Preoperative Differentiation of Spinal Schwannoma and Meningioma Using Machine Learning-Based Models: A Systematic Review and Meta-Analysis.

Abstract

Regarding the differences in surgical approaches for spinal schwannomas and meningiomas, preoperative differentiation of spinal schwannomas and meningiomas can be important in managing these lesions. This study evaluated the diagnostic performance of machine learning (ML)-based models in the differentiation of spinal schwannomas and meningiomas. On December 18, 2024, a comprehensive search was conducted. The data for the best-performing model were used to calculate pooled sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratio. Six studies with 644 patients were included, encompassing 364 schwannomas (59.9%) and 258 meningiomas (40.1%). Deep learning-based models (66.7%, 4/6) were the most frequent, followed by ML-based models (33.3%, 2/6). The best performance models' AUC and accuracy ranged from 0.876 to 0.998 and 0.8 to 0.982, respectively. Our findings showed a pooled sensitivity rate of 91% (95%CI: 81%-96%), a specificity rate of 92% (95%CI: 84%-96%), and a diagnostic odds ratio of 97.34 (95%CI: 23.5-403.6), concurrent with an AUC of 0.944. ML-based models have a high diagnostic accuracy in preoperative differentiation of spinal schwannomas and meningiomas.