Influential factors in deep learning classification of clear cell renal carcinoma cancer

Deep learning has been successful in predicting tumor malignancy. Clear cell renal carcinoma (ccRCC) diagnosis may help in decision making between active surveillance and definitive intervention. In this study, we investigate the effects of different factors on the performance of deep learning classification of ccRCC using T2w images. We demonstrate that the performance of 15 different CNN models varied substantially. The choice of CNN models, the cropped image size, and the type of inputs greatly altered the performance of ccRCC classification. We achieved the best AUC value of 0.81, which is close to the reported performance of radiologists.