Classification of renal tumour using convolutional neural networks to detect oncocytoma. Issue 133 (December 2020)

• Record Type: Journal Article
• Authors: Pedersen, Mikkel; Andersen, Michael Brun; Christiansen, Henning; Azawi, Nessn H.
• Is Part Of: European journal of radiology. Issue 133(2020)
• Publication Date: 2020-12
• Subjects: CNN convolutional neural networks -- RCC renal cell carcinoma -- US ultrasound -- CT computed tomography -- IQR interquartile range -- kV kilo voltage -- RUC area under the curve; Deep learning -- Machine learning -- Oncocytoma -- Renal cell carcinoma; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.109343
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Purpose: To investigate the ability of convolutional neural networks (CNNs) to facilitate differentiation of oncocytoma from renal cell carcinoma (RCC) using non-invasive imaging technology. Methods: Data were collected from 369 patients between January 2015 and September 2018. True labelling of scans as benign or malignant was determined by subsequent histological findings post-surgery or ultrasound-guided percutaneous biopsy. The data included 20, 000 2D CT images. Data were randomly divided into sets for training (70 %), validation (10 %) and independent testing (20 %, DataTest_1). A small dataset (DataTest_2) was used for additional validation of the training model. Data were divided into sets at the patient level, rather than by individual image. A modified version of the ResNet50V2 was used. Accuracy of detecting benign or malignant renal mass was evaluated by a 51 % majority vote of individual image classifications to determine the classification for each patient. Results: Test results from DataTest_1 indicate an area under the curve (AUC) of 0.973 with 93.3 % accuracy and 93.5 % specificity. Results from DataTest_2 indicate an AUC of 0.946 with 90.0 % accuracy and 98.0 % specificity when evaluation is performed image by image. There is no case in which multiple false negative images originate from the same patient. When evaluated with 51 % majority of scans for each patient, the accuracy rises to 100 % and the incidence of false negatives falls to zero. Conclusion: CNNs and deep learning technology can classify renal tumour masses as oncocytoma with high accuracy. This diagnostic method could prevent overtreatment for patients with renal masses.