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 th...

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Veröffentlicht in:2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2012-01, Vol.2012, p.5258-5261
Hauptverfasser: Gholami, B., Norton, I., Tannenbaum, A. R., Agar, N. Y. R.
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:1094-687X
1557-170X
1558-4615
DOI:10.1109/EMBC.2012.6347180