Abstract #1206

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

Durgesh Kumar Dwivedi¹, Yin Xi^1,2, Ananth J. Madhuranthakam^1,3, Michael Fulkerson¹, Alberto Diaz de Leon¹, Yee Ng¹, Matthew Lewis¹, Jeffrey A. Cadeddu⁴, Aditya Bagrodia⁴, Vitali Margulis⁴, Payal Kapur^4,5,6, and Ivan Pedrosa^1,3,4,6

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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