AIMC Topic: Isocitrate Dehydrogenase
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A machine learning analysis of a "normal-like" IDH-WT diffuse glioma transcriptomic subgroup associated with prolonged survival reveals novel immune and neurotransmitter-related actionable targets.
BMC medicine
Oct 16, 2020
BACKGROUND: Classification of primary central nervous system tumors according to the World Health Organization guidelines follows the integration of histologic interpretation with molecular information and aims at providing the most precise prognosis...
A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.
Scientific reports
Jul 16, 2020
Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have i...
Isocitrate Dehydrogenase
Preoperative Care
Chromosomes, Human, Pair 10
Chromosomes, Human, Pair 7
X-linked Nuclear Protein
Brain Neoplasms
Male
Magnetic Resonance Imaging
Artificial Intelligence
Cyclin-Dependent Kinase Inhibitor p16
Retrospective Studies
Decision Trees
Female
Humans
Glioblastoma
Sensitivity and Specificity
Gene Expression
Biomarkers, Tumor
Mutation
Aneuploidy
Image Interpretation, Computer-Assisted
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...
Glioma
Magnetic Resonance Imaging
DNA Methylation
Diagnosis, Differential
Neoplasm Proteins
Sensitivity and Specificity
Brain Neoplasms
Deep Learning
Image Processing, Computer-Assisted
Computational Biology
Positron-Emission Tomography
Radiotherapy Planning, Computer-Assisted
DNA Modification Methylases
DNA Repair Enzymes
Tumor Suppressor Proteins
Neuroimaging
Glioblastoma
Radiation Oncology
Neoplasm Grading
Neoplasm Recurrence, Local
Progression-Free Survival
Humans
Radiosurgery
Promoter Regions, Genetic
Isocitrate Dehydrogenase
Imaging Genomics
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...
A Multiple-Instance Learning-Based Convolutional Neural Network Model to Detect the Mutation in the Histopathology Images of Glioma Tissues.
Journal of computational biology : a journal of computational molecular cell biology
Jan 3, 2020
Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.
Scientific reports
Dec 30, 2019
Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a di...
Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.
Japanese journal of radiology
Nov 18, 2019
PURPOSE: To assess the performance of texture analysis of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps in predicting IDH1 status in high-grade gliomas (HGG).
Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm.
Clinical cancer research : an official journal of the American Association for Cancer Research
Sep 23, 2019
PURPOSE: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q ...
Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.
World neurosurgery
Feb 5, 2019
OBJECTIVE: Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favorable prognostic factor for glioblastoma (GBM) and is usually determined by sequencing or immunohistochemistry. An accurate prediction of IDH1 mutation status via noni...
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