AIMC Topic: Isocitrate Dehydrogenase

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neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival.

BMC bioinformatics Jul 23, 2021
BACKGROUND: Neoantigen based personalized immune therapies achieve promising results in melanoma and lung cancer, but few neoantigen based models perform well in IDH wild-type GBM, and the association between neoantigen intrinsic features and prognos...
Brain Neoplasms Deep Learning Cohort Studies Glioblastoma Isocitrate Dehydrogenase Humans
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Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type.

Scientific reports May 10, 2021
Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic var...
Deep Learning Contrast Media Magnetic Resonance Imaging Glioblastoma Feasibility Studies Humans Aged Image Interpretation, Computer-Assisted Brain Adult Datasets as Topic Retrospective Studies Brain Neoplasms DNA Mutational Analysis Female Isocitrate Dehydrogenase Male Middle Aged
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Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET.

Cancer imaging : the official publication of the International Cancer Imaging Society Mar 10, 2021
BACKGROUND: The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsuperv...
Male Humans Isocitrate Dehydrogenase Middle Aged Machine Learning Glioma Retrospective Studies Magnetic Resonance Imaging Female Positron-Emission Tomography Brain Neoplasms
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Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach.

BioMed research international Jan 22, 2021
PURPOSE: Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative an...
Humans Machine Learning Retrospective Studies Male Middle Aged Adolescent Decision Support Systems, Clinical Image Processing, Computer-Assisted Female Aged Young Adult Adult Brain Neoplasms Genotype Glioma Magnetic Resonance Imaging Mutation Isocitrate Dehydrogenase
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Noninvasive Determination of and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review.

AJNR. American journal of neuroradiology Nov 26, 2020
BACKGROUND: Determination of () status and, if -mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating ima...
Isocitrate Dehydrogenase Chromosomes, Human, Pair 1 Male Middle Aged Glioma Magnetic Resonance Imaging Mutation Humans Machine Learning Female Adult Brain Neoplasms
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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...
Transcriptome Glioma Mutation Survival Analysis Isocitrate Dehydrogenase Young Adult Adult Brain Neoplasms Male Middle Aged Prognosis Female Aged Aged, 80 and over Humans Machine Learning
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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...
Humans Artificial Intelligence Retrospective Studies Mutation Aneuploidy Magnetic Resonance Imaging Female Image Interpretation, Computer-Assisted Cyclin-Dependent Kinase Inhibitor p16 Chromosomes, Human, Pair 10 Sensitivity and Specificity Isocitrate Dehydrogenase Decision Trees Biomarkers, Tumor Preoperative Care Chromosomes, Human, Pair 7 Glioblastoma Brain Neoplasms X-linked Nuclear Protein Gene Expression Male
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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...
DNA Repair Enzymes Tumor Suppressor Proteins Neuroimaging Computational Biology Glioma Neoplasm Proteins Radiosurgery Neoplasm Recurrence, Local Magnetic Resonance Imaging Progression-Free Survival Isocitrate Dehydrogenase Promoter Regions, Genetic Glioblastoma DNA Methylation Positron-Emission Tomography Humans Radiation Oncology Imaging Genomics Diagnosis, Differential Radiotherapy Planning, Computer-Assisted Sensitivity and Specificity Neoplasm Grading DNA Modification Methylases Brain Neoplasms Deep Learning Image Processing, Computer-Assisted
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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.
DNA, Neoplasm Isocitrate Dehydrogenase Humans Machine Learning Biomarkers, Tumor ROC Curve Brain Neoplasms Glioma Magnetic Resonance Imaging Mutation DNA Mutational Analysis
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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...
Magnetic Resonance Imaging Predictive Value of Tests Mutation Survival Analysis Isocitrate Dehydrogenase Brain Neoplasms Male Middle Aged ROC Curve Glioma Humans Prognosis Machine Learning Retrospective Studies Brain Female Reproducibility of Results Adult
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