Magnetic Resonance Imaging Atlas-Based Volumetric Mapping of the Cervical Cord Gray Matter in Cervical Canal Stenosis

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

BACKGROUND CONTEXT

Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Degeneration of spinal discs, bony osteophyte growth and ligament pathology results in physical compression of the spinal cord contributing to damage of white matter tracts and grey matter cellular populations. This results in an insidious neurological and functional decline in patients which can lead to paralysis. Magnetic resonance imaging (MRI) confirms the diagnosis of DCM and is a prerequisite to surgical intervention, the only known treatment for this disorder. Unfortunately, there is a weak correlation between features of current commonly acquired MRI scans ("community MRI, cMRI") and the degree of disability experienced by a patient.

PURPOSE

This study examines the predictive ability of current MRI sequences relative to "advanced MRI" (aMRI) metrics designed to detect evidence of spinal cord injury secondary to degenerative myelopathy. We hypothesize that the utilization of higher fidelity aMRI scans will increase the effectiveness of machine learning models predicting DCM severity and may ultimately lead to a more efficient protocol for identifying patients in need of surgical intervention.

STUDY DESIGN/SETTING

Single institution analysis of imaging registry of patients with DCM.

PATIENT SAMPLE

A total of 296 patients in the cMRI group and 228 patients in the aMRI group.

OUTCOME MEASURES

Physiologic measures: accuracy of machine learning algorithms to detect severity of DCM assessed clinically based on the modified Japanese Orthopedic Association (mJOA) scale.

METHODS

Patients enrolled in the Canadian Spine Outcomes Research Network registry with DCM were screened and 296 cervical spine MRIs acquired in cMRI were compared with 228 aMRI acquisitions. aMRI acquisitions consisted of diffusion tensor imaging, magnetization transfer, T2-weighted, and T2*-weighted images. The cMRI group consisted of only T2-weighted MRI scans. Various machine learning models were applied to both MRI groups to assess accuracy of prediction of baseline disease severity assessed clinically using the mJOA scale for cervical myelopathy.

RESULTS

Through the utilization of Random Forest Classifiers, disease severity was predicted with 41.8% accuracy in cMRI scans and 73.3% in the aMRI scans. Across different predictive model variations tested, the aMRI scans consistently produced higher prediction accuracies compared to the cMRI counterparts.

CONCLUSIONS

aMRI metrics perform better in machine learning models at predicting disease severity of patients with DCM. Continued work is needed to refine these models and address DCM severity class imbalance concerns, ultimately improving model confidence for clinical implementation.

Utility of MRI in Quantifying Tissue Injury in Cervical Spondylotic Myelopathy

Cervical spondylotic myelopathy (CSM) is a progressive disease that worsens over time if untreated. However, the rate of progression can vary among individuals and may be influenced by various factors, such as the age of the patients, underlying conditions, and the severity and location of the spinal cord compression. Early diagnosis and prompt treatment can help slow the progression of CSM and improve symptoms. There has been an increased use of magnetic resonance imaging (MRI) methods in diagnosing and managing CSM. MRI methods provide detailed images and quantitative structural and functional data of the cervical spinal cord and brain, allowing for an accurate evaluation of the extent and location of tissue injury. This review aims to provide an understanding of the use of MRI methods in interrogating functional and structural changes in the central nervous system in CSM. Further, we identified several challenges hindering the clinical utility of these neuroimaging methods.

Reduced Spinal Cord Gray Matter in Patients with Fibromyalgia Using Opioids Long-term

Objective

Chronic pain involves alterations in brain gray matter volume (GMV). Moreover, opioid medications are known to reduce GMV in numerous brain regions involved in pain processing. However, no research has evaluated (1) chronic pain-related GMV alterations in the spinal cord or (2) the effect of opioids on spinal cord GMV. Accordingly, this study evaluated spinal cord GMV in health controls and patients with fibromyalgia who were using and not using opioids long-term.

Methods

We analyzed average C5 - C7 GMV of the spinal cord dorsal and ventral horns in separate female cohorts of healthy controls (HC, n = 30), fibromyalgia patients not using opioids (FMN, n = 31), and fibromyalgia patients using opioids long-term (FMO, n = 27). To assess the effect of group on average dorsal and ventral horn GMV, we conducted a one-way multivariate analysis of covariance.

Results

After controlling for age, we observed a significant effect of group on ventral horn GMV (p = 0.03, η2 = 0.09), and on dorsal horn GMV (p = 0.05, η2 = 0.08). Tukey's posthoc comparisons showed that, compared to HC participants, FMOs had significantly lower ventral (p = 0.01) and dorsal (p = 0.02) GMVs. Among FMOs only, ventral horn GMV was significantly positively associated with pain severity and interference, and both dorsal and ventral GMVs were significantly positively associated with cold pain tolerance.

Conclusion

Long-term opioid use may impact sensory processing in fibromyalgia via gray matter changes within the cervical spinal cord.

Using the cobweb classification system as a digital location system for the neurologic compression in cervical degenerative disease

OBJECTIVE

To provide the cobweb classification system (CCS) for the precise digital location and description of the neurological compression in cervical degenerative disease (CDD), and the reliability and the clinical subgroup analysis of the system were tested and analyzed.

METHODS

The CCS consisted of three parts: compression zones (1-12), degrees (a, b) and ossification (s, m, h). Computerized tomography (CT) and magnetic resonance imaging (MRI) images from 238 CDD patients were reviewed. All compression cases were classified by five independent reviewers with varied clinical experience in spine surgery. The reliability of the CCS was tested by calculating the kappa (κ) statistics value. Finally, 74 patients with anterior cervical surgery treatment were enrolled for the clinical subgroup analysis.

RESULTS

For the small compression, including single and double compression zones, there was a good interobserver reliability between the reviewers (κ coefficient = .855, P < .001). For the large compression with three or more involved zones, there was a fair reliability between the reviewers (κ coefficient = .696, P < .001). The whole intraobserver reliability was good (κ coefficient = .923, P < .001). For clinical practice, the operative time in the large compression and the m/h group was significantly longer than the small compression and the s group, respectively (P < .05), and the blood loss in the m/h group was significantly increased as well (P < .01). Though the preoperative Japanese Orthopedic Association score in Group b was lower than Group a (P < .05), all patients had achieved significant clinical improvement at last follow-up.

CONCLUSIONS

The CCS can be used to provide detailed and objective descriptions of the location, extent, and severity of neurological compressions in CDD with satisfactory reliability. Surgeons should pay more attention to the patient with large zone, degree b, and ossification compression, because the operation may be more challenging.

Fatty infiltration in cervical flexors and extensors in patients with degenerative cervical myelopathy using a multi-muscle segmentation model

Background

In patients with degenerative cervical myelopathy (DCM) that have spinal cord compression and sensorimotor deficits, surgical decompression is often performed. However, there is heterogeneity in clinical presentation and post-surgical functional recovery.

Objectives

Primary: a) to assess differences in muscle fat infiltration (MFI) in patients with DCM versus controls, b) to assess association between MFI and clinical disability. Secondary: to assess association between MFI pre-surgery and post-surgical functional recovery.

Study design

Cross-sectional case control study.

Methods

Eighteen patients with DCM (58.6 ± 14.2 years, 10 M/8F) and 25 controls (52.6 ± 11.8 years, 13M/12 F) underwent 3D Dixon fat-water imaging. A convolutional neural network (CNN) was used to segment cervical muscles (MFSS- multifidus and semispinalis cervicis, LC- longus capitis/colli) and quantify MFI. Modified Japanese Orthopedic Association (mJOA) and Nurick were collected.

Results

Patients with DCM had significantly higher MFI in MFSS (20.63 ± 5.43 vs 17.04 ± 5.24, p = 0.043) and LC (18.74 ± 6.7 vs 13.66 ± 4.91, p = 0.021) than controls. Patients with increased MFI in LC and MFSS had higher disability (LC: Nurick (Spearman’s ρ = 0.436, p = 0.003) and mJOA (ρ = -0.399, p = 0.008)). Increased MFI in LC pre-surgery was associated with post-surgical improvement in Nurick (ρ = -0.664, p = 0.026) and mJOA (ρ = -0.603, p = 0.049).

Conclusion

In DCM, increased muscle adiposity is significantly associated with sensorimotor deficits, clinical disability, and functional recovery after surgery. Accurate and time efficient evaluation of fat infiltration in cervical muscles may be conducted through implementation of CNN models.

Magnetization Transfer Ratio and Morphometrics of the Spinal Cord Associates with Surgical Recovery in Patients with Degenerative Cervical Myelopathy

OBJECTIVES

We assessed the prognostic value of the preoperative magnetization transfer ratio (MTR) and morphometrics of the spinal cord in patients with degenerative cervical myelopathy (DCM) in a longitudinal cohort study.

METHODS

Thirteen subjects with DCM underwent 3T magnetization transfer imaging. The MTR was calculated for the spinal cord regions and specific white matter tracts. Morphometric measures were extracted. Clinical (modified Japanese Orthopaedics Association [mJOA] and Nurick scale scores) and health-related quality of life scores were assessed before and after cervical decompression surgery. The association between the magnetic resonance imaging (MRI) metrics and postoperative recovery was assessed (Spearman's correlation). Receiver operating characteristics were used to assess the accuracy of MRI metrics in identifying ≥50% recovery in function.

RESULTS

Preoperative anterior cord MTRs were associated with recovery in mJOA scores (ρ = 0.608; P = 0.036; area under the curve [AUC], 0.66). Preoperative lateral cord MTR correlated with the neck disability index (ρ = 0.699; P = 0.011) and pain interference scale (ρ = 0.732; P = 0.007). Preoperative rubrospinal tract MTR was associated with mJOA score recovery (ρ = 0.573; P = 0.041; AUC, 0.86). Preoperative corticospinal tract and reticulospinal MTRs were related to recovery in pain interference scores (ρ = 0.591; P = 0.033; and ρ = 0.583; P = 0.035, respectively). Eccentricity of the cord was associated with Nurick scores (ρ = 0.606; P = 0.028) and mJOA scores (ρ = 0.651; P = 0.025; AUC, 0.92).

CONCLUSIONS

Preoperative MTR and eccentricity measurements of the spinal cord have prognostic value in assessing the response to surgery and recovery in patients with DCM. Advanced MRI and atlas-based postprocessing techniques can inform interventions and advance the healthcare received by patients with DCM.