Predicting Meningioma Consistency on Preoperative Neuroimaging Studies

Authors

Mark S. Shiroishi, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. Electronic address: Mark.Shiroishi@med.usc.edu.
Steven Y. Cen, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Benita Tamrazi, Pediatric Neuroradiology, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA 90027, USA.
Francesco D'Amore, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Alexander Lerner, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Kevin S. King, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Paul E. Kim, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Meng Law, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Darryl H. Hwang, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Orest B. Boyko, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Chia-Shang J. Liu, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.

Document Type

Article

Abstract

This article provides an overview of the neuroimaging literature focused on preoperative prediction of meningioma consistency. A validated, noninvasive neuroimaging method to predict tumor consistency can provide valuable information regarding neurosurgical planning and patient counseling. Most of the neuroimaging literature indicates conventional MRI using T2-weighted imaging may be helpful to predict meningioma consistency; however, further rigorous validation is necessary. Much less is known about advanced MRI techniques, such as diffusion MRI, MR elastography (MRE), and MR spectroscopy. Of these methods, MRE and diffusion tensor imaging appear particularly promising.

Medical Subject Headings

Brain (diagnostic imaging, pathology); Diffusion Magnetic Resonance Imaging (methods); Elasticity Imaging Techniques (methods); Humans; Magnetic Resonance Spectroscopy (methods); Meningeal Neoplasms (diagnostic imaging, pathology); Meningioma (diagnostic imaging, pathology); Tomography, Emission-Computed (methods)

Publication Date

4-1-2016

Publication Title

Neurosurgery clinics of North America

E-ISSN

1558-1349

Volume

27

Issue

2

First Page

145

Last Page

54

PubMed ID

27012379

Digital Object Identifier (DOI)

10.1016/j.nec.2015.11.007

Recommended Citation

Shiroishi, Mark S.; Cen, Steven Y.; Tamrazi, Benita; D'Amore, Francesco; Lerner, Alexander; King, Kevin S.; Kim, Paul E.; Law, Meng; Hwang, Darryl H.; Boyko, Orest B.; and Liu, Chia-Shang J., "Predicting Meningioma Consistency on Preoperative Neuroimaging Studies" (2016). Neuroradiology. 58.
https://scholar.barrowneuro.org/neuroradiology/58