Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI

Brain metastases and glioblastoma multiforme are the most aggressive and common brain tumours in adults and they require a different clinical management. Anatomical magnetic resonance imaging (MRI) or clinical history, cannot always clearly distinguish between them. This study describes and verifies the use of magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI) in combination with MRI for differential diagnosis of glioblastomas and metastases. Feature selection methods are applied to the magnetic resonance (MR) spectra of 121 patients and relevant features are detected. Different classification methods are used to distinguish glioblastoma multiforme and metastasis based on the single-voxel MR spectra, but no reliable differentiation is obtained: the accuracy varies from 50 to 78%. Next, MRSI and MRI data from 10 patients (5 glioblastomas, 5 solitary metastases) are used for differentiation purposes. The combination of multivoxel MR data and MRI data suggests a more clear differentiation between glioblastoma multiforme and brain metastasis. The results are visualized based on nosologic images, which are generated by including spectroscopic information in the segmented MR image. The methodology offers a new way that may support clinicians in decision making.

[1]  Sabine Van Huffel,et al.  Brain tumor classification based on long echo proton MRS signals , 2004, Artif. Intell. Medicine.

[2]  Sabine Van Huffel,et al.  Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  Tommaso Scarabino,et al.  Proton MR spectroscopy of cerebral gliomas at 3 T: spatial heterogeneity, and tumour grade and extent , 2008, European Radiology.

[5]  Z. Wu,et al.  In vivo single-voxel proton MR spectroscopy in the differentiation of high-grade gliomas and solitary metastases. , 2004, Clinical radiology.

[6]  Glyn Johnson,et al.  High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. , 2002, Radiology.

[7]  Makoto Ochi,et al.  Differentiation between high-grade glioma and metastatic brain tumor using single-voxel proton MR spectroscopy , 2001, European Radiology.

[8]  T. Carpenter,et al.  Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. , 2006, AJNR. American journal of neuroradiology.

[9]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[10]  F. Howe,et al.  Differentiation of metastases from high‐grade gliomas using short echo time 1H spectroscopy , 2004, Journal of magnetic resonance imaging : JMRI.

[11]  Sabine Van Huffel,et al.  Nosologic imaging of the brain: segmentation and classification using MRI and MRSI , 2009, NMR in biomedicine.

[12]  Yu-Ting Kuo,et al.  Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings , 2004, Neuroradiology.

[13]  J. Suykens,et al.  Classification of brain tumours using short echo time 1H MR spectra. , 2004, Journal of magnetic resonance.

[14]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[15]  Sabine Van Huffel,et al.  Fast nosological imaging using canonical correlation analysis of brain data obtained by two‐dimensional turbo spectroscopic imaging , 2008, NMR in biomedicine.