AIMC Topic: Meningioma

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A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subbands' textural fractal ch...

[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].

Revue medicale suisse
Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision and preservation of healthy tissues. It is effective for meningiomas, trigeminal neuralgia (TN), pitu...

An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.

Scientific reports
The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas fr...

Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.

NMR in biomedicine
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...

The Co-Pilot Project in wartime: lessons from Lviv, Ukraine.

Neurosurgical focus
OBJECTIVE: The ongoing war in Ukraine has introduced many challenges to an already overburdened and resource-limited medical system. Longitudinal collaborations, material support, educational outreach, and surgical mentorship are essential for improv...

Brain tumor classification using MRI images and deep learning techniques.

PloS one
Brain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims to address this challenge by introducing an automated brain tumor classification system that utilizes...

Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Meningiomas are the most prevalent benign intracranial tumors, and surgical intervention is the primary treatment. The physical characteristics of meningiomas, such as tumor stiffness or consistency, play a crucial role in the surgical approach. This...

Design and Development of Hypertuned Deep learning Frameworks for Detection and Severity Grading of Brain Tumor using Medical Brain MR images.

Current medical imaging
BACKGROUND: Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on bi...

Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.

Neurosurgery
BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructur...