Differentiating spinal intradural-extramedullary schwannoma from meningioma using MRI <i>T</i>
 2 weighted images

Takashima, Hiroyuki; Takebayashi, Toru; Yoshimoto, Mitsunori; Onodera, Maki; Terashima, Yoshiaki; Iesato, Noriyuki; Tanimoto, Katsumasa; Ogon, Izaya; Morita, Tomohiro; Yamashita, Toshihiko

doi:10.1259/bjr.20180262

Cited by 27 publications

(24 citation statements)

References 26 publications

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“…[4,5] Although both tumors are mostly benign, there is a substantial difference between the surgical techniques for removing each. [6,7] Since spinal meningiomas have a tendency to recur, complete tumor resection with dural excision is recommended whenever practicable to lower the recurrence rate. [6,8,9] Thus, radiologic differentiation between these two spinal tumors is a crucial step in surgical planning.…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12] Many studies have reported MRI to be useful in differentiating between intradural extramedullary schwannomas and meningiomas. [1,7,13,14] Besides locating the tumor, dural tail sign or neural foraminal extension was suggested as a useful imaging feature in differentiating between the two histologic subtypes. [4,7,13,14] Signal intensity of the tumor on T2-weighted image was also shown to be helpful for the differentiation; schwannomas have a tendency to show a hyperintense signal while a substantial portion of meningiomas shows an isointense signal compared to that of the spinal cord.…”

Section: Introductionmentioning

confidence: 99%

“…[1,7,13,14] Besides locating the tumor, dural tail sign or neural foraminal extension was suggested as a useful imaging feature in differentiating between the two histologic subtypes. [4,7,13,14] Signal intensity of the tumor on T2-weighted image was also shown to be helpful for the differentiation; schwannomas have a tendency to show a hyperintense signal while a substantial portion of meningiomas shows an isointense signal compared to that of the spinal cord. [7,13,14] However, most of the reported studies involve few cases, and the discriminating ability of each sign varies among studies.…”

Section: Introductionmentioning

confidence: 99%

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Differentiating between spinal schwannomas and meningiomas using MRI: A focus on cystic change

et al. 2020

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ObjectivesTo retrospectively determine the diagnostic ability of MRI in differentiating between intradural extramedullary spinal schwannomas and meningiomas. MethodsA total of 199 patients with spinal intradural extramedullary tumors who underwent preoperative contrast-enhanced MRI between January 2012 and December 2018 were included in this study. Two radiologists independently analyzed the presence of cystic change, dural tail sign, and neural foraminal extension. Clinical and MRI features between the two groups were compared by univariable and multivariable analyses using logistic regression. Interobserver agreements were calculated using kappa statistics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differentiating between spinal schwannomas and meningiomas using MRI: A focus on cystic change

et al. 2020

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“…The Mm contributes to two‐thirds of lumbar segmental stability and is said to play an important role in trunk function. We divided the fatty degeneration of the lumbar portion of the Mm into intramyocellular lipids (IMCL) and extramyocellular lipids (EMCL) components using MRS and found that IMCL was associated with chronic LBP …”

Section: Ivd and Advances In Diagnostic Imagingmentioning

confidence: 99%

“…We divided the fatty degeneration of the lumbar portion of the Mm into intramyocellular lipids (IMCL) and extramyocellular lipids (EMCL) components using MRS and found that IMCL was associated with chronic LBP. [25][26][27]…”

Section: Relationship Between the MM Fatty Degeneration And Lbpmentioning

confidence: 99%

Imaging diagnosis for intervertebral disc

Ogon

Takebayashi²,

Takashima

et al. 2019

JOR Spine

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Various functional magnetic resonance imaging (MRI) techniques have been investigated in recent years and are being used in clinical practice for the patients with low back pain (LBP). MRI is an important modality for diagnosing intervertebral disc (IVD) degeneration. In recent years, there have been several reported attempts to use MRI T2 mapping and MRI T1ρ mapping to quantify lumbar disc degeneration. MRI T2 mapping involves digitizing water content, proteoglycan content, and collagen sequence breakdown as relaxation times (T2 values) at each site. These digitized values are used to create a map, that is, then used to quantitatively evaluate the metabolite concentrations within IVD tissues. MRI T2 mapping utilizes the T2 relaxation time to quantify moisture content and the collagen sequence breakdown. MRI T1ρ mapping digitizes water molecule dispersion within the cartilaginous matrix to evaluate the degree of cartilaginous degeneration. Magnetic resonance spectroscopy is a less‐invasive diagnostic test that provides biochemical information. Adequate analysis of the IVD has not yet been performed, although there are indications of a relationship between the adipose content of the multifidus muscle in the low back and LBP. The ultra short TE technique has been recently used to investigate lumbar cartilaginous endplates. Unlike diagnosis based on contrast‐enhanced images of the IVD, which depends on the recurrence of pain that is determined subjectively, MRI‐based diagnosis is less‐invasive and based on objective imaging findings. It is therefore expected to play a key role in the diagnostic imaging of IVD conditions in the future.

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Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

Neromyliotis

Κalamatianos

Paschalis

et al. 2020

Magnetic Resonance Imaging

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Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI‐based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. Level of Evidence 5 Technical Efficacy Stage 2

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Differentiating spinal intradural-extramedullary schwannoma from meningioma using MRI T 2 weighted images

Abstract: 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.

Cited by 27 publications

References 26 publications

Differentiating between spinal schwannomas and meningiomas using MRI: A focus on cystic change

Differentiating between spinal schwannomas and meningiomas using MRI: A focus on cystic change

Imaging diagnosis for intervertebral disc

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

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