AIMC Topic: Meningeal Neoplasms

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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...
Humans Retrospective Studies Female Image Processing, Computer-Assisted Magnetic Resonance Imaging Meningioma Meningeal Neoplasms Male Middle Aged Deep Learning
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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.
Multiparametric Magnetic Resonance Imaging Magnetic Resonance Imaging Meningioma Meningeal Neoplasms Humans Retrospective Studies Deep Learning ROC Curve Adult Middle Aged
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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...
Meningeal Neoplasms Deep Learning Magnetic Resonance Imaging Humans Meningioma Retrospective Studies
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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.
Cohort Studies ROC Curve Magnetic Resonance Imaging Algorithms Retrospective Studies Deep Learning Meningioma Meningeal Neoplasms Humans
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Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model.

Sensors (Basel, Switzerland) Jan 4, 2022
With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tum...
Reproducibility of Results Brain Neoplasms Deep Learning Magnetic Resonance Imaging Humans Meningeal Neoplasms
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Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.

World neurosurgery Nov 28, 2020
BACKGROUND: The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to develop a predictive model of tumor consistency using the radiomic features of preoperative magnetic re...
Intraoperative Care Meningioma Elasticity Imaging Techniques Supratentorial Neoplasms Meningeal Neoplasms Diffusion Magnetic Resonance Imaging Decision Trees Bayes Theorem Preoperative Care Logistic Models Support Vector Machine Magnetic Resonance Imaging Retrospective Studies Female Aged Pilot Projects Computational Biology Male Middle Aged Humans Neural Networks, Computer Machine Learning
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Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma.

Neuroinformatics Sep 25, 2020
The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma le...
Retrospective Studies Deep Learning Humans Meningeal Neoplasms Child Neoplasm Grading Meningioma
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Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI.

European journal of radiology Aug 28, 2020
PURPOSE: To investigate the prediction performance of radiomic models based on multiparametric MRI in predicting the meningioma grade.
Preoperative Care Meningioma Meningeal Neoplasms Area Under Curve Multiparametric Magnetic Resonance Imaging Retrospective Studies Middle Aged Reproducibility of Results Humans ROC Curve Neoplasm Grading Female Male Predictive Value of Tests Machine Learning Image Interpretation, Computer-Assisted
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Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

Clinical neuroradiology Feb 14, 2020
PURPOSE: Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicate...
Image Processing, Computer-Assisted Meningioma Meningeal Neoplasms Humans Retrospective Studies Deep Learning Multiparametric Magnetic Resonance Imaging
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Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.

Magnetic resonance imaging Aug 16, 2019
BACKGROUND AND PURPOSE: Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis tha...
Middle Aged Aged ROC Curve Humans Sensitivity and Specificity Support Vector Machine Meningeal Neoplasms Male Machine Learning Area Under Curve Diffusion Magnetic Resonance Imaging Female Meningioma
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