AIMC Topic: Glioma
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Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.
Scientific reports
Jul 2, 2025
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...
Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.
Scientific reports
Jul 1, 2025
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...
3D-MRI brain glioma intelligent segmentation based on improved 3D U-net network.
PloS one
Jun 13, 2025
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.
Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms.
BMC neurology
Jun 5, 2025
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.
Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.
Science advances
Jun 4, 2025
Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factor...
Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children.
Cancer imaging : the official publication of the International Cancer Imaging Society
May 19, 2025
OBJECTIVES: Understanding the impact of epilepsy on pediatric brain tumors is crucial to diagnostic precision and optimal treatment selection. This study investigated MRI radiomics features, tumor location, voxel-based morphometry (VBM) for gray matt...
Semisupervised adaptive learning models for IDH1 mutation status prediction.
PloS one
May 5, 2025
The mutation status of isocitrate dehydrogenase1 (IDH1) in glioma is critical information for the diagnosis, treatment, and prognosis. Accurately determining such information from MRI data has emerged as a significant research challenge in recent yea...
Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.
Scientific reports
Apr 29, 2025
Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types-pituitary, meningioma, glioma, and normal-this ...
From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.
Neurosurgical review
Apr 29, 2025
Gliomas are the most common primary tumors of the central nervous system, and advances in genetics and molecular medicine have significantly transformed their classification and treatment. This study aims to predict the IDH1 genotype in gliomas using...
UBTD2 protein molecules emerges as a key prognostic protein marker in glioma: Insights from integrated omics and machine learning analysis of GRM7, NCAPG, CEP55, and other biomarkers.
International journal of biological macromolecules
Apr 24, 2025
Glioma is a malignant brain tumor with poor prognosis, and there is an urgent need to find effective biomarkers for early diagnosis and treatment. The aim of this study was to explore the potential of UBTD2 as a key prognostic protein marker for glio...
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