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Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation
MR imaging is central to the assessment of tumor burden and changes over time in neuro-
oncology. Several response assessment guidelines have been set forth by the Response …
oncology. Several response assessment guidelines have been set forth by the Response …
Novel imaging approaches for glioma classification in the Era of the World Health Organization 2021 update: A sco** review
V Richter, U Ernemann, B Bender - Cancers, 2024 - mdpi.com
Simple Summary The 2021 WHO classification of central nervous system (CNS) tumors is
challenging for neuroradiologists due to the central role of the molecular profile of tumors …
challenging for neuroradiologists due to the central role of the molecular profile of tumors …
Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma
Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable
responses to treatment, leading to tumor progression and adverse outcomes in cases where …
responses to treatment, leading to tumor progression and adverse outcomes in cases where …
Stepwise transfer learning for expert-level pediatric brain tumor MRI segmentation in a limited data scenario
Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning
pediatric brain tumor segmentation model using stepwise transfer learning. Materials and …
pediatric brain tumor segmentation model using stepwise transfer learning. Materials and …
Tumor location-weighted mri-report contrastive learning: A framework for improving the explainability of pediatric brain tumor diagnosis
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor
diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow …
diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow …
Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas
The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on
the determination of molecular status. It has been shown that genetic alterations in pLGG …
the determination of molecular status. It has been shown that genetic alterations in pLGG …
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour …
Purpose: Pediatric low-grade gliomas (pLGG) are the most common brain tumour in
children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI …
children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI …
Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks
Medical image analysis has significantly benefited from advancements in deep learning,
particularly in the application of Generative Adversarial Networks (GANs) for generating …
particularly in the application of Generative Adversarial Networks (GANs) for generating …
A review of deep learning for brain tumor analysis in MRI
Recent progress in deep learning (DL) is producing a new generation of tools across
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …
Applications of machine learning to MR imaging of pediatric low-grade gliomas
Introduction Machine learning (ML) shows promise for the automation of routine tasks
related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading …
related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading …