Automatic Lesion Morphometrics Reliably Predict Sensorimotor Recovery After Spinal Cord Injury: A Longitudinal, Multicenter Neuroimaging Study

OBJECTIVE: To evaluate automatic magnetic resonance imaging (MRI)-derived lesion morphometrics against manual measurements and their prognostic value for characterizing distinct acute to chronic neurological recovery trajectories after spinal cord injury (SCI). METHODS: We studied a retrospective, multicenter cohort of cervical and thoracolumbar SCI patients who underwent routine clinical MRI at 3-4 weeks post-SCI and neurological examinations over 6 months. Lesion severity was quantified using both established manual measurements and a novel automatic approach. We assessed the between-method agreement of MRI-derived lesion morphometrics, including lesion length, lesion width, and tissue bridge width, and their ability to stratify patients according to 6-month motor and sensory outcomes. Data-driven prediction models were used to identify clinically meaningful lesion measures and their respective thresholds, differentiating subgroups of specific recovery profiles. RESULTS: Eighty-three patients from eight sites were included. Automatically derived lesion morphometrics showed moderate to strong agreement with established manual measures (Spearman's ρ: 0.540-0.821, p < 0.001), capturing inter-individual differences in lesion severity and demonstrating comparable prognostic value. Lesion width represented the strongest predictor of 6-month neurological outcomes, splitting patients into recovery-specific subgroups. Patients with a smaller lesion width (threshold: 5.88-6.41 mm) demonstrated higher 6-month sensorimotor scores and superior recovery trajectories, while tissue bridges (threshold: 0.17 mm) further refined motor prediction-based stratification of more severely affected patients. INTERPRETATION: Automatic MRI measures of lesion severity hold promise as scalable, time-efficient in vivo biomarkers of SCI prognosis, with potential for clinical adoption to guide treatment decisions and multicenter trial planning.