Tanaka T, Huang Y, Marukawa Y, et al. - This study was carried out to assess the benefit of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. Between 2012 and 2016, researchers performed a retrospective study recruiting a total of 1,807 image sets from 168 pathologically diagnosed small (≤ 4 cm) solid renal masses with four CT phases (unenhanced, corticomedullary, nephrogenic, and excretory) in 159 individuals. This analysis categorized masses as malignant (n = 136) or benign (n = 32). There was no significant difference in the size of malignant and benign lesions. Multivariate analysis showed that the convolutional neural network (CNN) model of the corticomedullary phase was a significant predictor for malignancy compared with other CNN models, age, gender, and lesion size. The results of this study displayed that a deep learning method with a CNN provided acceptable differentiation of small (≤ 4 cm) solid renal masses in dynamic CT images, particularly in the corticomedullary image model.