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Abstract #0945

Deep learning prediction for clear cell renal carcinoma cancer compared with human and radiomics analysis

Junyu Guo1, Lauren Hinojosa1, Yin Xi1, Keith Husley1, and Ivan Pedrosa1
1Radiology, UT southwestern medical center, Dallas, TX, United States

Clear cell renal carcinoma cancer (ccRCC) is the most aggressive subtype among small renal masses. ccRCC identification can help in decision making between active surveillance and definitive intervention. Recently, a clear cell likelihood score (ccLS) using subjective interpretation of multiparametric MRI by radiologists was proposed. In this study, we investigate whether radiomics and deep learning (DL) technique can facilitate the prediction of ccRCC using T2-weighted images only. We compared the results of two different approaches, radiomics and DL, with the reported ccLS performance. Our results demonstrate that both radiomics and deep learning may provide useful information for identification of ccRCC.

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