Recursive feature elimination for brain tumor classification using desorption electrospray ionization mass spectrometry imaging.
The metabolism and composition of lipids is of increasing interest for understanding and detecting disease processes. Lipid signatures of tumor type and grade have been demonstrated using magnetic resonance spectroscopy. Clinical management and ultimate prognosis of brain tumors depend largely on the tumor type, subtype, and grade. Mass spectrometry, a well-known analytical technique used to identify molecules in a given sample based on their mass, can significantly improve the problem of tumor type classification. This work focuses on the problem of identifying lipid features to use as input for classification. Feature selection could result in improvements in classifier performance, discovery of biomarkers, improved data interpretation, and patient treatment.
Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA. bgholami@bwh.harvard.edu
Behnood
B
Gholami
Isaiah
I
Norton
Allen R
AR
Tannenbaum
Nathalie Y R
NY
Agar
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005910:Glioma / Q000175:diagnosis* / Q000378:metabolism*; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods*
D014408:Biomarkers, Tumor