title: Predicting Meningioma Consistency on Preoperative Neuroimaging Studies.
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.
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. Electronic address: Mark.Shiroishi@med.usc.edu.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Shiroishi
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Steven Y
authors.initials: SY
authors.lastname: Cen
authors.affiliation: Pediatric Neuroradiology, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA 90027, USA.
authors.forename: Benita
authors.initials: B
authors.lastname: Tamrazi
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Francesco
authors.initials: F
authors.lastname: D'Amore
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Lerner
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Kevin S
authors.initials: KS
authors.lastname: King
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Paul E
authors.initials: PE
authors.lastname: Kim
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Meng
authors.initials: M
authors.lastname: Law
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Darryl H
authors.initials: DH
authors.lastname: Hwang
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Orest B
authors.initials: OB
authors.lastname: Boyko
authors.affiliation: Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
authors.forename: Chia-Shang J
authors.initials: CS
authors.lastname: Liu
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D054459:Elasticity Imaging Techniques / Q000379:methods; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D008577:Meningeal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D008579:Meningioma / Q000000981:diagnostic imaging* / Q000473:pathology*; D014055:Tomography, Emission-Computed / Q000379:methods
keywords: Consistency; Firmness; MRI; Meningioma; Minimally invasive neurosurgery; Neurosurgical planning; Prediction; Texture