AIMC Topic: Glioblastoma

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Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks.

Mathematical biosciences
Glioblastoma is among the most aggressive brain tumors in adults, characterized by patient-specific invasion patterns driven by the underlying brain microstructure. In this work, we present a proof-of-concept for a mathematical model of GBL growth, e...

Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma.

Journal of translational medicine
BACKGROUND: Novel diagnostic criteria for glioblastoma (GBM) in the 2021 WHO classification emphasize the importance of integrating pathological and molecular features. Pathomics, which involves the extraction of digital pathology data, is gaining si...

Rapid diagnosis of TERT promoter mutation using Terahertz absorption spectroscopy in glioblastoma.

Scientific reports
Glioblastoma (GBM) is a highly aggressive brain tumor with poor outcomes and limited treatment options. The telomerase reverse transcriptase (TERT) promoter mutation, one of the key biomarkers in GBM, is linked to tumor progression and prognosis. Thi...

Mitochondrial dysfunction and cell death induced by Toona sinensis leaf extracts through MEK/ERK signaling in glioblastoma cells.

PloS one
Toona sinensis, a kind of phytochemicals in traditional Chinese medicine widely used in South-East Asia, has been recognized for its anticancer properties, particularly against various types of cancer. We aimed to evaluate the effectiveness of T. sin...

GLIO-Select: Machine Learning-Based Feature Selection and Weighting of Tissue and Serum Proteomic and Metabolomic Data Uncovers Sex Differences in Glioblastoma.

International journal of molecular sciences
Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biolo...

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

AJNR. American journal of neuroradiology
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
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
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
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
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...