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Órgano Oficial de difusión científica de la Asociación Mexicana de Cirujanos de Columna A. C. y de la Sociedad Iberolatinoamericana de Columna. SILACO.

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2026, Number 4

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Cir Columna 2026; 4 (4)

A new era in cervical myelopathy: the first microstructural classification impacting early diagnosis, treatment decision-making, and an algorithm for therapeutic optimization

Contreras GJ

Language: Spanish
References: 16
Page: 314-319
PDF size: 594.13 Kb.


ABSTRACT

Introduction: cervical spondylosis, one of the leading causes of degenerative cervical myelopathy (DCM), results from spinal canal narrowing secondary to multifactorial degenerative processes. Conventional imaging techniques often fail to detect early microstructural damage, delaying diagnosis and worsening clinical outcomes. Objectives: this study proposes an innovative diagnostic algorithm that integrates diffusion tensor imaging (DTI) and fractional anisotropy (FA) to identify early alterations associated with DCM, with the aim of improving classification, enabling timely intervention, and optimizing treatment outcomes. Material and methods: a literature review was conducted, including etiopathogenic, epidemiological, pathophysiological, diagnostic, and therapeutic aspects of cervical myelopathy across various degrees of stenosis, as well as the incorporation of advanced diffusion MRI techniques into the current diagnostic algorithm. Results: key elements in the gradual progression of cervical myelopathy were identified. The average reported diagnostic delay is 6.3 years, and patients typically require multiple consultations before receiving a definitive diagnosis. The literature highlights that fractional anisotropy enables more sensitive detection of spinal cord microstructural damage. Based on this evidence, previous cervical stenosis classifications were unified into a single system incorporating FA values, providing a more accurate representation of the degree of medullary involvement. Additionally, a revised diagnostic algorithm was proposed, integrating this classification to improve early detection and clinical decision-making. Conclusions: early recognition of microstructural damage is essential for optimizing the management of DCM. The adoption of an updated diagnostic algorithm and a diffusion-based adapted classification represents a crucial step toward the effective integration of these concepts into clinical practice.


REFERENCES

  1. Badhiwala JH, Wilson JR, Fehlings MG. Global burden of degenerative cervical myelopathy and the role of early diagnosis. Spine (Phila Pa 1976). 2020; 45: E401-E408.

  2. Nouri A, Martin AR, Kato S, et al. The relationship between MRI signal changes, spinal cord compression, and functional outcomes in degenerative cervical myelopathy. Spine J. 2021; 21: 1008-1018.

  3. Tetreault L, Rhee J, Prather H, et al. Change in function, pain, and quality of life following surgery for degenerative cervical myelopathy: a systematic review. Global Spine J. 2020; 10: 62S-76S.

  4. Fehlings MG, Tetreault LA, Riew KD, Middleton JW. Degenerative cervical myelopathy: diagnosis and management. Lancet Neurol. 2017; 16: 534-546.

  5. Behrbalk E, Salame K, Regev GJ, et al. Delayed diagnosis of cervical spondylotic myelopathy by primary care physicians. Neurosurg Focus. 2013; 35: E1.

  6. Ellingson BM, Salamon N, Holly LT. Imaging techniques in cervical spondylotic myelopathy: conventional and advanced MRI. Neuroimaging Clin N Am. 2023; 33: 247-260.

  7. Martin AR, De Leener B, Cohen-Adad J, et al. Monitoring for myelopathic progression with multiparametric quantitative MRI. Brain. 2018; 141: 3003-3016.

  8. Mohammadi M, Shokouhi G, Rezaei O, et al. Diffusion tensor imaging in diagnosing and evaluating degenerative cervical myelopathy: a systematic review and meta-analysis. Global Spine J. 2025; 15: 321-334.

  9. Wang X, Li J, Zhang Y, et al. Predictive value of dynamic diffusion tensor imaging for surgical outcomes in patients with cervical spondylotic myelopathy. BMC Med Imaging. 2024; 24: 115.

  10. Martin AR, Aleksanderek I, Cohen-Adad J, et al. Translating state-of-the-art spinal cord MRI techniques to clinical use: diffusion tensor imaging and microstructural biomarkers. Neuroimage Clin. 2022; 34: 102998.

  11. Shen Y, Ding WY, Shen HL, et al. Role of diffusion tensor imaging in diagnosing cervical spondylotic myelopathy and evaluating spinal cord injury. Spine (Phila Pa 1976). 2020; 45: E450-E457.

  12. Dong F, Shen Y, Ding W, et al. Quantitative analysis of diffusion tensor imaging parameters in cervical spondylotic myelopathy. Eur Spine J. 2021; 30: 329-337.

  13. Nukala M, Abraham J, Sreedharan S, et al. Diffusion tensor imaging metrics in cervical spondylotic myelopathy: correlation with neurological severity. J Clin Neurosci. 2022; 97: 134-140.

  14. Nagata K, Kiyonaga K, Ohashi T, Sagara M, Miyazaki S, Inoue A. Clinical value of magnetic resonance imaging for cervical myelopathy. Spine (Phila Pa 1976). 1990; 15(11): 1088-1096.

  15. Muhle C, Wiskirchen J, Weinert D, et al. Cervical spinal canal stenosis: MR imaging grading system. Radiology. 1998; 206: 391-396.

  16. Kang Y, Lee JW, Koh YH, et al. New MRI grading system for cervical canal stenosis. AJR Am J Roentgenol. 2011; 197: W134-W140.




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