ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018
MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
44 citing papers in PubMed, 92 citations in OpenAlex.
- Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.Scientific reports · 2025Article
- Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery.JMA journal · 2025Review
- Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.Biomedicines · 2024Review
- Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.Journal of radiation research · 2024Article
- Article
- CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Survival and grade of the glioma prediction using transfer learning.PeerJ. Computer science · 2023Article
- A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification.Journal of imaging · 2022Article
- The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.Translational cancer research · 2022Article
- An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment.Cancer communications (London, England) · 2022Review
- Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge.Cancers · 2022Article
- Article
- Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics.Journal of clinical medicine · 2022Article
- Artificial Intelligence in Neuro-Oncologic Imaging: A Brief Review for Clinical Use Cases and Future Perspectives.Brain tumor research and treatment · 2022Review
- Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer.Cancers · 2022Review
- Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the O6-methylguanine methyltransferase (MGMT) gene may impact the efficacy and sensitivity of temozolomide, and hence may affect overall patient survival. Microscopic genetic changes may manifest as macroscopic morphological changes in the brain tumors that can be detected using magnetic resonance imaging (MRI), which can serve as noninvasive biomarkers for determining methylation of MGMT regulatory regions. In this research, we use a compendium of brain MRI scans of GBM patients collected from The Cancer Imaging Archive (TCIA) combined with methylation data from The Cancer Genome Atlas (TCGA) to predict the methylation state of the MGMT regulatory regions in these patients. Our approach relies on a bi-directional convolutional recurrent neural network architecture (CRNN) that leverages the spatial aspects of these 3-dimensional MRI scans. Our CRNN obtains an accuracy of 67% on the validation data and 62% on the test data, with precision and recall both at 67%, suggesting the existence of MRI features that may complement existing markers for GBM patient stratification and prognosis. We have additionally presented our model via a novel neural network visualization platform, which we have developed to improve interpretability of deep learning MRI-based classification models.
Indexed as
Identifiers
29218894PMC5728677W2769012178What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.