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.

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.