A new approach for analyzing proton magnetic resonance spectroscopic images of brain tumors: nosologic images

Abstract

The histopathological characterization of brain tumors requires the use of biopsy, which is an invasive surgical procedure. However, a biopsy does not always show the real grade of the tumor because of tumor heterogeneity. Many studies have been done to assess the utility of in vivo proton magnetic resonance spectroscopic imaging ( 1 H MRSI) in noninvasive diagnosis of brain tumors [1][2][3][4][5][6][7][8][9][10][11][12][13] . The earliest studies [2][3][4][5]8 used single-voxel spectroscopy (acquisition of a spectrum for one volume of interest inside the tumor) and classification into histopathological groups based on the study of peak ratios. More recently, pattern recognition techniques have been used to improve the classification of the different brain tumor types 6,7,9,10,12,13 . These techniques allow an automatic classification of spectra based on an analysis of the whole spectrum after digitization or of a profile extracted from selected peak intensities 14,15 . Promising results have been obtained, with accuracy rates of 55-100%. However, individual variability, mainly due to the heterogeneity of the tissue within a voxel, results in a substantial overlap between different brain tumor types 2 and between high-and low-grade glial tumors 8 .Another approach to improving brain tumor characterization is based on 1 H MRSI in which spectra from all voxels of a selected slice are acquired simultaneously. 1 H MRSI shows that metabolites are distributed heterogeneously in the tumor 1,11,16 , but it is difficult to extract information about the histopathology of each voxel and about the histopathological heterogeneity of the tumor. A spectroscopic examination leads to a large amount of information: 64-100 spectra containing up to six peaks (Fig. 1), attributed to cholinecontaining compounds (3.2 parts per million (p.p.m.)), creatinecontaining compounds (3.0 p.p.m.), N-acetyl-aspartate (NAA; 2.0 p.p.m.), alanine (1.4 p.p.m.), lactate and/or lipids (1.3 p.p.m.) and lipids (0.9 p.p.m.). Several images of metabolite distribution (metabolite maps) can be obtained for each patient (Fig. 1). It is very difficult to extract pertinent information from visual inspection of these metabolite maps. The analysis might be limited to one or two voxels of the spectroscopic images, but the difficulty lies in deciding which ones are most representative of the tumor. One region of interest (ROI) might be defined in the tumor, with comparison of the averaged metabolite ratio obtained over this ROI (ref. 7), but then the information about tumor heterogeneity is lost. The main difficulty is combining the information contained in six metabolite maps into a single image giving voxel-by-voxel information about the tumor.Here we present a new approach for extracting information about brain tumor types and grades from 1 H MRSI data. We sought to design a tool that could be used routinely and easily by radiologists for assessing the potential of 1 H MRSI for brain tumor diagnosis, studying heterogeneity, stereotactic biopsy guidance and thera...

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

“…Both the -and the -error for the methods 9-16 decrease significantly compared with methods 1-8. To our knowledge only Szabo De Edelenyi et al 27 have combined MRI and MRSI features in a classifier before. In their approach, only information from the T 2 -weighted image was combined with MRSI information.…”

Imaging in neurooncology

“…63 An improved automated MRS analysis approach (nosologic imaging) enables correct differentiation between low-grade glioma, high-grade glioma, meningiomas, metastasis, necrosis, and healthy tissue in up to 90% of cases and shall facilitate a noninvasive diagnosis of lesion type. 64,65 Disadvantages of MRS include its low spatial resolution, which cannot fully address the anatomical and contrast heterogeneity of brain tumors observed with MRI. The role of PET has been primarily investigated in patients with gliomas as the most frequent and most difficult to treat primary brain tumors.…”

Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy

“…This study complements previous work in using combined MRI and spectroscopy features in classifying brain tumors [13][14][15][16]. We have chosen to apply similar methods of supervised pattern recognition and computer-aided diagnosis to study a very specific question in glioma imaging, thereby relating machine learning techniques with image segmentation and clinical prediction.…”

“…However, there has been somewhat less work on combining imaging data with spectroscopy. When this combination has been performed, the accuracy of the classification has been shown to be superior to using images or spectroscopy alone [13][14][15][16].…”