期刊
CLINICAL CANCER RESEARCH
卷 26, 期 8, 页码 1944-1952出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-19-0374
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类别
资金
- RSNA [RF1802]
- National Natural Science Foundation of China [8181101287]
- SIR Foundation Radiology Resident Research Grant
- National Cancer Institute of the National Institutes of Health [R03CA249554]
- National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health [5T32EB1680]
- National Cancer Institute (NCI) of the National Institutes of Health [F30CA239407]
- Nicole Foundation for Kidney Cancer Research
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: Preoperative MR images (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 multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.
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