Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy.

Purpose: To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs).

Methods: Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (K), and apparent diffusion coefficient (ADC) were performed.

Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models.

Objectives: To evaluate the additional prognostic value of multiparametric MR-based radiomics in patients with glioblastoma when combined with conventional clinical and genetic prognostic factors.

Methods: In this single-center study, patients diagnosed with glioblastoma between October 2007 and December 2019 were retrospectively screened and grouped into training and test sets with a 7:3 distribution. Segmentations of glioblastoma using multiparametric MRI were performed automatically via a convolutional-neural network.

MRI and Quantitative Magnetic Susceptibility Maps of the Brain after Serial Administration of Gadobutrol: A Longitudinal Follow-up Study.

Background The relationship between administration of macrocyclic gadolinium-based contrast agents and T1-weighted signal intensity (SI) change of the globus pallidus (GP) and dentate nucleus (DN) is, to the knowledge of the authors, not known. Purpose To determine if quantitative susceptibility mapping (QSM) can detect changes in magnetic susceptibility of the GP and DN after serial administration of macrocyclic gadobutrol in patients with primary brain tumors. Materials and Methods Patients diagnosed with primary brain tumors from August 2014 to February 2019 were eligible for this single-center retrospective study.

IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.

Purpose: This study aimed to determine whether MR-based radiomics of glioblastoma can predict the isocitrate dehydrogenase-1 (IDH1) mutation status and compare predictive performances between manual and fully automatic deep-learning segmentations.

Method: Forty-five glioblastoma patients with pretreatment T2-weighted MRIs were retrospectively evaluated. Manual segmentations of glioblastoma and peri-tumoral edema were trained via a deep neural network (V-Net).

Differential expression of MicroRNAs in patients with glioblastoma after concomitant chemoradiotherapy.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, and notorious for resistance to chemoradiotherapy. MicroRNAs (miRNAs) are significantly involved in the initiation and progression of numerous cancers; however, the role of miRNAs in recurrence of tumors remains unknown. Here we tried to identify novel miRNAs that are differentially expressed in recurrent GBM.