Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning

• Published in: BJU international Vol. 125; no. 4; pp. 553 - 560
• Main Authors: Baghdadi, Amir, Aldhaam, Naif A., Elsayed, Ahmed S., Hussein, Ahmed A., Cavuoto, Lora A., Kauffman, Eric, Guru, Khurshid A.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.04.2020
• Subjects: Artificial intelligence; Automation; Benign; chromophobe; Computed tomography; Computer vision; convolutional neural network; Deep learning; Image processing; Kidney cancer; Kidneys; Neural networks; Neuroimaging; oncocytoma; Renal cell carcinoma; Segmentation; Tumors; deep learning; chromophobe; renal cell carcinoma; convolutional neural network; artificial intelligence; oncocytoma
• ISSN: 1464-4096; 1464-410X
• DOI: 10.1111/bju.14985

Objectives To develop and evaluate the feasibility of an objective method using artificial intelligence (AI) and image processing in a semi‐automated fashion for tumour‐to‐cortex peak early‐phase enhancement ratio (PEER) in order to differentiate CD117(+) oncocytoma from the chromophobe subtype of renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on computed tomography imaging. Methods The CNN was trained and validated to identify the kidney + tumour areas in images from 192 patients. The tumour type was differentiated through automated measurement of PEER after manual segmentation of tumours. The performance of this diagnostic model was compared with that of manual expert identification and tumour pathology with regard to accuracy, sensitivity and specificity, along with the root‐mean‐square error (RMSE), for the remaining 20 patients with CD117(+) oncocytoma or ChRCC. Results The mean ± sd Dice similarity score for segmentation was 0.66 ± 0.14 for the CNN model to identify the kidney + tumour areas. PEER evaluation achieved accuracy of 95% in tumour type classification (100% sensitivity and 89% specificity) compared with the final pathology results (RMSE of 0.15 for PEER ratio). Conclusions We have shown that deep learning could help to produce reliable discrimination of CD117(+) benign oncocytoma and malignant ChRCC through PEER measurements obtained by computer vision.