Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review

Amy Yao1, Margaret Pain2, Priti Balchandani2

  • 1Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, Annenberg 8, One Gustave L Levy Pl, New York, NY, 10029, USA. amy.yao@icahn.mssm.edu.

Neurosurgical Review
|November 23, 2016
PubMed

Insights

Magnetic resonance imaging (MRI) can predict meningioma tumor consistency. T2-weighted MRI, particularly quantitative analysis, offers reliable prediction of tumor firmness, aiding surgical planning.

Area of Science:

  • Neurosurgery
  • Radiology
  • Oncology

Background:

  • Tumor consistency impacts surgical strategy and patient care.
  • Magnetic resonance imaging (MRI) assesses water content, potentially predicting tissue biomechanics.
  • Meningioma MRI signal intensity's ability to predict tumor consistency and subtype is debated.

Purpose of the Study:

  • To systematically review literature correlating preoperative MRI findings with meningioma tumor consistency.
  • To assess the reliability of MRI in predicting tumor firmness.
  • To evaluate the relationship between imaging characteristics, intraoperative findings, and WHO histopathological subtypes.

Main Methods:

  • Systematic review of PubMed database (since 1990).
  • Inclusion of case series and clinical studies correlating preoperative MRI with tumor consistency.
  • Analysis of T1-weighted, T2-weighted imaging, and MR elastography findings.

Main Results:

  • T2 signal intensity and MR elastography are useful predictors of tumor consistency; other techniques lack validation.
  • T1-weighted imaging showed no diagnostic or predictive value.
  • Quantitative T2 signal intensity assessment is more reliable than qualitative analysis for predicting consistency.

Conclusions:

  • Preoperative knowledge of meningioma firmness benefits surgical planning.
  • T2-weighted MRI is recommended for predicting tumor consistency, correlating with histological subtypes.
  • Standardized MRI quantification and further technique exploration can enhance neuroimaging prediction for meningiomas.