Feature selection in proton magnetic resonance spectroscopy data of brain tumors
Document typeConference report
PublisherUniversità degli Studi di Salerno
Rights accessOpen Access
In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and may minimize the negative impact of incorrectly targeted toxic or aggressive treatments. Moreover, the correct prediction of cancer types using non-invasive information –e.g. 1H-MRS data– could avoid patients to suffer collateral problems derived from exploration techniques that require surgery. A Feature Selection Algorithm specially designed to be use in 1H-MRS Proton Magnetic Resonance Spectroscopy data of brain tumors is presented. It takes advantage of a highly distinctive aspect in this data: some metabolite levels are notoriously different between types of tumors. Experimental read- ings on an international dataset show highly competitive models in terms of accuracy, complexity and medical interpretability.
CitationGonzález, F.F.; Belanche, Ll. Feature selection in proton magnetic resonance spectroscopy data of brain tumors. A: International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. "Proceedings of the CIBB 2011: 8th International meeting on computational intelligence methods for bioinformatics and biostatistics: Gargnano-Lago di Garda, Italy, June 30-July 2, 2011". Gargnano, Lago di Garda: Università degli Studi di Salerno, 2011, p. 1-8.