Abstract #1567
             Supervised pattern recognition for predicting the appearance of contrast-enhancement in high-grade gliomas using multimodality MRI and MRSI
             Chang S,  Nelson S,  Lee M
             University of California
             The appearance of contrast-enhancement in high-grade gliomas indicates a significant breakdown of the blood-brain barrier.  We are interested in determining which spectroscopic, diffusion, and perfusion parameters are most predictive of the appearance of contrast enhancement at a later date.  We hypothesize that data obtained prior to radiotherapy provide information that can be useful in predicting the location of the contrast-enhancing lesion after radiotherapy.  A genetic algorithm is used to select and scale features for an optimized k-nearest-neighbor classification algorithm.  By comparing the receiver-operating characteristic curves, we demonstrate that the optimized classifier significantly outperforms thresholding on any single-variable in the study.