AIMC Topic: Glioblastoma

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A comprehensive survey on the use of deep learning techniques in glioblastoma.

Artificial intelligence in medicine Jun 4, 2024
Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene...
Brain Neoplasms Deep Learning Glioblastoma Humans
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Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

BMC neuroscience May 25, 2024
INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatme...
Middle Aged Magnetic Resonance Imaging Neural Networks, Computer Humans Glioblastoma Algorithms Brain Neoplasms Aged Hypoxia Positron-Emission Tomography Male Adult Female
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Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.

Journal of cancer research and clinical oncology Apr 29, 2024
PURPOSE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-yea...
Magnetic Resonance Imaging Glioblastoma Glioma Brain Neoplasms Deep Learning Humans Image Processing, Computer-Assisted
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Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.

Scientific reports Apr 25, 2024
The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents...
Glioblastoma Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Brain Neoplasms Deep Learning Glioma Magnetic Resonance Imaging Neural Networks, Computer Brain Humans
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Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

Journal of molecular neuroscience : MN Apr 25, 2024
We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCG...
Male Middle Aged Transcriptome Female Brain Neoplasms Cancer Survivors Glioblastoma Tissue Inhibitor of Metalloproteinase-1 Nomograms Machine Learning Algorithms Humans
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Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis.

World neurosurgery Apr 3, 2024
BACKGROUND: Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor t...
Humans Machine Learning Glioma Glioblastoma Sensitivity and Specificity Brain Neoplasms
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How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review.

Clinical radiology Mar 19, 2024
BACKGROUND: Several studies have been published comparing deep learning (DL)/machine learning (ML) to radiologists in differentiating PCNSLs from GBMs with equivocal results. We aimed to perform this meta-analysis to evaluate the diagnostic accuracy ...
Magnetic Resonance Imaging Diagnosis, Differential Radiologists Humans Central Nervous System Neoplasms Glioblastoma Astrocytoma Machine Learning Brain Neoplasms Deep Learning Sensitivity and Specificity Lymphoma
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Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.

Scientific reports Jan 29, 2024
In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coeffi...
Hydrolases Humans Deep Learning Databases, Factual Machine Learning Glioblastoma
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Novel Operation Mechanism and Multifunctional Applications of Bubble Microrobots.

Advanced healthcare materials Jan 26, 2024
Microrobots have emerged as powerful tools for manipulating particles, cells, and assembling biological tissue structures at the microscale. However, achieving precise and flexible operation of arbitrary-shaped microstructures in 3D space remains a c...
Humans Robotics Tissue Engineering Equipment Design Glioblastoma
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Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.

Scientific reports Jan 25, 2024
Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the ...
Humans Magnetic Resonance Imaging Glioblastoma Brain Neoplasms Glioma Adult Deep Learning Contrast Media
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