Abstract
The incorporation of new biomedical technologies in the diagnosis and prognosis of cancer is changing medicine to an evidence-based diagnosis. We summarize some studies related to brain tumour research in Europe, based on the metabolic information provided by in vivo Magnetic Resonance Spectroscopy (MRS) and transcriptomic profiling observed by DNA microarrays. The first result presents the improvement in brain tumour diagnosis by combining Long TE and Short TE single voxel MR Spectra. Afterwards, a mixture model for binned and truncated data to characterize and classify MRS is reviewed. The classification of Glioblastomas Multiforme and Meningothelial Meningiomas using single-labeling cDNA-based microarrays was studied as proof of principle in the incorporation of genomic information to clinical diagnosis. Finally, we present a Decision Support System for in-vivo classification of brain tumours were the best inferred classifiers are deployed for their clinical use.
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García–Gómez, J.M. et al. (2007). Genomics and Metabolomics Research for Brain Tumour Diagnosis Based on Machine Learning. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_122
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DOI: https://doi.org/10.1007/978-3-540-73007-1_122
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