multicenter database of AISpatients were included in this study. All patients had bi-planar radiographs and 3D reconstruction of the entire spine. A clustering algorithm based on fuzzy c-means was utilized to identify sub-groups based on the following ten parameters measured on 3D reconstructions of the spine: Cobb angle, orientation of the plane of maximum curvature of the proximal thoracic, mid-thoracic (MT) and thoracolumbar (TLL) levels, axial rotation of the apical vertebra of the MT and TLL segments, T4-T12 thoracic kyphosis, and L1-S1 lumbar lordosis. Da Vinci views were also generated and analyzed for each patient in the study. A panel of four experienced spine surgeons from the SRS 3D Scoliosis Committee reviewed and evaluated each group to determine if cluster groups were clinically distinct from each other. RESULTS: The clustering algorithm was able to detect 11 sub-groups. The population size for each cluster varied from 11 to 290. Statistically significant differences were seen between the parameters for each group. Four spine surgeons reviewed the three most representative cases of each group and unanimously agreed that each cluster group represents a sub-group that was not defined in current classifications. CONCLUSIONS: This study presents a new method of classifying AIS based on a fuzzy clustering algorithm using parameters describing the 3D characteristics of the deformity. Further clinical validation is needed to confirm the usefulness of this classification system. LEVEL OF EVIDENCE: IV.
Authors: Alexander L Hornung; Christopher M Hornung; G Michael Mallow; J Nicolás Barajas; Augustus Rush; Arash J Sayari; Fabio Galbusera; Hans-Joachim Wilke; Matthew Colman; Frank M Phillips; Howard S An; Dino Samartzis Journal: Eur Spine J Date: 2022-03-27 Impact factor: 2.721
Authors: G Michael Mallow; Zakariah K Siyaji; Fabio Galbusera; Alejandro A Espinoza-Orías; Morgan Giers; Hannah Lundberg; Christopher Ames; Jaro Karppinen; Philip K Louie; Frank M Phillips; Robin Pourzal; Joseph Schwab; Daniel M Sciubba; Jeffrey C Wang; Hans-Joachim Wilke; Frances M K Williams; Shoeb A Mohiuddin; Melvin C Makhni; Nicholas A Shepard; Howard S An; Dino Samartzis Journal: Global Spine J Date: 2020-11-28