AIMC Topic: Isocitrate Dehydrogenase

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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...
Biomarkers, Tumor Brain Neoplasms Male Magnetic Resonance Imaging Cyclin-Dependent Kinase Inhibitor p16 Humans Artificial Intelligence Retrospective Studies Female Sensitivity and Specificity Image Interpretation, Computer-Assisted Isocitrate Dehydrogenase Preoperative Care Decision Trees Glioblastoma Gene Expression Mutation Aneuploidy Chromosomes, Human, Pair 10 Chromosomes, Human, Pair 7 X-linked Nuclear Protein
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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 Deep Learning Image Processing, Computer-Assisted Computational Biology Positron-Emission Tomography Glioblastoma Glioma Radiotherapy Planning, Computer-Assisted Neuroimaging Radiation Oncology Magnetic Resonance Imaging DNA Modification Methylases Neoplasm Grading DNA Methylation DNA Repair Enzymes Neoplasm Recurrence, Local Radiosurgery Diagnosis, Differential Tumor Suppressor Proteins Promoter Regions, Genetic Progression-Free Survival Neoplasm Proteins Isocitrate Dehydrogenase Imaging Genomics
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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.
ROC Curve Glioma Magnetic Resonance Imaging Mutation DNA Mutational Analysis Humans DNA, Neoplasm Machine Learning Isocitrate Dehydrogenase Biomarkers, Tumor Brain Neoplasms
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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...
Brain Neoplasms Male Middle Aged ROC Curve Glioma Prognosis Retrospective Studies Female Reproducibility of Results Adult Magnetic Resonance Imaging Predictive Value of Tests Mutation Survival Analysis Isocitrate Dehydrogenase Humans Machine Learning Brain
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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
Isocitrate Dehydrogenase Male Deep Learning Image Processing, Computer-Assisted Glioma Mutation Humans Neural Networks, Computer Female
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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...
Glioma Female Magnetic Resonance Imaging Reproducibility of Results Neoplasm Staging Biomarkers, Tumor Promoter Regions, Genetic Neoplasm Grading Male Isocitrate Dehydrogenase Mutation Image Processing, Computer-Assisted Telomerase Humans Neural Networks, Computer
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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).
Retrospective Studies Female Reproducibility of Results Adult Brain Neoplasms Male Middle Aged Glioma Magnetic Resonance Imaging Predictive Value of Tests Humans Machine Learning Brain Mutation Image Interpretation, Computer-Assisted Isocitrate Dehydrogenase
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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 ...
Magnetic Resonance Imaging Chromosomes, Human, Pair 1 Mutation Chromosomes, Human, Pair 19 Isocitrate Dehydrogenase Brain Neoplasms Chromosome Deletion Cytogenetic Analysis Male Middle Aged Humans ROC Curve Machine Learning Glioma Algorithms Female
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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...
Middle Aged Glioma Magnetic Resonance Imaging Glioblastoma Isocitrate Dehydrogenase Humans Machine Learning Algorithms Retrospective Studies Female Aged Aged, 80 and over Adult Brain Neoplasms Male
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Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.

Journal of cancer research and clinical oncology Feb 4, 2019
PURPOSE: Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.
Adolescent Genotype Glioma Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Young Adult Adult Brain Neoplasms Male Humans Middle Aged Machine Learning Isocitrate Dehydrogenase Female Aged Aged, 80 and over
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