Magnetic resonance spectroscopy of brain hemangiopericytomas: high myoinositol concentrations and discrimination from meningiomas.

OBJECT: Hemangiopericytomas are a rare type of brain tumor that are very similar to meningiomas in appearance and symptoms but require different treatment. It is not normally possible to distinguish between them by using magnetic resonance (MR) imaging and computerized tomography studies. However, discrimination may be possible by using in vivo MR spectroscopy (MRS) because the biochemical composition of these two lesions is different. The goal of this study was to describe the use of MRS in discriminating between these similar tumor types. METHODS: In vivo MRS spectra were acquired in 27 patients (three with hemangiopericytomas and 24 with meningiomas) by using a single-voxel proton brain examination system at 1.5 teslas with short- (20-msec) and long- (135-msec) echo times. In addition, brain biopsy specimens obtained by open craniotomy were frozen within 5 minutes of resection and stored in liquid nitrogen until they were used. The specimens were powdered, extracted with perchloric acid, redissolved in 2H2O2 and high-resolution in vitro MRS was used at 9.4 teslas to record their spectra. CONCLUSIONS: In this study the authors show that hemangiopericytomas could be clearly distinguished from meningiomas because they have a larger peak at 3.56 ppm. Measurements of extracts of the tumors and comparison of spectra acquired with MRS at long- (135-msec) and short- (20-msec) echo times established that this was due to the much higher levels of myoinositol in the hemangiopericytomas.

Towards a method for automated classification of 1H MRS spectra from brain tumours.

Recent studies have shown that MRS can substantially improve the non-invasive categorization of human brain tumours. However, in order for MRS to be used routinely by clinicians, it will be necessary to develop reliable automated classification methods that can be fully validated. This paper is in two parts: the first part reviews the progress that has been made towards this goal, together with the problems that are involved in the design of automated methods to process and classify the spectra. The second part describes the development of a simple prototype system for classifying 1H single voxel spectra, obtained at an echo time (TE) of 135 ms, of the four most common types of brain tumour (meningioma (MM), astrocytic (AST), oligodendroglioma (OD) and metastasis (ME)) and cysts. This system was developed in two stages: firstly, an initial database of spectra was used to develop a prototype classifier, based on a linear discriminant analysis (LDA) of selected data points. Secondly, this classifier was tested on an independent test set of 15 newly acquired spectra, and the system was refined on the basis of these results. The system correctly classified all the non-astrocytic tumours. However, the results for the the astrocytic group were poorer (between 55 and 100%, depending on the binary comparison). Approximately 50% of high grade astrocytoma (glioblastoma) spectra in our data base showed very little lipid signal, which may account for the poorer results for this class. Consequently, for the refined system, the astrocytomas were subdivided into two subgroups for comparison against other tumour classes: those with high lipid content and those without.

Genetic programming for classification and feature selection: analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies.

Genetic programming (GP) is used to classify tumours based on 1H nuclear magnetic resonance (NMR) spectra of biopsy extracts. Analysis of such data would ideally give not only a classification result but also indicate which parts of the spectra are driving the classification (i.e. feature selection). Experiments on a database of variables derived from 1H NMR spectra from human brain tumour extracts (n = 75) are reported, showing GP's classification abilities and comparing them with that of a neural network. GP successfully classified the data into meningioma and non-meningioma classes. The advantage over the neural network method was that it made use of simple combinations of a small group of metabolites, in particular glutamine, glutamate and alanine. This may help in the choice of the most informative NMR spectroscopy methods for future non-invasive studies in patients.

Pattern recognition analysis of 1H NMR spectra from perchloric acid extracts of human brain tumor biopsies.

Pattern recognition techniques (factor analysis and neural networks) were used to investigate and classify human brain tumors based on the 1H NMR spectra of chemically extracted biopsies (n = 118). After removing information from lactate (because of variable ischemia times), unsupervised learning suggested that the spectra separated naturally into two groups: meningiomas and other tumors. Principal component analysis reduced the dimensionality of the data. A back-propagation neural network using the first 30 principal components gave 85% correct classification of meningiomas and nonmeningiomas. Simplification by vector rotation gave vectors that could be assigned to various metabolites, making it possible to use or to reject their information for neural network classification. Using scores calculated from the four rotated vectors due to creatine and glutamine gave the best classification into meningiomas and nonmeningiomas (89% correct). Classification of gliomas (n = 47) gave 62% correct within one grade. Only inositol showed a significant correlation with glioma grade.

Diagnosis of brain abscess by magnetic resonance spectroscopy. Report of two cases.

Two cases of brain abscess were diagnosed by combining magnetic resonance spectroscopy (MRS) and magnetic resonance (MR) imaging. The resonances observed in vivo were assigned by means of an in vitro MRS study of the exudates extracted during surgical aspiration of the abscesses. The technique of MRS was demonstrated to be very powerful in the differential diagnosis of brain abscesses from other brain pathologies such as neoplasms. Amino acids, probably originating from extracellular proteolysis, and other compounds, such as acetate, arising from bacterial metabolism, were visible in the MRS spectra of the abscess, whereas they are not present in spectra of neoplasms. In this sense, MRS complemented the information provided by MR imaging to achieve a correct diagnosis of brain abscesses and could be added to routine MR examinations with only a small increase in cost and time.