AIMC Topic: Ependymoma

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AI-Driven innovations for managing ependymoma in neurosurgery.

Neurosurgical review Sep 11, 2024
Neurosurgical Procedures Humans Neurosurgery Ependymoma Artificial Intelligence Brain Neoplasms
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Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme.

NMR in biomedicine Aug 20, 2024
This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled p...
Diagnosis, Differential Glioblastoma Ependymoma Middle Aged ROC Curve Magnetic Resonance Imaging Aged Radiomics Machine Learning Young Adult Humans Brain Neoplasms Retrospective Studies Adult Male Female
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Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

Academic radiology Mar 20, 2024
RATIONALE AND OBJECTIVES: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to expl...
Male Infant Adolescent Female Radiomics Brain Neoplasms Image Interpretation, Computer-Assisted Cerebellar Neoplasms Medulloblastoma Child, Preschool Child Diagnosis, Differential Humans Multiparametric Magnetic Resonance Imaging Machine Learning Ependymoma Retrospective Studies
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Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.

Brain pathology (Zurich, Switzerland) Jan 11, 2024
Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the exist...
Humans Child, Preschool Middle Aged Neural Networks, Computer Child Spinal Cord Young Adult Female Ependymoma Deep Learning Adult DNA Methylation Aged Spinal Cord Neoplasms Adolescent Male
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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...
Medulloblastoma Ependymoma Diffusion Magnetic Resonance Imaging Astrocytoma Cerebellar Neoplasms Humans Child Machine Learning Pediatrics Female Infant Brain Neoplasms Adolescent Male Child, Preschool
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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...
Infratentorial Neoplasms Image Interpretation, Computer-Assisted Astrocytoma Ependymoma Child Female Infant Humans Magnetic Resonance Imaging Infant, Newborn Adolescent Machine Learning Diagnosis, Differential Male Child, Preschool
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A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images.

Clinical cancer research : an official journal of the American Association for Cancer Research Jan 5, 2024
PURPOSE: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images.
Area Under Curve Ependymoma Phenylphosphonothioic Acid, 2-Ethyl 2-(4-Nitrophenyl) Ester Clinical Decision-Making Deep Learning Magnetic Resonance Imaging Humans Retrospective Studies
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