AIMC Topic: Meningioma
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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 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...
Humans
Neural Networks, Computer
Machine Learning
Supratentorial Neoplasms
Magnetic Resonance Imaging
Decision Trees
Bayes Theorem
Preoperative Care
Intraoperative Care
Meningioma
Middle Aged
Elasticity Imaging Techniques
Meningeal Neoplasms
Pilot Projects
Diffusion Magnetic Resonance Imaging
Retrospective Studies
Computational Biology
Female
Logistic Models
Aged
Support Vector Machine
Male
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...
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.
Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study.
CMAJ open
Feb 18, 2020
BACKGROUND: As artificial intelligence (AI) approaches in research increase and AI becomes more integrated into medicine, there is a need to understand perspectives from members of the Canadian public and medical community. The aim of this project wa...
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...
An enhanced deep learning approach for brain cancer MRI images classification using residual networks.
Artificial intelligence in medicine
Dec 10, 2019
Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis ...
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...
Image-Guided Robotic Radiosurgery for Treatment of Recurrent Grade II and III Meningiomas. A Single-Center Study.
World neurosurgery
Jul 12, 2019
OBJECTIVE: Stereotactic radiosurgery (SRS) has been increasingly applied for malignant meningiomas as an alternative to conventionally fractioned radiation therapy. We performed a retrospective analysis of an institutional patient cohort with maligna...
Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis.
Journal of neuroradiology = Journal de neuroradiologie
Jun 18, 2019
PURPOSE: To assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant haemangiopericytoma (HPC) from angiomatous meningioma (AM).
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