AIMC Topic: Isocitrate Dehydrogenase
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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.
Contrast enhancement is a prognostic factor in IDH1/2 mutant, but not in wild-type WHO grade II/III glioma as confirmed by machine learning.
European journal of cancer (Oxford, England : 1990)
Dec 7, 2018
BACKGROUND: Mutation of the isocitrate dehydrogenase (IDH) gene and co-deletion on chromosome 1p/19q is becoming increasingly relevant for the evaluation of clinical outcome in glioma. Among the imaging parameters, contrast enhancement (CE) in WHO II...
Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.
Journal of neuro-oncology
May 16, 2018
INTRODUCTION: Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a gl...
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.
AJNR. American journal of neuroradiology
May 10, 2018
BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimenta...
Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.
Scientific reports
Apr 17, 2018
We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and textur...
Residual Convolutional Neural Network for the Determination of Status in Low- and High-Grade Gliomas from MR Imaging.
Clinical cancer research : an official journal of the American Association for Cancer Research
Nov 22, 2017
Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative r...
Magnetic Resonance Imaging
Predictive Value of Tests
Neoplasm Grading
Mutation
Datasets as Topic
Preoperative Period
Isocitrate Dehydrogenase
Brain Neoplasms
Male
Middle Aged
Image Processing, Computer-Assisted
Glioma
Aged
Aged, 80 and over
Young Adult
Adult
Humans
Neural Networks, Computer
Brain
Retrospective Studies
Female
Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Sep 4, 2017
MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is...
Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.
Scientific reports
Jul 14, 2017
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in ...
Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.
Neuro-oncology
Nov 4, 2024
BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the backgro...
Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction.
Neuro-oncology
Jun 3, 2024
BACKGROUND: This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in gl...
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