AIMC Topic: Meningioma
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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.
Male
Middle Aged
Deep Learning
Adolescent
Aged
Young Adult
Adult
Sensitivity and Specificity
Preoperative Care
Meningioma
Meningeal Neoplasms
Meninges
Cohort Studies
Humans
Magnetic Resonance Imaging
Algorithms
Area Under Curve
Neoplasm Grading
Prognosis
Retrospective Studies
Female
Reproducibility of Results
A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.
Sensors (Basel, Switzerland)
Apr 28, 2019
Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neur...
Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
Neuroradiology
Apr 4, 2019
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this...
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...
Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector.
Journal of medical systems
Nov 3, 2018
Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma's can occur in many parts of the brain and...
A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.
BMC veterinary research
Oct 22, 2018
BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, an...
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...
eNAL++: a new and effective off-line correction protocol for rotational setup errors when using a robotic couch.
Journal of applied clinical medical physics
Nov 8, 2015
Cone-beam CTs (CBCTs) installed on a linear accelerator can be used to provide fast and accurate automatic six degrees of freedom (6DoF) vector displacement information of the patient position just prior to radiotherapy. These displacement correction...
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