TY - JOUR
T1 - Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy
AU - García-Gómez, Juan M.
AU - Luts, Jan
AU - Julià-Sapé, Margarida
AU - Krooshof, Patrick
AU - Tortajada, Salvador
AU - Robledo, Javier Vicente
AU - Melssen, Willem
AU - Fuster-García, Elies
AU - Olier, Iván
AU - Postma, Geert
AU - Monleón, Daniel
AU - Moreno-Torres, Àngel
AU - Pujol, Jesús
AU - Candiota, Ana Paula
AU - Martínez-Bisbal, M. Carmen
AU - Suykens, Johan
AU - Buydens, Lutgarde
AU - Celda, Bernardo
AU - Van Huffel, Sabine
AU - Arús, Carles
AU - Robles, Montserrat
PY - 2009/2
Y1 - 2009/2
N2 - Justification: 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. Materials and 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. © 2008 The Author(s).
AB - Justification: 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. Materials and 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. © 2008 The Author(s).
KW - Brain tumors
KW - Decision support systems
KW - Magnetic resonance spectroscopy
KW - Multicenter evaluation study
KW - Pattern classification
U2 - 10.1007/s10334-008-0146-y
DO - 10.1007/s10334-008-0146-y
M3 - Article
C2 - 18989714
SN - 1352-8661
VL - 22
SP - 5
EP - 18
JO - Magma: Magnetic Resonance Materials in Physics, Biology, and Medicine
JF - Magma: Magnetic Resonance Materials in Physics, Biology, and Medicine
IS - 1
ER -