A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors.

OBJECTIVES: To investigate the effect of transfer learning on computed tomography (CT) images for the benign and malignant classification on renal tumors and to attempt to improve the classification accuracy by building patient-level models.

METHODS: One hundred ninety-two cases of renal tumors were collected and identified by pathologic diagnosis within 15 days after enhanced CT examination (66% male, 70% malignant renal tumors, average age of 62.27 ± 12.26 years). The InceptionV3 model pretrained by the ImageNet dataset was cross-trained to perform this classification. Five image-level models were established for each of the Slice, region of interest (ROI), and rectangular box region (RBR) datasets. Then, two patient-level models were built based on the optimal image-level models. The network's performance was evaluated through analysis of the receiver operating characteristic (ROC) and five-fold cross-validation.

RESULTS: In the image-level models, the test results of model trained on the Slice dataset [accuracy (ACC) = 0.69 and Matthews correlation coefficient (MCC) = 0.45] were the worst. The corresponding results on the ROI dataset (ACC = 0.97 and MCC = 0.93) were slightly better than those on the RBR dataset (ACC = 0.93 and MCC = 0.85) when freezing the weights before the mixed6 layer. Compared with the image-level models, both patient-level models could discriminate better (ACC increased by 2%-5%) on the RBR and Slice datasets.

CONCLUSIONS: Deep learning can be used to classify benign and malignant renal tumors from CT images. Our patient-level models could benefit from 3D data to improve the accuracy.