This article investigates methods for the accurate and robust differentiation of metastases from glioblastomas on the basis of single-voxel 1H MRS information. Single-voxel 1H MR spectra from a total of 109 patients (78 glioblastomas and 31 metastases) from the multicenter, international INTERPRET database, plus a test set of 40 patients (30 glioblastomas and 10 metastases) from three different centers in the Barcelona (Spain) metropolitan area, were analyzed using a robust method for feature (spectral frequency) selection coupled with a linear-in-the-parameters single-layer perceptron classifier. For the test set, a parsimonious selection of five frequencies yielded an area under the receiver operating characteristic curve of 0.86, and an area under the convex hull of the receiver operating characteristic curve of 0.91. Moreover, these accurate results for the discrimination between glioblastomas and metastases were obtained using a small number of frequencies that are amenable to metabolic interpretation, which should ease their use as diagnostic markers. Importantly, the prediction can be expressed as a simple formula based on a linear combination of these frequencies. As a result, new cases could be straightforwardly predicted by integrating this formula into a computer-based medical decision support system. This work also shows that the combination of spectra acquired at different TEs (short TE, 20-32ms; long TE, 135-144ms) is key to the successful discrimination between glioblastomas and metastases from single-voxel 1H MRS. © 2011 John Wiley & Sons, Ltd.
|Journal||NMR in Biomedicine|
|Publication status||Published - 1 Jun 2012|
- Feature selection
- High-grade malignant tumors
- Medical decision support system
- Pattern recognition
- SV H MRS 1