AIMC Topic: Isocitrate Dehydrogenase
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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...
Young Adult
Adult
Brain Neoplasms
Male
Middle Aged
Image Processing, Computer-Assisted
Glioma
Magnetic Resonance Imaging
Retrospective Studies
Female
Aged
Aged, 80 and over
Humans
Neural Networks, Computer
Brain
Predictive Value of Tests
Neoplasm Grading
Preoperative Period
Mutation
Datasets as Topic
Isocitrate Dehydrogenase
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 ...
Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.
Cancer medicine
Aug 1, 2025
OBJECTIVES: Accurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic effica...
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...
Insight into deep learning for glioma IDH medical image analysis: A systematic review.
Medicine
Feb 16, 2024
BACKGROUND: Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained increasing significance over the past decade in the context of glioma patien...
Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma.
Korean journal of radiology
Dec 1, 2023
Response to "Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma".
Korean journal of radiology
Dec 1, 2023
[A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities].
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
Aug 20, 2023
OBJECTIVE: To propose a Dual-Aware deep learning framework for genotyping of isocitrate dehydrogenase (IDH) in gliomas based on magnetic resonance amide proton transfer (APT) modality data as a means to assist non-invasive diagnosis of gliomas.
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