Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours

The results reported in this paper reveal the advantage of using a recently described variant of NMF, namely Convex-NMF, as an unsupervised method of source extraction from SV1H-MRS. Most of the sources extracted in our experiments closely correspond to the mean spectra of some of the analysed tumour types. This similarity allows accurate diagnostic predictions to be made both in fully unsupervised mode and using Convex-NMF as a DR step previous to standard supervised classification. The obtained results are comparable to, or more accurate than those obtained with supervised techniques.The unsupervised properties of Convex-NMF place this approach one step ahead of classical label-requiring supervised methods for the discrimination of brain tumour types, as it accounts for their increasingly recognised molecular subtype heterogeneity. The application of Convex-NMF in computer assisted decision support systems is expected to facilitate further improvements in the uptake of MRS-derived information by clinicians.The clinical investigation of an abnormal mass in the brain frequently starts with its non-invasive characterisation (localisation, infiltration, etc.), normally with a magnetic resonance imaging (MRI) study. Magnetic resonance spectroscopy (MRS) is another MR technique that, unlike MRI, provides insight into the biochemistry of tissue through a discrete signal in the frequency domain (a spectrum) containing information about the relative abundance of several low molecular weight metabolites, lipids and macromolecules in the millimolar range of concentration.This MR modality has been used in computer-based systems for diagnostic decision support [1], building on the increasing availability of data in electronic format [2,3]. However, for brain tumours and, more specifically, glial tumours, the computer-based discrimination of the grade or the specific subtype of tumour still leaves a "gray zone" of uncertainty between class labels [4-6]. Therefore, it would be d