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Abstract #0266

Identification of Sarcomatoid De-Differentiation in Renal Cell Carcinoma by Machine Learning on Multiparametric MRI

Asim M. Mazin1, Samuel H. Hawkins1, Olya Stringfield2, Jasreman Dhillon3, Brandon J. Manley4, Daniel K. Jeong5, and Natarajan Raghunand1
1Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL, United States, 2IRAT Shared Service, 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, 5Department of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, United States

We report a machine learning approach using Self-Organizing Maps (SOM) and Learning Vector Quantization (LVQ) to analyze multiparametric MRI for the purpose of differentiating between renal cell carcinoma tumor with (sRCC) and without (nsRCC) sarcomatoid de-differentiation, a transformation that is associated with poorer outcomes. The SOM+LVQ model was trained on mpMRI data from 9 nsRCC and 9 sRCC tumors, validated on a separate cohort of 3 nsRCC and 3 sRCC tumors, and tested on a held-out set of 5 nsRCC and 5 sRCC tumors. An overall classification accuracy of 70% was achieved on the test cohort.

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