Clear cell renal carcinoma cancer (ccRCC) is the most common subtype among renal masses. ccRCC identification helps in decision making between active surveillance and definitive intervention. A clear cell likelihood score (ccLS) using subjective interpretation of multiparametric MRI by radiologists was proposed recently. In this study, we investigate whether deep learning (DL) using the three main MR sequences for ccLS can facilitate the diagnosis of ccRCC. We compared the results of twelve trained DL models with the reported ccLS performance. Our results demonstrate that DL may achieve a performance comparable to radiologists and provide useful information for identification of ccRCC.