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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