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 SUV_max was decreased. Follow-up assessments were conducted. Two machine learning models using MRI T2-based radiomics were developed to identify stenotic levels and decreased SUV_max. (3) Results: Patients with normal SUV_max 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 SUV_max decline. The model outperformed clinicians in predicting SUV_max decline, improving the AUC by 10%. (4) Conclusion: Patients with preserved SUV_max 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.