Two-dimensional classification systems of the scoliotic spine are developed to assist with surgical planning [2, 10, 11]. Yet, variations in the surgical outcomes exist partially due to the fact that the 3D shapes of the scoliotic spines are not incorporated in these classification systems [12,13,14,15,16]. We developed a true 3D classification of the right thoracic scoliosis and attempted to apply the classification system via 2D images to identify the characteristics of these 3D subtypes on 2D radiographs. This study determined the reliability of this 3D classification method based on 2D radiographs to be moderate to strong among the raters suggesting that this 3D classification system has the potential to be used in orthopedic clinics without a need for excessive image post-processing.
The reliability of the 3D classification system proved to be within the range of the Lenke modifier kappa values in the cohort of our raters and superior to other classification system commonly used in the field of orthopedics. For example the kappa value for tibial plateau fractures classifications was 0.476 based on Schatzker classification [17]. Similarly the kappa for pediatric supracondylar fractures is reported at 0.475 for Wilkins-modified Gartland classification [18]. Our intraobserver reliability for the 3D classification and axial classification were at κ = 0.61 and 0.80 respectively suggesting an acceptable range for clinical applications. The intraobsever reliability varied among the five raters; however as the number of raters is small we did not make any relationship between the raters (surgeon versus radiologist, versus engineer) and the reliability scores.
Surgical planning in AIS aims to stabilize the spine while minimizing the number of the fused vertebrae [19, 20]. In doing that, short fusion or variations in the upper and lower instrumented vertebrae (UIV and LIV) may result in compensatory curve progression, postural compensation, and subsequently a need for revision surgery [5, 20]. Our classification primarily focuses on identifying the true 3D characteristics of these curves by utilizing axial information to augment our understanding of the curve in right thoracic AIS [4, 21]. Our previous results demonstrated that the vertebral level below the thoracic apex at which the direction of the vertebral rotation changes can identify the true number of the 3D curves and the axial subtypes in right thoracic AIS [4, 21]. The lemniscate axial type (with a rather sudden change in the direction of the vertebral rotation in the thoracolumbar region) responds better to shorter fusion whereas loop shaped axial types (with a long sweeping curve and change in the direction of the vertebral rotation in the lower lumbar region or no rotation at all below the apex) require an extension of the fusion to the lumbar spine [5] as the spine is comprised of only one long 3D curve. Yet, detailed surgical planning based on this classification remains to be further established. The criterion based on the vertebral rotation is close to the concept of the neutral vertebra (NV), however current definition tries to draw attention to whether the least rotated vertebra is located between two curves (if the vertebral rotates to the opposite direction below NV i.e., lemniscate axial group) or part of the structural curve (if the vertebral rotation does not change below the NV, i.e., loop shaped axial group) [5]. A previous risk stratification analysis based on the 3D classification that was presented here showed that the subtypes with loop shape axial projection benefit from longer fusion that includes the entire 3D curve whereas fusion of one of the 3D curves in subtypes with lemniscate shaped axial projection can improve the rate of spontaneous lumbar Cobb angle correction [5]. The raters in our study could identify the axial subtypes with excellent reliability only by considering the pattern of the vertebrae rotation in the thoracolumbar/lumbar sections. This axial classification of the right thoracic AIS, as described in this study, can provide a better understanding of the nature of the compensatory curves in right thoracic AIS and assist with surgical decision-making.
As our 3D classification includes the sagittal alignment of the spine, attention should be paid to natural patient positioning during radiograph acquisition [22, 23]. As the importance of sagittal alignment in clinical evaluation of scoliosis is emphasized [21, 24, 25], patient positioning methods that do not change the postural alignment are critical for sagittal evaluation of the spine. Considering the sagittal profile in natural standing position, Types 1 and 2 have a negative SVA thus excessive transition of the UIV anteriorly, while imparting kyphosis, can result in proximal junctional kyphosis due to over-working of the posterior elements [5, 26]. Types 4 and 5 have a positive SVA. Imparting large kyphosis may result in disturbing the harmonious spino-pelvic alignment and developing compensatory mechanism [26]. Finally, the changes in the position of UIV are of greater importance in the curve types without a proximal kyphosis i.e., Types 2 and 4 as shown in a previous analysis [5]. This shows the importance of the proximal kyphosis which was not considered in previous sagittal classification of the spine [27, 28].
All raters primarily used the flowchart to perform the classifications (Fig. 2a). A total of 4 subjects were excluded from the current analysis because a large dissimilarity between the 3D spinal curvature of these patients and the original cluster centers as determined in our previous work [4, 5] was observed (Fig. 3 and Table 1). When visually evaluated, these patients had one or more characteristics that did not fit in the 3D description of any of the clusters (Figs. 1 and 2). For example, case 1 presented with a hypokyphosis and flat sagittal profile, as seen in Type 4, but with slight rotation of the thoracolumbar curve to the opposite direction of the thoracic curve, similar to what is seen in Types 1, 3, and 5 (Fig. 3-a). Case 2 had a sagittal profile similar to Type2 but slight vertebral rotation in the thoracolumbar region (Types 1, 3, or 5) was observed. Case 3 had a sagittal profile similar to Type 3 or 5 however no vertebral rotation in the thoracolumbar region was observed, suggesting a Type 2 or 4. Finally, case 4 had Type 4’s sagittal profile but the frontal curve, based on the vertebral rotation below the neutral vertebra, was classified as Types 1, 3, or 5. Analysis of a larger database can determine whether these cases should be considered as additional subgroups of Lenke1 AIS. Extra attntion needs to be paid during the radiograph acquisation and avoid patient positionings that alter the natural sagittal profile [23].
Advancements in deep learning in medical imaging can further facilitate this classification method. Such automated methods can determine new subgroups if the similarity between the patients’ subtypes increased and form new groups using an online processing algorithm. This is the subject of our future work. Using this 3D classifications algorithm [29] while including true 3D parameters of the spine [30], method needs to be expanded to other Lenke types and validated. Finally, an external validation of this classification is required to show whether this classification system can be used reliably in other hospitals and research centers.