Cystic meningioma

El-Fiki, Mohamed; El-Henawy, Y.; Abdelrahman, Nayer

doi:10.1007/bf01411258

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…In addition, El-Fiki et al (1996) correlated the location of the cyst and its content and also described four types ( Type D includes meningiomas with the presence of both clear extratumoural or peritumoural cysts and small dark brown intratumoural cyst [22]. The use of this classification showed in our case a Type C cystic meningioma (Figure 3(a)).…”

Section: Discussionsupporting

confidence: 60%

Management and Diagnosis Challenges in Cystic Meningioma. A Case Report and Review of Literature

Hemama¹,

Bankole²,

Denou³

et al. 2020

OJMN

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Background: Cystic meningioma is a rare variety of meningioma. It represents 4% to 7% of all meningiomas. The authors report a case of intracranial cystic meningioma with a review of literature. Case Report: A 59-year-old female presented with a history of headache followed by left side hemiplegia. CT scan and MRI of the brain showed a right parietal tumor with double solid and cystic components thought to be pilocytic astrocytoma or metastasis preoperatively. At surgery, the extraaxial solid and cystic lesion had a well defined capsule that could be easily separated from the perilesional cortical surface. The tumor was totally removed and the cyst was punctioned. The histological study showed a cystic meningioma WHO Grade I. A follow-up at 10 months after surgery, the patient was going well and recovered from the hemiplegiaa. Conclusion: Cystic meningioma is an uncommon tumor that should be considered in the differential diagnosis of brain tumors with a cystic component. The preoperative diagnosis and management could be in some cases challenging.

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Section: Discussionsupporting

confidence: 60%

Management and Diagnosis Challenges in Cystic Meningioma. A Case Report and Review of Literature

Hemama¹,

Bankole²,

Denou³

et al. 2020

OJMN

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“…The pathogenesis of cyst formation is tumour degeneration and necrosis which produces a macro-cavitation attributed to intracellular regressive process [64]. The content of the cyst may be CSF, xanthochromic fluid, clear fluid or haemorrhagic fluid [65].…”

Section: Other Rare Variants Of Meningioma 141 Cystic Meningiomamentioning

confidence: 99%

Molecular alterations, histopathology and squash cytology of Meningioma

Kumar Chowhan,

Agrawal

2024

Meningioma - The Essentials From Bench to Bedside

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Meningioma originates from the arachnoid cap cells of duramater. It is a slow growing tumour of Central Nervous System. It is seen more commonly in females at around 66 years of age. The most common genetic abnormality is monosomy of chromosome 22. Since the inception of World Health Organisation (WHO), meningioma was graded based on histo-morphological appearance. At present, they are categorised into 3 grades. This chapter describes the molecular alterations, histopathological grading, histopathological subtypes and squash cytology of meningioma.

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“…This work focuses on estimating the microstructural parameters of solid tumors in brain 18–20 with the IMPULSED model. The IMPULSED model combines one PGSE and multiple OGSE acquisitions to quantify the cytological features.…”

Section: Introductionmentioning

confidence: 99%

“…For example, it has successfully been applied for measuring cytological features of solid tumors in breast, 9,15 liver, 16 and prostate 17 of human in vivo. This work focuses on estimating the microstructural parameters of solid tumors in brain [18][19][20] with the IMPULSED model. The IMPULSED model combines one PGSE and multiple OGSE acquisitions to quantify the cytological features.…”

Section: Introductionmentioning

confidence: 99%

IMPULSED model based cytological feature estimation with U‐Net: Application to human brain tumor at 3T

Kang

Lan

et al. 2022

Magnetic Resonance in Med

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This work introduces and validates a deep-learning-based fitting method, which can rapidly provide accurate and robust estimation of cytological features of brain tumor based on the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting with diffusion-weighted MRI data. Methods: The U-Net was applied to rapidly quantify extracellular diffusion coefficient (D ex ), cell size (d), and intracellular volume fraction (v in ) of brain tumor. At the training stage, the image-based training data, synthesized by randomizing quantifiable microstructural parameters within specific ranges, was used to train U-Net. At the test stage, the pre-trained U-Net was applied to estimate the microstructural parameters from simulated data and the in vivo data acquired on patients at 3T. The U-Net was compared with conventional non-linear least-squares (NLLS) fitting in simulations in terms of estimation accuracy and precision.Results: Our results confirm that the proposed method yields better fidelity in simulations and is more robust to noise than the NLLS fitting. For in vivo data, the U-Net yields obvious quality improvement in parameter maps, and the estimations of all parameters are in good agreement with the NLLS fitting. Moreover, our method is several orders of magnitude faster than the NLLS fitting (from about 5 min to <1 s). Conclusion:The image-based training scheme proposed herein helps to improve the quality of the estimated parameters. Our deep-learning-based fitting method can estimate the cell microstructural parameters fast and accurately.

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Cystic meningioma

Cited by 14 publications

References 19 publications

Management and Diagnosis Challenges in Cystic Meningioma. A Case Report and Review of Literature

Management and Diagnosis Challenges in Cystic Meningioma. A Case Report and Review of Literature

Molecular alterations, histopathology and squash cytology of Meningioma

IMPULSED model based cytological feature estimation with U‐Net: Application to human brain tumor at 3T

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