[HTML][HTML] 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 …
Stepwise Transfer Learning for Expert-Level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario
“Just Accepted” papers have undergone full peer review and have been accepted for
publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout …
publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout …
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 …
Multiparametric MRI Along with Machine Learning Informs on Molecular Underpinnings, Prognosis, and Treatment Response in Pediatric Low-Grade Glioma
In this study, we present a comprehensive radiogenomic analysis of pediatric low-grade
gliomas (pLGGs), combining treatment-naïve multiparametric MRI and RNA sequencing. We …
gliomas (pLGGs), combining treatment-naïve multiparametric MRI and RNA sequencing. We …
Longitudinal risk prediction for pediatric glioma with temporal deep learning
Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern
and severity are heterogeneous and challenging to predict with established clinical and …
and severity are heterogeneous and challenging to predict with established clinical and …