MRI-Based Machine Learning and Radiomics Methods for Assessing Spinal Cord Function in Patients with Mild Cervical Spondylotic Myelopathy.

(1)

Background

Patients with mild cervical spondylotic myelopathy (CSM) who delay surgery risk progression. While PET evaluates spinal cord function, its cost and radiation limit its use. (2)

Methods

In this prospective study, patients with mild cervical spondylosis underwent preoperative 18F-FDG PET-MRI. Narrowed spinal levels were classified based on whether SUVmax was decreased. Follow-up assessments were conducted. Two machine learning models using MRI T2-based radiomics were developed to identify stenotic levels and decreased SUVmax. (3)

Results

Patients with normal SUVmax showed greater symptom improvement. The radiomics models performed well, with AUCs of 0.981/0.962 (training/testing) for stenosis detection and 0.830/0.812 for predicting SUVmax decline. The model outperformed clinicians in predicting SUVmax decline, improving the AUC by 10%. (4)

Conclusion

Patients with preserved SUVmax have better outcomes. MRI-based radiomics shows potential for identifying stenosis and predicting spinal cord function changes for preoperative assessment, though larger studies are needed to validate its clinical utility.