Benefits of Combined MRI Sequences in Meningioma Consistency Prediction: A Prospective Study of 287 Consecutive Patients
- PMID: 36570751
- PMCID: PMC9771632
- DOI: 10.1055/s-0042-1758849
Benefits of Combined MRI Sequences in Meningioma Consistency Prediction: A Prospective Study of 287 Consecutive Patients
Abstract
Objective Consistency of meningiomas is one of the most important factors affecting the completeness of removal and major risks of meningioma surgery. This study used preoperative magnetic resonance imaging (MRI) sequences in single and in combination to predict meningioma consistency. Methods The prospective study included 287 intracranial meningiomas operated on by five attending neurosurgeons at Chiang Mai University Hospital from July 2012 through June 2020. The intraoperative consistency was categorized in four grades according to the method of surgical removal and intensity of ultrasonic aspirator, then correlated with preoperative tumor signal intensity pattern on MRI including T1-weighted image, T2-weighted image (T2WI), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted image (DWI), which were described as hypointensity, isointensity, and hyperintensity signals which were blindly interpreted by one neuroradiologist. Results Among 287 patients, 29 were male and 258 female. The ages ranged from 22 to 83 years. A total of 189 tumors were situated in the supratentorial space and 98 were in the middle fossa and infratentorial locations. Note that 125 tumors were found to be of soft consistency (grades 1, 2) and 162 tumors of hard consistency (grades 3, 4). Hyperintensity signals on T2WI, FLAIR, and DWI were significantly associated with soft consistency of meningiomas (relative risk [RR] 2.02, 95% confidence interval [CI] 1.35-3.03, p = 0.001, RR 2.19, 95% CI 1.43-3.35, p < 0.001, and RR 1.47, 95% CI 1.02-2.11, p = 0.037, respectively). Further, chance to be soft consistency significantly increased when two and three hyperintensity signals were combined (RR 2.75, 95% CI 1.62-4.65, p ≤ 0.001, RR 2.79, 95% CI 1.58-4.93, p < 0.001, respectively). Hypointensity signals on T2WI, FLAIR, and DWI were significantly associated with hard consistency of meningiomas (RR 1.82, 95% CI 1.18-2.81, p = 0.007, RR 1.80, 95% CI 1.15-2.83, p = 0.010, RR 1.67, 95% CI 1.07-2.59, p = 0.023, respectively) and chance to be hard consistency significantly increased when three hypointensity signals were combined (RR 1.82, 95% CI 1.11-2.97, p = 0.017). Conclusion T2WI, FLAIR, and DWI hyperintensity signals of the meningiomas was solely significantly associated with soft consistency and predictive value significantly increased when two and three hyperintensity signals were combined. Each of hypointensity signals on T2WI, FLAIR, and DWI was significantly associated with hard consistency of tumors and tendency to be hard consistency significantly increased when hypointensity was found in all three sequences.
Keywords: combined MRI; consistency; meningioma consistency; prediction; preoperative MRI; sequences.
Asian Congress of Neurological Surgeons. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ).
Conflict of interest statement
Conflict of Interest None declared.
Figures
References
-
- Romani R, Tang W J, Mao Y. Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas. Acta Neurochir (Wien) 2014;156(10):1837–1845. - PubMed
-
- Bai Y, Zhang R, Zhang X. Magnetic resonance fingerprinting for preoperative meningioma consistency prediction. Acad Radiol. 2022;29(08):e157–e165. - PubMed
-
- Cepeda S, Arrese I, García-García S. Meningioma consistency can be defined by combining the radiomic features of magnetic resonance imaging and ultrasound elastography. A pilot study using machine learning classifiers. World Neurosurg. 2021;146:e1147–e1159. - PubMed
-
- Kashimura H, Inoue T, Ogasawara K. Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg. 2007;107(04):784–787. - PubMed
LinkOut - more resources
Full Text Sources
