title: Classification of astrocytomas and oligodendrogliomas from mass spectrometry data using sparse kernel machines.
abstract: Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample's histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry.
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. jhuang@gatech.edu
authors.forename: Jacob
authors.initials: J
authors.lastname: Huang
authors.forename: Behnood
authors.initials: B
authors.lastname: Gholami
authors.forename: Nathalie Y R
authors.initials: NY
authors.lastname: Agar
authors.forename: Isaiah
authors.initials: I
authors.lastname: Norton
authors.forename: Wassim M
authors.initials: WM
authors.lastname: Haddad
authors.forename: Allen R
authors.initials: AR
authors.lastname: Tannenbaum
mesh_terms: D000465:Algorithms; D001254:Astrocytoma / Q000145:classification*; D006801:Humans; D009837:Oligodendroglioma / Q000145:classification*; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods*