Latest AI and machine learning research in brain cancer for healthcare professionals.
Currently, the focus of intraoperative imaging in brain tumor surgery is beginning to shift to optical methods such as optical coherence tomography (OCT), Raman spectroscopy, confocal laser endomicroscopy (CLE), and fluorescence lifetime imaging (FLIM). Optical imaging technologies provide in vivo and real-time high-resolution images of tissues. "Optical biopsy" can be considered as an alternative...
Gliomas are aggressive brain tumors that pose serious health risks. Deep learning aids in lesion segmentation, but CNN and Transformer-based models often lack context modeling or demand heavy computation, limiting real-time use on mobile medical devices. We propose GaMNet, integrating the NMamba module for global modeling and a multi-scale CNN for efficient local feature extraction. To improve i...
INTRODUCTION: Presently, heavy particle ion radiation therapy is commonly utilized for the treatment of deep-seated malignancies, such as brain tumors...
Sixteen sites in the coastal city of Qingdao, including eight national control sites, seven provincial control sites, and one background site, were se...
PURPOSE: This study aims to create a deep learning (DL) model capable of accurately delineating the ventricles, and by extension, the periventricular ...
Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemother...
This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by ...
High-grade gliomas, particularly glioblastoma (MeSH:Glioblastoma), are among the most aggressive and lethal central nervous system tumors, necessitati...
The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public heal...
INTRODUCTION: Artificial intelligence (AI) is now transforming medical imaging, with extensive ramifications for nearly every aspect of diagnostic ima...
Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-q...
Magnetic resonance imaging (MRI) offers superb-quality images, but its accessibility is limited by high costs, posing challenges for patients requir...
DNA microarray technology enables the simultaneous measurement of expression levels of thousands of genes, thereby facilitating the understanding of...
Background: Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and t...
Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI...
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiom...
Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We u...
Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machin...
BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and MR components and radiation exposure associated w...
PURPOSE: Artificial intelligence (AI) tools could improve clinical decision making or exacerbate inequities because of bias. African American (AA) men...