Differentiating Spinal Intradural-extramedullary Schwannoma from Meningioma Using MRI T Weighted Images

Overview

Journal Br J Radiol

Publisher Oxford University Press

Specialty Radiology

Date 2018 Jul 28

PMID 30052467

Citations 12

Authors

Hiroyuki Takashima

Tsuneo Takebayashi

Mitsunori Yoshimoto

Maki Onodera

Yoshinori Terashima

Noriyuki Iesato

Katsumasa Tanimoto

Izaya Ogon

Tomonori Morita

Toshihiko Yamashita

Affiliations

Soon will be listed here.

Abstract

Objective:: Prior studies advocate the subjective visual differences between meningioma and schwannoma on T weighted images, however objective measurement of signal intensity differences may be useful in certain cases. The aim of this study was to investigate whether an objective evaluation of SIs on T weighted images would be useful to differentiate spinal schwannomas from meningiomas.

Methods:: The patients with spinal MRIs demonstrating path proven and subsequently treated intradural extramedullary spinal tumors were selected between April 2008 and May 2017. Regions of interest (ROIs) were measured in the tumor and subcutaneous fat on the same image, and we calculated the SI ratio between tumor and fat ROIs.

Results:: Twenty patients each with meningioma and schwannoma were enrolled. The SI ratios of schwannomas were significantly higher than those of meningiomas (both researcher 1 and 2: p = 0.002). The areas under the curve by researchers 1 and 2 were 0.780. The cutoff value of SI ratio by both of researchers 1 and 2 to differentiate between schwannomas from meningiomas was 0.420 (sensitivity: 80.0%, specificity: 70.0-75.0%).

Conclusion:: The SI ratio, calculated from the SIs of the tumor and fat on T weighted images, is useful for differentiating spinal schwannomas from meningiomas to obtain an accurate diagnosis.

Advances In Knowledge:: Signal intensity ratio of the spinal tumor and fat on T weighted images is useful for differentiating schwannomas from meningiomas to obtain an accurate diagnosis.

Citing Articles

Cavernoma of the cauda equina mimicking schwannoma: A case report.

Naseri R, Haghighi-Morad M, Mohammadi Manesh Z, Bidari Zerehpoosh F, Vahedi H, Ashayeri H Radiol Case Rep. 2025; 20(4):2140-2143.

PMID: 39967587 PMC: 11833503. DOI: 10.1016/j.radcr.2025.01.043.

Clinical, molecular, and genetic features of spinal meningiomas.

Deska-Gauthier D, Hachem L, Wang J, Landry A, Yefet L, Gui C Neurooncol Adv. 2024; 6(Suppl 3):iii73-iii82.

PMID: 39430393 PMC: 11485713. DOI: 10.1093/noajnl/vdae123.

Analysis of the current status and hot topics in spinal schwannoma imaging research based on bibliometrics.

Abudueryimu A, Shoukeer K, Ma H Front Neurol. 2024; 15:1408716.

PMID: 39318871 PMC: 11421035. DOI: 10.3389/fneur.2024.1408716.

Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.

Xu Z, Wang Y, Wang Y, Feng Y, Ye J, Cheng Z Quant Imaging Med Surg. 2024; 14(5):3628-3642.

PMID: 38720862 PMC: 11074726. DOI: 10.21037/qims-23-1194.

Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging.

Ito S, Nakashima H, Segi N, Ouchida J, Oda M, Yamauchi I J Clin Med. 2023; 12(15).

PMID: 37568477 PMC: 10419638. DOI: 10.3390/jcm12155075.

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