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.
Keywords Magnetic resonance spectroscopy - Pattern classification - Brain tumors - Decision support systems - Multicenter evaluation study