AIMC Topic: Glioma
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A combined attention mechanism for brain tumor segmentation of lower-grade glioma in magnetic resonance images.
Computers in biology and medicine
Jul 1, 2025
Low-grade gliomas (LGGs) are among the most problematic brain tumors to reliably segment in FLAIR MRI, and effective delineation of these lesions is critical for clinical diagnosis, treatment planning, and patient monitoring. Nevertheless, convention...
CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification.
Computers in biology and medicine
Jul 1, 2025
The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models....
REPAIR: Reciprocal assistance imputation-representation learning for glioma diagnosis with incomplete MRI sequences.
Medical image analysis
Jul 1, 2025
The absence of MRI sequences is a common occurrence in clinical practice, posing a significant challenge for prediction modeling of non-invasive diagnosis of glioma (GM) via fusion of multi-sequence MRI. To address this issue, we propose a novel unif...
Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for ...
Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading.
BMC medical informatics and decision making
May 30, 2025
PURPOSE: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.
Does Whole Brain Radiomics on Multimodal Neuroimaging Make Sense in Neuro-Oncology? A Proof of Concept Study.
Studies in health technology and informatics
May 15, 2025
Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feasible, albeit less common, approach in neuro-oncology research. This study aims to evaluate the relationship between WB radiomic features, derived from...
Machine learning for grading prediction and survival analysis in high grade glioma.
Scientific reports
May 15, 2025
We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 gra...
An automated cascade framework for glioma prognosis via segmentation, multi-feature fusion and classification techniques.
Biomedical physics & engineering express
May 13, 2025
Glioma is one of the most lethal types of brain tumors, accounting for approximately 33% of all diagnosed brain tumor cases. Accurate segmentation and classification are crucial for precise glioma characterization, emphasizing early detection of mali...
Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and...
Towards Optical Biopsy in Glioma Surgery.
International journal of molecular sciences
May 9, 2025
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 (FLI...
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