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Abstract #0035

Radio-pathomic tumor probability maps in glioma patients using autopsy tissue samples as ground truth

Samuel Bobholz1, Allison Lowman2, Michael Brehler2, Savannah Duenweg1, John Sherman1, Fitzgerald Kyereme2, Elizabeth Cochran3, Dylan Coss3, Jennifer Connelly4, Wade Mueller5, Mohit Agarwal2, Anjishnu Banerjee6, and Peter LaViolette2,7
1Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States, 2Radiology, Medical College of Wisconsin, Milwaukee, WI, United States, 3Pathology, Medical College of Wisconsin, Milwaukee, WI, United States, 4Neurology, Medical College of Wisconsin, Milwaukee, WI, United States, 5Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States, 6Biostatistics, Medical College of Wisconsin, Milwaukee, WI, United States, 7Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, United States

Synopsis

This study used autopsy tissue samples to develop multi-stage radio-pathomic models of tumor probability in glioma patients. Three models were trained to predict cell density, extracellular fluid density, and cytoplasm density segmented from autopsy samples using T1, T1C, FLAIR, and ADC intensity. A fourth model was then trained to predict tumor probability from pathological annotations using the cellularity, extracellular fluid, and cytoplasm segmentations as input. The combined models were then able to non-invasively estimate tumor probability using MRI. These maps identified regions of tumor beyond the contrast-enhancing region and discriminated between areas of tumor and vasogenic edema within FLAIR hyperintensity.

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