Abstract #2132

Autopsy-based radio-pathomic maps of tumor probability delineate tumor presence within radiological segmentations

Samuel A Bobholz¹, Allison K Lowman¹, Savannah R Duenweg², Aleksandra Winiarz², Margaret Stebbins², Fitzgerald Kyereme¹, Jennifer Connelly³, Dylan Coss⁴, Wade M Mueller⁵, Mohit Agarwal¹, Anjishnu Banerjee⁶, and Peter S LaViolette^1,7

¹Radiology, Medical College of Wisconsin, Milwaukee, WI, United States, ²Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States, ³Neurology, Medical College of Wisconsin, Milwaukee, WI, United States, ⁴Pathology, 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

Keywords: Tumors, Cancer, Machine learning, glioblastoma, radio-pathomicsThis study applied autopsy-based radio-pathomic maps to the pre-surgical PENN-GBM dataset to test the hypothesis that the predicted tumor composition of the contrast-enhancing and FLAIR-hyperintense regions identify distinct pathological features of glioblastoma. We find that greater predicted tumor within the contrast-enhancing region is indicative of IDH1-wildtype mutation status, and show that larger tumors tend to have less predicted tumor within contrast-enhancement and more tumor within non-enhancing FLAIR hyperintensity. This technique could be used to non-invasively identify more aggressive tumors.

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