AIMC Topic: Glioma
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Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning.
Scientific reports
Aug 27, 2024
Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor im...
Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning.
Journal of neuro-oncology
Aug 24, 2024
PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resona...
Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning.
Science advances
Aug 23, 2024
High-grade glioma (HGG) is an aggressive brain tumor. Sex is an important factor that differentially affects survival outcomes in HGG. We used an end-to-end deep learning approach on hematoxylin and eosin (H&E) scans to (i) identify sex-specific hist...
Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients.
PloS one
Aug 20, 2024
BACKGROUND: There is ongoing uncertainty about the effectiveness of various adjuvant treatments for low-grade gliomas (LGGs). Machine learning (ML) models that predict individual treatment effects (ITE) and provide treatment recommendations could hel...
Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas.
Scientific reports
Aug 17, 2024
The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features ...
Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types.
Journal of imaging informatics in medicine
Aug 16, 2024
Primary diffuse central nervous system large B-cell lymphoma (CNS-pDLBCL) and high-grade glioma (HGG) often present similarly, clinically and on imaging, making differentiation challenging. This similarity can complicate pathologists' diagnostic effo...
Self-Supervised Image Denoising of Third Harmonic Generation Microscopic Images of Human Glioma Tissue by Transformer-Based Blind Spot (TBS) Network.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser intensity and inherent noise of the imaging system, the noise level o...
Predicting telomerase reverse transcriptase promoter mutation in glioma: A systematic review and diagnostic meta-analysis on machine learning algorithms.
The neuroradiology journal
Aug 5, 2024
BackgroundGlioma is one of the most common primary brain tumors. The presence of the telomerase reverse transcriptase promoter (pTERT) mutation is associated with a better prognosis. This study aims to investigate the TERT mutation in patients with g...
Deep learning for rapid virtual H&E staining of label-free glioma tissue from hyperspectral images.
Computers in biology and medicine
Aug 1, 2024
Hematoxylin and eosin (H&E) staining is a crucial technique for diagnosing glioma, allowing direct observation of tissue structures. However, the H&E staining workflow necessitates intricate processing, specialized laboratory infrastructures, and spe...
Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.
Journal of magnetic resonance imaging : JMRI
Jul 29, 2024
This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature o...
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