A Comparison Between Retaining and Resecting the Posterior Longitudinal Ligament in Percutaneous Endoscopic Transforaminal Discectomy for Disc Herniation: A Retrospective Cohort Study

Normative Measurements of L1-S1 Segmental Angulation, Disk Space Height, and Neuroforaminal Dimensions Using Computed Tomography

BACKGROUND AND OBJECTIVES

To establish normative anatomic measurements of lumbar segmental angulation (SA) and disk space height (DSH) in relation to neuroforaminal dimensions (NFDs), and to uncover the influence of patient demographic and anthropometric characteristics on SA, DSH, and NFDs.

METHODS

NFDs, SA, and anterior, middle, and posterior DSH were measured using computed tomography of 969 patients. NFDs were defined as sagittal anterior-to-posterior width, foraminal height, and area. Statistical analyses were performed to assess associations among SA, DSH, NFDs, and patient height, weight, body mass index, sex, and ethnicity.

RESULTS

SA and DSH measurements increased moving caudally from L1 to S1. Foraminal width decreased moving caudally from L1 to S1. Foraminal height and area demonstrated unimodal distribution patterns with the largest values clustered at L2-L3 on the right side and L3-L4 on the left. Significant differences in SA, DSH, and NFD measurements were observed based on the disk level. Inconsistent, marginal NFD differences were observed based on laterality. Across all disk levels, only weak-to-moderate correlations were observed between SA and DSH in relation to NFDs. Patient height, weight, and body mass index were only weakly associated with SA, DSH, and NFDs. Based on patient sex, significant differences were observed for SA, DSH, and NFD measurements from L1 to S1, with males demonstrating consistently larger values compared with females. Based on patient race and ethnicity, significant differences in SA and NFD measurements were observed from L1 to S1.

CONCLUSION

This study describes 48 450 normative measurements of L1-S1 SA, DSH, and NFDs. These measurements serve as representative models of normal anatomic dimensions necessary for several applications including surgical planning and diagnosis of foraminal stenosis. Normative values of SA and DSH are not moderately or strongly associated with NFDs. SA, DSH, and NFDs are influenced by sex and ethnicity, but are not strongly or moderately influenced by patient anthropometric factors.

Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen

OBJECTIVE

3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time-consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4-5 level.

METHODS

A total of 100 participants (mean age: 42.2 ± 14.0 years; 52 males and 48 females; mean body mass index, 22.7 ± 3.2 kg/m² ), were enrolled in this prospective study between March and July 2020. All participants were scanned on L4-5 level with a 3T MR unit using 3D T2-weighted sampling perfection with application-optimized contrast with various flip-angle evolutions (SPACE) sequences. The lumbar spine's vertebra bone structures (VBS) and intervertebral discs (IVD) were manually segmented by skilled surgeons according to their anatomical outlines from MRI. Then all manual segmentation were saved and used for training. An automated segmentation method based on a 3D U-shaped architecture network (3D-UNet) was introduced for the automated segmentation of lumbar spinal structures. A number of quantitative metrics, including dice similarity coefficient (DSC), precision, and recall, were used to evaluate the performance of the automated segmentation method on MRI. Wilcoxon signed-rank test was applied to compare morphometric parameters, including foraminal area, height and width of 3D LIVF models between automatic and manual segmentation. The intra-class correlation coefficient was used to assess the test-retest reliability and inter-observer reliability of multiple measurements for these morphometric parameters of 3D LIVF models.

RESULTS

The automatic segmentation performance of all spinal structures (VBS and IVD) was found to be 0.918 (healthy levels: 0.922; unhealthy levels: 0.916) for the mean DSC, 0.922 (healthy levels: 0.927; unhealthy levels: 0.920) for the mean precision, and 0.917 (healthy levels: 0.918; unhealthy levels: 0.917) for the mean recall in the test dataset. It took approximately 2.5 s to achieve each automated segmentation, far less than the 240 min for manual segmentation. Furthermore, no significant differences were observed in the foraminal area, height and width of the 3D LIVF models between manual and automatic segmentation images (P > 0.05).

CONCLUSION

A method of automated MRI segmentation based on deep learning algorithms was capable of rapidly generating accurate segmentation of spinal structures and can be used to construct 3D LIVF models from MRI at the L4-5 level.

Knowledge Areas and New Trends in Lumbar Disc Herniation Research: Bibliometrics and Knowledge Mapping Analysis

OBJECTIVE

To determine the coalitions and impact of authors, countries, institutions, and journals, evaluate the knowledge base, find the hotspot trends, and identify the emerging topics in lumbar disc herniation (LDH).

METHOD

The articles related to LDH were obtained from the Web of Science Core Collection on August 21, 2021. Two scientometric software (CiteSpace 5.8.R.1 and VOSviewer 1.6.17) were used to perform bibliometric and knowledge-map analysis.

RESULTS

From the set parameters, 4642 articles were included in the literature. Although the total number of publications fluctuated between 2001 and 2020, a general trend toward increase was observed. Respectively, the most productive country and institution in the field were the United States and Wooridul Spine Hospital. The most active and cited authors were Lee and Weinstein. Spine was the most impactful and cited journal. Weinstein (JAMA 296:2441-2450, 2006) had the highest number of co-citations and Weinstein(N Engl J Med 358:794-810, 2008) had the highest number of citations. The keyword "low back pain" was ranked first for frequency and total link strength, whereas "risk factor" was ranked first for centrality. Topics including pathogenesis (disc herniation), examination methods (MRI), treatment methods (non-surgical treatment, surgical treatment), surgical options (laminectomy, discectomy), clinical observations (double-blind, efficacy, outcome, learning curve), and evaluation of efficacy (meta-analysis) of LDH have been the focus of leading-edge research in 2001-2020.

CONCLUSION

Using bibliometric methods, this study mapped the knowledge map of LDH research in the past 20 years. The study identifies existing trends to provide a framework for further research.