^{1}H MRS spectroscopy in brain tumors : in search of the highest efficacy

Abstract

Many thousands of published papers, a lot of communications and daily practice show the outstanding role of proton spectroscopy (^{1}H MRS) in neurooncology. Paradoxically, in several clinical centres (neurosurgery ones) we can observe the lack of full acceptance of the reliability of this method. Presumably, the reason for this attitude lies in non-uniform methodology and quite superficial treatment of spectroscopic results. At the beginning of this paper, we would like to stress that fortunately, there is a certain number of publications which order criteria of tumor's malignancy, its tissue characterisation and the degree of malignancy. In this paper we intend to point out the proper ways of improving reliability of ^{1}H MRS in brain tumor diagnosis. Obviously, the quality of spectroscopic exams is influenced by methodology and manner of result interpretation (e.g. just simple visual inspection of MRS spectra is not sufficient for correct diagnosis). Currently, imaging diagnosis of brain tumors is based on many modalities (DTI, perfusion CT, MR, PET, SPECT) which are sufficiently effective in detection and differentiation of the lesions; in this context, we can say that ^{1}H MRS spectroscopy constitutes the element of multimodal diagnostic approach for brain tumors. Nevertheless, the current papers concerning MR spectroscopy stress its significant role in making therapeutic decision, particularly as an alternative for surgery treatment and solution of the recurrence of tumor or/and extent of the tumor's spreading. Over 15 years of experience with ^{1}H MRS in diagnosis of brain tumors convinced us that these methods have very high diagnostic and decision-making value provided that it is properly applied and interpreted correctly. It is also important to choose the statistic methods that will show the best discriminators (markers) for particular pathologies. Authors' intention is to recommend the best ways to form the right diagnosis considering technological requirements, methodology and result interpretation