Ependymal Tumours

C71-C72WHO Vol. 6 (CNS5, 2021)

Central Nervous System (CNS)

Prognosis

This section is being prepared by our editorial process.

🔬 Histological Types

📚 Latest Research

2026-09-10

Contiguous T12-L1 Recapping Laminoplasty for Myxopapillary Ependymoma at the Conus Medullaris: A Case Report.

Ono K, et al

A novel contiguous T12-L1 recapping laminoplasty technique enabled complete en bloc resection of a 41-mm myxopapillary ependymoma at the conus medullaris in a 42-year-old woman with no postoperative neurological deterioration. The procedure, performed using a T-saw with T12 osteotomy directed toward the transverse process and L1 pedicle transection, allowed wide thoracolumbar exposure while preserving and reconstructing the posterior spinal elements via T12 suturing and L1 pedicle screw fixation. At 3-year follow-up, no tumour recurrence was observed, suggesting this approach offers both oncological and functional advantages for a challenging anatomical location.

JBJS case connector

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2026-09-09

[Intramedullary spinal cord tumors : MRI diagnosis and differential diagnosis].

Yilmaz U

A narrative review published in Radiologie demonstrates that structured MRI assessment frequently enables reliable preoperative differential diagnosis of intramedullary spinal cord tumors, with ependymomas, astrocytomas, and hemangioblastomas accounting for the majority of cases. Key imaging features — including tumor location, longitudinal extent, enhancement pattern, hemorrhagic components, syringomyelia, and flow voids — substantially narrow the differential diagnosis without surgery. The review highlights that recent WHO classifications now incorporate molecular markers such as MYCN-amplified spinal ependymoma and H3 K27-altered diffuse midline glioma, reflecting a paradigm shift toward biology-based tumor categorization. Clinicians must additionally consider inflammatory, vascular, and ischemic disorders as important non-neoplastic mimics when evaluating spinal cord lesions on MRI.

Radiologie (Heidelberg, Germany)

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2026-09-07 • AI

Deep Learning Pipeline for Automatic Segmentation, Classification, and Molecular Subtyping of Three Pediatric Posterior Fossa Tumors Using T2-Weighted MRI.

Jin Y, et al

A fully automated deep learning pipeline using standard T2-weighted MRI has been developed and validated to segment, classify, and molecularly subtype three pediatric posterior fossa tumors — medulloblastoma, ependymoma, and pilocytic astrocytoma — achieving tumor segmentation Dice scores of 0.94–0.96 and tumor-type classification accuracy of 0.824–0.918 (Cohen's kappa 0.722–0.873) across internal and external test sets in a multicenter cohort of 1,305 patients. Separate models for molecular subtyping reached an accuracy of 0.794 (Cohen's kappa 0.605) for medulloblastoma and 0.789 (Cohen's kappa 0.538) for ependymoma. This noninvasive, fully automated approach could enable preoperative differentiation of tumor types and molecular subtypes in children without the need for surgical sampling, directly informing treatment planning and prognosis.

Journal of magnetic resonance imaging : JMRI

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