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Abstract #3292

Machine Learning to Identify Sarcomatoid De-Differentiation in Renal Cell Carcinoma by Multiparametric MRI

Nicolas Rognin1, Daniel Jeong2, Jasreman Dhillon3, Michael Poch4, and Natarajan Raghunand1

1Department of Cancer Imaging & Metabolism, Moffitt Cancer Center, Tampa, FL, United States, 2Department of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, United States, 3Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, FL, United States, 4Department of Genitourinary Oncology, Moffitt Cancer Center, Tampa, FL, United States

We developed a machine learning application to detect renal cell carcinoma (RCC) tumors with sarcomatoid de-differentiation, a rare form of aggressive cancer with poor prognosis. Proof-of-concept was demonstrated by analyzing multiparametric MRI volumetric data of 24 tumors, of which 11 were sarcomatoid RCC and 13 were non-sarcomatoid clear cell RCC. Our machine correctly classified 10 out of 11 sarcomatoid RCC cases (91% sensitivity) and 10 out of 13 clear cell RCC cases (77% specificity), with an overall classification accuracy of 20 out of 24 tumors (83%).

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