We have located links that may give you full text access.
Magnetic resonance imaging spectrum of spinal meningioma.
Clinical Imaging 2019 May
PURPOSE: To evaluate magnetic resonance (MR) imaging findings of spinal meningioma and to determine the radiological subtypes based on the MR imaging findings and their respective clinical features.
MATERIAL AND METHODS: Data for 105 patients with surgically treated and histopathologically diagnosed spinal meningiomas at our hospital between May 1, 2003 and May 1, 2017 were evaluated in this study. Two radiologists reviewed the characteristics of spinal meningiomas on MR images and categorized the spinal meningiomas into subtypes based on MR imaging findings.
RESULTS: Most spinal meningiomas showed higher signal intensity than that of the spinal cord but lower than that of the subcutaneous fat on T2-weighted images (WI). 56 cases (54%) showed adjacent spinal cord signal changes. Meningiomas could be categorized according to MR imaging findings into type A: dural-based tumors with a homogeneous signal intensity and intense contrast enhancement (81 cases, 77%); type B: round or oval-shaped tumors with an internal hypointense portion on T2-weighted images (18 cases, 17%); type C: en plaque tumors (three cases, 3%); and type D: tumors with unusual findings and a heterogeneous appearance (three cases, 3%). All type C patients showed spinal cord signal changes.
CONCLUSIONS: Spinal meningioma showed slightly high signal intensity rather than high signal intensity on T2-weighted images. Spinal cord signal changes were present in more than half of the cases. Clinical differences were observed among the different MR imaging types.
MATERIAL AND METHODS: Data for 105 patients with surgically treated and histopathologically diagnosed spinal meningiomas at our hospital between May 1, 2003 and May 1, 2017 were evaluated in this study. Two radiologists reviewed the characteristics of spinal meningiomas on MR images and categorized the spinal meningiomas into subtypes based on MR imaging findings.
RESULTS: Most spinal meningiomas showed higher signal intensity than that of the spinal cord but lower than that of the subcutaneous fat on T2-weighted images (WI). 56 cases (54%) showed adjacent spinal cord signal changes. Meningiomas could be categorized according to MR imaging findings into type A: dural-based tumors with a homogeneous signal intensity and intense contrast enhancement (81 cases, 77%); type B: round or oval-shaped tumors with an internal hypointense portion on T2-weighted images (18 cases, 17%); type C: en plaque tumors (three cases, 3%); and type D: tumors with unusual findings and a heterogeneous appearance (three cases, 3%). All type C patients showed spinal cord signal changes.
CONCLUSIONS: Spinal meningioma showed slightly high signal intensity rather than high signal intensity on T2-weighted images. Spinal cord signal changes were present in more than half of the cases. Clinical differences were observed among the different MR imaging types.
Full text links
Related Resources
Trending Papers
Executive Summary: State-of-the-Art Review: Unintended Consequences: Risk of Opportunistic Infections Associated with Long-term Glucocorticoid Therapies in Adults.Clinical Infectious Diseases 2024 April 11
Autoimmune Hemolytic Anemias: Classifications, Pathophysiology, Diagnoses and Management.International Journal of Molecular Sciences 2024 April 13
Clinical practice guidelines on the management of status epilepticus in adults: A systematic review.Epilepsia 2024 April 13
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app