Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools

A Systematic Review of Current Terminology for Conditions Preceding Degenerative Cervical Myelopathy: Evidence Synthesis to Inform an AO Spine Expert Opinion Statement

Study DesignSystematic review.ObjectivesThe pre-symptomatic state of Degenerative Cervical Myelopathy (DCM), wherein degenerative changes and spinal cord compression are seen without clinical findings, is poorly understood and inconsistently categorised. Clear identification may elucidate the temporality of DCM development. Therefore, a systematic assessment was undertaken of current terminology for pre-DCM states, with the objective of standardising definitions and informing an AO Spine expert position statement.MethodsMedline and Embase were searched for all studies on asymptomatic spinal compression or clinical findings preceding DCM, returning 3585 studies. After screening, 96 studies were included in the final analysis. The terminology used for pre-DCM states and their definitions were extracted, along with their frequencies or speciality/country of author in the literature.ResultsMultiple terms were used to represent pre-DCM stages, including "asymptomatic" (86 studies), "non-myelopathic" (26 studies), "without myelopathy" (15 studies), "pre-symptomatic" (9 studies) and "sub-clinical" (7 studies). "asymptomatic" was associated with the greatest inconsistency. Some defined this as patients with radiological signs of spinal degeneration with/without spinal cord compression but no clinical signs of myelopathy, whereas others used the term synonymously with healthy controls. This inconsistency is particularly challenging in clinical studies in which DCM patients are compared to those with pre-DCM states and/or healthy volunteers.ConclusionThere is substantial inconsistency in the terms used to describe pre-DCM states. There is no clear relationship between the terms used and the country or speciality of the main author. Standardised definitions for these disease states should be agreed and used in future studies.

Spinal Cord Tract Integrity in Degenerative Cervical Myelopathy

BACKGROUND AND OBJECTIVES

Degenerative cervical myelopathy (DCM) is the most common cause of spinal dysfunction globally. Despite surgical intervention, motor dysfunction may persist in many patients. The purpose of this study was to comprehensively examine specific spinal cord tract changes in patients with DCM, to better understand potential substrates for compensatory recovery of function.

METHODS

Cervical spinal cord MRI scans with diffusion tensor imaging were performed in patients with DCM and in healthy volunteers. Spinal Cord Toolbox was used to register the PAM50 template, which includes a probabilistic atlas of the white matter tracts of the spinal cord, to the imaging data. Fractional anisotropy (FA) was extracted for each tract at C3 above the level of maximal compression and compared between patients with DCM and healthy volunteers and between patients with mild vs moderate to severe DCM.

RESULTS

We included 25 patients with DCM (13 mild and 12 moderate to severe) and 6 healthy volunteers. FA was significantly reduced in DCM subjects relative to healthy volunteers for the lateral corticospinal tract (mild DCM vs healthy ∆ = -0.13, P = .018; moderate to severe DCM vs healthy ∆ = -0.11, P = .047), fasciculus gracilis (mild DCM vs healthy ∆ = -0.16, P = .010; moderate to severe DCM vs healthy ∆ = -0.13, P = .039), and fasciculus cuneatus (mild DCM vs healthy ∆ = -0.16, P = .007; moderate to severe DCM vs healthy ∆ = -0.15, P = .012). There were no differences in FA for all tracts between mild and moderate-to-severe DCM subjects.

CONCLUSION

Patients with DCM had altered diffusion tensor imaging signal in their lateral corticospinal tract, fasciculus gracilis, and fasciculus cuneatus in comparison with healthy volunteers. These findings indicate that DCM is characterized by injury to these structures, which suggests that other tracts within the cord could potentially act as substrates for compensatory motor recovery.

Is the type and/or co-existence of degenerative spinal pathology associated with the occurrence of degenerative cervical myelopathy? A single centre retrospective analysis of individuals with MRI defined cervical cord compression

BACKGROUND

Degenerative cervical myelopathy (DCM) arises from spinal degenerative changes injuring the cervical spinal cord. Most cord compression is incidental, referred to as asymptomatic spinal cord compression (ASCC). How and why ASCC differs from DCM is poorly understood. In this paper, we study a local cohort to identify specific types and groups of degenerative pathology more likely associated with DCM than ASCC.

METHODS

This study was a retrospective cohort analysis (IRB Approval ID: PRN10455). The frequency of degenerative findings between those with ASCC and DCM patients were compared using network analysis, hierarchical clustering, and comparison to existing literature to identify potential subgroups in a local cohort (N = 155) with MRI-defined cervical spinal cord compression. Quantitative measures of spinal cord compression (MSCC and MCC) were used to confirm their relevance.

RESULTS

ELF (8.7 %, 95 % CI 3.8-13.6 % vs 35.7 %, 95 % CI 27.4-44.0 %) Congenital Stenosis (3.9 %, 95 % CI 0.6-7.3 % vs 25.0 %, 95 % CI 17.5-32.5 %), and OPLL (0.0 %, 95 % CI 0.0-0.0 % vs 3.6 %, 95 % CI 0.3-6.8 %) were more likely in patients with DCM. Comparative network analysis indicated loss of lordosis was associated with ASCC, whilst ELF with DCM. Hierarchical Cluster Analysis indicated four sub-groups: multi-level disc disease with ELF, single-level disc disease without loss of lordosis and OPLL with DCM, and single-level disc disease with loss of lordosis with ASCC. Quantitative measures of cord compression were higher in groups associated with DCM, but similar in patients with single-level disc disease and loss of lordosis.

CONCLUSIONS

This study identified four subgroups based on degenerative pathology requiring further investigation.

Magnetic resonance image segmentation of the compressed spinal cord in patients with degenerative cervical myelopathy using convolutional neural networks

PURPOSE

Spinal cord segmentation is the first step in atlas-based spinal cord image analysis, but segmentation of compressed spinal cords from patients with degenerative cervical myelopathy is challenging. We applied convolutional neural network models to segment the spinal cord from T2-weighted axial magnetic resonance images of DCM patients. Furthermore, we assessed the correlation between the cross-sectional area segmented by this network and the neurological symptoms of the patients.

METHODS

The CNN architecture was built using U-Net and DeepLabv3 + and PyTorch. The CNN was trained on 2762 axial slices from 174 patients, and an additional 517 axial slices from 33 patients were held out for validation and 777 axial slices from 46 patients for testing. The performance of the CNN was evaluated on a test dataset with Dice coefficients as the outcome measure. The ratio of CSA at the maximum compression level to CSA at the C2 level, as segmented by the CNN, was calculated. The correlation between the spinal cord CSA ratio and the Japanese Orthopaedic Association score in DCM patients from the test dataset was investigated using Spearman's rank correlation coefficient.

RESULTS

The best Dice coefficient was achieved when U-Net was used as the architecture and EfficientNet-b7 as the model for transfer learning. Spearman's r_s between the spinal cord CSA ratio and the JOA score of DCM patients was 0.38 (p = 0.007), showing a weak correlation.

CONCLUSION

Using deep learning with magnetic resonance images of deformed spinal cords as training data, we were able to segment compressed spinal cords of DCM patients with a high concordance with expert manual segmentation. In addition, the spinal cord CSA ratio was weakly, but significantly, correlated with neurological symptoms. Our study demonstrated the first steps needed to implement automated atlas-based analysis of DCM patients.

A natural history study to track brain and spinal cord changes in individuals with Friedreich's ataxia: TRACK-FA study protocol

INTRODUCTION

Drug development for neurodegenerative diseases such as Friedreich's ataxia (FRDA) is limited by a lack of validated, sensitive biomarkers of pharmacodynamic response in affected tissue and disease progression. Studies employing neuroimaging measures to track FRDA have thus far been limited by their small sample sizes and limited follow up. TRACK-FA, a longitudinal, multi-site, and multi-modal neuroimaging natural history study, aims to address these shortcomings by enabling better understanding of underlying pathology and identifying sensitive, clinical trial ready, neuroimaging biomarkers for FRDA.

METHODS

200 individuals with FRDA and 104 control participants will be recruited across seven international study sites. Inclusion criteria for participants with genetically confirmed FRDA involves, age of disease onset ≤ 25 years, Friedreich's Ataxia Rating Scale (FARS) functional staging score of ≤ 5, and a total modified FARS (mFARS) score of ≤ 65 upon enrolment. The control cohort is matched to the FRDA cohort for age, sex, handedness, and years of education. Participants will be evaluated at three study visits over two years. Each visit comprises of a harmonized multimodal Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) scan of the brain and spinal cord; clinical, cognitive, mood and speech assessments and collection of a blood sample. Primary outcome measures, informed by previous neuroimaging studies, include measures of: spinal cord and brain morphometry, spinal cord and brain microstructure (measured using diffusion MRI), brain iron accumulation (using Quantitative Susceptibility Mapping) and spinal cord biochemistry (using MRS). Secondary and exploratory outcome measures include clinical, cognitive assessments and blood biomarkers.

DISCUSSION

Prioritising immediate areas of need, TRACK-FA aims to deliver a set of sensitive, clinical trial-ready neuroimaging biomarkers to accelerate drug discovery efforts and better understand disease trajectory. Once validated, these potential pharmacodynamic biomarkers can be used to measure the efficacy of new therapeutics in forestalling disease progression.

CLINICAL TRIAL REGISTRATION

ClinicalTrails.gov Identifier: NCT04349514.

The Prevalence of Degenerative Cervical Myelopathy-Related Pathologies on Magnetic Resonance Imaging in Healthy/Asymptomatic Individuals: A Meta-Analysis of Published Studies and Comparison to a Symptomatic Cohort

Degenerative cervical myelopathy (DCM) is a progressive cervical spinal cord injury brought about by mechanical stress from degenerative changes in the cervical spine. It is typically diagnosed on clinical symptoms and examination findings together with MRI findings. In this study, we explore the significance of these degenerative pathology to onset of DCM by performing the first meta-analysis on the prevalence of degenerative features reported on MRI amongst healthy and asymptomatic populations and compare this to the prevalence of degenerative features reported on MRI amongst a symptomatic population calculated in a previous review. We conducted a systematic review and meta-analysis in accordance with PRISMA guidelines, cognizant of their adaptation for epidemiological studies. A search strategy was used to identify original research carrying out MRI screening of cervical spines of asymptomatic patients in MEDLINE and Embase from 1985 to present day. The search yielded a total of 1098 studies of which 17 were included in this meta-analysis covering a total of 5059 patients. Ossification of posterior longitudinal ligament (pooled asymptomatic prevalence of 0.4%, 95% Confidence Interval [0.1%, 0.8%]), enlargement of ligamentum flavum (pooled asymptomatic prevalence of 11.8%, 95% Confidence Interval [5.3%, 18.4%]) and degenerative multilevel disc pathology (pooled asymptomatic prevalence of 64.5%, 95% Confidence Interval [48.3%, 80.8%], I² 100%) were found to be significantly lower in asymptomatic populations. Symptomatic populations have a prevalence of 10.5% (95% Confidence Interval [7.7%, 13.3%]) for ossification of posterior longitudinal ligament, 56.8% (95% Confidence Interval [52.3%, 61.3%]) for enlargement of ligamentum flavum and 89.7% (95% Confidence Interval [86.9%, 92.5%]) for degenerative multilevel disc pathology [18]. Understanding the natural history of DCM is a recognised research priority, and whilst these perspectives require further evaluation, they may be of significant relevance to the evolving biomechanical understanding of the disease.

A New Framework for Investigating the Biological Basis of Degenerative Cervical Myelopathy [AO Spine RECODE-DCM Research Priority Number 5]: Mechanical Stress, Vulnerability and Time

STUDY DESIGN

Literature Review (Narrative).

OBJECTIVE

To propose a new framework, to support the investigation and understanding of the pathobiology of DCM, AO Spine RECODE-DCM research priority number 5.

METHODS

Degenerative cervical myelopathy is a common and disabling spinal cord disorder. In this perspective, we review key knowledge gaps between the clinical phenotype and our biological models. We then propose a reappraisal of the key driving forces behind DCM and an individual's susceptibility, including the proposal of a new framework.

RESULTS

Present pathobiological and mechanistic knowledge does not adequately explain the disease phenotype; why only a subset of patients with visualized cord compression show clinical myelopathy, and the amount of cord compression only weakly correlates with disability. We propose that DCM is better represented as a function of several interacting mechanical forces, such as shear, tension and compression, alongside an individual's vulnerability to spinal cord injury, influenced by factors such as age, genetics, their cardiovascular, gastrointestinal and nervous system status, and time.

CONCLUSION

Understanding the disease pathobiology is a fundamental research priority. We believe a framework of mechanical stress, vulnerability, and time may better represent the disease as a whole. Whilst this remains theoretical, we hope that at the very least it will inspire new avenues of research that better encapsulate the full spectrum of disease.

Anderson D, W. Cadotte D. Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy

With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.