Proton chemical shift imaging, metabolic maps, and single voxel spectroscopy of glial brain tumors

SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial

Background

The revised 2016 WHO-Classification of CNS-tumours now integrates molecular information of glial brain tumours for accurate diagnosis as well as for the development of targeted therapies. In this prospective study, our aim is to investigate the predictive value of MR-spectroscopy in order to establish a solid preoperative molecular stratification algorithm of these tumours. We will process a 1H MR-spectroscopy sequence within a radiomics analytics pipeline.

Methods

Patients treated at our institution with WHO-Grade II, III and IV gliomas will receive preoperative anatomical (T2- and T1-weighted imaging with and without contrast enhancement) and proton MR spectroscopy (MRS) by using chemical shift imaging (MRS) (5 × 5 × 15 mm³ voxel size). Tumour regions will be segmented and co-registered to corresponding spectroscopic voxels. Raw signals will be processed by a deep-learning approach for identifying patterns in metabolic data that provides information with respect to the histological diagnosis as well patient characteristics obtained and genomic data such as target sequencing and transcriptional data.

Discussion

By imaging the metabolic profile of a glioma using a customized chemical shift 1H MR spectroscopy sequence and by processing the metabolic profiles with a machine learning tool we intend to non-invasively uncover the genetic signature of gliomas. This work-up will support surgical and oncological decisions to improve personalized tumour treatment.

Trial registration

This study was initially registered under another name and was later retrospectively registered under the current name at the German Clinical Trials Register (DRKS) under DRKS00019855.

Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype?

The purpose of this study was to identify markers from perfusion, diffusion, and chemical shift imaging in glioblastomas (GBMs) and to correlate them with genetically determined and previously published patterns of structural magnetic resonance (MR) imaging. Twenty-six patients (mean age 60 years, 13 female) with GBM were investigated. Imaging consisted of native and contrast-enhanced 3D data, perfusion, diffusion, and spectroscopic imaging. In the presence of minor necrosis, cerebral blood volume (CBV) was higher (median ± SD, 2.23% ± 0.93) than in pronounced necrosis (1.02% ± 0.71), p_corr = 0.0003. CBV adjacent to peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity was lower in edema (1.72% ± 0.31) than in infiltration (1.91% ± 0.35), p_corr = 0.039. Axial diffusivity adjacent to peritumoral FLAIR hyperintensity was lower in severe mass effect (1.08*10^-3 mm²/s ± 0.08) than in mild mass effect (1.14*10^-3 mm²/s ± 0.06), p_corr = 0.048. Myo-inositol was positively correlated with a marker for mitosis (Ki-67) in contrast-enhancing tumor, r = 0.5, p_corr = 0.0002. Changed CBV and axial diffusivity, even outside FLAIR hyperintensity, in adjacent normal-appearing matter can be discussed as to be related to angiogenesis pathways and to activated proliferation genes. The correlation between myo-inositol and Ki-67 might be attributed to its binding to cell surface receptors regulating tumorous proliferation of astrocytic cells.

Mapping of glycine distributions in gliomas

BACKGROUND AND PURPOSE

Increased glycine concentration in the brain is associated with altered metabolism in cancer and can be detected by using in vivo MR spectroscopy. This has been proposed as a marker for grade IV gliomas; however, little is known about the potential significance and frequency of in vivo glycine observation. The purpose of this study was to examine the rate of occurrence and spatial distribution of glycine observation with respect to other MR imaging parameters.

MATERIALS AND METHODS

Data from volumetric whole-brain MR spectroscopic imaging of 59 subjects with glioma were analyzed with glycine included in the spectral model. The associations of the signal amplitude and spatial distributions of glycine with findings from contrast-enhanced T1, perfusion, and diffusion MR imaging were then examined.

RESULTS

Glycine was detected in 24% of all studies, though with a wide range of signal amplitude and extent of the spatial distributions. While more commonly seen in grade IV tumors (42% of studies), relatively large concentrations were also detected in grade II and III gliomas. Coanalysis with other metabolites indicated a strong association with choline and that glycine was frequently seen to be overlapping with, and adjacent to, areas of high lactate concentration. Increased glycine was always associated with contrast enhancement and areas of increased cerebral blood flow, but without any clear association with other image parameters.

CONCLUSIONS

Detection of increased glycine in gliomas appears to identify a subgroup of tumors and areas of increased proliferation.

High-resolution magic angle spinning magnetic resonance spectroscopy detects glycine as a biomarker in brain tumors

The non-essential amino acid neurotransmitter glycine (Gly) may serve as a biomarker for brain tumors. Using 36 biopsies from patients with brain tumors [12 glioblastoma multiforme (GBM); 10 low-grade (LG), including 7 schwannoma and 3 pylocytic astrocytoma; 7 meningioma (MN); 7 brain metastases (MT), including 3 adenocarcinoma and 4 breast cancer] and 9 control biopsies from patients undergoing surgery for epilepsy, we tested the hypothesis that the presence of glycine may distinguish among these brain tumor types. Using high-resolution magic angle spinning (HRMAS) ¹H magnetic resonance spectroscopy (MRS), we determined a theoretically optimum echo time (TE) of 50 ms for distinguishing Gly signals from overlapping myo-inositol (Myo) signals and tested our methodology in phantom and biopsy specimens. Quantitative analysis revealed higher levels of Gly in tumor biopsies (all combined) relative to controls; Gly levels were significantly elevated in LG, MT and GBM biopsies (P≤0.05). Residual Myo levels were elevated in LG and MT and reduced in MN and GBM (P<0.05 vs. control levels). We observed higher levels of Gly in GBM as compared to LG tumors (P=0.05). Meanwhile, although Gly levels in GBM and MT did not differ significantly from each other, the Gly:Myo ratio did distinguish GBM from MT (P<0.003) and from all other groups, a distinction that has not been adequately made previously. We conclude from these findings that Gly can serve as a biomarker for brain tumors and that the Gly:Myo ratio may be a useful index for brain tumor classification.

Non-invasive detection of glycine as a biomarker of malignancy in childhood brain tumours using in-vivo 1H MRS at 1.5 tesla confirmed by ex-vivo high-resolution magic-angle spinning NMR

Management of brain tumours in children would benefit from improved non-invasive diagnosis, characterisation and prognostic biomarkers. Metabolite profiles derived from in-vivo MRS have been shown to provide such information. Studies indicate that using optimum a priori information on metabolite contents in the construction of linear combination (LC) models of MR spectra leads to improved metabolite profile estimation. Glycine (Gly) is usually neglected in such models due to strong overlap with myo-inositol (mI) and a low concentration in normal brain. However, biological studies indicate that Gly is abundant in high-grade brain tumours. This study aimed to investigate the quantitation of Gly in paediatric brain tumours using MRS analysed by LCModel, and its potential as a non-invasive biomarker of malignancy. Single-voxel MRS was performed using PRESS (TR 1500 ms, TE 30 ms/135 ms) on a 1.5 T scanner. Forty-seven cases (18 high grade (HG), 17 low grade (LG), 12 ungraded) were retrospectively selected if both short-TE and long-TE MRS (n = 33) or short-TE MRS and high-resolution magic-angle spinning (HRMAS) of matched surgical samples (n = 15) were available. The inclusion of Gly in LCModel analyses led to significantly reduced fit residues for both short-TE and long-TE MRS (p < 0.05). The Gly concentrations estimated from short-TE MRS were significantly correlated with the long-TE values (R = 0.91, p < 0.001). The Gly concentration estimated by LCModel was significantly higher in HG versus LG tumours for both short-TE (p < 1e-6) and long-TE (p = 0.003) MRS. This was consistent with the HRMAS results, which showed a significantly higher normalised Gly concentration in HG tumours (p < 0.05) and a significant correlation with the normalised Gly concentration measured from short-TE in-vivo MRS (p < 0.05). This study suggests that glycine can be reliably detected in paediatric brain tumours using in-vivo MRS on standard clinical scanners and that it is a promising biomarker of tumour aggressiveness.

1H MR spectroscopic imaging with short and long echo time to discriminate glycine in glial tumours

OBJECT

To investigate glycine (Gly) concentrations in low- and high-grade gliomas based on (1)H MR spectroscopic imaging (MRSI) with short and long echo time (TE). Myoinositol (MI) and Gly appear at the same resonance frequency of 3.56 ppm, but due to strong coupling the MI signal dephases more rapidly. Therefore, their contribution to the 3.56 ppm signal should be distinguishable comparing MRSI data acquired at short and long TE.

MATERIALS AND METHODS

(1)H MRSI (TE = 30 and 144 ms) was performed at 3 T in 29 patients with histopathological confirmed World Health Organization (WHO) grade II-IV gliomas and in FIVE healthy subjects. All spectra from the gliomas revealed increase of the 3.56 ppm resonance in the short TE spectra. Signal intensities of Gly and MI were differentiated either by analysing the short to long TE ratio of the resonance or by performing a weighted difference. Gly concentrations were compared between high-grade (WHO III-IV) and low-grade gliomas.

RESULTS

High-grade gliomas showed significantly higher Gly concentrations compared to low-grade gliomas.

CONCLUSION

Appropriate data processing of short and long TE (1)H MRSI provides a tool to distinguish and to quantify Gly and MI concentrations in gliomas. As Gly seems to be a marker of malignancy, more dedicated spectroscopic methods to differentiate these metabolites are justified.

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra

A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low-grade gliomas and high-grade malignant tumours--glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone.

Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomas

In this study, we developed a method to improve the delineation of intrinsic brain tumors based on the changes in metabolism due to tumor infiltration. Proton magnetic resonance spectroscopic imaging ((1)H-MRSI) with a nominal voxel size of 0.45 cm(3) was used to investigate the spatial distribution of choline-containing compounds (Cho), creatine (Cr) and N-acetyl-aspartate (NAA) in brain tumors and normal brain. Ten patients with untreated gliomas were examined on a 1.5 T clinical scanner using a MRSI sequence with PRESS volume preselection. Metabolic maps of Cho, Cr, NAA and Cho/NAA ratios were calculated. Tumors were automatically segmented in the Cho/NAA images based on the assumption of Gaussian distribution of Cho/NAA values in normal brain using a limit for normal brain tissue of the mean + three times the standard deviation. Based on this threshold, an area was calculated which was delineated as pathologic tissue. This area was then compared to areas of hyperintense signal caused by the tumor in T2-weighted MRI, which were determined by a region growing algorithm in combination with visual inspection by two experienced clinicians. The area that was abnormal on (1)H-MRSI exceeded the area delineated via T2 signal changes in the tumor (mean difference 24%) in all cases. For verification of higher sensitivity of our spectroscopic imaging strategy we developed a method for coregistration of MRI and MRSI data sets. Integration of the biochemical information into a frameless stereotactic system allowed biopsy sampling from the brain areas that showed normal T2-weighted signal but abnormal (1)H-MRSI changes. The histological findings showed tumor infiltration ranging from about 4-17% in areas differentiated from normal tissue by (1)H-MRSI only. We conclude that high spatial resolution (1)H-MRSI (nominal voxel size = 0.45 cm(3)) in combination with our segmentation algorithm can improve delineation of tumor borders compared to routine MRI tumor diagnosis.

Proton magnetic resonance spectroscopic imaging in brain tumor diagnosis

The current state of standard tumor diagnostics using contrast-enhanced MRI and biopsy is assessed in this review, and the progress of proton magnetic resonance spectroscopy (MRS) over the last 15 years is discussed. We summarize MRS basics and describe a typical magnetic resonance session for noninvasive routine tumor diagnostics at 1.5 T, including two-dimensional magnetic resonance spectroscopic imaging (MRSI). The results that can be obtained from such procedures are illustrated with clinical examples. Attention is turned to cutting-edge methodologic and clinical research at 3 T, with examples using high-resolution or very short echo-time three-dimensional MRSI. The current status and limitations in proton MRSI are discussed, and we look to the potential of faster data collection and even higher field strength.

A Chemometric Approach for Brain Tumor Classification Using Magnetic Resonance Imaging and Spectroscopy

A new classification approach was developed to improve the noninvasive diagnosis of brain tumors. Within this approach, information is extracted from magnetic resonance imaging and spectroscopy data, from which the relative location and distribution of selected tumor classes in feature space can be calculated. This relative location and distribution is used to select the best information extraction procedure, to identify overlapping tumor classes, and to calculate probabilities of class membership. These probabilities are very important, since they provide information about the reliability of classification and might provide information about the heterogeneity of the tissue. Classification boundaries were calculated by setting thresholds for each investigated tumor class, which enabled the classification of new objects. Results on histopathologically determined tumors are excellent, demonstrated by spatial maps showing a high probability for the correctly identified tumor class and, moreover, low probabilities for other tumor classes.

The glycine peak in brain diseases

Glycine is an excitatory amino acid, a neurotransmitter for the brain. A recent experimental study by a 9.3T laboratory spectrometer identified the peak of pure glycine at 3.52 ppm, and in a clinical case this peak was demonstrated at 3.50 ppm by a 1.5 T clinical scanner. This study was undertaken to investigate the brain diseases having the glycine peak. An experiment with a 1.5 T clinical MRI unit was performed. Two grams of pure glycine was dissolved in 200 cc of distilled water and the solution was frozen, and proton MR spectroscopy (TR=1500 ms, TE=20 ms) was obtained. Nine patients with various diseases studied by two-dimensional chemical shift spectroscopy (hybrid CSI) with TR=1500 ms, and TE=40 ms are included in the study. Ten normal cases were available for comparison. In the experiment with the clinical MRI unit, the glycine peak was centered at 3.50 ppm. The disease processes associated with distinct glycine peaks at 3.50 ppm included infarction, high-grade astrocytoma, megalencephalic leukoencephalopathy with cysts, Leigh's disease, adrenoleukodystrophy, congenital muscular dystrophy, Rasmussen's encephalitis, gliosis in neuronal migrational disorder, and hamartoma in tuberous sclerosis. None of the control cases displayed a glycine peak. In conclusion, glycine has a peak centered at 3.50 ppm in in vivo environments. It is distinct from the myoinositol peak. Detection of glycine in a wide variety of brain diseases ranging from infarction, tumor, leukoencephalopathies, infection to gliosis likely reflects presence of excitotoxic brain damage or a disturbance of neurotransmitting mechanisms in these conditions.

1H MR spectroscopy of brain tumours and masses

Accurate diagnosis is essential for optimum management and treatment of patients with brain tumours. Proton magnetic resonance spectroscopy ((1)H MRS) provides information non-invasively on tumour biochemistry and has been shown to provide important additional information to that obtained by conventional radiology. We review the current status of (1)H MRS in classifying brain tumour type and grade, for monitoring response to therapy and progression to higher grade, and as a molecular imaging technique for determining tumour extent for treatment planning.

Use of proton magnetic resonance spectroscopy of the brain to differentiate gliomatosis cerebri from low-grade glioma

OBJECT

Gliomatosis cerebri (GC), a rare entity characterized by a widespread infiltration of brain by tumor, lacks objective and quantitative diagnostic criteria. Single-voxel spectroscopy and chemical shift imaging (two-dimensional proton magnetic resonance [MR] spectroscopy) were performed using both short (20- or 22-msec) and long (135-msec) echo times in nine patients suffering from GC, nine patients with low-grade gliomas (LGGs), and 25 healthy volunteers to establish the precise metabolic pattern of this uncommon brain neoplasm.

METHODS

The gliomatosis infiltration was characterized by markedly elevated levels of creatine-phosphocreatine (Cr) and mvo-inositol (Ins), a reduced level of N-acetyl aspartate (NAA), and a moderately elevated level of choline-containing compounds (Cho). This pattern differs strikingly from LGGs, which are characterized by elevated levels of Cho and Ins, markedly reduced levels of NAA, and low-to-normal Cr concentrations. Although the distinction between GC and LGG, based on histological and MR imaging criteria, is a matter of debate, MR spectroscopy produces valuable information for the differentiation between these two entities and, hence, the choice of therapeutic strategy. It also provides new insight into the pathophysiology of GC because elevated Cr and Ins levels may be related to proliferation of glial elements or, more probably, activation of normal glia. Elevated levels of Cho reflect cellular proliferation and reduced NAA corresponds to reversible neuronal injury and/or focal invasion by the tumor process.

CONCLUSIONS

Owing to the unfavorable clinical outcome associated with GC compared with that associated with LGG, the findings of this study illustrate the diagnostic and prognostic value of proton MR spectroscopy in the characterization of infiltrating gliomas.

On the incorporation of multi-modality image registration into the radiotherapy treatment planning process

A technique is presented that allows the direct use of physiological image sets in the radiation therapy treatment planning process. When fused to the treatment planning CT, physiological image studies may allow one to define physiological tumor subvolumes consisting of areas of possible chronic hypoxia, areas of high perfusion, areas of high diffusion, and areas containing high choline concentrations. These physiological tumor subvolumes could be selectively boosted to increase local control of malignant brain tumors once one has determined which of these physiological tumor subvolumes predicts for local tumor recurrence after conventional radiotherapy. In this technique a user assisted automatic registration technique is used that is based on an analytical estimate for the transformation matrix needed to register two rigid bodies. The only user input needed is three non-collinear points selected based on landmarks in the primary image and the corresponding three points in the secondary image. Since this registration technique uses two sets of at least three user-defined landmark points each of which has some selection error associated with it, the final registration will have an error that depends only on the selection error associated with the point sets. Since physiological image studies are acquired at the same setting as the T1- w MRI their spatial orientation with respect to the T1- w MRI is known. Therefore, the registration of multiple physiological image studies to the treatment planning CT can be accomplished by first correlating them to the T1- w MRI, and in a second step the T1- w MRI is then registered to the treatment planning CT. The desired registration of the physiological image studies to the treatment planning CT is then accomplished by simply composing the appropriate transformation matrices.

Characterization of oligodendrogliomas using short echo time 1H MR spectroscopic imaging

Oligodendroglial tumors may not be distinguished easily from other brain tumors based on clinical presentation and magnetic resonance imaging (MRI) alone. Identification of these tumors however may have therapeutic consequences. The purpose of this study was to characterize and identify oligodendrogliomas by their metabolic profile as measured by (1)H MR spectroscopic imaging (MRSI). Fifteen patients with oligodendroglial tumors (eight high-grade oligodendrogliomas, seven low-grade oligodendrogliomas) underwent MRI and short echo time (1)H MRSI examinations. Five main metabolites found in brain MR spectra were quantified and expressed as ratios of tumor to contralateral white matter tissue. The level of lipids plus lactate was also assessed in the tumor. For comparison six patients with a low grade astrocytoma were also included in the study. The metabolic profile of oligodendrogliomas showed a decreased level of N-acetylaspartate and increased levels of choline-containing compounds and glutamine plus glutamate compared with white matter. The level of glutamine plus glutamate was significantly higher in low-grade oligodendrogliomas than in low-grade astrocytomas and may serve as a metabolic marker in diagnosis and treatment planning. In high-grade oligodendrogliomas large resonances of lipids plus lactate were observed in contrast to low-grade tumors.

Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy

Proton spectroscopy can noninvasively provide useful information on brain tumor type and grade. Short- (30 ms) and long- (136 ms) echo time (TE) (1)H spectra were acquired from normal white matter (NWM), meningiomas, grade II astrocytomas, anaplastic astrocytomas, glioblastomas, and metastases. Very low myo-Inositol ([mI]) and creatine ([Cr]) were characteristic of meningiomas, and high [mI] characteristic of grade II astrocytomas. Tumor choline ([Cho]) was greater than NWM and increased with grade for grade II and anaplastic astrocytomas, but was highly variable for glioblastomas. Higher [Cho] and [Cr] correlated with low lipid and lactate (P < 0.05), indicating a dilution of metabolite concentrations due to necrosis in high-grade tumors. Metabolite peak area ratios showed no correlation with lipids and mI/Cho (at TE = 30 ms), and Cr/Cho (at TE = 136 ms) best correlated with tumor grade. The quantified lipid, macromolecule, and lactate levels increased with grade of tumor, consistent with progression from hypoxia to necrosis. Quantification of lipids and macromolecules at short TE provided a good marker for tumor grade, and a scatter plot of the sum of alanine, lactate, and delta 1.3 lipid signals vs. mI/Cho provided a simple way to separate most tumors by type and grade.

Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study

Automated pattern recognition techniques are needed to help radiologists categorize MRS data of brain tumors according to histological type and grade. A major question is whether a computer program "trained" on spectra from one hospital will be able to classify those from another, particularly if the acquisition protocol is different. A subset of 144 histopathologically validated brain tumor spectra in the INTERPRET database, obtained from three of the collaborating centers, was grouped into meningiomas, low-grade astrocytomas, and "aggressive tumors" (glioblastomas and metastases). Spectra from two centers formed the training set (94 spectra) while the third acted as the test set (50 spectra). Linear discriminant analysis successfully classified 48/50 in the test set; the remaining two were atypical cases. When the training and test sets were combined, 133 of the 144 spectra were correctly classified using the leave-one-out procedure. These spectra had been obtained using different sequences (STEAM and PRESS), different echo times (20, 30, 31, and 32 ms), different repetition times (1600 and 2000 ms), and different manufacturers' instruments (GE and Philips). Pattern recognition algorithms are less sensitive to acquisition parameters than had been expected.

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.

Infantile tuberous sclerosis changes in the brain: proton MR spectroscopy findings

A parietal hamartoma of a three-month-old boy with tuberous sclerosis was studied with magnetic resonance (MR) imaging, and proton MR spectroscopy. MR spectra were obtained with the single-voxel PRESS (point resolved spectroscopy; TR = 1500 ms, TE = 135 ms) sequence, in a 8 cc region of interest. Apparently low NAA/Cho (0.28), and NAA/Cr (0.37) ratios were noted in the hamartoma, that could suggest a neoplasm. The lesion and the surrounding brain tissue were studied again after seven months with spectroscopic imaging using the chemical shift sequence (TR = 1500 ms. TE = 40 ms). This study revealed apparently improved NAA/Cho (2.63), NAA/Cr (2.13) ratios in the hamartoma compared to the initial examination at three months of age, excluding the possibility of a neoplasm.

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.

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.

Assignment of glial brain tumors in humans by in vivo 1H-magnetic resonance spectroscopy and multidimensional metabolic classification

This study presents a simple approach for the noninvasive assignment of glial brain tumors according to malignancy by single-voxel proton magnetic resonance spectroscopy at short echo times (TE < or = 50 milliseconds). Based on peak area ratios, a five-dimensional data set was obtained for each investigated subject. This vector was then projected along metabolic coordinates in a two-dimensional metabolic space. These coordinates had been determined in a previous study (Hagberg G et al., 1995, Magn Reson Med 34: 242-252). Tumor assignment was done without any knowledge of histology by comparing the location of the new cases to the features of the previous study. All 11 investigated glioblastomas multiforme, as well as 4 of 5 astrocytomas grade II, could easily be assigned to the groups of high- and low-grade tumors, respectively. Classification was more difficult in the case of a cystic astrocytoma grade II and one astrocytoma grade III. Two spectra measured in normal-appearing matter of glioblastoma patients were not classified as healthy. Using single-voxel proton magnetic resonance spectroscopy at short echo times with the knowledge of a base study, a straightforward, fast, and noninvasive differential diagnosis of glial brain tumors is possible.