A Radiomics-Driven Model to Distinguish Between Clinically Similar Myxopapillary Ependymomas and Lumbosacral Schwannomas.

BACKGROUND AND OBJECTIVES: Myxopapillary ependymomas (MPE) and intradural lumbosacral schwannomas may be challenging to distinguish based on presenting characteristics and preoperative imaging. Accurate differentiation is crucial, as MPEs carry a risk of cerebrospinal fluid dissemination and warrant earlier intervention, a more tailored surgical strategy, consideration for adjuvant radiation, and frequent surveillance. Here, we describe our institutional experience with these tumors and develop a radiomics-based machine learning model to help distinguish them on preoperative imaging. METHODS: Institutional surgical records from 2011 to 2025 were queried and clinical data were extracted for the retrospective cohort analysis. Tumors were manually segmented in ITK-Snap from T1 postcontrast images, and radiomics features were extracted using the PyRadiomics package. An ensemble of random forest, k-nearest neighbors, and naive Bayes classifiers was trained on a subset of radiomics features using nested cross-validation. RESULTS: Our cohort included 101 cases, including 32 MPEs, 61 intradural schwannomas, and 8 dumbbell schwannomas with a circumscribed intradural component. Twenty-four consecutive tumors (3 MPEs and 21 schwannomas) were used as a held-out pseudoprospective test set. No significant difference in presenting International Standards for Neurological Classification of Spinal Cord Injury grade was observed (P = .558). Our radiomics model incorporated segmentations with inter-rater DICE scores >0.83 for all tumors. Cross-validation (area under the receiver operating characteristic = 0.895) and test (area under the receiver operating characteristic = 0.984) discriminatory performance was high and identified biologically relevant features, such as maximum 3-dimensional diameter, that were significantly increased (P < .001) in MPEs, likely due to longitudinal tumor growth along the filum. Excluding scoliotic patients did not significantly alter discrimination, suggesting robustness to vertebral column malalignment that may coexist with intradural tumors. CONCLUSION: A radiomics-based machine learning model demonstrated excellent discriminative ability between MPE and lumbosacral schwannoma, achieving high accuracy and robustness to vertebral alignment variations. These results suggest that radiomics-based models may be developed into a useful tool for preoperative planning and patient counseling.