Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients
- URL: http://arxiv.org/abs/2512.14232v1
- Date: Tue, 16 Dec 2025 09:37:20 GMT
- Title: Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients
- Authors: Rawan Alyahya, Asrar Alruwayqi, Atheer Alqarni, Asma Alkhaldi, Metab Alkubeyyer, Xin Gao, Mona Alshahrani,
- Abstract summary: Presence of MGMT promoter methylation significantly affects how well chemotherapy works for patients with Glioblastoma Multiforme (GBM)<n>Currently, confirmation of MGMT promoter methylation relies on invasive brain tumor tissue biopsies.<n>We propose a new multi-view approach that considers spatial relationships between MRI views to detect MGMT methylation status.
- Score: 6.414625387280682
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: The presence of MGMT promoter methylation significantly affects how well chemotherapy works for patients with Glioblastoma Multiforme (GBM). Currently, confirmation of MGMT promoter methylation relies on invasive brain tumor tissue biopsies. In this study, we explore radiogenomics techniques, a promising approach in precision medicine, to identify genetic markers from medical images. Using MRI scans and deep learning models, we propose a new multi-view approach that considers spatial relationships between MRI views to detect MGMT methylation status. Importantly, our method extracts information from all three views without using a complicated 3D deep learning model, avoiding issues associated with high parameter count, slow convergence, and substantial memory demands. We also introduce a new technique for tumor slice extraction and show its superiority over existing methods based on multiple evaluation metrics. By comparing our approach to state-of-the-art models, we demonstrate the efficacy of our method. Furthermore, we share a reproducible pipeline of published models, encouraging transparency and the development of robust diagnostic tools. Our study highlights the potential of non-invasive methods for identifying MGMT promoter methylation and contributes to advancing precision medicine in GBM treatment.
Related papers
- Explainable Deep Radiogenomic Molecular Imaging for MGMT Methylation Prediction in Glioblastoma [0.7614628596146601]
The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene promoter is a critical molecular biomarker.<n>Traditional methods for determining MGMT status rely on invasive biopsies.<n>This study presents a radiogenomic molecular imaging analysis framework for the non-invasive prediction of MGMT promoter methylation.
arXiv Detail & Related papers (2026-01-11T19:16:19Z) - The Multi-View Paradigm Shift in MRI Radiomics: Predicting MGMT Methylation in Glioblastoma [0.0]
Non-invasive inference of molecular tumor characteristics from medical imaging is a central goal of radiogenomics.<n>We introduce a multi-view latent representation learning framework based on variational autoencoders (VAE) to integrate complementary radiomic features.<n>The proposed approach preserves modality-specific structure while enabling effective multimodal integration.
arXiv Detail & Related papers (2025-12-26T16:32:19Z) - Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI [7.144319861722029]
Unsupervised anomaly detection (UAD) presents a complementary alternative to supervised learning for brain tumor segmentation in MRI.<n>We propose a novel Multimodal Vision Transformer Autoencoder (MViT-AE) trained exclusively on healthy brain MRIs to detect and localize tumors.<n>Our method achieves clinically meaningful tumor localization, with lesion-wise Dice Similarity Coefficient of 0.437 (Whole Tumor), 0.316 (Tumor Core), and 0.350 (Enhancing Tumor) on the test set, and an anomaly Detection Rate of 89.4% on the validation set.
arXiv Detail & Related papers (2025-10-17T14:26:30Z) - Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification [24.801687550103217]
This study proposes a novel double ensembling framework, consisting of ensembled pre-trained deep learning (DL) models for feature extraction and ensembled fine-tuned machine learning (ML) models to efficiently classify brain tumors.<n>Specifically, our method includes extensive preprocessing and augmentation, transfer learning concepts by utilizing various pre-trained deep convolutional neural networks and vision transformer networks to extract deep features from brain MRI.
arXiv Detail & Related papers (2025-07-16T12:22:11Z) - MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation.<n>Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning.<n>Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
arXiv Detail & Related papers (2025-03-18T15:39:44Z) - Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors [2.104687387907779]
We introduce HT-CNNs, an ensemble of Hybrid Transformers and Convolutional Neural Networks optimized through transfer learning for varied brain tumor segmentation.<n>This method captures spatial and contextual details from MRI data, fine-tuned on diverse datasets representing common tumor types.<n>Our findings underscore the potential of transfer learning and ensemble approaches in medical image segmentation, indicating a substantial enhancement in clinical decision-making and patient care.
arXiv Detail & Related papers (2024-12-11T09:52:01Z) - Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation [48.107348956719775]
We introduce Mask-Enhanced SAM (M-SAM), an innovative architecture tailored for 3D tumor lesion segmentation.
We propose a novel Mask-Enhanced Adapter (MEA) within M-SAM that enriches the semantic information of medical images with positional data from coarse segmentation masks.
Our M-SAM achieves high segmentation accuracy and also exhibits robust generalization.
arXiv Detail & Related papers (2024-03-09T13:37:02Z) - Cross-modality Guidance-aided Multi-modal Learning with Dual Attention
for MRI Brain Tumor Grading [47.50733518140625]
Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly.
We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
arXiv Detail & Related papers (2024-01-17T07:54:49Z) - Genetic InfoMax: Exploring Mutual Information Maximization in
High-Dimensional Imaging Genetics Studies [50.11449968854487]
Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits.
Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS.
We introduce a trans-modal learning framework Genetic InfoMax (GIM) to address the specific challenges of GWAS.
arXiv Detail & Related papers (2023-09-26T03:59:21Z) - MGMT promoter methylation status prediction using MRI scans? An
extensive experimental evaluation of deep learning models [0.0]
We employ deep learning models to predict the methylation status of the MGMT promoter in glioblastoma tumors using MRI scans.
Our results show no correlation between these models' performance to assure the accuracy and reliability of deep learning systems in cancer diagnosis.
arXiv Detail & Related papers (2023-04-03T07:55:42Z) - Artificial-intelligence-based molecular classification of diffuse
gliomas using rapid, label-free optical imaging [59.79875531898648]
DeepGlioma is an artificial-intelligence-based diagnostic screening system.
DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
arXiv Detail & Related papers (2023-03-23T18:50:18Z) - Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI
Scans using Deep Learning Models? [0.0]
Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal.
To identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis.
A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor.
arXiv Detail & Related papers (2022-01-16T16:44:21Z) - Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR
Images using a GAN [59.60954255038335]
The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators.
Experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
arXiv Detail & Related papers (2020-06-26T02:50:09Z) - Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size.
The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
arXiv Detail & Related papers (2020-02-03T22:25:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.