Predicting Postoperative Neurological Outcomes in Metastatic Spinal Tumor Surgery Using Machine Learning

Retrospective analysis of data collected across multiple centers.

To develop machine learning models for predicting neurological outcomes 1 month postoperatively in patients with metastatic spinal tumors undergoing surgery, and to identify key factors influencing neurological recovery.

The increasing prevalence of spinal metastases has led to a growing need for surgical intervention to address mechanical instability and neurological deficits. Predicting postoperative neurological status, as assessed by the Frankel classification, can provide valuable insights for surgical planning and patient counseling. Traditional prognostic models have shown limitations in capturing the complexity of neurological recovery patterns.

We analyzed data from 244 patients who underwent spinal surgery for metastatic disease across 38 institutions. The primary outcome was functional ambulation, defined as Frankel grades D or E at 1 month postoperatively. Four machine learning algorithms (random forest, XGBoost, LightGBM, and CatBoost) were used to build predictive models. Feature selection employed the Boruta algorithm and variance inflation factor analysis to reduce multicollinearity.

Among the 244 patients, the proportion of ambulatory patients (Frankel grades D or E) increased from 36.8% preoperatively to 63.1% at 1 month postoperatively. The random forest model achieved the highest area under the receiver operating characteristic curve of 0.8516, followed by XGBoost (0.8351), CatBoost (0.8331), and LightGBM (0.8098). SHapley Additive exPlanations analysis identified preoperative Frankel classification, transfer ability, inflammatory markers (C-reactive protein and white blood cell-lymphocyte), and surgical timing as the most important predictors of postoperative outcomes.

Machine learning models showed strong predictive performance in assessing postoperative neurological status of patients with metastatic spinal tumors. Key factors, including preoperative neurological function, functional ability, and inflammation markers, significantly influenced outcomes. These findings could inform surgical decision-making and help set realistic postoperative expectations while potentially improving patient care through more accurate outcome prediction.