Background: The consistency of a meningioma is one of the important factors in determining the surgical outcome. If the surgeon is aware of the consistency of a meningioma preoperatively, the surgical plans will be influenced. A few papers have described the correlation between consistency of meningiomas and their magnetic resonance imaging (MRI) findings. However, prediction of consistency with MRI is still difficult. We have tried to predict the consistency of meningiomas with MRI findings more precisely.
Methods And Results: Fifty patients diagnosed as having intracranial meningiomas were studied with 1.5 Tesla MRI. We compared the MRI findings with tumor consistency. The intensities of the tumors were categorized into three grades (low, iso, and high) compared to that of the gray matter. T1-weighted images had no specifics, but T2-weighted images and proton density images were useful for the prediction of tumor consistency. Hyperintensity on protein density (PD) and T2-weighted images was a sign of a soft tumor.
Conclusion: We presume that T2 and PD are useful for predicting consistency of meningiomas, and their water content is one of the main factors in their consistency. Histology may be one of the factors helpful in defining the consistency of a tumor. In this series, we found no relationship between histology and MRI findings, nor between histology and consistency. If the meningioma is believed to be hard, preoperative endovascular embolization is beneficial, which will induce necrosis of the meningioma and make it soft enough to be removed more easily and safety.
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BMJ Case Rep
January 2025
Radiodiagnosis, AIIMS Nagpur, Nagpur, Maharashtra, India.
A boy in his middle childhood presented with a gradually enlarging, mildly tender swelling in the left frontal region, noticed after minor trauma. Skull radiograph and non-enhanced CT revealed a diffuse sclerotic lesion involving the left frontal bone and overlying subcutaneous soft tissue, suggestive of an intraosseous haemangioma. Contrast-enhanced MRI showed an expansile, hypointense lesion in the frontal bone on the left side with enhancing extraosseous components and a small extra-axial cyst.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Background/objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation.
Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research.
J Neurosurg
January 2025
Departments of1Neurological Surgery.
Objective: Tumor consistency, or fibrosity, affects the ability to optimally resect meningiomas, especially with recent trends evolving toward minimally invasive approaches. The authors' team previously validated a practical 5-point scale for intraoperative grading of meningioma consistency. The impact of meningioma consistency on surgical management and outcomes, however, has yet to be explored.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Pathology Department, Salah Azeiz Institute, 1006, Tunis, Tunisia.
Follicular dendritic cell sarcoma (FDCS) is a rare malignancy, often challenging to diagnose due to its nonspecific presentation and resemblance to other neoplasms. This case highlights a locally advanced nasopharyngeal FDCS initially misdiagnosed as a meningioma, underscoring the importance of differential diagnosis in unusual tumor presentations. A 77-year-old patient presented with nasal obstruction for 3 months.
View Article and Find Full Text PDFNeuroinformatics
January 2025
Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist, Taipei, 112304, Taiwan.
Background: Meningioma, the most common primary brain tumor, presents significant challenges in MRI-based diagnosis and treatment planning due to its diverse manifestations. Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and efficiency of meningioma segmentation from MRI scans. This systematic review and meta-analysis assess the effectiveness of CNN models in segmenting meningioma using MRI.
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