AIMC Topic: Glioblastoma
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Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.
AJNR. American journal of neuroradiology
May 2, 2025
This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients ...
Reirradiation for recurrent glioblastoma: the significance of the residual tumor volume.
Journal of neuro-oncology
May 1, 2025
PURPOSE: Recurrent glioblastoma has a poor prognosis, and its optimal management remains unclear. Reirradiation (re-RT) is a promising treatment option, but long-term outcomes and optimal patient selection criteria are not well established.
Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.
Scientific reports
Apr 24, 2025
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...
Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma.
Journal of neuro-oncology
Apr 17, 2025
PURPOSE: Analyzing post-treatment MRIs from glioblastoma patients can be challenging due to similar radiological presentations of disease progression and treatment effects. Identifying radiomics features (RFs) revealing progressive glioblastoma can c...
Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by systems pharmacology and machine learning.
British journal of pharmacology
Apr 14, 2025
BACKGROUND AND PURPOSE: Glioblastoma (GBM), the most frequent and aggressive brain tumour in adults, is associated with a dismal prognostic despite intensive treatment involving surgery, radiotherapy and temozolomide (TMZ)-based chemotherapy. The ini...
Identification of Recurrence-associated Gene Signatures and Machine Learning-based Prediction in IDH-Wildtype Histological Glioblastoma.
Journal of molecular neuroscience : MN
Apr 14, 2025
Glioblastoma (GBM) is a highly aggressive brain tumor with frequent recurrence, yet the molecular mechanisms driving recurrence remain poorly understood. Identifying recurrence-associated genes may improve prognosis and treatment strategies. We appli...
Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA neutrophil prognostic model.
Biology direct
Apr 5, 2025
BACKGROUND: Neutrophils play a key role in the tumor microenvironment (TME); however, their functions in glioblastoma (GBM) are overlooked and insufficiently studied. A detailed analysis of GBM-associated neutrophil (GBMAN) subpopulations may offer n...
A predictive model for MGMT promoter methylation status in glioblastoma based on terahertz spectral data.
Analytical biochemistry
Mar 29, 2025
O-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a crucial biomarker in glioblastoma (GBM) that influences response to temozolomide. Traditional detection methods, such as gene sequencing, are time-consuming and limited to postope...
Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.
Acta neurochirurgica
Feb 24, 2025
PURPOSE: Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms...
Feasibility of using Gramian angular field for preprocessing MR spectroscopy data in AI classification tasks: Differentiating glioblastoma from lymphoma.
European journal of radiology
Jan 29, 2025
OBJECTIVES: To convert 1D spectra into 2D images using the Gramian angular field, to be used as input for convolutional neural network for classification tasks such as glioblastoma versus lymphoma.
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