MRI Patterns Distinguish AQP4 Antibody Positive Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis

Laura Clarke1, Simon Arnett1, Wajih Bukhari1

  • 1Menzies Health Institute Queensland, Gold Coast, Griffith University, Southport, QLD, Australia.

Frontiers in Neurology
|September 27, 2021
PubMed

Insights

Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are CNS inflammatory diseases. Specific MRI features can help distinguish NMOSD from MS, with machine learning models achieving over 85% accuracy.

Area of Science:

  • Neuroimmunology
  • Radiology
  • Medical Imaging

Background:

  • Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are inflammatory central nervous system (CNS) diseases.
  • Distinguishing between NMOSD and MS can be challenging due to overlapping clinical and MRI features.

Purpose of the Study:

  • To evaluate the diagnostic utility of MRI features in differentiating NMOSD from MS.
  • To develop predictive models for accurate diagnosis of NMOSD and MS using MRI.

Main Methods:

  • Cross-sectional analysis of MRI data from aquaporin-4 (AQP4) antibody-positive NMOSD cases and age/sex-matched MS cases.
  • Identification and definition of NMOSD and MS MRI lesions through literature review.
  • Machine learning algorithms used to develop predictive models based on identified MRI features.

Main Results:

  • Specific MRI lesion characteristics were associated with either NMOSD or MS.
  • NMOSD-associated lesions include: longitudinally extensive, "bright spotty", whole (axial), and gadolinium-enhancing spinal cord lesions; bilateral and gadolinium-enhancing optic nerve lesions; and nucleus tractus solitarius, periaqueductal, or hypothalamic brain lesions.
  • MS-associated lesions include: ovoid, Dawson's fingers, pyramidal corpus callosum, periventricular, temporal lobe, and T1 black hole brain lesions.
  • Machine learning models accurately predicted over 85% of diagnoses using initial imaging.

Conclusions:

  • Distinct MRI features reliably differentiate NMOSD and MS.
  • Predictive models incorporating these MRI features can accurately diagnose over 85% of NMOSD and MS cases.
  • These findings enhance diagnostic capabilities for these debilitating CNS inflammatory diseases.