AIMC Topic: Meningioma
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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...
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
Radiomics
Male
Middle Aged
Adolescent
Female
Aged
Young Adult
Adult
Craniopharyngioma
Diagnosis, Differential
Support Vector Machine
Central Nervous System Cysts
Meningioma
Magnetic Resonance Imaging
Meningeal Neoplasms
Pituitary Neoplasms
Sella Turcica
Adenoma
Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.
Scientific reports
Apr 29, 2025
Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types-pituitary, meningioma, glioma, and normal-this ...
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...
MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 9, 2025
Multimodal medical images reveal different characteristics of the same anatomy or lesion, offering significant clinical value. Deep learning has achieved widespread success in medical image analysis with large-scale labeled datasets. However, annotat...
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 ...
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.
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.
T1-weighted MRI-based brain tumor classification using hybrid deep learning models.
Scientific reports
Feb 27, 2025
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....
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...
Middle Aged
Case-Control Studies
Spectrum Analysis, Raman
Spectroscopy, Fourier Transform Infrared
Metabolomics
Chromatography, Liquid
Liquid Chromatography-Mass Spectrometry
Tandem Mass Spectrometry
Aged
Adult
Meningioma
Biomarkers, Tumor
Humans
Meningeal Neoplasms
Male
Machine Learning
Prognosis
Female
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