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Abstract #0409

Statistical multiscale mapping of IDH1, MGMT, and microvascularity in human brain tumors from multiparametric MR and registered core biopsy

Jason Glenn Parker1, Emily E Diller2, Sha Cao3, Jeremy T Nelson4, Kristen Yeom5, Chang Ho1, and Robert Lober6
1Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States, 2School of Health Sciences, Purdue University, West Lafayette, IN, United States, 3Biostatistics, Indiana University School of Medicine, Indianapolis, IN, United States, 4Military Health Institute, University of Texas Health San Antonio, San Antonio, TX, United States, 5Neuroradiology, Lucile Salter Packard Children’s Hospital and Stanford University Medical Center, Palo Alto, CA, United States, 6Neurosurgery, Dayton Children's Hospital, Dayton, OH, United States

We demonstrate statistical relationships between routine multiparametric imaging signatures and underlying cellular and molecular properties of brain tumors. We apply advanced statistical methods to correct for the family-wise error rate problem associated with whole-brain statistical parametric mapping, and show that the results have strong agreement with surgical biopsy. These results imply that cellular and molecular mapping of tumor heterogeneity from minimally-invasive images may be possible in the near future.

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