A metabolomic data fusion approach to support gliomas grading.

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems.

Survival in patients with newly diagnosed conventional glioblastoma: a modified prognostic score based on a single-institution series.

Aims And Background: Recursive partioning analysis (RPA) is commonly used to define the stratification of patients with glioblastoma. Epigenetic silencing of the O6-methylguanine methyltransferase (MGMT) gene by promoter methylation plays an important role in regulating MGMT expression in gliomas and this is an established independent prognostic factor. We tested a prognostic scoring system including all clinical variables used by RPA classification (age, ECOG performance status and type of surgery) and MGMT gene promoter methylation status.