title: Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.
abstract: BACKGROUND
  Automatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004-2009), which builds upon previous expertise from the INTERPRET project (2000-2002) has allowed such an evaluation to take place.
  
  
  METHODS
  A total of 253 pairwise classifiers for glioblastoma, meningioma, metastasis, and low-grade glial diagnosis were inferred based on 211 SV short TE INTERPRET MR spectra obtained at 1.5 T (PRESS or STEAM, 20-32 ms) and automatically pre-processed. Afterwards, the classifiers were tested with 97 spectra, which were subsequently compiled during eTUMOUR.
  
  
  RESULTS
  In our results based on subsequently acquired spectra, accuracies of around 90% were achieved for most of the pairwise discrimination problems. The exception was for the glioblastoma versus metastasis discrimination, which was below 78%. A more clear definition of metastases may be obtained by other approaches, such as MRSI + MRI.
  
  
  CONCLUSIONS
  The prediction of the tumor type of in-vivo MRS is possible using classifiers developed from previously acquired data, in different hospitals with different instrumentation under the same acquisition protocols. This methodology may find application for assisting in the diagnosis of new brain tumor cases and for the quality control of multicenter MRS databases.
authors.affiliation: IBIME-Itaca, Universidad Politécnica de Valencia, Camino de Vera, Valencia, Spain. juanmig@upv.es
authors.forename: Juan M
authors.initials: JM
authors.lastname: García-Gómez
authors.forename: Jan
authors.initials: J
authors.lastname: Luts
authors.forename: Margarida
authors.initials: M
authors.lastname: Julià-Sapé
authors.forename: Patrick
authors.initials: P
authors.lastname: Krooshof
authors.forename: Salvador
authors.initials: S
authors.lastname: Tortajada
authors.forename: Javier Vicente
authors.initials: JV
authors.lastname: Robledo
authors.forename: Willem
authors.initials: W
authors.lastname: Melssen
authors.forename: Elies
authors.initials: E
authors.lastname: Fuster-García
authors.forename: Iván
authors.initials: I
authors.lastname: Olier
authors.forename: Geert
authors.initials: G
authors.lastname: Postma
authors.forename: Daniel
authors.initials: D
authors.lastname: Monleón
authors.forename: Angel
authors.initials: A
authors.lastname: Moreno-Torres
authors.forename: Jesús
authors.initials: J
authors.lastname: Pujol
authors.forename: Ana-Paula
authors.initials: AP
authors.lastname: Candiota
authors.forename: M Carmen
authors.initials: MC
authors.lastname: Martínez-Bisbal
authors.forename: Johan
authors.initials: J
authors.lastname: Suykens
authors.forename: Lutgarde
authors.initials: L
authors.lastname: Buydens
authors.forename: Bernardo
authors.initials: B
authors.lastname: Celda
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
authors.forename: Carles
authors.initials: C
authors.lastname: Arús
authors.forename: Montserrat
authors.initials: M
authors.lastname: Robles
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D001932:Brain Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005060:Europe; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor