ArticleScientific reports2023
Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 72 citations in OpenAlex.
- A data-driven scientific approach to explore the relationship between MGMT methylation and imaging phenotype in glioblastoma.Radiological physics and technology · 2026Article
- Multimodal imaging and AI in brain tumor surgery: current tools and emerging integration.npj biomedical innovations · 2026Review
- MGMT promoter methylation across glioma subtypes: biological relevance, treatment response, and survival outcomes.Molecular biology reports · 2026Review
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.Scientific reports · 2026Article
- Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- IDH Mutation Assessment in Gliomas from Anatomical MRI Using Deep Learning: A Comparative Analysis of Centralized and Federated Learning Frameworks.Diagnostics (Basel, Switzerland) · 2026Article
- Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.International journal of biomedical imaging · 2026Article
- Integrating Deep Learning and Radiogenomics: A Novel Approach to Glioblastoma Segmentation and MGMT Methylation Prediction.Journal of imaging · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas.Biomedicines · 2025Review
- Can diffusion-based generated magnetic resonance images predict glioma methylation accurately?Quantitative imaging in medicine and surgery · 2025Article
- Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability.Diagnostics (Basel, Switzerland) · 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
- Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.PloS one · 2025Article
- Correlations ofMolecular and clinical oncology · 2025Article
- Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.International journal of biomedical imaging · 2025Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 10 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F
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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.