Discrimination between spinal meningioma and schwannoma using MRI machine learning-based radiomics texture analysis

Aim To identify radiomics parameters that distinguish spinal meningiomas from schwannomas and evaluate machine learning models using MRI data. Methods Patients with histopathologic diagnoses of spinal meningioma (<em>n</em> = 25) and schwannoma (<em>n</em> = 26) who underwent pre-surgical MRI were enrolled. Semantic features, including tumor location, longest diameter, foraminal extension, cystic changes, spinal cord compression, enhancement patterns, dural tail, and ginkgo leaf signs, were assessed using a single 3.0 T scanner. Radiomics features were extracted from T1-weighted, T2-weighted, STIR, and post-gadolinium T1-weighted images. Support Vector Machine (SVM) models were trained and validated using conventional MRI, radiomics features, and their combination. Results A significant gender difference was found, with more females in the meningioma group (84%, <em>P</em> = .039). Other significant factors included the longest tumor dimension (<em>P</em> = .03), presence of a dural tail sign (<em>P</em> < .001), intratumoral cystic changes (<em>P</em> = .003), ginkgo leaf sign (<em>P</em> = .01), and spinal cord compression (<em>P</em> < .001). Of the 444 extracted radiomics parameters, 186 demonstrated good reproducibility (ICC ≥ 0.75). Among these, 49 were retained for model construction. Model 1 (conventional MRI) achieved an AUC of 0.902, an accuracy of 0.882, a sensitivity of 0.884, and a specificity of 0.880. Model 2 (radiomics features) achieved an AUC of 0.909, an accuracy of 0.843, a sensitivity of 0.961, and a specificity of 0.720. Model 3 (combined features) demonstrated the highest performance with an AUC of 0.997, accuracy of 0.960, sensitivity of 1.000, and specificity of 0.920. Conclusion Combining radiomics with conventional MRI improves diagnostic accuracy in differentiating spinal meningiomas from schwannomas, supporting radiomics as a valuable non-invasive tool for preoperative diagnosis. Multicenter studies are needed to validate these findings and expand clinical applications.