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Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
Published on: August 8, 2019
Evaluación Cuantitativa Mejorada de la Degeneración de las Articulaciones Facetarias Lumbares: Refinamiento del
Xiaowen Liu1, Mingjian Sun2, Shouyu He3
1Department of Spine Surgery Affiliated Hospital of Qingdao University Qingdao Shandong China.
Background:
Low back pain (LBP) is a leading cause of disability, often progressing to chronic low back pain (CLBP), which often involves facet joint degeneration. The Weishaupt grading system is widely used to evaluate facet joint degeneration, but precise, quantitative methods for early diagnosis are lacking. This study aims to develop a predictive model for facet joint degeneration using magnetic resonance imaging (MRI)-derived curve fitting and some radiographic parameters, enhancing early, quantitative assessment and providing more accurate analysis for effective treatment planning.
Methods:
This study included 48 patients (161 joints), aged 20-80 years. Imaging parameters such as intervertebral space height (ISH), lumbar range of motion (ROM-L), intervertebral disc degeneration, facet joint degeneration, facet joint cross-sectional area (FJCSA), vertebral bone quality (VBQ) scores, subcutaneous fat tissue thickness (SFTT) and grayscale values of lumbar facet joint were analyzed. Statistical correlations were evaluated using spearman rank correlation and ordinal logistic regression. Curve fitting was performed to analyze and model the grayscale value changes in lumbar facet joints.
Results:
Correlation analysis revealed positive correlations between facet joint degeneration and intervertebral disc degeneration, FJCSA and SFTT, while negative correlations were observed with ISH and ROM-L. Ordinal logistic regression identified ROM-L and FJCSA as significant factors influencing degeneration. The locally estimated scatterplot smoothing (LOESS) curve fitting method of grayscale value for facet joint degeneration provided a more accurate predictive model, refining the joint degeneration grading system for improved quantitative assessment.
Conclusion:
The study identified some radiographic factors, including ISH, FJCSA, ROM-L, SFTT and intervertebral disc degeneration, associated with facet joint degeneration. This study refined the joint degeneration grading system using LOESS curve fitting, providing a more quantitative approach for evaluating facet joint degeneration. This grading system offered greater precision and reliability in clinical assessments, supporting better diagnostic accuracy for patients with facet joint-related CLBP.
