BACKGROUND AND PURPOSE: An autoencoder can learn representative time–signal
intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility …

Background The aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes
of gliomas using an interpretable deep learning application for dynamic susceptibility …

Background Combining MRI techniques with machine learning methodology is rapidly
gaining attention as a promising method for staging of brain gliomas. This study assesses …

Purpose To augment the analysis of dynamic susceptibility contrast material–enhanced
magnetic resonance (MR) images to uncover unique tissue characteristics that could …

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic
resonance imaging (MRI) do not capture complex dynamic information of time-signal …

Objectives To investigate whether radiomic features extracted from dynamic susceptibility
contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular …

Objective The current study aimed to evaluate the clinical practice for hemodynamic tissue
signature (HTS) method in IDH genotype prediction in three groups derived from high-grade …

Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely
used for studying blood perfusion in brain tumors. While the time-dependent change of MRI …

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because
of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared …

Purpose Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene
promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is …