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Glioblastoma

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Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

GigaScience
BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field ...

Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs.

Studies in health technology and informatics
INTRODUCTION: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15-18 months. Therefore, it is helpful to be able to assess a patient's OS to tailor treatment more s...

Real-time estimation of the optimal coil placement in transcranial magnetic stimulation using multi-task deep learning.

Scientific reports
Transcranial magnetic stimulation (TMS) has emerged as a promising neuromodulation technique with both therapeutic and diagnostic applications. As accurate coil placement is known to be essential for focal stimulation, computational models have been ...

Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme.

NMR in biomedicine
This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled p...

Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.

Scientific reports
Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-grade glioma (LGG) is a less severe. The prognosis for glioma remains poor even after various treatment...

Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.

Magnetic resonance in medicine
PURPOSE: To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Radiology. Artificial intelligence
Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free su...

Artificial intelligence and omics in malignant gliomas.

Physiological genomics
Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemothe...

Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
OBJECTIVE: Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data,...