DNA methylation-based classification using the Heidelberg Classifier is a state-of-the-art data-driven method for molecular diagnosis of central nervous system (CNS) tumors. However, many pediatric low-grade glioma (pLGG) samples fail to yield a conf...
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Sep 27, 2025
PURPOSE: Tumors are heterogeneous and consist of subregions, also known as tumor habitats, each of which corresponds to a group of tissues with similar structural, metabolic or functional characteristics. This study aims to visualize and quantify int...
This study aimed to validate the utility of commercially available vendor-neutral deep learning (DL) image enhancement software for improving the image quality of multiparametric MRI for gliomas in a multinational setting. A total of 294 patients fro...
Brain metastases (BMs) are the most common intracranial tumors and stereotactic radiotherapy improved the life quality of patient with BMs, while it requires more time and experience to delineate BMs precisely by oncologists. Deep Learning techniques...
International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
Sep 9, 2025
OBJECTIVE: To develop a deep learning method for fast and accurate prediction of Specific Absorption Rate (SAR) distributions in the human head to support real-time hyperthermia treatment planning (HTP) of brain cancer patients.
Gliomas are known to have different sub-regions within the tumor, including the edema, necrotic, and active tumor regions. Segmenting of these regions is very important for glioma treatment decisions and management. This paper aims to demonstrate the...
Journal of molecular neuroscience : MN
Aug 27, 2025
Low-grade gliomas (LGGs) represent a complex and aggressive category of brain tumors. Despite recent advancements in molecular subtyping and characterization, the necessity to identify additional molecular subtypes and biomarkers remains. To delineat...
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 26, 2025
BACKGROUND: Machine learning (ML) applied to radiomics has revolutionized neuro-oncological imaging, yet the diagnostic performance of ML models based specifically on ^18F-FDG PET features in glioma remains poorly characterized.
Brain tumor classification (BTC) from Magnetic Resonance Imaging (MRI) is a critical diagnosis task, which is highly important for treatment planning. In this study, we propose a hybrid deep learning (DL) model that integrates VGG16, an attention mec...
Glioblastoma is an aggressive, malignant primary brain tumour and the most prevalent histological type of glioma. Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patie...
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