AIMC Topic: Glioma
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Evaluating the clinical utility of artificial intelligence assistance and its explanation on the glioma grading task.
Artificial intelligence in medicine
Jan 2, 2024
Clinical evaluation evidence and model explainability are key gatekeepers to ensure the safe, accountable, and effective use of artificial intelligence (AI) in clinical settings. We conducted a clinical user-centered evaluation with 35 neurosurgeons ...
Detection and classification of brain tumor using hybrid deep learning models.
Scientific reports
Dec 27, 2023
Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diag...
Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.
Operative neurosurgery (Hagerstown, Md.)
Dec 22, 2023
BACKGROUND AND OBJECTIVES: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy ...
Intraoperative molecular diagnosis of glioma through combination of radiofrequency signals from ultrasound and deep learning.
EBioMedicine
Dec 16, 2023
Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas.
European journal of radiology
Dec 13, 2023
PURPOSE: Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is typically obtained invasively from excised tumor tissue; however, these mut...
Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.
EBioMedicine
Dec 2, 2023
BACKGROUND: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultra...
Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.
Journal of magnetic resonance imaging : JMRI
Nov 29, 2023
BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored.
The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis.
World neurosurgery
Nov 22, 2023
BACKGROUND: Diffuse midline gliomas (DMGs) encompass a set of tumors, and those tumors with H3K27 M mutation carry a poor prognosis. In recent years, machine learning (ML)-based radiomics have shown promising results in predicting gene mutation statu...
Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms.
Scientific reports
Nov 2, 2023
This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVI...
A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas.
Cancer imaging : the official publication of the International Cancer Imaging Society
Oct 27, 2023
BACKGROUND: The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a ...
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