Object Malignant gliomas are incurable, primary brain neoplasms noted for their potential to
extensively invade brain parenchyma. Current methods of clinical imaging do not elucidate …

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE)
high-grade glioma (HGG) cell populations driving tumor progression. Here, we present …

Radiogenomics uses machine-learning (ML) to directly connect the morphologic and
physiological appearance of tumors on clinical imaging with underlying genomic features. Despite …

Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain
were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that …

The explosion of medical imaging data along with the advent of big data analytics has
launched an exciting era for clinical research. One factor affecting the ability to aggregate large …

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor
segmentation in MRI. However, validation is required prior to routine clinical use. We report the …

Background Sex is recognized as a significant determinant of outcome among glioblastoma
patients, but the relative prognostic importance of glioblastoma features has not been …

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain
tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging …

Purpose Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM),
there is little definitive data on the underlying cause of the differences in patient …

Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema
on magnetic resonance imaging (MRI) is important for understanding disease evolution …