Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging

Purpose: With increasing incidence of renal mass, it is important to make a pre-treatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. Experimental Design: Preoperative MR images (T2-weighted and T1-post contrast sequences) of 1162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables, T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. Results: Among the 1162 renal lesions, 655 were malignant and 507 were benign. Compared to a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, p=0.004). Compared to all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, p=0.053), sensitivity (0.92 vs. 0.80, p=0.017) and specificity (0.41 vs. 0.35, p=0.450). Compared to the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, p=0.081), sensitivity (0.92 vs. 0.79, p=0.012) and specificity (0.41 vs. 0.39, p=0.770). Conclusions:Deep learning can non-invasive distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity and specificity comparable to experts and radiomics.