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.
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.
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).
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.
Clinical cancer research : an official journal of the American Association for Cancer Research. 2020 Jan 14 [Epub ahead of print]
Ianto Lin Xi, Yijun Zhao, Robin Wang, Marcello Chang, Subhanik Purkayastha, Ken Chang, Raymond Y Huang, Alvin C Silva, Martin Vallières, Peiman Habibollahi, Yong Fan, Beiji Zou, Terence P Gade, Paul J Zhang, Michael C Soulen, Zishu Zhang, Harrison X Bai, S William Stavropoulos
Department of Radiology, Hospital of the University of Pennsylvania., Department of Radiology, Second Xiangya Hospital of Central South University., Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University., Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital., Radiology, Brigham and Women's Hospital., Department of Radiology, Mayo Clinic., Medical Physics Unit, McGill University., Nuclear Medicine/Radiology, Massachusetts General Hospital., Radiology, University of Pennsylvania., School of Informatics and Engineering, Central South University., Cancer Biology, University of Pennsylvania., Pathology and Laboratory Medicine, University of Pennsylvania., Division of Interventional Radiology, University of Pennsylvania., Radiology, The 2nd Xiangya Hospital of Central South University., Department of Radiology, Hospital of the University of Pennsylvania .