AIMC Topic: 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.
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...
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).
A deep learning radiomics model for preoperative grading in meningioma.
European journal of radiology
May 1, 2019
OBJECTIVES: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI.
Humans
Algorithms
Prognosis
Reproducibility of Results
Aged
Young Adult
Retrospective Studies
Female
Area Under Curve
Neoplasm Grading
Preoperative Care
Meningioma
Deep Learning
Adolescent
Meningeal Neoplasms
Cohort Studies
Meninges
Magnetic Resonance Imaging
Adult
Sensitivity and Specificity
Male
Middle Aged
Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.
European radiology
Nov 15, 2018
OBJECTIVES: Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), app...
The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest.
European radiology
Aug 7, 2018
OBJECTIVES: The preoperative prediction of the WHO grade of a meningioma is important for further treatment plans. This study aimed to assess whether texture analysis (TA) based on apparent diffusion coefficient (ADC) maps could non-invasively classi...
Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.
European radiology
Jun 25, 2018
OBJECTIVES: Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine ...
Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.
Veterinary journal (London, England : 1997)
Apr 4, 2018
An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and...
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