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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