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Abstract #0264

Proof-of-principle for endogenous signal classification towards voxel-wise tumor detection using statistical machine learning

Artur Hahn1,2, Julia Kr├╝wel-Bode3, Yannis Seemann2, Sarah Schuhegger2, Johann M. E. Jende1, Anja Hohmann4, Volker J. F. Sturm1, Ke Zhang5, Sabine Heiland1, Martin Bendszus1, Michael O. Breckwoldt1,6, Christian H. Ziener1,5, and Felix T. Kurz1,5
1Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany, 2Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany, 3Molecular Mechanisms of Tumor Invasion (V077), German Cancer Research Center (DKFZ), Heidelberg, Germany, 4Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany, 5Department of Radiology (E010), German Cancer Research Center (DKFZ), Heidelberg, Germany, 6Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany

Based on the microvasculature of entire healthy and tumor-bearing mouse brains, imaged with high-resolution fluorescence microscopy, the transverse relaxation process within virtual MRI voxels was simulated. Extended parametrizations of the non-Lorentzian signal decay were used to train support vector machine and random forest classifiers to differentiate healthy brain and tumor voxel signals. A proof-of-principle is presented with U87 and GL261 glioblastoma at different SNR levels. This automated workflow enables the in-silico development of specialized MRI sequences to maximize classification accuracy with minimal NMR measurements for experimental analogies.

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