BACKGROUND: Glioma is characterized by a poor prognosis and limited possibilities for treatment. Previous studies have developed prediction models for glioma using genetic, clinical, pathological, imaging and other aspects; however, few studies have ...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 10, 2025
Gliomas are the most common malignant primary brain tumors, and grading their severity, particularly the diagnosis of low-grade gliomas, remains a challenging task for clinicians and radiologists. With advancements in deep learning and medical image ...
Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We collected multi-omics data from 575 TCGA diffuse-glioma patients (156 IDH-wild-type WHO-grade 4 gli...
Proceedings of the National Academy of Sciences of the United States of America
Jul 8, 2025
Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essential for the management of glioma patients. Our study focused on tumor regions segmentation and predicting the World Health Organization (WHO) grade, i...
Epilepsy is a common manifestation in patients with lower grade glioma (LGG), often presenting as the initial symptom in approximately 70% of cases. This study aimed to identify clinical and pathological markers for epileptic seizures in patients wit...
Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of...
Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmentations, demanding extensive time and resources from radiologists. We propose a weakly supervised ap...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 16, 2025
This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and...
PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection.
BACKGROUND: Glioma is a common primary malignant brain tumor. This study aimed to develop a predictive model for glioma risk by these screened key SNPs in the Chinese Han population.
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