AIMC Topic: Brain Neoplasms
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Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.
IEEE journal of biomedical and health informatics
Apr 4, 2025
The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pat...
Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16.
Cancer biomarkers : section A of Disease markers
Apr 4, 2025
BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for the development of an automated cancer detection system, particularly for MRI images. By leveraging deep learning and image processing techniques, we a...
Multimodal imaging platform for enhanced tumor resection in neurosurgery: integrating hyperspectral and pCLE technologies.
International journal of computer assisted radiology and surgery
Apr 3, 2025
PURPOSE: This work presents a novel multimodal imaging platform that integrates hyperspectral imaging (HSI) and probe-based confocal laser endomicroscopy (pCLE) for improved brain tumor identification during neurosurgery. By combining these two modal...
Machine learning fusion for glioma tumor detection.
Scientific reports
Apr 2, 2025
The early detection of brain tumors is very important for treating them and improving the quality of life for patients. Through advanced imaging techniques, doctors can now make more informed decisions. This paper introduces a framework for a tumor d...
Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.
AJNR. American journal of neuroradiology
Apr 2, 2025
BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high ac...
Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice.
Journal of translational medicine
Mar 31, 2025
BACKGROUND: High-grade gliomas are among the most aggressive and deadly brain tumors, highlighting the critical need for improved prognostic markers and predictive models. Recent studies have identified the expression of IL7R as a significant risk fa...
Neoplasm Grading
Proportional Hazards Models
Multivariate Analysis
Interleukin-7 Receptor alpha Subunit
Kaplan-Meier Estimate
Humans
Receptors, Interleukin-7
Machine Learning
Glioma
Support Vector Machine
Magnetic Resonance Imaging
Aged
Male
Adult
Middle Aged
Algorithms
Radiomics
ROC Curve
Prognosis
Brain Neoplasms
Female
Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis.
Scientific reports
Mar 30, 2025
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact that the features ...
A predictive model for MGMT promoter methylation status in glioblastoma based on terahertz spectral data.
Analytical biochemistry
Mar 29, 2025
O-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a crucial biomarker in glioblastoma (GBM) that influences response to temozolomide. Traditional detection methods, such as gene sequencing, are time-consuming and limited to postope...
Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.
BMC medical imaging
Mar 26, 2025
BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focu...
Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.
PloS one
Mar 24, 2025
Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on manual intervention and often have problems with efficiency and accuracy, which may lead to misdiagnos...
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