BACKGROUND: For patients with meningioma, surgical procedures are different because of the status of sinus invasion. However, there is still no suitable technique to identify the status of sinus invasion in patients with meningiomas. We aimed to buil...
PURPOSE: This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.
With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tum...
BACKGROUND: The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to develop a predictive model of tumor consistency using the radiomic features of preoperative magnetic re...
The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma le...
PURPOSE: Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicate...
BACKGROUND AND PURPOSE: Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis tha...
OBJECTIVE: Stereotactic radiosurgery (SRS) has been increasingly applied for malignant meningiomas as an alternative to conventionally fractioned radiation therapy. We performed a retrospective analysis of an institutional patient cohort with maligna...
Journal of neuroradiology = Journal de neuroradiologie
Jun 18, 2019
PURPOSE: To assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant haemangiopericytoma (HPC) from angiomatous meningioma (AM).
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.