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Abstract #0871

Deep Learning Based on MR Imaging for Differentiation of Low and High Fuhrman Grade Renal Cell Carcinoma

Harrison X. Bai1 and Yijun Zhao2

1University of Pennsylvania, Philadelphia, PA, United States, 2The Second Xiangya Hospital of Central South University, Changsha, China

The inability to determine aggressiveness of RCC based on pretreatment imaging makes it challenging for physicians to select best benefit treatment. We aimed to differentiate low grade (Fuhrman I–II) from high grade (Fuhrman III–IV) RCC using a deep learning model based on routine MR imaging. 297 patients with 300 RCC lesions in a multicenter cohort were included. A residual convolutional neural network model combining MR images and three clinical variables was built, which demonstrated high accuracy when compared to expert evaluation. Deep learning can non-invasively predict Fuhrman grade of RCC using conventional MR imaging in a multi-institutional dataset.

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