AIMC Topic: Brain Neoplasms

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Improving brain tumor diagnosis: A self-calibrated 1D residual network with random forest integration.

Brain research
Medical specialists need to perform precise MRI analysis for accurate diagnosis of brain tumors. Current research has developed multiple artificial intelligence (AI) techniques for the process automation of brain tumor identification. However, existi...

Optimizing workflows for metastatic central nervous system disease: a systematic review and proposed guidelines.

Journal of neuro-oncology
INTRODUCTION: Brain and spine metastases are a major cause of morbidity and mortality in patients with malignancy. Aside from advancements in medical, surgical, and radiation therapies, patient workflow optimization augments care. Here, we present th...

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...

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Yonsei medical journal
PURPOSE: Black-blood (BB) magnetic resonance images (MRI) offer superior image contrast for the detection and segmentation of brain metastases (BMs). This study investigated the efficacy and accuracy of deep learning (DL) architectures and post-proce...

Artificial intelligence applications in the screening and classification of glioblastoma.

Journal of neurosurgical sciences
Glioblastoma is the most aggressive primary brain tumor, with poor prognosis following initial identification. Current diagnostic methods, including neuroimaging and molecular pathology, face several limitations in tumor delineation, differentiation ...

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...