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Quantitative MRI Assessment of Post-Surgical Spinal Cord Injury Through Radiomic Analysis. J Imaging 2024 Dec 08;10(12)

Date

12/27/2024

Pubmed ID

39728209

Pubmed Central ID

PMC11678099

DOI

10.3390/jimaging10120312

Scopus ID

2-s2.0-85213496292 (requires institutional sign-in at Scopus site)

Abstract

This study investigates radiomic efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization or prognosis and therapy guidance. 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, which are 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. A total of 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. 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 the highest efficacy. 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.

Author List

Sharafi A, Klein AP, Koch KM

Authors

Andrew P. Klein MD Chief, Associate Professor in the Radiology department at Medical College of Wisconsin
Kevin M. Koch PhD Center Director, Professor in the Radiology department at Medical College of Wisconsin