AIMC Topic: Glioma
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Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.
Scientific reports
Apr 24, 2025
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...
MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 9, 2025
Multimodal medical images reveal different characteristics of the same anatomy or lesion, offering significant clinical value. Deep learning has achieved widespread success in medical image analysis with large-scale labeled datasets. However, annotat...
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...
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...
Algorithms
Prognosis
Female
Aged
Adult
Radiomics
Brain Neoplasms
Humans
Machine Learning
Glioma
Proportional Hazards Models
Support Vector Machine
Multivariate Analysis
Magnetic Resonance Imaging
Kaplan-Meier Estimate
Neoplasm Grading
Male
Interleukin-7 Receptor alpha Subunit
Receptors, Interleukin-7
Middle Aged
ROC Curve
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 ...
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...
MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.
Scientific reports
Mar 3, 2025
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
T1-weighted MRI-based brain tumor classification using hybrid deep learning models.
Scientific reports
Feb 27, 2025
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....
Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.
Scientific reports
Feb 26, 2025
Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especially in regions with limited access to medical expertise. However, existing methods often overlook edge pixel information during tumor segmentation, l...
Performance of Machine Learning Models in Predicting BRAF Alterations Using Imaging Data in Low-Grade Glioma: A Systematic Review and Meta-Analysis.
World neurosurgery
Feb 25, 2025
BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prognostication and management strategy. Recent advances in artificial intelligence (AI) have resulted i...
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