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 …
Radiogenomics: bridging the gap between imaging and genomics for precision oncology
Genomics allows the tracing of origin and evolution of cancer at molecular scale and
underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker …
underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker …
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 …
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 …
Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma
A Fathi Kazerooni, A Kraya, KS Rathi, MC Kim… - Nature …, 2025 - nature.com
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 …
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 …
Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas
Background Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is
challenging to predict by conventional clinical, radiographic, and genomic factors. We …
challenging to predict by conventional clinical, radiographic, and genomic factors. We …
Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping 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 …
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 …
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 …