AIMC Topic: Glioma
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An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.
Computational and mathematical methods in medicine
Jul 14, 2020
Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In t...
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
May 11, 2020
BACKGROUND: Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use o...
Humans
Sensitivity and Specificity
Brain Neoplasms
Radiosurgery
Promoter Regions, Genetic
Isocitrate Dehydrogenase
Imaging Genomics
DNA Modification Methylases
DNA Repair Enzymes
Tumor Suppressor Proteins
Neuroimaging
Neoplasm Proteins
Glioblastoma
Radiation Oncology
Neoplasm Grading
Neoplasm Recurrence, Local
Progression-Free Survival
Positron-Emission Tomography
Radiotherapy Planning, Computer-Assisted
Glioma
Magnetic Resonance Imaging
DNA Methylation
Diagnosis, Differential
Deep Learning
Image Processing, Computer-Assisted
Computational Biology
DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images.
International journal of computer assisted radiology and surgery
May 5, 2020
PURPOSE: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fl...
Artificial intelligence in glioma imaging: challenges and advances.
Journal of neural engineering
Apr 30, 2020
Primary brain tumors including gliomas continue to pose significant management challenges to clinicians. While the presentation, the pathology, and the clinical course of these lesions are variable, the initial investigations are usually similar. Pat...
Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images.
Computers in biology and medicine
Apr 22, 2020
Gliomas are the most common malignant brain tumors with different grades that highly determine the rate of survival in patients. Tumor segmentation and grading using magnetic resonance imaging (MRI) are common and essential for diagnosis and treatmen...
Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.
Neuroradiology
Apr 6, 2020
Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dyna...
PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.
European radiology experimental
Apr 3, 2020
PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consor...
Brain tumor classification using modified local binary patterns (LBP) feature extraction methods.
Medical hypotheses
Mar 25, 2020
Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tum...
Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.
European radiology
Mar 19, 2020
OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML.
Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.
European radiology
Mar 11, 2020
BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features cou...
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