AIMC Topic: Glioma
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Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.
Neuro-oncology
Apr 1, 2022
BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive appr...
Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.
Neuro-oncology
Feb 1, 2022
BACKGROUND: Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric...
Artificial intelligence technologies in clinical neurooncology.
Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko
Jan 1, 2022
Neurooncology in the 21 century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and ...
Introduction to Deep Learning in Clinical Neuroscience.
Acta neurochirurgica. Supplement
Jan 1, 2022
The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyz...
Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study.
Turkish neurosurgery
Jan 1, 2022
AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time.
An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients.
Briefings in bioinformatics
Nov 5, 2021
Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are highly heterogeneous. Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status can identify patient subsets with different prognosis. However,...
3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks.
Journal of Zhejiang University. Science. B
Jun 15, 2021
To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of th...
THE SAFETY AND EFFICACY OF ROBOT-ASSISTED STEREOTACTIC BIOPSY FOR BRAIN GLIOMA: EARLIEST INSTITUTIONAL EXPERIENCES AND EVALUATION OF LITERATURE.
Acta clinica Croatica
Jun 1, 2021
Robot-assisted brain tumor biopsy is becoming one of the most important innovative technologies in neurosurgical practice. The idea behind its engagement is to advance the safety and efficacy of the biopsy procedure, which is much in demand when plan...
Dual-responsive biohybrid neutrobots for active target delivery.
Science robotics
Mar 24, 2021
Swimming biohybrid microsized robots (e.g., bacteria- or sperm-driven microrobots) with self-propelling and navigating capabilities have become an exciting field of research, thanks to their controllable locomotion in hard-to-reach areas of the body ...
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