AIMC Topic: DNA Modification Methylases
Clear Filters Showing 11 to 20 of 23 articles
The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.
Cancer medicine
Aug 28, 2021
This retrospective study has been conducted to validate the performance of deep learning-based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the ...
Brain Neoplasms
Male
Middle Aged
Deep Learning
Age Factors
Predictive Value of Tests
Humans
Retrospective Studies
Female
Aged
Adult
Tumor Suppressor Proteins
Proportional Hazards Models
Chemoradiotherapy
Radiotherapy
Isocitrate Dehydrogenase
Karnofsky Performance Status
Glioblastoma
Bayes Theorem
Sex Factors
Survival Analysis
DNA Modification Methylases
DNA Repair Enzymes
MRI-Based Deep-Learning Method for Determining Glioma Promoter Methylation Status.
AJNR. American journal of neuroradiology
Mar 4, 2021
BACKGROUND AND PURPOSE: () promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining promoter methylation status using T2 weighted Images (T2WI) only.
DNA Methylation
Humans
Brain Neoplasms
Area Under Curve
Neural Networks, Computer
Male
DNA Modification Methylases
Reproducibility of Results
Middle Aged
DNA Repair Enzymes
Deep Learning
Aged
Tumor Suppressor Proteins
Glioma
Adult
Promoter Regions, Genetic
Magnetic Resonance Imaging
Sensitivity and Specificity
Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma.
Magnetic resonance imaging
Sep 25, 2020
INTRODUCTION: Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to impro...
Humans
Machine Learning
Middle Aged
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
DNA Methylation
Glioblastoma
Survival Analysis
Retrospective Studies
Female
Aged
Adult
Brain Neoplasms
Male
DNA Modification Methylases
DNA Repair Enzymes
Tumor Suppressor Proteins
Semantics
Promoter Regions, Genetic
Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis.
BioMed research international
Sep 23, 2020
Methylation of the O-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of M...
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...
Radiosurgery
Promoter Regions, Genetic
Isocitrate Dehydrogenase
Imaging Genomics
Radiotherapy Planning, Computer-Assisted
DNA Modification Methylases
DNA Repair Enzymes
Tumor Suppressor Proteins
Neuroimaging
Humans
Computational Biology
Sensitivity and Specificity
Glioma
Brain Neoplasms
Magnetic Resonance Imaging
Deep Learning
DNA Methylation
Glioblastoma
Image Processing, Computer-Assisted
Diagnosis, Differential
Radiation Oncology
Neoplasm Proteins
Neoplasm Grading
Neoplasm Recurrence, Local
Progression-Free Survival
Positron-Emission Tomography
Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.
Cancer reports (Hoboken, N.J.)
Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
Tumor Suppressor Protein p53
Antineoplastic Combined Chemotherapy Protocols
Tumor Microenvironment
Humans
Glioblastoma
Brain Neoplasms
Tumor Suppressor Proteins
Machine Learning
Clonal Evolution
Immune Checkpoint Inhibitors
Brain
Computational Biology
Precision Medicine
Cancer Vaccines
DNA Modification Methylases
Prognosis
Mutation
DNA Mutational Analysis
Models, Genetic
Genetic Heterogeneity
DNA Repair Enzymes
Chemoradiotherapy, Adjuvant
Drug Resistance, Neoplasm
Microtubules
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...
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...
Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma.
Computer methods and programs in biomedicine
Dec 30, 2016
BACKGROUND AND OBJECTIVE: The O-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. ...
Sensitive detection of DNA methyltransferase using the dendritic rolling circle amplification-induced fluorescence.
Analytica chimica acta
Dec 27, 2016
The analysis of DNA methylation and MTase activities is very important in the early clinical diagnosis of cancer, on purposes of providing insights into the mechanism of gene repression and developing novel drugs of treating methylation-related disea...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823