Purpose To systematically review and evaluate the methodological quality of studies using
radiomics for diagnostic and predictive purposes in patients with intracranial meningioma …

Simple Summary Meningiomas are typically benign, common extra-axial tumors of the
central nervous system. Routine clinical assessment by radiologists presents some …

In this work, we demonstrate that it is possible to create supervised machine-learning
models using a support vector machine and random forest algorithms to separate yeast cells …

Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance
images (MRIs), a process that is prone to human error and is also time consuming. Recent …

Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are
two contemporary computational methods that can identify discrete changes in cell and …

Purpose This study aims to combine deep learning features with radiomics features for the
computer-assisted preoperative assessment of meningioma consistency. Methods 202 …

Gray level co-occurrence matrix (GLCM) analysis is a contemporary and innovative
computer-based algorithm that can be used for the quantification of subtle changes in a …

Preoperative identification of intracranial meningiomas with aggressive behaviour may help
in choosing the optimal treatment strategy. Radiomics is emerging as a powerful diagnostic …

Diagnosing brain tumors typically starts with an investigation of the brain's Magnetic
Resonance (MR) imaging. However, such a manual analysis of MR images is tedious and …

Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy
tissue, can be very important for operation planning and further treatment. Radiomics is a …