Abstract #1396

Multi-Variate Pattern Analysis for Identification of Metabolites that are Predictive of Malignant Transformation in Gliomas using HRMAS Spectra from Image Guided Tissue Samples

Alexandra Constantin¹, Adam Elkhaled², Trey Jalbert², Radhika Srinivasan², Soonmee Cha³, Susan M. Chang³, Ruzena Bajcsy¹, Sarah J. Nelson²

¹Electrical Engineering & Computer Science, University of California, Berkeley, CA, United States; ²Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, United States; ³Department of Neurological Surgery, University of California, San Franisco, CA, United States

In this study, we applied multivariate pattern recognition methods to HRMAS spectra from image guided tissue samples in order to identify metabolites that are predictive of malignant transformations in gliomas and to accurately detect those patients exhibiting malignant transformations. Our method extracts a small subset of features in the HRMAS spectra and uses it to build a parsimonious model capable of discriminating between patients with different tumor grades with over 90% accuracy. The features used in our model are traced back to known metabolites in the corresponding chemical shift range, thus identifying a useful set of metabolites to acquire in-vivo.