Background: Meningioma consistency is one of the most critical factors affecting the difficulty of surgery. Although many studies have attempted to predict meningioma consistency via magnetic resonance imaging findings, no definitive method has been established, because most have been based on qualitative evaluations. Therefore, the present study examined the potential of the T2 relaxation time (T2 value), a tissue-specific quantitative parameter, for assessment of meningioma consistency.
Methods: Eighteen surgically treated meningiomas in 16 patients were included in the present study. Preoperatively, the T2 values of all meningiomas were calculated pixel by pixel, and a T2 value distribution map was generated. A total of 27 tumor specimens (multiple specimens were procured if heterogeneous) were taken from these meningiomas, with each localization identified intraoperatively using image guidance. The consistency of the specimens was measured with a durometer, originally a device for measuring the hardness of material such as elastic rubber, and their water content was subsequently measured using wet and dry measurements.
Results: A significant correlation was found between the T2 values of the matched locations identified by image guidance intraoperatively and the consistency measured using the durometer (r = -0.722; P < 0.01) and the water content (r = 0.621; P = 0.01). In addition, the water content correlated significantly with the durometer consistency (r = -0.677; P < 0.01).
Conclusions: The T2 values could be a reliable quantitative predictor of meningioma consistency, and the T2 value distribution map, which elucidated the internal structure of the tumor in detail, could provide helpful information for surgical resection.
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http://dx.doi.org/10.1016/j.wneu.2021.10.135 | DOI Listing |
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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