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

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

Neuroradiology, 2023 · DOI: https://doi.org/10.1007/s00234-023-03187-w · Published: June 30, 2023

Spinal Cord InjuryNeuroimagingSpinal Disorders

Simple Explanation

This study introduces a new method for objectively measuring spinal cord signal intensity to aid in the diagnosis of degenerative cervical myelopathy (DCM). The current method relies on subjective assessment, leading to inconsistencies. The researchers used a fully automated process to quantify T2 signal intensity (T2-SI) from MRI scans of patients with DCM and healthy volunteers. This involves automatically segmenting the spinal cord using a trained computer algorithm. The study found that the variability in T2-SI was significantly higher in DCM patients compared to healthy volunteers. This new method shows potential for more objective DCM diagnosis and treatment planning.

Study Duration
July 2018 and Jan 2022
Participants
114 symptomatic patients and 88 healthy volunteers
Evidence Level
Not specified

Key Findings

  • 1
    The T2-SI curves of patients with T2 hyperintensities showed significantly higher signal variability compared to matched healthy controls, as reflected by standard deviation and range.
  • 2
    The T2 myelopathy index (T2-MI), representing the percentage of T2-SI range from the mean, was significantly higher in T2-positive segments compared to controls.
  • 3
    ROC analysis indicated excellent differentiation between DCM patients and healthy volunteers for all three parameters: standard deviation, range, and T2-MI.

Research Summary

This study introduces a fully automated method for quantifying T2 signal intensity (T2-SI) in the spinal cord to improve the objective diagnosis of degenerative cervical myelopathy (DCM). The automated process involves segmenting the spinal cord using a trained convolutional neural network (CNN) and analyzing the T2-SI curve to derive parameters such as standard deviation, range, and a novel T2 myelopathy index (T2-MI). The results demonstrated that T2-SI variability and T2-MI were significantly higher in DCM patients with T2 hyperintensities compared to healthy volunteers, indicating the potential for more objective and standardized DCM diagnosis.

Practical Implications

Improved DCM Diagnosis

The automated T2-SI quantification can lead to a more objective and standardized diagnosis of radiological DCM, reducing rater-dependency.

Optimized Treatment Recommendation

Objective radiological DCM diagnosis could optimize treatment recommendation.

Potential Clinical Correlation

The extent of signal variability is expected to be associated to the severity of myelopathy symptoms and anticipated outcome after treatment.

Study Limitations

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