AIMC Topic: Meningioma
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Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.
World neurosurgery
Aug 12, 2023
BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional ap...
Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.
European radiology
Apr 13, 2023
OBJECTIVES: To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.
BMC medical informatics and decision making
Jan 23, 2023
BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and ...
Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.
Scientific reports
Sep 14, 2022
Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter...
Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning".
Journal of magnetic resonance imaging : JMRI
Jul 1, 2022
Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.
Journal of magnetic resonance imaging : JMRI
Jul 1, 2022
BACKGROUND: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated vo...
Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study.
European radiology
Apr 14, 2022
OBJECTIVES: Develop and evaluate a deep learning-based automatic meningioma segmentation method for preoperative meningioma differentiation using radiomic features.
A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions.
European journal of radiology
Feb 12, 2022
BACKGROUND: For patients with meningioma, surgical procedures are different because of the status of sinus invasion. However, there is still no suitable technique to identify the status of sinus invasion in patients with meningiomas. We aimed to buil...
A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.
Neuroradiology
Jan 17, 2022
PURPOSE: This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.
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