Abstract #2656
IDH mutation prediction in glioma patients using 7T 3D-FID-MRSI
Sukrit Sharma1, Cornelius Cadrien1,2, Philipp Lazen 1, Julia Furtner3, Roxane Licandro4, Alexandra Lipkal5, Eva Heckova1, Lukas Hingerl1, Stanislav Motyka1, Stephan Gruber 1, Bernhard Strasser1, Barbara Kiesel2, Mario Mischkulnig2, Matthias Preusser6, Thomas Roetzer-Pejrimovsky7, Adelheid Wöhrer7, Michael Weber8, Christian Dorfer2, Karl Rössler2, Siegfried Trattnig5, Wolfgang Bogner1, Georg Widhalm2, and Gilbert Hangel1,2
1High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 2Department of Neurosurgery, Medical University of Vienna, Vienna, Austria, 3Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 4Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna, Vienna, Austria, 5Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria, 6Division of Oncology, Department of Inner Medicine I, Medical University of Vienna, Vienna, Austria, 7Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria, 8Division of Medical Imaging and Nuclear Medicine, Medical University of Vienna, Vienna, Austria
Synopsis
We used brain MRSI to explore the differentiation of gliomas regarding IDH-1 and Wildtype mutation. We found significant differencesin metabolites like Gln/NAA, Ins/NAA, and GPC+PCh/NAA using the Mann-Whitney-Wilcoxon test. More metabolites with signifi-cant differentiation were used for IDH classification using Random Forest, which also showed better classification results with additionalmetabolites than GPC+PCh/NAA alone. The ROC curve with more than GPC+PCh/NAA as feature yields an AUC value of 81 %.
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