ArticleJournal of digital imaging2017
Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
77 citing papers in PubMed, 200 citations in OpenAlex.
- Brain foundation models with hypergraph dynamic adapter for brain disease analysis.Pattern recognition · 2026Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Emerging Trends in Artificial Intelligence in Neuro-Oncology.Current oncology reports · 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
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine.Current oncology reports · 2024Review
- Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.Scientific reports · 2024Article
- Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.Biomedicines · 2024Review
- Article
- The unintended consequences of artificial intelligence in paediatric radiology.Pediatric radiology · 2024Article
- DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks.Genes · 2024Article
- Empowering Radiographers: A Call for Integrated AI Training in University Curricula.International journal of biomedical imaging · 2024Article
- Deep learning modeling using mammography images for predicting estrogen receptor status in breast cancer.American journal of translational research · 2024Article
- Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine.Biomicrofluidics · 2024Article
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care.BMC health services research · 2023Article
- A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.Journal of digital imaging · 2023Article
- MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation.Cancers · 2023Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p < 0.001). We report a method that alleviates the need of extensive preprocessing and acts as a proof of concept that deep neural architectures can be used to predict molecular biomarkers from routine medical images.
Indexed as
Identifiers
What 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.