Assessment of Maximum Spinal Deformity in Scoliosis: A Literature Review

Patient and surgical predictors of 3D correction in posterior spinal fusion: a systematic review

BACKGROUND

Restoration of three-dimensional (3D) alignment is critical in correcting patients with adolescent idiopathic scoliosis using posterior spinal fusion (PSF). However, current studies mostly rely on 2D radiographs, resulting in inaccurate assessment of surgical correction and underlying predictive factors. While 3D reconstruction of biplanar radiographs is a reliable and accurate tool for quantifying spinal deformity, no study has reviewed the current literature on its use in evaluating surgical prognosis.

PURPOSE

To summarize the current evidence on patient and surgical factors affecting sagittal alignment and curve correction after PSF based on 3D parameters derived from reconstruction of biplanar radiographs.

METHODS

A comprehensive search was conducted by three independent investigators on Medline, PubMed, Web of Science, and Cochrane Library to obtain all published information on predictors of postoperative alignment and correction after PSF. Search items included "adolescent idiopathic scoliosis," "stereoradiography," "three-dimensional," "surgical," and "correction." The inclusion and exclusion criteria were carefully defined to include clinical studies. Risk of bias was assessed with the Quality in Prognostic Studies tool, and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development, and Evaluations approach. 989 publications were identified, with 444 unique articles subjected to full-text screening. Ultimately, 41 articles were included.

RESULTS

Strong predictors of better curve correction included preoperative normokyphosis (TK > 15°), a corresponding rod contour, intraoperative vertebral rotation and translation, and upper and lower instrumented vertebrae selected based on sagittal and axial inflection points. For example, for Lenke 1 patients with junctional vertebrae above L1, fusion to NV-1 (1 level above the neutral vertebra) achieved optimal curve correction while preserving motion segments. Pre-op coronal Cobb angle and axial rotation, distal junctional kyphosis, pelvic incidence, sacral slope, and type of instrument were identified as predictors with moderate evidence. For Lenke 1C patients, > 50% LIV rotation was found to increase spontaneous lumbar curve correction. Pre-op thoracolumbar apical translation and lumbar lordosis, Ponte osteotomies, and rod material were found to be predictors with low evidence.

CONCLUSIONS

Rod contouring and UIV/LIV selection should be based on preoperative 3D TK in order to achieve normal postoperative alignment. Specifically, Lenke 1 patients with high-lying rotations should be fused distally at NV-1, while hypokyphotic patients with large lumbar curves and truncal shift should be fused at NV to improve lumbar alignment. Lenke 1C curves should be corrected using > 50% LIV rotation counterclockwise to the lumbar rotation. Further investigation should compare surgical correction between pedicle-screw and hybrid constructs using matched cohorts. DJK and overbending rods are potential predictors of postoperative alignment.

Model-Based Radiation-Free Assessment of Scoliosis: A Principle Validation Study

Purpose

Adolescent scoliosis is one of the common pediatric spinal diseases which has a high risk of progression due to the rapid growth of the skeleton during the growing stage therefore needs regular clinical monitoring including X-rays. Because X-rays could lead to ionizing radiation-related health problems, an ionizing radiation-free, non-invasive method is presented here to estimate the degree of scoliosis and to potentially support the medical assessment.

Methods

The radiation-free body scanner provides a 3D surface scan of the torso. A basic 3D structure of the human ribcage and vertebral column was modeled and simulated with computer-aided design software and finite element method calculation. For comparison with X-rays, courses of vertebral columns derived from 3D torso images and 3D models were analyzed with respect to their apex positions and angles.

Results

The methods show good results in the estimation of the apex positions of scoliosis. Strong correlations (R = 0.8924) were found between the apex and Cobb angle from X-rays. Similar correlations (R = 0.8087) was obtained between the apex angles extracted from X-rays and the combination of torso scan images with 3D model simulations. Promising agreement was obtained between the spinal trajectories extracted from X-ray and 3D torso images.

Conclusions

Very strong correlations suggest that the apex angle could potentially be used for scoliosis assessment in follow-up examinations in complement to the Cobb angle. However, further improvements of the methods and tests on a larger number of data set are necessary before their introduction into the clinical application.

An automatic method for feature segmentation of human thoracic and lumbar vertebrae

BACKGROUND AND OBJECTIVE

Because of the three-dimensional distribution of morphological features of human vertebrae and the whole spine, in recent years, to make more precise diagnoses and to design optimized surgical procedures, new protocols have been proposed based on analysing their three-dimensional (3D) models. In the related literature, processes of segmentation and morphological features recognition are essentially performed by a skilled operator that selects the interesting areas. So, being affected by the preparation and experience of the operator, this produces an evaluation that is poorly reproducible and repeatable for the uncertainties of a typical manual measurement process.

METHODS

To overcome this limitation, in this paper a new automatic method is proposed for feature segmentation and recognition of human vertebrae. The proposed computer-based method, starting from 3D high density discretized models of thoracic and lumbar vertebrae, automatically performs both the semantic and geometric segmentation of their morphological features. The segmentation and recognition rules codify some important definitions used in the traditional manual method, considering all the vertebra morphology information that is invariant inter-subject.

RESULTS

The automatic method proposed here is verified by analysing many real vertebrae, both acquired using a 3D scanner and coming from Computerized Tomography (CT) scans. The obtained results are critically discussed and compared with the traditional manual methods for vertebra analysis. The method has proven to be robust and reliable in the segmentation and recognition of morphological features of vertebrae. Furthermore, the proposed automatic method avoids the blurring of quantitative parameters get from vertebrae, resulting from poor repeatability and reproducibility of manual methods used in the state-of-the-art.

CONCLUSIONS

Starting from the automatic segmentation and recognition here proposed, it is possible to automatically calculate the parameters of thoracic or lumbar vertebrae used in archaeology, medicine, or biomechanics or define their new ones.