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Meningiomas: MR and histopathologic features.

A D Elster1, V R Challa, T H Gilbert

  • 1Department of Radiology, Bowman Gray School of Medicine, Wake Forest University, Winston-Salem, NC 27103.

Radiology
|March 1, 1989
PubMed
Summary
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Magnetic resonance (MR) imaging can predict meningioma subtypes. T2-weighted images are key, correlating with histology in over 75% of cases, aiding in diagnosis.

Area of Science:

  • Neuroradiology
  • Neuro-oncology
  • Pathology

Background:

  • Meningiomas are common primary tumors of the central nervous system.
  • Accurate preoperative histologic subtyping of meningiomas is challenging.
  • Magnetic resonance (MR) imaging is crucial for evaluating intracranial masses.

Purpose of the Study:

  • To correlate magnetic resonance (MR) imaging findings with the predominant histologic patterns of meningiomas.
  • To assess the utility of T1- and T2-weighted MR images in differentiating meningioma subtypes.
  • To determine if secondary MR imaging features can improve histologic prediction.

Main Methods:

  • Blind evaluation of MR imaging appearances in 40 biopsy-proved meningiomas.
  • Correlation of MR imaging features with predominant histologic subtypes (fibroblastic, transitional, syncytial, angioblastic, mixed).

Related Experiment Videos

  • Analysis of T1-weighted, T2-weighted, and secondary imaging features (edema, cysts, calcifications).

    • Main Results:

    • T1-weighted images showed limited value in differentiating subtypes, with most tumors being isointense or hypointense to cortex.
    • T2-weighted image signal intensity strongly correlated with histopathology in over 75% of cases.
    • Markedly hypointense T2 tumors were fibroblastic/transitional; markedly hyperintense T2 tumors were syncytial/angioblastic. Secondary features aided prediction in isointense tumors.

    Conclusions:

    • The varied MR appearance of meningiomas has a distinct histologic basis.
    • Histologic subtype prediction is possible in over three-fourths of meningiomas using MR imaging, particularly T2-weighted sequences.
    • Combining T2-weighted signal intensity with secondary features enhances diagnostic accuracy for meningioma subtypes.