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Prediction of Survival Outcomes in Patients with Glioma Using Magnetic Resonance Imaging (MRI): A Systematic Review and Meta-Analysis.
J Integr Neurosci
January 2025
Department of Radiology, Huzhou Central Hospital, The Affiliated Central Hospital of Huzhou University, 313000 Huzhou, Zhejiang, China.
Background: Glioma is the most common malignancy in the central nervous system. Even with optimal therapies, glioblastoma (the most aggressive form of glioma) is incurable, with only 26.5% of patients having a 2-year survival rate.
View Article and Find Full Text PDFAssessing the Validity of Diffusion Weighted Imaging Models: A Study in Patients with Post-Surgical Lower-Grade Glioma.
J Clin Med
January 2025
Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands.
Diffusion weighted imaging (DWI) is used for monitoring purposes for lower-grade glioma (LGG). While the apparent diffusion coefficient (ADC) is clinically used, various DWI models have been developed to better understand the micro-environment. However, the validity of these models and how they relate to each other is currently unknown.
View Article and Find Full Text PDFSafety of Cerebral Intra-Arterial Chemotherapy for the Treatment of Malignant Brain Tumours.
J Clin Med
January 2025
Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Universitaire de Sherbrooke, 12e Avenue Nord, Porte 6, Sherbrooke, QC J1H 5N4, Canada.
: Cerebral intra-arterial chemotherapy (CIAC) has been demonstrated to achieve tumoricidal concentrations in cerebral tumour cells that are otherwise unachievable due to the presence of the blood-brain barrier. In this study, we sought to analyze the safety of CIAC in a cohort of patients treated at the Centre intégré universitaire de santé et de services sociaux de l'Estrie-Centre hospitalier universitaire de Sherbrooke (CIUSSS-CHUS). : Treatments consisted of monthly CIAC.
View Article and Find Full Text PDFComputed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study.
Cancers (Basel)
January 2025
Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany.
Background/objectives: In recent years, numerous studies have been published on determining the WHO grade of central nervous system (CNS) tumors using machine learning algorithms. These studies are usually based on magnetic resonance imaging (MRI) and sometimes also on positron emission tomography (PET) images. To date, however, there are virtually no corresponding studies based on routinely generated computed tomography (CT) images.
View Article and Find Full Text PDFDeep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS).
Cancers (Basel)
January 2025
Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain.
Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset.
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