Background: Endplate defects are commonly seen in patients with lumbar degenerative disease. However, little is known about the presence of endplate defects in patients with degenerative spondylolisthesis (DS). In the present study, we have introduced a classification system for endplate defects in patients with DS using routine magnetic resonance imaging findings and explored the correlative factors.
Methods: Endplate defects were classified into 3 major categories (rim defects, focal defects, and erosive defects) and 5 subtypes (anterior type, posterior type, arc type at the anterior rim, notch type, and Schmorl's nodes). The incidence rates of the endplate defects were compared between the slippage and nonslippage levels. The correlations between the endplate defects and age, sex, disc degeneration, Modic changes (MCs), body mass index, slippage segment, and slippage degree were analyzed.
Results: Endplate defects were present in 47.43% of the endplates in DS. The most common endplate defects were rim defects. The occurrence of endplate defects, especially anterior defects, was more common at the slippage levels. Endplate defects were associated with age and closely related to MCs and the severity of disc degeneration. The slippage degree, slippage segment, body mass index, and sex differences were not associated with endplate defects in our study. The results obtained using this novel classification system were stable and consistent.
Conclusions: The results from the present study have shown that the novel radiological classification system of endplate defects is reliable. Endplate defects were associated with slippage but not with the slippage degree or slippage segment differences in DS. The correlation between endplate defects and age and between MCs and disc degeneration were important features on the magnetic resonance imaging scans of patients with DS.
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http://dx.doi.org/10.1016/j.wneu.2020.05.163 | DOI Listing |
N Am Spine Soc J
December 2024
Department of Information Engineering, University of Brescia, via Branze 38, Brescia 25123, Italy.
Background: In recent years, the integration of Artificial Intelligence (AI) models has revolutionized the diagnosis of Low Back Pain (LBP) and associated disc pathologies. Among these, SpineNetV2 stands out as a state-of-the-art, open-access model for detecting and grading various intervertebral disc pathologies. However, ensuring the reliability and applicability of AI models like SpineNetV2 is paramount.
View Article and Find Full Text PDFNeuromuscul Disord
December 2024
INMG-PGNM, UMR CNRS 5261 - INSERM U1315, Université Lyon 1, Lyon, France; Centre de Biotechnologie Cellulaire, Hospices Civils De Lyon, Lyon, France.
Skelet Muscle
October 2024
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Background: Amyotrophic lateral sclerosis (ALS) is a devastating and incurable neurodegenerative disease. Accumulating evidence strongly suggests that intrinsic muscle defects exist and contribute to disease progression, including imbalances in whole-body metabolic homeostasis. We have previously reported that tumour necrosis factor (TNF)-like weak inducer of apoptosis (TWEAK) and fibroblast growth factor inducible 14 (Fn14) are significantly upregulated in skeletal muscle of the SOD1 ALS mouse model.
View Article and Find Full Text PDFVet Radiol Ultrasound
September 2024
Division of Diagnostic Imaging, Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht, The Netherlands.
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