Differences between spinal cord injury and cervical compressive myelopathy in intramedullary high-intensity lesions on T2-weighted magnetic resonance imaging: A retrospective study.
Naosuke KameiKazuyoshi NakanishiToshio NakamaeTakayuki TamuraYuji TsuchikawaTaiki MoisakosTakahiro HaradaToshiaki MaruyamaNobuo AdachiPublished in: Medicine (2022)
Increases in aging populations have raised the number of patients with cervical spinal cord injury (SCI) without fractures due to compression of the cervical spinal cord. In such patients, it is necessary to clarify whether SCI or cervical compressive myelopathy (CCM) is the cause of disability after trauma. This study aimed to clarify the differences in magnetic resonance imaging (MRI) features between SCI and CCM. Overall, 60 SCI patients and 60 CCM patients with intramedullary high-intensity lesions on T2-weighted MRI were included in this study. The longitudinal lengths of the intramedullary T2 high-intensity lesions were measured using sagittal MRI sections. Snake-eye appearance on axial sections was assessed as a characteristic finding of CCM. The T2 values of the high-intensity lesions and normal spinal cords at the first thoracic vertebra level were measured, and the contrast ratio was calculated using these values. The longitudinal length of T2 high-intensity lesions was significantly longer in SCI patients than in CCM patients. Snake-eye appearance was found in 26 of the 60 CCM patients, but not in SCI patients. On both the sagittal and axial images, the contrast ratio was significantly higher in the SCI group than in the CCM group. Based on these results, a diagnostic scale was created. This scale made it possible to distinguish between SCI and CCM with approximately 90% accuracy.
Keyphrases
- high intensity
- spinal cord injury
- spinal cord
- end stage renal disease
- magnetic resonance imaging
- chronic kidney disease
- newly diagnosed
- ejection fraction
- contrast enhanced
- peritoneal dialysis
- prognostic factors
- resistance training
- magnetic resonance
- computed tomography
- neuropathic pain
- multiple sclerosis
- deep learning
- network analysis