Generalizable model to predict new or progressing compression fractures in tumor-infiltrated thoracolumbar vertebrae in an all-comer population

OBJECTIVE Neurosurgical evaluation is required in the setting of spinal metastases at high risk for leading to a vertebral body fracture. Both irradiated and nonirradiated vertebrae are affected. Understanding fracture risk is critical in determining management, including follow-up timing and prophylactic interventions. Herein, the authors report the results of a machine learning model that predicts the development or progression of a pathological vertebral compression fracture (VCF) in metastatic tumor–infiltrated thoracolumbar vertebrae in an all-comer population. METHODS A multi-institutional all-comer cohort of patients with tumor containing vertebral levels spanning T1 through L5 and at least 1 year of follow-up was included in the study. Clinical features of the patients, diseases, and treatments were collected. CT radiomic features of the vertebral bodies were extracted from tumor-infiltrated vertebrae that did or did not subsequently fracture or progress. Recursive feature elimination (RFE) of both radiomic and clinical features was performed. The resulting features were used to create a purely clinical model, purely radiomic model, and combined clinical-radiomic model. A Spine Instability Neoplastic Score (SINS) model was created for a baseline performance comparison. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity (with 95% confidence intervals) with tenfold cross-validation. RESULTS Within 1 year from initial CT, 123 of 977 vertebrae developed VCF. Selected clinical features included SINS, SINS component for < 50% vertebral body collapse, SINS component for "none of the prior 3" (i.e., "none of the above" on the SINS component for vertebral body involvement), histology, age, and BMI. Of the 2015 radiomic features, RFE selected 19 to be used in the pure radiomic model and the combined clinical-radiomic model. The best performing model was a random forest classifier using both clinical and radiomic features, demonstrating an AUROC of 0.86 (95% CI 0.82–0.9), sensitivity of 0.78 (95% CI 0.70–0.84), and specificity of 0.80 (95% CI 0.77–0.82). This performance was significantly higher than the best SINS-alone model (AUROC 0.75, 95% CI 0.70–0.80) and outperformed the clinical-only model but not in a statistically significant manner (AUROC 0.82, 95% CI 0.77–0.87). CONCLUSIONS The authors developed a clinically generalizable machine learning model to predict the risk of a new or progressing VCF in an all-comer population. This model addresses limitations from prior work and was trained on the largest cohort of patients and vertebrae published to date. If validated, the model could lead to more consistent and systematic identification of high-risk vertebrae, resulting in faster, more accurate triage of patients for optimal management.