AIMC Topic: Meningioma

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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...
Brain Deep Learning Meningioma Meningeal Neoplasms Humans Neural Networks, Computer
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Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning".

Journal of magnetic resonance imaging : JMRI Jul 1, 2022
Deep Learning Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Humans Meningioma 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...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Meningioma Female Male Middle Aged Meningeal Neoplasms Humans Retrospective Studies
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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.
Humans Retrospective Studies Adult Middle Aged Meningioma Meningeal Neoplasms Multiparametric Magnetic Resonance Imaging Deep Learning ROC Curve Magnetic Resonance Imaging
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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...
Humans Retrospective Studies Deep Learning Magnetic Resonance Imaging Meningioma Meningeal Neoplasms
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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.
Meningeal Neoplasms ROC Curve Magnetic Resonance Imaging Meningioma Humans Deep Learning Cohort Studies Algorithms Retrospective Studies
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Resection of Intracranial Tumors with a Robotic-Assisted Digital Microscope: A Preliminary Experience with Robotic Scope.

World neurosurgery May 27, 2021
BACKGROUND: Magnified intraoperative visualization is of paramount importance during microsurgical procedures. Although the introduction of the operating microscope represented one of the most relevant innovations in modern neurosurgery, surgical vis...
Meningioma Astrocytoma Adult Brain Neoplasms Surgery, Computer-Assisted Male Feasibility Studies Humans Magnetic Resonance Imaging Microscopy Robotics Female Microsurgery Middle Aged Aged Robotic Surgical Procedures Imaging, Three-Dimensional Neurosurgical Procedures
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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...
Support Vector Machine Magnetic Resonance Imaging Decision Trees Intraoperative Care Meningioma Bayes Theorem Preoperative Care Middle Aged Pilot Projects Computational Biology Logistic Models Female Supratentorial Neoplasms Elasticity Imaging Techniques Aged Humans Male Meningeal Neoplasms Neural Networks, Computer Machine Learning Diffusion Magnetic Resonance Imaging Retrospective Studies
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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...
Humans Retrospective Studies Deep Learning Child Neoplasm Grading Meningioma Meningeal Neoplasms
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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.
Meningioma Meningeal Neoplasms Multiparametric Magnetic Resonance Imaging Middle Aged ROC Curve Predictive Value of Tests Image Interpretation, Computer-Assisted Female Humans Area Under Curve Preoperative Care Neoplasm Grading Reproducibility of Results Machine Learning Male Retrospective Studies
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