Magnetic resonance classification of lumbar intervertebral disc degeneration
- PMID: 11568697
- DOI: 10.1097/00007632-200109010-00011
Magnetic resonance classification of lumbar intervertebral disc degeneration
Abstract
Study design: A reliability study was conducted.
Objectives: To develop a classification system for lumbar disc degeneration based on routine magnetic resonance imaging, to investigate the applicability of a simple algorithm, and to assess the reliability of this classification system.
Summary of background data: A standardized nomenclature in the assessment of disc abnormalities is a prerequisite for a comparison of data from different investigations. The reliability of the assessment has a crucial influence on the validity of the data. Grading systems of disc degeneration based on state of the art magnetic resonance imaging and corresponding reproducibility studies currently are sparse.
Methods: A grading system for lumbar disc degeneration was developed on the basis of the literature. An algorithm to assess the grading was developed and optimized by reviewing lumbar magnetic resonance examinations. The reliability of the algorithm in depicting intervertebral disc alterations was tested on the magnetic resonance images of 300 lumbar intervertebral discs in 60 patients (33 men and 27 women) with a mean age of 40 years (range, 10-83 years). All scans were analyzed independently by three observers. Intra- and interobserver reliabilities were assessed by calculating kappa statistics.
Results: There were 14 Grade I, 82 Grade II, 72 Grade III, 68 Grade IV, and 64 Grade V discs. The kappa coefficients for intra- and interobserver agreement were substantial to excellent: intraobserver (kappa range, 0.84-0.90) and interobserver (kappa range, 0.69-0.81). Complete agreement was obtained, on the average, in 83.8% of all the discs. A difference of one grade occurred in 15.9% and a difference of two or more grades in 1.3% of all the cases.
Conclusion: Disc degeneration can be graded reliably on routine T2-weighted magnetic resonance images using the grading system and algorithm presented in this investigation.
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