Feature selection in proton magnetic resonance spectroscopy data of brain tumors
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/18003
Tipus de documentText en actes de congrés
Data publicació2011
EditorUniversità degli Studi di Salerno
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
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.
CitacióGonzález, 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.
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
cibb-2011-CSFS.pdf | 129,5Kb | Visualitza/Obre |