AIMC Topic: Glioma

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Resting state fMRI feature-based cerebral glioma grading by support vector machine.

International journal of computer assisted radiology and surgery
PURPOSEĀ : Tumor grading plays an essential role in the optimal selection of solid tumor treatment. Noninvasive methods are needed for clinical grading of tumors. This study aimed to extract parameters of resting state blood oxygenation level-dependen...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Computational biology and chemistry
Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for gliomas of different grades and molecular types. However, current glioma molecular subtyping systems ...

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Computers in biology and medicine
Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity demands greater transparency in artificial intelligence (AI) systems. This is noticeable when users mu...

Multimodal MRI radiomics enhances epilepsy prediction in pediatric low-grade glioma patients.

Journal of neuro-oncology
BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based ...

Integrative multi-omics analysis reveals BEST1 as a potential tumor-associated gene in gliomas.

Neuroscience
BACKGROUND: The newest glioma classification in WHO 2021 emphasizes the importance of gene mutations in the glioma molecular pathogenesis. Our research aims to look for new glioma-related genes that have the potential to be therapeutic targets.

Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes.

Cancer discovery
UNLABELLED: Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer diagnosis. In this study, we analyzed genome-wide c...

Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.

Cancer medicine
OBJECTIVES: Accurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic effica...

Diagnostic Performance of ChatGPT-4.0 in Histopathological Analysis of Gliomas: A Single Institution Experience.

Neuropathology : official journal of the Japanese Society of Neuropathology
This study aimed to evaluate the performance of ChatGPT-4.0 as a diagnostic support tool for pathologists in identifying different types of gliomas based on histopathological data and to compare its performance with that of another artificial intelli...

Three-Dimensional Visualisation of Blood Vessels in Human Gliomas Using Tissue Clearing and Deep Learning.

Neuropathology and applied neurobiology
Gliomas, with their intricate and aggressive nature, call for a detailed visualisation of their vasculature. Traditional 2D imaging often overlooks the spatial heterogeneity of tumours. Our study overcomes this by combining tissue clearing, 3D-confoc...

Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.

Neuro-oncology
Diffuse midline glioma (DMG) is a rare, aggressive, and fatal tumor that largely occurs in the pediatric population. To improve outcomes, it is important to characterize DMGs, which can be performed via magnetic resonance imaging (MRI) assessment. Re...