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Abstract #1206

Radiomic analysis of MRI in Clear Cell Renal Carcinoma: Non-invasive prediction of High Grade Histology

Durgesh Kumar Dwivedi1, Yin Xi1,2, Ananth J. Madhuranthakam1,3, Michael Fulkerson1, Alberto Diaz de Leon1, Yee Ng1, Matthew Lewis1, Jeffrey A. Cadeddu4, Aditya Bagrodia4, Vitali Margulis4, Payal Kapur4,5,6, and Ivan Pedrosa1,3,4,6

1Radiology, UT Southwestern Medical Center, Dallas, TX, United States, 2Clinical Science, UT Southwestern Medical Center, Dallas, TX, United States, 3Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States, 4Urology, UT Southwestern Medical Center, Dallas, TX, United States, 5Pathology, UT Southwestern Medical Center, Dallas, TX, United States, 6Kidney Cancer Program, UT Southwestern Medical Center, Dallas, TX, United States

Clear cell RCC (ccRCC), the most common and aggressive subtype of kidney cancer is a very heterogenous disease. Percutaneous biopsies are limited to determine tumor grade, particularly in larger, heterogeneous tumors. MRI can diagnose ccRCC histology with reasonable accuracy. However, efforts to distinguish indolent from aggressive ccRCCs using MRI have produced only modest results. Here we apply quantitative radiomic analysis based on first-order (histogram) and second-order (texture feature) statistics to MRI data for the prediction of high grade ccRCC. Our findings suggest that radiomic analysis of MRI data can help in the prediction of tumor grade in ccRCC.

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