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

Maki, Satoshi; Koda, Masao; Kitamura, Mitsuhiro; Inada, Taigo; Kamiya, Koshiro; M, Ota; Iijima, Yasushi; Saito, Junya; Masuda, Yoshitada; Matsumoto, Koji; Kojima, Masatoshi; Obata, Takayuki; Takahashi, Kazuhisa; Yamazaki, Masashi; Furuya, Takeo

doi:10.1007/s00586-017-5191-7

Cited by 30 publications

(33 citation statements)

References 41 publications

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“…Furthermore, multilevel compression was also not predictive of a poorer neurological outcome. While, these results are consistent with some studies found in literature [8,[37][38][39][40], other studies, notably those derived from the AOSpine multicenter studies on DCM, have shown contradictory results [11,41]. Our results may partially be due to our relatively small cohort and may have been underpowered to detect statistically significant differences.…”

Section: Discussionsupporting

confidence: 91%

“…This is consistent with the concept that FA can be used in the assessment of the degree of severity of DCM with greater accuracy. [12][13][14]34,43] Our results support what has been previously suggested-higher values of preoperative FA can be considered as a positive prognostic factor of functional recovery [13,34,37]. It was interesting to note that, FA values were not different between best and normal responders at 3-months, and this potentially indicates a 3-month FA MRI may be too early to detect changes.…”

Section: Discussionsupporting

confidence: 87%

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Degenerative Cervical Myelopathy: How to Identify the Best Responders to Surgery?

Severino

Nouri

Tessitore

2020

JCM

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Surgery is the only definitive treatment for degenerative cervical myelopathy (DCM), however, the degree of neurological recovery is often unpredictable. Here, we assess the utility of a multidimensional diagnostic approach, consisting of clinical, neurophysiological, and radiological parameters, to identify patients likely to benefit most from surgery. Thirty-six consecutive patients were prospectively analyzed using the modified Japanese Orthopedic Association (mJOA) score, MEPs/SSEPs and advance and conventional MRI parameters, at baseline, and 3- and 12-month postoperatively. Patients were subdivided into “normal” and “best” responders (<50%, ≥50% improvement in mJOA), and correlation between Diffusion Tensor Imaging (DTI) parameters, mJOA, and MEP/SSEP latencies were examined. Twenty patients were “best” responders and 16 were “normal responders”, but there were no statistical differences in age, T2 hyperintensity, and midsagittal diameter between them. There was a significant inverse correlation between the MEPs central conduction time and mJOA in the preoperative period (p = 0.0004), and a positive correlation between fractional anisotropy (FA) and mJOA during all the phases of the study, and statistically significant at 1-year (r = 0.66, p = 0.0005). FA was significantly higher amongst “best responders” compared to “normal responders” preoperatively and at 1-year (p = 0.02 and p = 0.009). A preoperative FA > 0.55 was predictor of a better postoperative outcome. Overall, these results support the concept of a multidisciplinary approach in the assessment and management of DCM.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 87%

Degenerative Cervical Myelopathy: How to Identify the Best Responders to Surgery?

Severino

Nouri

Tessitore

2020

JCM

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“…We included 7 articles studying the correlation between preoperative DTI metrics (FA) and postoperative mJOA scale, reported from 2013 to 2019. Two articles 9,14 claimed a nonsignificant correlation, while the rest 5,[15][16][17][18] claimed a significant correlation. Note, Shabani et al 18 showed a negative correlation between preoperative FA and the postoperative DmJOA (difference between post-and preoperative mJOA), while the other studies showed a positive correlation.…”

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confidence: 96%

The Evaluation and Prediction of Laminoplasty Surgery Outcome in Patients with Degenerative Cervical Myelopathy Using Diffusion Tensor MRI

Han

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: DTI has been proved valuable for the diagnosis of degenerative cervical myelopathy, whereas its capacity for predicting the outcome of surgery is still under debate. Here we conduct a prospective cohort study to analyze the capacity of DTI for evaluating and predicting laminoplasty surgery outcome for degenerative cervical myelopathy. MATERIALS AND METHODS: We recruited 55 patients with degenerative cervical myelopathy who underwent DTI before surgery and at 3-and 6-month follow-up stages, and 20 healthy subjects. For clinical assessment, the modified Japanese Orthopedic Association scale was recorded for each patient at different stages. DTI metrics were compared between patients before surgery and healthy subjects. Spearman correlation and receiver operating characteristic were used to analyze the evaluation and prediction capacity of DTI for the modified Japanese Orthopedic Association scale, respectively. We analyzed different vertebral levels: maximal compression level, average of all compression levels, and C2 level. RESULTS: DTI metrics were significantly different between patients before surgery and healthy subjects. Before surgery, DTI for the maximal compression level or DTI for the average of all compression levels had no significant correlation with the modified Japanese Orthopedic Association scale. For all stages, DTI at the C2 level was correlated with the modified Japanese Orthopedic Association scale. DTI metrics at the C2 level before surgery were significantly correlated with the postoperative modified Japanese Orthopedic Association scale recovery rate. Receiver operating characteristic analysis demonstrated that fractional anisotropy at C2 was capable of predicting the postoperative modified Japanese Orthopedic Association scale recovery rate (P ¼ .04). CONCLUSIONS: The DTI metrics before laminoplasty surgery, especially fractional anisotropy at the C2 level, have the potential for evaluating and predicting the degenerative cervical myelopathy surgery outcome. ABBREVIATIONS: AC ¼ all compression; AD ¼ axial diffusivity; DCM ¼ degenerative cervical myelopathy; FA ¼ fractional anisotropy; MC ¼ maximal compression; MD ¼ mean diffusivity; mJOA ¼ modified Japanese Orthopedic Association scale; RD ¼ radial diffusivity; ROC ¼ receiver operating characteristic

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“…Diffusion tensor imaging (DTI) has been demonstrated to have prognostic value for CM . In CM, demyelination is believed to be the main pathological change in the spinal cord, and this demyelination changes the organization of nerve fiber bundles, resulting in changes to DTI‐related signals.…”

Section: Introductionmentioning

confidence: 99%

Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods

Jin

Luk

2019

NMR in Biomedicine

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Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy-five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one-year follow-up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k-nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF-SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10-fold cross-validations. The accuracy of the classifications reached 74.2% ± 1.6% for LR, 85.6% ± 1.4% for KNN, 89.7% ± 1.6% for RBF-SVM, and 59.2% ± 3.8% for the deep leaning model. The RBF-SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% ± 3.4% and 92.4% ± 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM. KEYWORDS artificial intelligence (AI), cervical myelopathy (CM), diffusion tensor imaging (DTI), prognosis 1 | INTRODUCTION Cervical myelopathy (CM) is a common spinal cord dysfunction that affects millions of people worldwide. 1 Currently, surgical intervention is considered to be the most immediate way to provide relief from the spinal cord compression and to promote neurological recovery when coupled with active post-operative neural rehabilitation. 2,3 An accurate prognosis for the surgery would provide useful assistance to surgeons, helping them to decide on the most appropriate treatment option, manage patient expectations, and plan postoperative rehabilitation. 4 Several prognosticators of surgical outcomes have already been proposed. 5,6 However, the value of these prognosticators remains controversial, and researchers continue to search for more effective prognostic methods.Diffusion tensor imaging (DTI) has been demonstrated to have prognostic value for CM. [7][8][9] In CM, demyelination is believed to be the main pathological change in the spinal cord, and this demyelination changes the organization of nerve fiber bundles, resulting in changes to DTI-related signals. Based on this principle, researchers have used DTI to study CM. Also, it has been reported that DTI has great potential for distinguishing CM patients from healthy subjects and for predicting the exact cervical levels causing symptoms. [10][11][12] The pathological status of the spinal cord,

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

Abstract: 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.

Cited by 30 publications

References 41 publications

Degenerative Cervical Myelopathy: How to Identify the Best Responders to Surgery?

Degenerative Cervical Myelopathy: How to Identify the Best Responders to Surgery?

The Evaluation and Prediction of Laminoplasty Surgery Outcome in Patients with Degenerative Cervical Myelopathy Using Diffusion Tensor MRI

Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods

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