AIMC Topic: Meningioma
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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...
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...
A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.
Scientific reports
Jan 10, 2025
Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separabl...
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...
Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning.
Analytical biochemistry
Dec 27, 2024
Brain tumors are among the most dangerous, due to their location in the organ that governs all life processes. Moreover, the high differentiation of these poses a challenge in diagnostics. Therefore, this study focused on the chemical differentiation...
Prognostic models for progression-free survival in atypical meningioma: Comparison of machine learning-based approach and the COX model in an Asian multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 19, 2024
BACKGROUND AND PURPOSE: Atypical meningiomas are prevalent intracranial tumors with varied prognoses and recurrence rates. The role of adjuvant radiotherapy (ART) in atypical meningiomas remains debated. This study aimed to develop and validate a pro...
Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.
Acta neurochirurgica
Dec 17, 2024
PURPOSE: Meningiomas are the most common primary brain tumour and account for over one-third of cases. Traditionally, estimations of morbidity and mortality following surgical resection have depended on subjective assessments of various factors, incl...
Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.
Academic radiology
Nov 29, 2024
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (...
MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning.
Journal of X-ray science and technology
Nov 27, 2024
BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging c...
Application of Artificial Intelligence in Prediction of Ki-67 Index in Meningiomas: A Systematic Review and Meta-Analysis.
World neurosurgery
Nov 18, 2024
BACKGROUND: The Ki-67 index is a histopathological marker that has been reported to be a crucial factor in the biological behavior and prognosis of meningiomas. Several studies have developed artificial intelligence (AI) models to predict the Ki-67 b...
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