Semi-automated detection of cervical spinal cord compression with the Spinal Cord Toolbox

Abstract

Background: Degenerative cervical spinal cord compression is becoming increasingly prevalent, yet the MRI criteria that define compression are vague, and vary between studies. This contribution addresses the detection of compression by means of the Spinal Cord Toolbox and assesses the variability of the morphometric parameters extracted with it.Methods: Prospective cross-sectional study. Two types of MRI examination, 3 and 1.5 T, were performed on 66 healthy controls and 118 participants with cervical spinal cord compression. Morphometric parameters from 3T MRI obtained by Spinal Cord Toolbox (cross-sectional area, solidity, compressive ratio, torsion) were combined in multivariate logistic regression models with the outcome (binary dependent variable) being the presence of compression determined by two radiologists. Inter-trial (between 3 and 1.5 T) and inter-rater (three expert raters and SCT) variability of morphometric parameters were assessed in a subset of 35 controls and 30 participants with compression.Results: The logistic model combining compressive ratio, cross-sectional area, solidity, torsion and one binary indicator, whether or not the compression was set at level C6/7, demonstrated outstanding compression detection (area under curve =0.947). The single best cut-off for predicted probability calculated

Normalizing Spinal Cord Compression Morphometric Measures: Application in Degenerative Cervical Myelopathy

“…Various morphometric measures were shown to be sensitive to spinal cord compression 16,17 and DCM progression 4,5 . Therefore, we implemented the normalization approach for several morphometric measures provided by SCT's sct_process_segmentation.…”

“…Only a few studies have used automatic or semi-automatic measures to compute spinal cord morphometrics 5,16,17,[31][32][33] . The majority of the studies measured the AP diameter on the midsagittal slice 1,12,29,[34][35][36] .…”

“…Computing MSCC with other shape metrics will likely increase the ability to detect and characterise various types of compressions 37 . Some studies directly used the AP diameter or CSA in their analysis without self-normalization with healthy levels above and below the compression sites 4,16,32,33,38 . Spinal cord morphometry has a large inter-subject variability, especially for CSA, AP and RL diameters 10,11,14 and between males and females 11,14,15,[39][40][41] .…”

“…Similarly, age-related changes in spinal cord size and shape can also impact spinal cord morphometrics and may require different normalization strategies for different age groups 15 . Previous studies proposed different normalization strategies to mitigate this variability, like normalizing to age-matched healthy control 16 or taking into account confounding variables like age and brain volume 14,15,17 . However, an age– and sex-matched healthy control cohort is not always available, measures like brain volume are not commonly accessible within spinal cord studies, and there is no clear consensus on which demographic and anatomical factors to use for normalization.…”

Evidence-based commentary on the diagnosis, management, and further research of degenerative cervical spinal cord compression in the absence of clinical symptoms of myelopathy

“…The imaging definitions of DCC based on both quantitative and qualitative methods are vague, with no generally accepted quantitative parameter as a hallmark of DCC. The criteria used for DCC vary between studies, leading to bias in meta-analyses and making multicenter studies difficult (17,25). Additionally, repeated MRI in longitudinal follow-up of mild DCM and ADCC requires reliable quantitative measures to assess the potential progression of radiological outcomes.…”