Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.
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.
García-Gómez
Juan M
JM
IBIME-Itaca, Universidad Politécnica de Valencia, Camino de Vera, Valencia, Spain. juanmig@upv.es
Luts
Jan
J
Julià-Sapé
Margarida
M
Krooshof
Patrick
P
Tortajada
Salvador
S
Robledo
Javier Vicente
JV
Melssen
Willem
W
Fuster-García
Elies
E
Olier
Iván
I
Postma
Geert
G
Monleón
Daniel
D
Moreno-Torres
Angel
A
Pujol
Jesús
J
Candiota
Ana-Paula
AP
Martínez-Bisbal
M Carmen
MC
Suykens
Johan
J
Buydens
Lutgarde
L
Celda
Bernardo
B
Van Huffel
Sabine
S
Arús
Carles
C
Robles
Montserrat
M
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
D014408:Biomarkers, Tumor