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

Ianto Lin Xi, Yijun Zhao, Robin Wang, Marcello Chang, Subhanik Purkayastha, Ken Chang, Raymond Y. Huang, Alvin C. Silva, Martin Valliéres, Peiman Habibollahi, Yong Fan, Beiji Zou, Terence P. Gade, Paul J. Zhang, Michael C. Soulen, Zishu Zhang, Harrison X. Bai, S. William Stavropoulos

Research output: Contribution to journalArticlepeer-review


Purpose: With increasing incidence of renal mass, it is important to make a pretreatment 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: PreoperativeMRimages (T2-weighted and T1-postcontrast sequences) of 1,162 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 and 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 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learningmodel had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learningmodel 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 with 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 noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multiinstitutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.

Original languageEnglish (US)
Pages (from-to)1944-1952
Number of pages9
JournalClinical Cancer Research
Issue number8
StatePublished - Apr 15 2020
Externally publishedYes

ASJC Scopus subject areas

  • General Medicine


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