Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging
| Autor: | Martin Vallières, Alvin C. Silva, Zishu Zhang, Subhanik Purkayastha, Yong Fan, Ken Chang, Terence P. Gade, Raymond Y. Huang, Michael C. Soulen, S. William Stavropoulos, Beiji Zou, I. Xi, Paul J. Zhang, Marcello Chang, Robin Wang, Peiman Habibollahi, Harrison X. Bai, Yijun Zhao |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Cancer Research medicine.medical_specialty Adolescent 030218 nuclear medicine & medical imaging Diagnosis Differential Young Adult 03 medical and health sciences Deep Learning 0302 clinical medicine Text mining Predictive Value of Tests Renal cell carcinoma medicine Carcinoma Humans Child Carcinoma Renal Cell Aged Retrospective Studies Aged 80 and over Artificial neural network business.industry Deep learning Retrospective cohort study Middle Aged medicine.disease Magnetic Resonance Imaging Mr imaging Kidney Neoplasms Oncology Child Preschool 030220 oncology & carcinogenesis Predictive value of tests Female Neural Networks Computer Radiology Artificial intelligence business Algorithms |
| Zdroj: | Clinical Cancer Research. 26:1944-1952 |
| ISSN: | 1557-3265 1078-0432 |
| Popis: | 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 learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with 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 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. |
| Databáze: | OpenAIRE |
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