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Abstract #3694

Machine learning based contrast-enhancement and IDH status prediction of gliomas using 7T MR spectroscopic imaging

Florian Schwarzhans1, Geevarghese George1, Cornelius Cadrien2,3, Amirreza Mahbod1, Wolfgang Bogner2,4, Olgica Zaric1, Matthias Preusser5, Thomas Rötzer-Pejrimovsky6, Georg Widhalm3, Karl Rössler3,4, Siegfried Trattnig2,4, Ramona Woitek1, Julia Furtner1, and Gilbert Hangel2,3,4
1Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria, 2High-Field MR Center - 7T MR, Department of Biomedical imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 3Department of Neurosurgery, Medical University of Vienna, Vienna, Austria, 4Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria, 5Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria, 6Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria

Synopsis

Keywords: Tumors (Pre-Treatment), Brain

Motivation: IDH-mutant diffuse adult type gliomas almost invariably lead to fatality. The INDIGO trial found vorasidenib significantly improving progression-free survival in non-contrast enhancing IDH mutant CNS grade 2 glioma patients.

Goal(s): The purpose of this study was to non-invasively predict both contrast enhancement and IDH mutation in glioma patients.

Approach: We employed a machine learning approach on 7T MRSI data to forecast IDH mutation status and contrast-enhancing tumor tissue in adult diffuse gliomas.

Results: Our models performed well in the training and the testing set (AUC ≥ 0.8) for both, IDH mutation and contrast enhancement prediction.

Impact: With regard to emerging IDH inhibition therapies in IDH mutant non-contrast enhancing diffuse gliomas, non-invasive prediction of IDH mutation status and contrast enhancement are of utmost importance for glioma patients. 7T MRSI can be successfully applied to this task.

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Keywords