AIMC Topic: Temozolomide

Clear Filters Showing 1 to 8 of 8 articles

A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.

Journal of digital imaging Jan 5, 2023
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (...
Adult Brain Neoplasms Deep Learning Magnetic Resonance Imaging Humans Brain DNA Repair Enzymes Tumor Suppressor Proteins Temozolomide DNA Methylation O(6)-Methylguanine-DNA Methyltransferase Glioblastoma DNA Modification Methylases
View on PubMed DOI

Exploration of biomedical knowledge for recurrent glioblastoma using natural language processing deep learning models.

BMC medical informatics and decision making Oct 13, 2022
BACKGROUND: Efficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published articles, searching for this knowledge...
Lomustine Humans Deep Learning Chronic Disease Bevacizumab Nivolumab Natural Language Processing Temozolomide Clinical Trials as Topic Glioblastoma
View on PubMed DOI

Personalized oncology with artificial intelligence: The case of temozolomide.

Artificial intelligence in medicine Aug 12, 2019
PURPOSE: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability.
Maximum Tolerated Dose Temozolomide Bayes Theorem Artificial Intelligence Models, Biological Algorithms Tumor Burden Computer Simulation Humans Brain Neoplasms Dose-Response Relationship, Drug
View on PubMed DOI

Optimal dynamic regimens with artificial intelligence: The case of temozolomide.

PloS one Jun 26, 2018
We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et...
Humans Temozolomide Artificial Intelligence Antineoplastic Agents, Alkylating Dose-Response Relationship, Drug Models, Biological Monte Carlo Method Algorithms Neoplasms Treatment Outcome
View on PubMed DOI

Pseudo progression identification of glioblastoma with dictionary learning.

Computers in biology and medicine Apr 1, 2016
OBJECTIVE: Although the use of temozolomide in chemoradiotherapy is effective, the challenging clinical problem of pseudo progression has been raised in brain tumor treatment. This study aims to distinguish pseudo progression from true progression.
Female Male Image Processing, Computer-Assisted Humans Machine Learning Temozolomide Diffusion Magnetic Resonance Imaging Glioblastoma Dacarbazine Chemoradiotherapy
View on PubMed DOI

A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide.

Scientific reports May 8, 2025
Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and ...
Antineoplastic Agents, Alkylating Glioblastoma Cell Line, Tumor Humans Cell Cycle Drug Resistance, Neoplasm DNA Modification Methylases Machine Learning Cell Proliferation Temozolomide DNA Repair Enzymes Tumor Suppressor Proteins Brain Neoplasms Cell Movement
View on PubMed DOI

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Briefings in bioinformatics Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Male Middle Aged Gene Expression Profiling Transcriptome Adult Tumor Microenvironment Brain Neoplasms Neoplastic Stem Cells Glioblastoma Patient Selection Programmed Cell Death 1 Receptor Immunotherapy Gene Expression Regulation, Neoplastic Treatment Outcome Humans Female Aged Machine Learning Prognosis Atlases as Topic Survival Analysis Temozolomide Antineoplastic Agents, Alkylating
View on PubMed DOI

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2018
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the...
Humans Computational Biology Databases, Genetic Tumor Suppressor Proteins Dacarbazine Neural Networks, Computer Magnetic Resonance Imaging DNA Repair Enzymes Regulatory Sequences, Nucleic Acid Temozolomide DNA Modification Methylases Brain Neoplasms DNA Methylation Drug Resistance, Neoplasm Antineoplastic Agents, Alkylating Glioblastoma
View on PubMed
Popular Topics
Recent Journals