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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