AIMC Topic: Meningeal Neoplasms

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Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study.

BMC medical imaging Jul 1, 2025
BACKGROUND: Meningioma consistency critically impacts surgical planning, as soft tumors are easier to resect than hard tumors. Current assessments of tumor consistency using MRI are subjective and lack quantitative accuracy. Integrating deep learning...
Retrospective Studies Female Aged Adult Humans Deep Learning Magnetic Resonance Imaging Meningioma Meningeal Neoplasms Sensitivity and Specificity Radiomics Male Middle Aged
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Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

BMC medical imaging May 3, 2025
OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. Thi...
Humans Machine Learning Retrospective Studies Sella Turcica Craniopharyngioma Central Nervous System Cysts Adolescent Support Vector Machine Magnetic Resonance Imaging Pituitary Neoplasms Adenoma Diagnosis, Differential Meningioma Adult Meningeal Neoplasms Female Radiomics Aged Male Young Adult Middle Aged
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Deep learning for automated segmentation of brain edema in meningioma after radiosurgery.

BMC medical imaging Apr 22, 2025
BACKGROUND: Although gamma Knife radiosurgery (GKRS) is commonly used to treat benign brain tumors, such as meningioma, irradiating the surrounding brain tissue can lead to perifocal edema within a few months after the procedure. Volumetric assessmen...
Adult Male Middle Aged Deep Learning Humans Female Aged Meningeal Neoplasms Magnetic Resonance Imaging Brain Edema Radiosurgery Meningioma
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Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review.

F1000Research Mar 25, 2025
INTRODUCTION: Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies ...
Machine Learning Prognosis Humans Neoplasm Recurrence, Local Meningioma Meningeal Neoplasms Radiomics Magnetic Resonance Imaging
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Development of Hybrid radiomic Machine learning models for preoperative prediction of meningioma grade on multiparametric MRI.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia Mar 5, 2025
PURPOSE: To develop and compare machine learning models for distinguishing low and high grade meningiomas on multiparametric MRI.
Multiparametric Magnetic Resonance Imaging Adult Radiomics Male Middle Aged Magnetic Resonance Imaging Neoplasm Grading Meningioma Meningeal Neoplasms Machine Learning Retrospective Studies Female Aged Humans
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Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.

Cancer imaging : the official publication of the International Cancer Imaging Society Feb 28, 2025
OBJECTIVE: Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.
Deep Learning ROC Curve Magnetic Resonance Imaging Neoplasm Invasiveness Humans Preoperative Care Meningioma Meningeal Neoplasms Cranial Sinuses Adult Retrospective Studies Radiomics Female Male Aged Middle Aged
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Serum Metabolomics Profiling Coupled with Machine Learning Identifies Potential Diagnostic and Prognostic Candidate Markers in Meningioma Using Raman Spectroscopy, ATR-FTIR, and LC-MS/MS.

Journal of proteome research Feb 25, 2025
Meningioma, the most prevalent brain tumor, poses significant challenges due to its unclear transition from low-grade to aggressive forms, with limited knowledge about grade-specific markers. We have utilized vibrational spectroscopic techniques such...
Meningioma Meningeal Neoplasms Middle Aged Case-Control Studies Spectrum Analysis, Raman Aged Humans Spectroscopy, Fourier Transform Infrared Adult Machine Learning Metabolomics Biomarkers, Tumor Prognosis Chromatography, Liquid Male Female Liquid Chromatography-Mass Spectrometry Tandem Mass Spectrometry
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MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach.

Sensors (Basel, Switzerland) Feb 25, 2025
The firmness of meningiomas is a critical factor that impacts the surgical approach recommended for patients. The conventional approaches that couple image processing techniques with radiologists' visual assessments of magnetic resonance imaging (MRI...
Humans Neural Networks, Computer Magnetic Resonance Imaging Meningioma Meningeal Neoplasms Machine Learning Algorithms Deep Learning Image Processing, Computer-Assisted
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Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.

Neurosurgical review Jan 24, 2025
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering rece...
Meningioma Meningeal Neoplasms Machine Learning Radiomics Deep Learning Neoplasm Grading Humans
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Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Neuroinformatics Dec 28, 2024
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 acc...
Magnetic Resonance Imaging Humans Meningioma Neural Networks, Computer Meningeal Neoplasms Image Processing, Computer-Assisted
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