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

1H-MRSI of radiation effects in normal-appearing white matter: dose-dependence and impact on automated spectral classification

PURPOSE

To identify radiation-induced changes in healthy white-matter spectra in the first six months following radiotherapy, and assess the impact of these changes on an automated algorithm for detecting spectral abnormalities.

MATERIALS AND METHODS

1H-MRSI was performed on 10 patients with grade IV gliomas who were to undergo radiation therapy. Choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) ratios were studied as a function of dose and time. The impact of these spectral changes on a spectral analysis algorithm was evaluated.

RESULTS

The Cho/NAA ratios rose to values of 0.66 +/- 0.15, 0.75 +/- 0.21, and 0.73 +/- 0.15 two months after therapy, compared to immediate post-therapy values of 0.56 +/- 0.15, 0.60 +/- 0.16, and 0.61 +/- 0.15 for the < 25, 25-50, and > 50 Gy dose groups, respectively. These maxima were followed by a dose-dependent recovery. A similar trend was found in the Cho/Cr ratio. The automated spectral analysis system incorporated the changing Cho/NAA ratio into a global redefinition of healthy tissue, but did not account for dose-dependent spatial variations in Cho/NAA ratios.

CONCLUSION

Radiation significantly alters the spectra of healthy tissues in the first six months after radiotherapy. This suggests that the radiation dose distribution should be considered during analysis of post-therapy spectra.

Parameterized evaluation of macromolecules and lipids in proton MR spectroscopy of brain diseases

Short echo time (TE) proton MR spectra of the brain include signals of several metabolites as well as macromolecules. In various pathologies, such as brain tumors and multiple sclerosis (MS), the presence of mobile lipids or pathologically altered macromolecules may provide useful additional diagnostic information. A reliable quantitation of these resonances, however, is often not possible due to the lack of adequate prior knowledge. Furthermore, even if advanced fitting procedures are used, a reliable evaluation of metabolites in the presence of pathological lipids or macromolecules often fails if the latter are omitted in the spectral evaluation. In this study, a method is presented for the simultaneous evaluation of all visible components, including metabolites, lipids, and macromolecules, by the use of the fitting procedure LCModel. A standard basis set of brain metabolites was extended by inclusion of parameterized components for macromolecules and lipids that were derived from metabolite-nulled in vivo spectra of normal brain and high-grade gliomas, respectively. The improved spectral quantitation is demonstrated in glial brain tumors and MS lesions as well as in normal brain. It is pointed out that both macromolecules and lipids must be included to provide a proper spectral evaluation.

Adult primitive neuroectodermal tumor: proton MR spectroscopic findings with possible application for differential diagnosis

PURPOSE

To assess the utility of proton magnetic resonance (MR) spectroscopy in the clinical categorization of primitive neuroectodermal tumors (PNETs) in adults.

MATERIALS AND METHODS

In vivo proton MR spectroscopy was performed with an echo time of 136 msec in nine adults with PNET, and findings were retrospectively compared with spectroscopic findings of 22 meningiomas, 12 low-grade astrocytomas, eight anaplastic astrocytomas, 23 glioblastomas, and 21 metastases. Nine resonances were semiquantitatively evaluated. Statistical analysis was performed by using Kruskal-Wallis and Mann-Whitney U tests. The Hochberg correction was applied for multiple comparisons. Results were prospectively validated in 24 tumors of the six types included in the study.

RESULTS

The resonances of choice for identifying PNET were alanine (P <.001) and glutamate and glutamine (P =.004), both decreased with respect to meningioma; choline increased with respect to low-grade (P <.001) and anaplastic astrocytoma (P =.055); and lipids at 1.30 ppm decreased and choline and other trimethyl-amine-containing compounds increased with respect to glioblastoma (P <.001 and P =.004, respectively) and metastasis (P <.001 and P =.021, respectively). We developed an algorithm for bilateral differential diagnosis between PNET and other tumor types. The leave-one-out method was used to test the five possible differential situations in the retrospective data set, with the following results: PNET versus meningioma, 31/23/5/3 (number of total/correct/unclassifiable/incorrect procedures); PNET versus low-grade astrocytoma, 21/19/2/0; PNET versus anaplastic astrocytoma, 17/6/9/2; PNET versus glioblastoma, 32/28/2/2; and PNET versus metastasis, 30/27/1/2. In total, 131 consecutive procedures produced 103 (79%) correct classifications and nine (7%) misclassifications. Twenty-five (78%) of 32 possible procedures in the prospective independent test set produced correct classifications and four (13%) produced incorrect classifications.

CONCLUSION

In vivo proton MR spectroscopy provides useful information in clinical differentiation between PNETs and common brain tumors in adults.

Common processing of in vivo MR spectra

This introductory article addresses approaches currently in use to process in vivo spectra. First, a brief overview is given of the information content represented by the parameters of MR signals. Subsequently, common steps in the processing of MR spectra such as pre-processing, normalisation and quantification and the use of prior knowledge are described. Finally, some prospects for more advanced processing are given.

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.