AIMC Topic: Astrocytoma
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Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.
Scientific reports
Feb 4, 2021
To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocyt...
Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.
Scientific reports
Nov 23, 2020
Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurren...
Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach.
Radiology
Aug 4, 2020
Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To d...
Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Apr 23, 2020
Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologi...
Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.
International journal of radiation oncology, biology, physics
Jul 22, 2019
PURPOSE: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly accurate machine learning model to perform glioma grading.
Diffusion Tensor Imaging
Anisotropy
Diffusion Magnetic Resonance Imaging
Astrocytoma
Oligodendroglioma
Humans
Diagnosis, Differential
Glioblastoma
Neoplasm Grading
Datasets as Topic
Aged, 80 and over
Male
Young Adult
Middle Aged
Machine Learning
Adult
Adolescent
Retrospective Studies
Brain Neoplasms
Glioma
Female
Aged
Diffuse intrinsic pontine gliomas in children: Interest of robotic frameless assisted biopsy. A technical note.
Neuro-Chirurgie
Oct 27, 2016
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...
A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors.
Journal of medical systems
Aug 15, 2015
The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. ...
Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma.
Korean journal of radiology
Dec 1, 2023
Response to "Medical Statistics Unlock the Gateway to Further Research: Using Deep Learning to Predict CDKN2A/B Homozygous Deletion in Isocitrate Dehydrogenase-Mutant Astrocytoma".
Korean journal of radiology
Dec 1, 2023
Deep learning based on preoperative magnetic resonance (MR) images improves the predictive power of survival models in primary spinal cord astrocytomas.
Neuro-oncology
Jun 2, 2023
BACKGROUND: Prognostic models for spinal cord astrocytoma patients are lacking due to the low incidence of the disease. Here, we aim to develop a fully automated deep learning (DL) pipeline for stratified overall survival (OS) prediction based on pre...
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