Deep Learning from CT Scans Assists Diagnosis of Papillary Renal Cell Carcinoma Subtypes

To develop a generalizable and automatic Deep Learning model using 2D U-Net and LSTM algorithms to segment and discriminate two subtypes of papillary renal cell carcinoma (PRCC) through multiphase computed tomography (CT) images. Total 71 patients proven PRCC were retrospectively included. Following data pre-processing steps, automatic localization of region of interests (ROIs) and classification algorithm from multiphase CT images were implemented. A nested cross-validation was used for model optimization. The segmentation part was a 2D U-Net trained on the VISCERAL challenge dataset leveraging transfer learning, while the classification part was developed using spatial-frequency nonlocal convolutional LSTM network based on convolutional neural network (CNN). Further, the images were intensively discriminated by two urologists and the results of urologists were compared with that of the model. The trained 2D U-Net architecture automatic fixated most of its attention on the region of tumor masses in corticomedullary and nephrographic phases. After the training process of the CNN-based LSTM network, it performed for discrimination of two subtypes of PRCC with overall accuracy, sensitivity, and specificity of 78.5%, 66.7% and 81.5%, respectively, compared to those of the average performance of two experienced urologists with 66.7%, 88.5 and 68.9%, respectively. The CNN-based model on multiphase CT images can discriminate the two subtypes of PRCC with a competitive and superior performance and also shows up far faster than urologists which may be useful and practical in clinical work.