AIMC Topic: Glioblastoma

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Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like comput...

Diffuse intrinsic pontine gliomas in children: Interest of robotic frameless assisted biopsy. A technical note.

Neuro-Chirurgie
INTRODUCTION: Diffuse intrinsic pontine gliomas (DIPG) constitute 10-15% of all brain tumors in the pediatric population; currently prognosis remains poor, with an overall survival of 7-14 months. Recently the indication of DIPG biopsy has been enlar...

Brain tumor segmentation with Deep Neural Networks.

Medical image analysis
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors ...

Improve Glioblastoma Multiforme Prognosis Prediction by Using Feature Selection and Multiple Kernel Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Glioblastoma multiforme (GBM) is a highly aggressive type of brain cancer with very low median survival. In order to predict the patient's prognosis, researchers have proposed rules to classify different glioma cancer cell subtypes. However, survival...

Pseudo progression identification of glioblastoma with dictionary learning.

Computers in biology and medicine
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.

Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping.

BMC genomics
BACKGROUND: Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss o...

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment...

Metasurface-enhanced terahertz imaging for glioblastoma in orthotopic xenograft mouse model combined with neural network decision making.

Biosensors & bioelectronics
Terahertz (THz) optical sensing and imaging offer significant potential in a range of biological and medical applications owing to their low-energy, non-ionizing nature, and ultra-broadband spectral information, which includes numerous molecular fing...

Assessing the toxicological impact of DEGDB plasticizer exposure on glioblastoma multiforme via network toxicology, machine learning and in vitro methods.

Environmental pollution (Barking, Essex : 1987)
Diethylene glycol dibenzoate (DEGDB) is a novel environmentally friendly plasticizer. However, toxicological studies on DEGDB remain limited, and its potential harmful effects on the malignant progression of glioblastoma are still unclear. Further sy...