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.