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Glioma

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Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway.

Frontiers in immunology
BACKGROUND: Recent years have seen persistently poor prognoses for glioma patients. Therefore, exploring the molecular subtyping of gliomas, identifying novel prognostic biomarkers, and understanding the characteristics of their immune microenvironme...

Predicting survival in malignant glioma using artificial intelligence.

European journal of medical research
Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use...

Can we rely on machine learning algorithms as a trustworthy predictor for recurrence in high-grade glioma? A systematic review and meta-analysis.

Clinical neurology and neurosurgery
Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine lear...

Detecting IDH and TERTp mutations in diffuse gliomas using H-MRS with attention deep-shallow networks.

Computers in biology and medicine
BACKGROUND: Preoperative and noninvasive detection of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations in glioma is critical for prognosis and treatment planning. This study aims to develop deep lear...

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.

Journal of cellular and molecular medicine
It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as it is an important factor that determines the evolution and treatment response of tumours. This wor...

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significa...

A deep ensemble learning framework for glioma segmentation and grading prediction.

Scientific reports
The segmentation and risk grade prediction of gliomas based on preoperative multimodal magnetic resonance imaging (MRI) are crucial tasks in computer-aided diagnosis. Due to the significant heterogeneity between and within tumors, existing methods ma...

Radiomics in glioma: emerging trends and challenges.

Annals of clinical and translational neurology
Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning alg...

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

NeuroImage
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Genes to cells : devoted to molecular & cellular mechanisms
Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activi...