LIVS Dictionary: reference values for lumbar reference values for lumbar intervertebral spacing in a Chinese population.

Abstract

Retrospective, cross-sectional study. Injuries to the lumbar spine may alter the lumbar intervertebral space (LIVS). This study aimed to establish the LIVS Dictionary, a standardized anatomical reference system for LIVS, and to evaluate its ability to detect ligamentous injuries using sex-specific normative data. This study presents the first computed tomography (CT)-based LIVS Dictionary from 457 asymptomatic subjects. It identifies sex-specific differences, age-linked degeneration patterns, and demonstrates its utility in detecting anterior ligament injuries, particularly among females. Enhanced whole-abdominal CT scans from 457 asymptomatic adults were retrospectively analyzed to construct the LIVS Dictionary. Seven anatomical regions encompassing 28 LIVS parameters were quantified using 3D Slicer. A validation cohort of 87 patients with five distinct types of lumbar fractures was included. Statistical analyses involved t -tests, Cohen's d, uniform manifold approximation and projection clustering, and Elastic Net receiver operating characteristic (ROC) curve analysis with 95% confidence intervals (CIs). Most LIVS parameters were significantly larger in males (25 of 28 parameters, p<0.05), particularly in the anterior regions. Age analysis revealed sex-specific degeneration patterns: males showed progressive LIVS reduction with age, whereas females exhibited localized decline after 80 years. Incidental anatomical variants included lumbarization in 5.25% and sacralization in 1.09% of the healthy cohort. Among fracture types, transverse process fractures produced the greatest LIVS alterations. The Elastic Net model demonstrated excellent predictive performance (area under the ROC curve=0.90; 95% CI, 0.88-0.92; accuracy=0.94). Lumbar spine injuries produce quantifiable alterations in LIVS morphology. The LIVS Dictionary provides a validated framework for identifying these changes and captures sex-, age-, and fracture-related morphological variations with high diagnostic robustness. This framework establishes a foundation for clinical evaluation, biomechanical modeling, and forensic applications related to spinal stability.