Purpose: This study investigates radiomics efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization for prognosis and therapy guidance. Background: Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows qualitative injury evaluation, standard imaging alone has limitations for precise SCI diagnosis, severity stratification, and pathology characterization needed to guide prognosis and therapy. Radiomics enables quantitative tissue phenotyping by extracting a high-dimensional set of descriptive texture features from medical images. However, the efficacy of postoperative radiomic quantification in the presence of metal-induced MRI artifacts from spinal instrumentation has yet to be fully explored. Methods: 50 healthy controls and 12 SCI patients post-stabilization surgery underwent 3D multi-spectral MRI. Automated spinal cord segmentation was followed by radiomic feature extraction. Supervised machine learning categorized SCI versus controls, injury severity, and lesion location relative to instrumentation. Results: Radiomics differentiated SCI patients (Matthews correlation coefficient (MCC) 0.97; accuracy 1.0), categorized injury severity (MCC: 0.95; ACC: 0.98) and localized lesions (MCC: 0.85; ACC: 0.90). Combined T1 and T2 features outperformed individual modalities across tasks with gradient boosting models showing highest efficacy. Conclusion: The radiomic framework achieved excellent performance, differentiating SCI from controls and accurately categorizing injury severity. The ability to reliably quantify SCI severity and localization could potentially inform diagnosis, prognosis, and guide therapy. Further research is warranted to validate radiomic SCI biomarkers and explore clinical integration.