Meta-analysis of the predictive value of diffusion tensor imaging in cervical spondylotic myelopathy

doi:10.3877/cma.j.issn.2095-123X.2021.06.003

Abstract

Abstract:

Objective

To systematically evaluate the predictive value of the anisotropy parameter (FA) and the apparent diffusion coefficient (ADC) of the common variables of diffusion tensor imaging (DTI) for cervical spondylotic myelopathy (CSM).

Methods

DTI cohort studies of CSM patients and healthy controls were retrieved from PubMed, Cochrane Library, Embase, Web of Science and Wanfang database from 2000 to 2021. The modified Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the included literature. The Meta-analysis of continuous variable data was performed using Stata 14.0 software.

Results

A total of 10 studies were included, including 327 patients with CSM and 208 healthy controls. In the comparison of FA values, there were 282 patients in the CSM group and 199 healthy controls. The Level of maximal cord compression LMC in CSM patients was lower than that in healthy controls, and the results were statistically significant (SMD=-2.32, 95%CI: -2.56--2.06, P<0.001). In the comparison of ADC values, there were 177 patients in the CSM group and 132 in the healthy control group. The LMC in the CSM group was higher than that in the healthy control group, and the results were statistically significant (SMD=1.95, 95%CI: 1.67-2.23, P<0.001).

Conclusion

As DTI parameters, FA value and ADC value have potential ability to predict CSM, and DTI may be an important part in the diagnosis of CSM.

Key words: Cervical spondylotic myelopathy, Diffusion tensor imaging, Apparent diffusion coefficient, Fractional anisotropy, Meta-analysis

Yan Feng, Yandong Fan, Jinze Wu, Nuermaimaiti Mangsuer·, Jumai Pairihati·, Kun Luo. Meta-analysis of the predictive value of diffusion tensor imaging in cervical spondylotic myelopathy[J]. Chinese Journal of Brain Diseases and Rehabilitation(Electronic Edition), 2021, 11(06): 331-335.

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Figures/Tables 7

References 24

[1]	Smith ZA, Khayatzadeh S, Bakhsheshian J, et al. Dimensions of the cervical neural foramen in conditions of spinal deformity: an ex vivo biomechanical investigation using specimen-specific CT imaging[J]. Eur Spine J, 2016, 25(7): 2155-2165.
[2]	Kara B, Celik A, Karadereler S, et al. The role of DTI in early detection of cervical spondylotic myelopathy: a preliminary study with 3-T MRI[J]. Neuroradiology, 2011, 53(8): 609-616.
[3]	Demir A, Ries M, Moonen CT, et al. Diffusion-weighted MR imaging with apparent diffusion coefficient and apparent diffusion tensor maps in cervical spondylotic myelopathy[J]. Radiology, 2003, 229(1): 37-43.
[4]	Vedantam A, Jonathan A, Rajshekhar V. Association of magnetic resonance imaging signal changes and outcome prediction after surgery for cervical spondylotic myelopathy[J]. J Neurosurg Spine, 2011, 15(6): 660-666.
[5]	Ellingson BM, Salamon N, Holly LT. Advances in MR imaging for cervical spondylotic myelopathy[J]. Eur Spine J, 2015, 24 Suppl 2(Suppl 2): 197-208.
[6]	Banaszek A, Bladowska J, Podgórski P, et al. Role of diffusion tensor MR imaging in degenerative cervical spine disease: a review of the literature[J]. Clin Neuroradiol, 2016, 26(3): 265-276.
[7]	Wang K, Chen Z, Zhang F, et al. Evaluation of DTI parameter ratios and diffusion tensor tractography grading in the diagnosis and prognosis prediction of cervical spondylotic myelopathy[J]. Spine (Phila Pa 1976), 2017, 42(4): E202-E210.
[8]	Jiang W, Han X, Guo H, et al. Usefulness of conventional magnetic resonance imaging, diffusion tensor imaging and neurite orientation dispersion and density imaging in evaluating postoperative function in patients with cervical spondylotic myelopathy[J]. J Orthop Translat, 2018, 15: 59-69.
[9]	Cui JL, Li X, Chan TY, et al. Quantitative assessment of column-specific degeneration in cervical spondylotic myelopathy based on diffusion tensor tractography[J]. Eur Spine J, 2015, 24(1): 41-47.
[10]	Chen X, Kong C, Feng S, et al. Magnetic resonance diffusion tensor imaging of cervical spinal cord and lumbosacral enlargement in patients with cervical spondylotic myelopathy[J]. J Magn Reson Imaging, 2016, 43(6): 1484-1491.
[11]	Toktas ZO, Tanrıkulu B, Koban O, et al. Diffusion tensor imaging of cervical spinal cord: a quantitative diagnostic tool in cervical spondylotic myelopathy[J]. J Craniovertebr Junction Spine, 2016, 7(1): 26-30.
[12]	Ying J, Zhou X, Zhu M, et al. The contribution of diffusion tensor imaging to quantitative assessment on multilevel cervical spondylotic myelopathy[J]. Eur Neurol, 2016, 75(1-2): 67-74.
[13]	Liu Y, Kong C, Cui L, et al. Correlation between diffusion tensor imaging parameters and clinical assessments in patients with cervical spondylotic myelopathy with and without high signal intensity[J]. Spinal Cord, 2017, 55(12): 1079-1083.
[14]	Rao A, Soliman H, Kaushal M, et al. Diffusion tensor imaging in a large longitudinal series of patients with cervical spondylotic myelopathy correlated with long-term functional outcome[J]. Neurosurgery, 2018, 83(4): 753-760.
[15]	Iwasaki M, Yokohama T, Oura D, et al. Decreased value of highly accurate fractional anisotropy using 3-Tesla ZOOM diffusion tensor imaging after decompressive surgery in patients with cervical spondylotic myelopathy: aligned fibers effect[J]. World Neurosurg X, 2019, 4: 100056.
[16]	Han X, Ma X, Li D, et al. The evaluation and prediction of laminoplasty surgery outcome in patients with degenerative cervical myelopathy using diffusion tensor MRI[J]. AJNR Am J Neuroradiol, 2020, 41(9): 1745-1753.
[17]	Wu W, Yang Z, Zhang T, et al. Microstructural changes in compressed cervical spinal cord are consistent with clinical symptoms and symptom duration[J]. Spine (Phila Pa 1976), 2020, 45(16): E999-E1005.
[18]	Ulubaba HE, Saglik S, Yildirim IO, et al. Effectiveness of diffusion tensor imaging in determining cervical spondylotic myelopathy[J]. Turk Neurosurg, 2021, 31(1): 67-72.
[19]	Vedantam A, Rao A, Kurpad SN, et al. Diffusion tensor imaging correlates with short-term myelopathy outcome in patients with cervical spondylotic myelopathy[J]. World Neurosurg, 2017, 97: 489-494.
[20]	Facon D, Ozanne A, Fillard P, et al. MR diffusion tensor imaging and fiber tracking in spinal cord compression[J]. AJNR Am J Neuroradiol, 2005, 26(6): 1587-1594.
[21]	Rajasekaran S, Yerramshetty JS, Chittode VS, et al. The assessment of neuronal status in normal and cervical spondylotic myelopathy using diffusion tensor imaging[J]. Spine (Phila Pa 1976), 2014, 39(15): 1183-1189.
[22]	Rajasekaran S, Prasad V, Shetty J, et al. Diffusion tensor imaging as a prognostic investigation in cervical myelopathy-comparison of pre- and postsurgical decompression DTI indices and tractography in 56 subjects[J]. Global Spine J, 2012, 2(1-suppl): 137-157.
[23]	Mamata H, Jolesz FA, Maier SE. Apparent diffusion coefficient and fractional anisotropy in spinal cord: age and cervical spondylosis-related changes[J]. J Magn Reson Imaging, 2005, 22(1): 38-43.
[24]	Wang SQ, Li X, Cui JL, et al. Prediction of myelopathic level in cervical spondylotic myelopathy using diffusion tensor imaging[J]. J Magn Reson Imaging, 2015, 41(6): 1682-1688.

研究	发表时间	国家	研究类型	研究人数		平均年龄（岁）		结局指标	临床评估
研究	发表时间	国家	研究类型	CSM	CG	CSM	CG	结局指标	临床评估
Cui等^[9]	2015	中国	队列研究	23	20	59.0	46.0	FA	JOA
Chen等^[10]	2015	中国	队列研究	10	10	53.4	49.3	FA/ADC	JOA
Ying等^[12]	2016	中国	队列研究	32	21	54.3	53.6	FA/ADC	JOA
Toktas等^[11]	2016	土耳其	队列研究	21	21	53.8	69.0	FA/ADC	JOA
Liu等^[13]	2017	中国	队列研究	40	42	55.6	51.2	FA/ADC	JOA
Rao等^[14]	2018	美国	队列研究	44	24	53.9	52.3	FA	JOA
Iwasaki等^[15]	2019	日本	队列研究	28	12	71.3	73.8	FA	JOA
Han等^[16]	2020	中国	队列研究	55	20	58.6	57.2	FA	JOA
Wu等^[17]	2020	中国	队列研究	29	29	47.5	45.5	FA/ADC	JOA
Ulubaba等^[18]	2021	土耳其	队列研究	45	9	-	-	FA/ADC	JOA

研究	发表时间	国家	研究类型	研究人数		平均年龄（岁）		结局指标	临床评估
研究	发表时间	国家	研究类型	CSM	CG	CSM	CG	结局指标	临床评估
Cui等^[9]	2015	中国	队列研究	23	20	59.0	46.0	FA	JOA
Chen等^[10]	2015	中国	队列研究	10	10	53.4	49.3	FA/ADC	JOA
Ying等^[12]	2016	中国	队列研究	32	21	54.3	53.6	FA/ADC	JOA
Toktas等^[11]	2016	土耳其	队列研究	21	21	53.8	69.0	FA/ADC	JOA
Liu等^[13]	2017	中国	队列研究	40	42	55.6	51.2	FA/ADC	JOA
Rao等^[14]	2018	美国	队列研究	44	24	53.9	52.3	FA	JOA
Iwasaki等^[15]	2019	日本	队列研究	28	12	71.3	73.8	FA	JOA
Han等^[16]	2020	中国	队列研究	55	20	58.6	57.2	FA	JOA
Wu等^[17]	2020	中国	队列研究	29	29	47.5	45.5	FA/ADC	JOA
Ulubaba等^[18]	2021	土耳其	队列研究	45	9	-	-	FA/ADC	JOA

研究	选择				基于设计或分析所得的队列可比性	结局			总分
研究	暴露队列的代表性	非暴露队列的代表性	暴露的确定	研究开始前没有受试者发生结局事件	基于设计或分析所得的队列可比性	结局事件的评估	随访是否充分	随访完整性	总分
Cui等^[9]	1	1	1	1	1	1	1	1	8
Chen等^[10]	1	1	1	1	2	1	1	1	9
Ying等^[12]	1	1	1	1	1	1	1	1	8
Toktas等^[11]	1	0	1	1	1	1	1	1	7
Liu等^[13]	1	1	1	1	1	1	1	1	8
Rao等^[14]	1	1	1	1	2	1	1	1	9
Iwasaki等^[15]	1	0	1	1	2	1	1	1	8
Han等^[16]	1	1	1	1	1	1	1	1	8
Wu等^[17]	1	0	1	1	1	1	1	1	7
Ulubaba等^[18]	1	1	1	1	2	1	1	1	9

研究	选择				基于设计或分析所得的队列可比性	结局			总分
研究	暴露队列的代表性	非暴露队列的代表性	暴露的确定	研究开始前没有受试者发生结局事件	基于设计或分析所得的队列可比性	结局事件的评估	随访是否充分	随访完整性	总分
Cui等^[9]	1	1	1	1	1	1	1	1	8
Chen等^[10]	1	1	1	1	2	1	1	1	9
Ying等^[12]	1	1	1	1	1	1	1	1	8
Toktas等^[11]	1	0	1	1	1	1	1	1	7
Liu等^[13]	1	1	1	1	1	1	1	1	8
Rao等^[14]	1	1	1	1	2	1	1	1	9
Iwasaki等^[15]	1	0	1	1	2	1	1	1	8
Han等^[16]	1	1	1	1	1	1	1	1	8
Wu等^[17]	1	0	1	1	1	1	1	1	7
Ulubaba等^[18]	1	1	1	1	2	1	1	1	9

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