Machine Learning-Based Prediction of Independent Ambulation Following Intramedullary Spinal Cord Tumor Resection.

BACKGROUND AND OBJECTIVES: Intramedullary spinal cord tumor (IMSCT) resection carries a high risk of postoperative neurological deficit because of neural tract manipulation and myelotomy. Although short-term and long-term neurological recovery represent key treatment outcomes, current prognostication methods are lacking and would benefit from further complex analysis. METHODS: From March 2009 to August 2025, all adult IMSCT resections at our institution were reviewed. Demographic, oncologic, and perioperative data were extracted from electronic medical records. This included preoperative and follow-up neurological examination data in the form of American Spinal Injury Association Impairment Scale (AIS) grading, Modified McCormick Scale (MMCS), and ambulatory status. Independent ambulation served as the primary outcome for 4 machine learning models. Each model was sequentially evaluated using area under the receiver operating characteristic curve (AUROC). RESULTS: Fifty-four patients underwent 55 surgeries for IMSCT resection. Encapsulated lesions predominated IMSCT pathology, with grade II ependymoma comprising 28 (50.9%) resections, 5 hemangioblastomas (9.1%), and 5 cavernous hemangiomas (9.1%). Gross total resection was achieved in 36 cases (65.5%), with encapsulated tumors more readily achieving gross total resection vs unencapsulated (84.6% vs 18.8%, P < .01). By 4 weeks, conversion of MMCS, but not AIS grade, significantly correlated with concurrent ambulatory conversion (P < .01 vs P = .15). At 6 months, both AIS grade conversion (P < .01) and MMCS conversion (P < .01) significantly correlated with ambulatory conversion. For predicting ambulation at latest follow-up from 4 weeks postoperatively, the comprehensive granular model achieved an AUROC of 0.833, outperforming the AIS grade (0.583), American Spinal Injury Association Motor Score (0.667), and MMCS (0.667) models. By the 6-month follow-up, the comprehensive granular model achieved strong discrimination (AUROC 1.00). CONCLUSION: Follow-up IMSCT data demonstrate a postoperative lability that stabilizes by 6 months into a reliably modeled outcome. By enhancing the granularity of recovery data, accurate independent ambulation modeling may improve counseling for patients with IMSCT.