Combined use of neuroradiology and 1H-MR spectroscopy may provide an intervention limiting diagnosis of glioblastoma multiforme.

PURPOSE: To evaluate the accuracy of (1)H-MR spectroscopy ((1)H-MRS) as an intervention limiting diagnostic tool for glioblastoma multiforme. GBM is the most common and aggressive primary brain tumor, with mean survival under a year. Oncological practice currently requires histopathological diagnosis before radiotherapy. MATERIALS AND METHODS: Eighty-nine patients had clinical computed tomography (CT) and MR imaging and 1.5T SV SE (1)H-MRS with PRESS localization for neuroradiological diagnosis and tumor classification with spectroscopic and automated pattern recognition analysis (TE 30 ms, TR 2000 ms, spectral width 2500 Hz and 2048 data points, 128-256 signal averages were acquired, depending on voxel size (8 cm(3) to 4 cm(3)). Eighteen patients from a cohort of 89 underwent stereotactic biopsy. RESULTS: The 18 stereotactic biopsies revealed 14 GBM, 2 grade II astrocytomas, 1 lymphoma, and 1 anaplastic astrocytoma. All 14 biopsied GBMs were diagnosed as GBM by a protocol combining an individual radiologist and an automated spectral pattern recognition program. CONCLUSION: In patients undergoing stereotactic biopsy combined neuroradiological and spectroscopic evaluation diagnoses GBM with accuracy that could replace the need for biopsy. We do not advocate the replacement of biopsy in all patients; instead our data suggest a specific intervention limiting role for the use of (1)H-MRS in brain tumor diagnosis.

Correlations between in vivo (1)H MRS and ex vivo (1)H HRMAS metabolite measurements in adult human gliomas.

PURPOSE: To assess how accurately ex vivo high-resolution magic angle spinning (HRMAS) proton magnetic resonance spectroscopy ((1)H MRS) from small biopsy tissues relate to in vivo (1)H MRS (from larger tumor volumes) in human astrocytomas. MATERIALS AND METHODS: In vivo (PRESS, TE = 30 msec) and ex vivo (presaturation) (1)H spectra of 17 human astrocytomas (4 grade II, 1 grade III and 12 glioblastomas) were quantified using LCModel. Concentrations of 11 metabolites and 2 lipid/macromolecules were retrospectively compared, with histogram analysis of the in vivo MRI data used to evaluate tumor heterogeneity. RESULTS: For homogeneous-appearing tumors, significant correlations were found between in vivo and ex vivo (1)H MRS concentrations of those metabolites known to be metabolically stable in postmortem tissues (eg, creatine, myo-inositol, total cholines, and the approximately 1.3 and 0.9 ppm lipids). Anaerobic glycolysis during biopsy surgical removal depletes the tissue of glucose, increasing alanine and lactate, and resulted in no correlation between these in vivo and ex vivo metabolite concentrations. CONCLUSION: Within defined limitations, ex vivo astrocytoma biopsy HRMAS (1)H spectra have similar metabolic profiles to that obtained in vivo and therefore detailed ex vivo characterization of glioma biopsies can directly relate to the original tumor.

Toward accurate quantification of metabolites, lipids, and macromolecules in HRMAS spectra of human brain tumor biopsies using LCModel.

High-resolution magic angle spinning (HRMAS) (1)H MR spectroscopy of biopsy samples provides detailed biochemical profiles that can be related to the lower-resolution spectra obtained in vivo. Nevertheless, there is still significant overlap of many resonance peaks and contributions from broad lipid and macromolecule resonances that impede accurate quantification. We determined a minimum set of in vitro metabolite and simulated lipid and macromolecule resonances needed for LCModel analysis and quantification of brain tumor biopsy HRMAS spectra. We also demonstrate the quality of the LCModel fit for the four main brain tumor types (astrocytoma grade II, glioblastoma, metastasis, and meningioma). Our data suggest that when fitting resonances of coupled spins systems in high-resolution spectra, interactions between metabolites and the macromolecular environment of the biopsy may cause small peak shifts not found in the solution spectra. However, LCModel is shown to provide a user-independent method of analyzing HRMAS brain tumor spectra.

An assessment of the effects of sample ischaemia and spinning time on the metabolic profile of brain tumour biopsy specimens as determined by high-resolution magic angle spinning (1)H NMR.

High-resolution magic angle spinning (HRMAS) (1)H NMR of biopsy tissue provides a biochemical profile that has potential diagnostic and prognostic value, and can aid interpretation of the lower-resolution (1)H-NMR spectra obtained in vivo. However, the biochemical profile obtained may be affected by experimental factors such as a period of ischaemia before snap-freezing of the biopsy tissue for subsequent analysis and the mechanical stress of the spinning procedure of HRMAS itself. We have used normal rat brain cortex as a 'gold standard', either funnel-frozen or deliberately allowed to become ischaemic for set periods of time before snap-freezing, to quantitatively investigate these two effects. In addition, we have compared biochemical changes that occur in normal rat brain during HRMAS (spun continuously at 5 kHz for 4 h at 4 degrees C as could be required for a two-dimensional acquisition) with those that occur in biopsy samples from low-grade and high-grade adult human astrocytomas, during the same HRMAS procedure. Significant changes due to delayed initial sample freezing were noted in metabolites associated with glycolysis (alanine, glucose and lactate), as expected. However, for the funnel-frozen rat tissue at 4 degrees C, there were even more significant changes, which appear to be the result of extended spinning at 5 kHz. In particular, the 18% total creatine increase observed is unlikely to be de novo synthesis of creatine. More likely, the asymptotic exponential increase in creatine suggests an exponential release of an NMR-invisible bound creatine store as a result of tissue damage from mechanical stress of sample spinning. Overall, it appears that tissue ischaemia during biopsy excision and delays in snap-freezing may have less significant effects on metabolite profile than the prolonged spinning times required for two-dimensional HRMAS, and this must be accounted for when results are being interpreted.

Serum alpha 2-HS glycoprotein predicts survival in patients with glioblastoma.

BACKGROUND: Glioblastoma, the most common primary brain tumor, has variable prognosis. We aimed to identify serum biomarkers that predict survival of patients with glioblastoma. METHODS: In phase 1 (biomarker discovery), SELDI-TOF mass spectra were studied in 200 serum samples from 58 control subjects and 36 patients with grade II astrocytoma, 15 with anaplastic astrocytoma, and 91 with glioblastoma. To identify potential biomarkers, we searched for peptide peaks that changed progressively in size with increasing malignancy. One peak, identified as the B-chain of alpha 2-Heremans-Schmid glycoprotein (AHSG), was less prominent with increasing tumor grade. We therefore investigated AHSG as a survival predictor in glioblastoma. We measured serum AHSG by turbidimetry and determined indices of malignancy, including tumor proliferation (Ki67 immunolabel) and necrosis (tumor lipids on magnetic resonance spectroscopy). In phase 2 (biomarker validation), the prognostic power of AHSG was validated in an independent group of 72 glioblastoma patients. RESULTS: Median survival was longer (51 vs 29 weeks) in glioblastoma patients with normal vs low serum AHSG concentrations (hazard ratio 2.7, 95% CI 1.5-5.0, P <0.001), independent of age and Karnofsky score. Serum AHSG inversely correlated with Ki-67 immunolabeling and tumor lipids. A prognostic index combining serum AHSG with patient age and Karnofsky score separated glioblastoma patients with short (<3 months) and long (>2 years) median survival. The prognostic value of serum AHSG was validated in a different cohort of glioblastoma patients. CONCLUSIONS: We conclude that serum AHSG concentration, measured before starting treatment, predicts survival in patients with glioblastoma.

An investigation of human brain tumour lipids by high-resolution magic angle spinning 1H MRS and histological analysis.

NMR-visible lipid signals detected in vivo by 1H MRS are associated with tumour aggression and believed to arise from cytoplasmic lipid droplets. High-resolution magic angle spinning (HRMAS) 1H MRS and Nile Red staining were performed on human brain tumour biopsy specimens to investigate how NMR-visible lipid signals relate to viable cells and levels of necrosis across different grades of glioma. Presaturation spectra were acquired from 24 adult human astrocytoma biopsy samples of grades II (8), III (2) and IV (14) using HRMAS 1H MRS and quantified using LCModel to determine lipid concentrations. Each biopsy sample was then refrozen, cryostat sectioned, and stained with Nile Red, to determine the number of lipid droplets and droplet size distribution, and with Haematoxylin and Eosin, to determine cell density and percentage necrosis. A strong correlation (R=0.92, P<0.0001) was found between the number of Nile Red-stained droplets and the approximately 1.3 ppm lipid proton concentration by 1H MRS. Droplet sizes ranged from 1 to 10 microm in diameter, and the size distribution was constant independent of tumour grade. In the non-necrotic biopsy samples, the number of lipid droplets correlated with cell density, whereas in the necrotic samples, there were greater numbers of droplets that showed a positive correlation with percentage necrosis. The correlation between 1H MRS lipid signals and number of Nile Red-stained droplets, and the presence of lipid droplets in the non-necrotic biopsy specimens provide good evidence that the in vivo NMR-visible lipid signals are cytoplasmic in origin and that formation of lipid droplets precedes necrosis.

MRS quality assessment in a multicentre study on MRS-based classification of brain tumours.

This paper reports on quality assessment of MRS in the European Union-funded multicentre project INTERPRET (International Network for Pattern Recognition of Tumours Using Magnetic Resonance; http://azizu.uab.es/INTERPRET), which has developed brain tumour classification software using in vivo proton MR spectra. The quality assessment consisted of both MR system quality assurance (SQA) and quality control (QC) of spectral data acquired from patients and healthy volunteers. The system performance of the MR spectrometers at all participating centres was checked bimonthly by a short measurement protocol using a specially designed INTERPRET phantom. In addition, a more extended SQA protocol was performed yearly and after each hardware or software upgrade. To compare the system performance for in vivo measurements, each centre acquired MR spectra from the brain of five healthy volunteers. All MR systems fulfilled generally accepted minimal system performance for brain MRS during the entire data acquisition period. The QC procedure of the MR spectra in the database comprised automatic determination of the signal-to-noise ratio (SNR) in a water-suppressed spectrum and of the line width of the water resonance (water band width, WBW) in the corresponding non-suppressed spectrum. Values of SNR > 10 and WBW < 8 Hz at 1.5 T were determined empirically as conservative threshold levels required for spectra to be of acceptable quality. These thresholds only hold for SNR and WBW values using the definitions and data processing described in this article. A final QC check consisted of visual inspection of each clinically validated water-suppressed metabolite spectrum by two, or, in the case of disagreement, three, experienced MR spectroscopists, to detect artefacts such as large baseline distortions, exceptionally broadened metabolite peaks, insufficient removal of the water line, large phase errors, and signals originating from outside the voxel. In the end, 10% of 889 spectra with completed spectroscopic judgement were discarded.

Apparent T(2) relaxation times of lipid and macromolecules: a study of high-grade tumor spectra.

PURPOSE: To determine T(2) relaxation times of lipid and macromolecules (Lip/MMs) observed by (1)H magnetic resonance spectroscopy ((1)H MRS) of metastases (MET) and glioblastomas (GBM), so that they may be better characterized. MATERIALS AND METHODS: (1)H spectra were acquired at multiple echo times from brain lesions using point-resolved spectroscopy sequence (PRESS) at TE = 30 msec either with metabolite-nulling (six GBM and 11 MET), or without metabolite-nulling (four MET and one mucocele). All lesions were previously untreated and had subsequent histopathological classification. RESULTS: The T(2) of the 1.3-ppm Lip/MM peak was concentration-dependent, but at high concentrations it was significantly different (P = 0.015) between GBM (42 +/- 6 msec) and MET (63 +/- 18 msec). The broad 2.05-ppm and 0.09-ppm Lip/MM peaks had similar T(2)s in MET and GBM. The T(2) of the narrow 2.05-ppm Lip/MM peak sometimes observed had a T(2) of 100 +/- 17 msec in MET and 75 msec in the mucocele. CONCLUSION: We observed significantly higher T(2) of the 1.3-ppm Lip/MM peak in MET compared with GBM at high 1.3-ppm proton concentrations, in agreement with a higher 1.3/0.9-ppm peak ratio found in MET. The relatively long T(2) of the 2.05-ppm Lip/MM peak sometimes observed in MET may cause it to be confused with N-acetyl aspartate (NAA).

Differentiation of metastases from high-grade gliomas using short echo time 1H spectroscopy.

PURPOSE: To determine if short echo time (TE) (1)H magnetic resonance spectroscopy (MRS) can distinguish between intracranial metastases and glioblastomas. MATERIALS AND METHODS: TE 30-msec spectra were acquired (1.5 T) from voxels entirely within tumors from 23 glioblastoma patients and 24 metastases patients (3 breast carcinomas, 1 bladder carcinoma, 8 lung carcinomas, 3 probable lung carcinomas, 6 melanomas, 1 stomach carcinoma, and 2 undetermined). Spectra were analyzed quantitatively (LCModel) to determine metabolite, lipid, and macromolecule concentrations. All tumors were previously untreated and classified histopathologically. RESULTS: The lipid peak area (LPA) ratio (total peak area at ca. delta1.3 to that at ca. delta0.9) was 2.6 +/- 0.6 (N = 25) for glioblastomas and 3.8 +/- 1.4 (N = 34) for metastases (P < 0.0001). There were no significant differences in metabolite or lipid concentrations between the tumor groups. The LPA ratio provided 80% sensitivity and 80% specificity for discriminating metastases from glioblastomas. CONCLUSION: Lipid and macromolecule (LM) signals can dominate (1)H spectra of high-grade tumors and have characteristics that allow significant discrimination of metastases from glioblastomas. Work is now needed to determine the source and biophysical characteristics of these LM signals to further improve differentiation by optimizing the data acquisition and analysis protocol.

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.