Diffusion Tensor Imaging in a Large Longitudinal Series of Patients With Cervical Spondylotic Myelopathy Correlated With Long-Term Functional Outcome

Rao, A. Muralikrishna; Soliman, Hesham; Kaushal, Mayank; Motovylyak, Olesya; Vedantam, Aditya; Budde, Matthew D.; Schmit, Brian D.; Wang, Marjorie C.; Kurpad, Shekar N.

doi:10.1093/neuros/nyx558

Cited by 27 publications

(32 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, few studies have assessed the combination of electrophysiology in conjunction with the commonly used clinical and advanced MRI parameters. While our study did not support some of the findings of previous authors that remain supported with low evidence, our findings with regards to FA are in accordance with others [34,45,46] and strongly support the effectiveness of DTI analysis in both assessing the clinical status and predicting the surgical outcome in DCM patients.…”

Section: Limitationscontrasting

confidence: 54%

“…New MRI techniques, such as the DTI, in combination with neurophysiological assessment can help identify those patients with higher probabilities of improving after surgical decompression [22]. Similarly to previous research [45], FA in "best responders" were significantly higher than those of the "normal responders", both preoperatively and 1-year follow-up. Moreover, there was also a statistical difference in the FA values of the most stenotic level in all the phases of the study between the two groups, and a significant relationship between a preoperative FA > 0.55 at the most compressed level and a better clinical outcome (RR > 50%) at 1-year.…”

Section: Discussionmentioning

confidence: 51%

See 1 more Smart Citation

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

Severino

Nouri

Tessitore

2020

JCM

View full text Add to dashboard Cite

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.

show abstract

Section: Limitationscontrasting

confidence: 54%

Section: Discussionmentioning

confidence: 51%

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

Severino

Nouri

Tessitore

2020

JCM

View full text Add to dashboard Cite

show abstract

“…This study is subject to some limitations. Although the sample size is comparable with those of most previous studies, it was still rather small for an AI study, especially for the deep learning model. Therefore, it was quite difficult to separate an independent testing set for the evaluation, and cross‐validation was used as a replacement.…”

Section: Discussionmentioning

confidence: 60%

“…Currently, the function of the existing prognostic indicators used in clinical practice is under debate, and the DTI technique is believed to achieve a better prognostic result for CM because of its ability to reflect the integrity of the spinal cord according to microstructural changes in the de‐ and remyelination process . Currently, DTI analysis for prognosis of CM is mostly performed via individual analysis of the whole‐cord‐based features at the MCCL . However, such features do not provide a comprehensive description of the status of the spinal cord, especially when they are individually analyzed.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Jin

Luk

2019

NMR in Biomedicine

View full text Add to dashboard Cite

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,

show abstract

The use of diffusion tensor imaging in spinal pathology: a comprehensive literature review

Scullen,

Milburn,

Aria

et al. 2024

Eur Spine J

View full text Add to dashboard Cite

Study design We reviewed the available literature systematically without meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Objective To evaluate contemporary literature on use of spinal diffusion tensor imaging(sDTI) in spinal pathology. Background sDTI reveals the location and functional state of critical long tracts and is a potentially useful adjunct in disease management. Methods Studies were included if they presented or discussed data from investigative or therapeutic procedures involving sDTI on human subjects in the setting of surgically amenable spinal pathology. Studies were excluded if they were (1) restricted to computational models investigating parameters using data not obtained clinically, (2) about cranial DTI methods, (3) about spinal pathology data not related to surgical management, (4) discussions or overviews of methods/techniques with minimal inclusion of objective experimental or clinical data. Results Degenerative pathologies of interest were restricted to either cervical myelopathy (22/29,75.9%) or lumbar spondylosis 7/29,24.1%). Mass-occupying lesions included intradural pathology and discussed preoperative (7/9,77.8%) and intraoperative imaging(2/9,22.2%) as an adjunct to surgery 22.2%. Traumatic pathology focused on spinal cord injury prognosis and severity grading. Conclusions sDTI seems useful in surgical decision making and outcome measurements and in establishing clinical prognoses over a wide range of surgical pathologies. Further research is warranted with longer follow-up and larger population sizes in a prospective and controlled protocol.

show abstract

Diffusion Tensor Imaging in a Large Longitudinal Series of Patients With Cervical Spondylotic Myelopathy Correlated With Long-Term Functional Outcome

Abstract: In the largest longitudinal study of this kind, FA promises a valid biomarker for severity of CSM and postoperative improvement. FA is an objective measure of function and could provide a basis for prognosis. FA is particularly useful if preoperative values are less than 0.55.

Cited by 27 publications

References 42 publications

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

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

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

The use of diffusion tensor imaging in spinal pathology: a comprehensive literature review

Contact Info

Product

Resources

About