Loukas G. Astrakas1,2, Konstantinos D. Blekas3, Ovidiu C. Andronesi1,4, Michael N. Mindrinos5, Peter M. Black6, Laurence G. Rahme7, A Aria Tzika1,4
1NMR Surgical Laboratory, Department of Surgery, Massachusetts General Hospital and Shriners Burns Institute, Harvard Medical School, Boston, MA, United States; 2Department of Medical Physics, University of Ioannina, Ioannina, Greece; 3Department of Computer Science, University of Ioannina, Ioannina, Greece; 4Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center of Biomedical Imaging, Boston, MA, United States; 5Stanford Genome Technology Center, Department of Biochemistry, Stanford University School of Medicine, Palo Alto, CA, United States; 6Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States; 7Molecular Surgery Laboratory, Department of Surgery, Massachusetts General Hospital and Shriners Burn Institute, Harvard Medical School, Boston, MA, United States
Our aim was to develop a novel approach that combines high-resolution magic angle spinning (HRMAS) H1 NMR and genomics in the same biopsies to improve prognostication of brain tumors. We employed a linear Support Vector Machine combined with the robust minimum redundancy maximum relevance feature selection scheme, and applied our algorithm to combined HRMAS 1H MRS and microarray data of the same adult brain tumor biopsies. Our results demonstrate that we are able to produce accurate and meaningful data and introduce a novel classification scheme that predicts a clinically meaningful parameter such as survival better than either method alone.