Skip to main content
SpringerLink
Log in

Diffusion tensor imaging can predict surgical outcomes of patients with cervical compression myelopathy

  • Original Article
  • Published:
European Spine Journal Aims and scope Submit manuscript

Abstract

Purpose

The aim of this study was to assess the potential role of diffusion tensor imaging (DTI) as a predictor of surgical outcomes in patients with cervical compressive myelopathy (CCM). Surgical decompression is often recommended for symptomatic CCM. It is important to know the prognosis of surgical outcomes and to recommend appropriate timing for surgery.

Methods

We enrolled 26 patients with CCM who underwent surgery. The Japanese Orthopaedic Association (JOA) score for cervical myelopathy was evaluated before and 6 months after surgery. Surgical outcomes were regarded as good if there was a change in JOA score of three points or more, or the recovery rate of JOA score was 50% or more. The patients were examined using a 3.0 T magnetic resonance system before surgery. Measured diffusion parameters were fractional anisotropy (FA) and mean diffusivity (MD). The correlations between DTI parameters and surgical outcomes were analyzed.

Results

Both change and recovery rate of JOA score moderately correlated with FA. Furthermore, the area under the receiver–operator characteristic curve based on FA for prognostic precision of surgical outcomes indicates that FA is a good predictive factor. The cut-off values of FA for predicting good surgical outcomes evaluated by change and recovery rate of JOA score were 0.65 and 0.57, respectively. Neither change nor recovery rate of JOA score correlated with MD.

Conclusions

FA in spinal cord DTI can moderately predict surgical outcomes. DTI can serve as a supplementary tool for decision-making to guide surgical intervention in patients with CCM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Tetreault LA, Karpova A, Fehlings MG (2013) Predictors of outcome in patients with degenerative cervical spondylotic myelopathy undergoing surgical treatment: results of a systematic review. Eur Spine J. doi:10.1007/s00586-013-2658-z

    Google Scholar 

  2. Yoon ST, Raich A, Hashimoto RE et al (2013) Predictive factors affecting outcome after cervical laminoplasty. Spine (Phila Pa 1976) 38:S232–S252. doi:10.1097/BRS.0b013e3182a7eb55

  3. Tetreault LA, Kopjar B, Vaccaro A et al (2013) A clinical prediction model to determine outcomes in patients with cervical spondylotic myelopathy undergoing surgical treatment: data from the prospective, multi-center AOSpine North America study. J Bone Joint Surg Am 95:1659–1666. doi:10.2106/JBJS.L.01323

    Article  PubMed  Google Scholar 

  4. Tetreault LA, Karpova A, Fehlings MG (2015) Predictors of outcome in patients with degenerative cervical spondylotic myelopathy undergoing surgical treatment: results of a systematic review. Eur Spine J 24(Suppl 2):236–251. doi:10.1007/s00586-013-2658-z

    Article  PubMed  Google Scholar 

  5. Chung SS, Lee CS, Chung KH (2002) Factors affecting the surgical results of expansive laminoplasty for cervical spondylotic myelopathy. Int Orthop 26:334–338. doi:10.1007/s00264-002-0372-2

    Article  PubMed  PubMed Central  Google Scholar 

  6. Karpova A, Arun R, Davis AM et al (2013) Predictors of surgical outcome in cervical spondylotic myelopathy. Spine (Phila Pa 1976) 38:392–400. doi:10.1097/BRS.0b013e3182715bc3

  7. Tetreault L A, Dettori JR, Wilson JR et al (2013) Systematic review of magnetic resonance imaging characteristics that affect treatment decision making and predict clinical outcome in patients with cervical spondylotic myelopathy. Spine (Phila Pa 1976) 38:S89–110. doi:10.1097/BRS.0b013e3182a7eae0

  8. Arima H, Sakamoto S, Naito K et al (2015) Prediction of the efficacy of surgical intervention in patients with cervical myelopathy by using diffusion tensor 3T-magnetic resonance imaging parameters. J craniovertebral junction spine 6:120–124. doi:10.4103/0974-8237.161593

    Article  Google Scholar 

  9. Nakamura M, Fujiyoshi K, Tsuji O et al (2012) Clinical significance of diffusion tensor tractography as a predictor of functional recovery after laminoplasty in patients with cervical compressive myelopathy. J Neurosurg Spine 17:147–152. doi:10.3171/2012.5.SPINE1196

    Article  PubMed  Google Scholar 

  10. Maki S, Koda M, Ota M et al (2015) Reduced field-of-view diffusion tensor imaging of the spinal cord shows motor dysfunction of the lower extremities in patients with cervical compression myelopathy. Spine (Phila Pa 1976). doi:10.1097/BRS.0000000000001123

  11. Wen CY, Cui JL, Liu HS et al (2014) Is diffusion anisotropy a biomarker for disease severity and surgical prognosis of cervical spondylotic myelopathy? Radiology 270:197–204. doi:10.1148/radiol.13121885

    Article  PubMed  Google Scholar 

  12. Martin AR, Aleksanderek I, Cohen-Adad J et al (2016) Translating state-of-the-art spinal cord MRI techniques to clinical use: a systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI. NeuroImage Clin 10:192–238. doi:10.1016/j.nicl.2015.11.019

    Article  PubMed  Google Scholar 

  13. Guan X, Fan G, Wu X et al (2015) Diffusion tensor imaging studies of cervical spondylotic myelopathy: a systemic review and meta-analysis. PLoS One 10:e0117707. doi:10.1371/journal.pone.0117707

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ellingson B, Salamon N, Holly L (2013) Advances in MR imaging for cervical spondylotic myelopathy. Eur Spine J 24:1–12. doi:10.1007/s00586-013-2915-1

    Google Scholar 

  15. Keřkovský M, Bednařík J, Jurová B et al (2017) Spinal cord MR diffusion properties in patients with degenerative cervical cord compression. J Neuroimaging 27:149–157. doi:10.1111/jon.12372

    Article  PubMed  Google Scholar 

  16. Wei L, Wang S, Zheng Z et al (2016) Analysis of the diffusion tensor imaging parameters of a normal cervical spinal cord in a healthy population. J Spinal Cord Med. doi:10.1080/10790268.2016.1244905 (Epub ahead of print)

  17. Basser PJ (1995) Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8:333–344

    Article  CAS  PubMed  Google Scholar 

  18. Wen CY, Cui JL, Lee MP et al (2013) Quantitative analysis of fiber tractography in cervical spondylotic myelopathy. Spine J 13:697–705. doi:10.1016/j.spinee.2013.02.061

    Article  PubMed  Google Scholar 

  19. Kohno K, Kumon Y, Oka Y et al (1997) Evaluation of prognostic factors following expansive laminoplasty for cervical spinal stenotic myelopathy. Surg Neurol 48:237–245

    Article  CAS  PubMed  Google Scholar 

  20. Tetreault L, Nouri A, Kopjar B et al (2015) The minimum clinically important difference of the modified Japanese Orthopaedic Association scale in patients with degenerative cervical myelopathy. Spine (Phila Pa 1976) 40:1653–9. doi:10.1097/BRS.0000000000001127

  21. Acharya S, Srivastava A, Virmani S et al (2010) Resolution of physical signs and recovery in severe cervical spondylotic myelopathy after cervical laminoplasty. Spine (Phila Pa 1976) 35:E1083–7. doi:10.1097/BRS.0b013e3181df1a8e

  22. Saritas EU, Cunningham CH, Lee JH et al (2008) DWI of the spinal cord with reduced FOV single-shot EPI. Magn Reson Med 60:468–473. doi:10.1002/mrm.21640

    Article  PubMed  Google Scholar 

  23. Jones JG, Chen SY, Lebel RM et al (2013) Diffusion tensor imaging correlates with the clinical assessment of disease severity in cervical spondylotic myelopathy and predicts outcome following surgery. AJNR Am J Neuroradiol 34:471–478. doi:10.3174/ajnr.A3199

    Article  CAS  PubMed  Google Scholar 

  24. Wang K, Chen Z, Zhang F et al (2017) Evaluation of DTI parameter ratios and diffusion tensor tractography grading in the diagnosis and prognosis prediction of cervical spondylotic myelopathy. Spine (Phila Pa 1976) 42: E202–E210. doi:10.1097/BRS.0000000000001784

  25. Rindler RS, Chokshi FH, Malcolm JG et al (2017) spinal diffusion tensor imaging in evaluation of preoperative and postoperative severity of cervical spondylotic myelopathy: systematic review of literature. World Neurosurg 99:150–158. doi:10.1016/j.wneu.2016.11.141

    Article  PubMed  Google Scholar 

  26. Tetreault L, Kopjar B, Nouri A et al (2017) The modified Japanese Orthopaedic Association scale: establishing criteria for mild, moderate and severe impairment in patients with degenerative cervical myelopathy. Eur Spine J 26:78–84. doi:10.1007/s00586-016-4660-8

    Article  PubMed  Google Scholar 

  27. Chibbaro S, Benvenuti L, Carnesecchi S et al (2006) Anterior cervical corpectomy for cervical spondylotic myelopathy: experience and surgical results in a series of 70 consecutive patients. J Clin Neurosci 13:233–238. doi:10.1016/j.jocn.2005.04.011

    Article  CAS  PubMed  Google Scholar 

  28. Wada E, Yonenobu K, Suzuki S et al (1999) Can intramedullary signal change on magnetic resonance imaging predict surgical outcome in cervical spondylotic myelopathy? Spine (Phila Pa 1976) 24:455–461 (discussion 462)

  29. Park YS, Nakase H, Kawaguchi S et al (2006) Predictors of outcome of surgery for cervical compressive myelopathy: retrospective analysis and prospective study. Neurol Med Chir (Tokyo) 46:231–238 (discussion 238–9)

  30. Suda K, Abumi K, Ito M et al (2003) Local kyphosis reduces surgical outcomes of expansive open-door laminoplasty for cervical spondylotic myelopathy. Spine (Phila Pa 1976) 28:1258–1262. doi:10.1097/01.BRS.0000065487.82469.D9

  31. Suri A, Chabbra RPS, Mehta VS et al (2003) Effect of intramedullary signal changes on the surgical outcome of patients with cervical spondylotic myelopathy. Spine J 3:33–45

    Article  PubMed  Google Scholar 

  32. Kim H-J, Moon S-H, Kim H-S et al (2008) Diabetes and smoking as prognostic factors after cervical laminoplasty. J Bone Joint Surg Br 90:1468–1472. doi:10.1302/0301-620X.90B11.20632

    Article  PubMed  Google Scholar 

  33. Vedantam A, Jonathan A, Rajshekhar V (2011) Association of magnetic resonance imaging signal changes and outcome prediction after surgery for cervical spondylotic myelopathy. J Neurosurg Spine 15:660–666. doi:10.3171/2011.8.SPINE11452

    Article  PubMed  Google Scholar 

  34. Okada Y, Ikata T, Yamada H et al (1993) Magnetic resonance imaging study on the results of surgery for cervical compression myelopathy. Spine (Phila Pa 1976) 18:2024–2029

  35. Wang LF, Zhang YZ, Shen Y et al (2010) Using the T2-weighted magnetic resonance imaging signal intensity ratio and clinical manifestations to assess the prognosis of patients with cervical ossification of the posterior longitudinal ligament. J Neurosurg Spine 13:319–323. doi:10.3171/2010.3.SPINE09887

    Article  PubMed  Google Scholar 

  36. Zhang YZ, Shen Y, Wang LF et al (2010) Magnetic resonance T2 image signal intensity ratio and clinical manifestation predict prognosis after surgical intervention for cervical spondylotic myelopathy. Spine (Phila Pa 1976) 35:E396–E399. doi:10.1097/BRS.0b013e3181c6dbc4

  37. Zhang JT, Meng FT, Wang S et al (2015) Predictors of surgical outcome in cervical spondylotic myelopathy: focusing on the quantitative signal intensity. Eur Spine J 24:2941–2945. doi:10.1007/s00586-015-4109-5

    Article  PubMed  Google Scholar 

  38. DeBoy CA, Zhang J, Dike S et al (2007) High resolution diffusion tensor imaging of axonal damage in focal inflammatory and demyelinating lesions in rat spinal cord. Brain 130:2199–2210. doi:10.1093/brain/awm122

    Article  PubMed  Google Scholar 

  39. Budde MD, Kim JH, Liang HF et al (2007) Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn Reson Med 57:688–695. doi:10.1002/mrm.21200

    Article  PubMed  Google Scholar 

  40. Machino M, Yukawa Y, Ito K et al (2014) Impact of diabetes on the outcomes of cervical laminoplasty: a prospective cohort study of more than 500 patients with cervical spondylotic myelopathy. Spine (Phila Pa 1976) 39:220–227. doi:10.1097/BRS.0000000000000102

  41. Singh A, Crockard HA (2001) Comparison of seven different scales used to quantify severity of cervical spondylotic myelopathy and post-operative improvement. J Outcome Meas 5:798–818

    PubMed  Google Scholar 

  42. Bae YJ, Lee JW, Lee E (2017) Cervical compressive myelopathy: flow analysis of cerebrospinal fluid using phase-contrast magnetic resonance imaging. Eur Spine J 26:40–48. doi:10.1007/s00586-016-4874-9

    Article  PubMed  Google Scholar 

  43. Cadotte A, Cadotte DW, Livne M et al (2015) Spinal cord segmentation by one dimensional normalized template matching: a novel, quantitative technique to analyze advanced magnetic resonance imaging data. PLoS One 10:e0139323. doi:10.1371/journal.pone.0139323

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Dr. Nellie Byun for proofreading the manuscript. Initiative for Accelerating Regulatory Science in Innovative Drug, Medical Device and Regenerative Medicine, the Ministry of Health, Labour and Welfare, National Mutual Insurance Federation of Agricultural Cooperatives, and The General Insurance Association of Japan grant funds supported this work.

Author information

Authors and Affiliations

  1. Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, 1-8-1 Inohana Chuou-ku, Chiba, Chiba, 260-8670, Japan

    Satoshi Maki, Masao Koda, Mitsuhiro Kitamura, Taigo Inada, Koshiro Kamiya, Mitsutoshi Ota, Yasushi Iijima, Junya Saito, Kazuhisa Takahashi & Takeo Furuya

  2. Department of Radiology, Chiba University Hospital, 1-8-1 Inohana Chuou-ku, Chiba, Chiba, 260-0856, Japan

    Yoshitada Masuda, Koji Matsumoto & Masatoshi Kojima

  3. Center for Charged Particle Therapy, National Institute of Radiological Sciences, 4-9-1 Anagawa Inage-ku, Chiba, Chiba, 263-8555, Japan

    Takayuki Obata

  4. Department of Orthopaedic Surgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan

    Masashi Yamazaki

Authors
  1. Satoshi Maki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masao Koda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mitsuhiro Kitamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Taigo Inada
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Koshiro Kamiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mitsutoshi Ota
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yasushi Iijima
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Junya Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yoshitada Masuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Koji Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Masatoshi Kojima
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Takayuki Obata
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Kazuhisa Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Masashi Yamazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Takeo Furuya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satoshi Maki.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest associated with this manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maki, S., Koda, M., Kitamura, M. et al. Diffusion tensor imaging can predict surgical outcomes of patients with cervical compression myelopathy. Eur Spine J 26, 2459–2466 (2017). https://doi.org/10.1007/s00586-017-5191-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00586-017-5191-7

Keywords

Search

Navigation