Fig. 3: Diagnostic accuracy of the deep learning model to differentiate aggressive from indolent renal tumors.

A Representative CT images of an indolent renal mass. B Automatic segmentation of the renal mass and kidney. C CAMs of the indolent and aggressive prediction for the indolent renal mass, where red color represents a region more significant to the designated classification. D Representative CT images of an aggressive renal mass. E Automatic segmentation of the renal mass and kidney. F CAMs of the indolent and aggressive prediction for the aggressive renal mass. G The receiver operating characteristic (ROC) curves of the deep learning-based model, the radiomics model, and nephrometry score nomogram on the internal test set. H The ROC curves of the deep learning-based model, the radiomics model, and nephrometry score nomogram on the external test set. I The ROC curves of the deep learning-based model, the radiomics model, and nephrometry score nomogram on the prospective test set. J The ROC curves of the deep learning-based model and nephrometry score nomogram on TCIA test set. In Fig. 2B, E yellow areas represent renal lesions identified by the nnU-Net model, while blue areas represent the kidney tissue also identified by the nnU-Net model. In Fig. 2C, F, the color intensity represents the level of importance for the deep learning model in making its decision. Red (designated as point 1) indicates the most important regions, green (designated as point 0.5) signifies regions of lesser importance, and blue (designated as point 0) denotes regions that are not important for the model’s decision. Source data for ROC curves are provided as a Source Data file.