Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.

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.

Clinical Decision Support Systems (CDSS) in GRID Environments.

This paper presents an architecture defined for searching and executing Clinical Decision Support Systems (CDSS) in a LCG2/GT2 Grid environment, using web-based protocols. A CDSS is a system that provides a classification of the patient illness according to the knowledge extracted from clinical practice and using the patient's information in a structured format. The CDSS classification engines can be installed in any site and can be used by different medical users from a Virtual Organization (VO). All users in a VO can consult and execute different classification engines that have been installed in the Grid independently of the platform, architecture or site where the engines are installed or the users are located. The present paper present a solution to requirements such as short-job execution, reducing the response delay on LCG2 environments and providing grid-enabled authenticated access through web portals. Resource discovering and job submission is performed through web services, which are also described in the article.