Automated signal intensity analysis of the spinal cord for detection of degenerative cervical myelopathy — a matched-pair MRI study

Abstract

Abstract Purpose Detection of T2 hyperintensities in suspected degenerative cervical myelopathy (DCM) is done subjectively in clinical practice. To gain objective quantification for dedicated treatment, signal intensity analysis of the spinal cord is purposeful. We investigated fully automated quantification of the T2 signal intensity (T2-SI) of the spinal cord using a high-resolution MRI segmentation. Methods Matched-pair analysis of prospective acquired cervical 3D T2-weighted sequences of 114 symptomatic patients and 88 healthy volunteers. Cervical spinal cord was segmented automatically through a trained convolutional neuronal network with subsequent T2-SI registration slice-by-slice. Received T2-SI curves were subdivided for each cervical level from C2 to C7. Additionally, all levels were subjectively classified concerning a present T2 hyperintensity. For T2-positive levels, corresponding T2-SI curves were compared to curves of age-matched volunteers at the identical level. Results Forty-nine patients showed subjective T2 hyperintensities at any level. The corresponding T2-SI curves showed higher signal variabilities reflected by standard deviation (18.51 vs. 7.47 a.u.; p < 0.001) and range (56.09 vs. 24.34 a.u.; p < 0.001) compared to matched controls. Percentage of the range from the mean absolute T2-SI per cervical level, introduced as “T2 myelopathy index” (T2-MI), was correspondingly significantly higher in T2-positive segments (23.99% vs. 10.85%; p < 0.001). ROC analysis indicated excellent differentiation for all three parameters (AUC 0.865–0.920). Conclusion This fully automated T2-SI quantification of the spinal cord revealed significantly increased signal variability for DCM patients compared to healthy volunteers. This innovative procedure and the applied parameters showed sufficient diagnostic accuracy, potentially diagnosing radiological DCM more objective to optimize treatment recommendation. Trial registration DRKS00012962 (17.01.2018) and DRKS00017351 (28.05.2019)